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Sample records for compartmental metabolic models

  1. Cellular compartmentalization of secondary metabolism

    Directory of Open Access Journals (Sweden)

    H. Corby eKistler

    2015-02-01

    Full Text Available Fungal secondary metabolism is often considered apart from the essential housekeeping functions of the cell. However, there are clear links between fundamental cellular metabolism and the biochemical pathways leading to secondary metabolite synthesis. Besides utilizing key biochemical precursors shared with the most essential processes of the cell (e.g. amino acids, acetyl CoA, NADPH, enzymes for secondary metabolite synthesis are compartmentalized at conserved subcellular sites that position pathway enzymes to use these common biochemical precursors. Co-compartmentalization of secondary metabolism pathway enzymes also may function to channel precursors, promote pathway efficiency and sequester pathway intermediates and products from the rest of the cell. In this review we discuss the compartmentalization of three well-studied fungal secondary metabolite biosynthetic pathways for penicillin G, aflatoxin and deoxynivalenol, and summarize evidence used to infer subcellular localization. We also discuss how these metabolites potentially are trafficked within the cell and may be exported.

  2. Analysis of a Compartmental Model of Endogenous Immunoglobulin G Metabolism with Application to Multiple Myeloma.

    Science.gov (United States)

    Kendrick, Felicity; Evans, Neil D; Arnulf, Bertrand; Avet-Loiseau, Hervé; Decaux, Olivier; Dejoie, Thomas; Fouquet, Guillemette; Guidez, Stéphanie; Harel, Stéphanie; Hebraud, Benjamin; Javaugue, Vincent; Richez, Valentine; Schraen, Susanna; Touzeau, Cyrille; Moreau, Philippe; Leleu, Xavier; Harding, Stephen; Chappell, Michael J

    2017-01-01

    Immunoglobulin G (IgG) metabolism has received much attention in the literature for two reasons: (i) IgG homeostasis is regulated by the neonatal Fc receptor (FcRn), by a pH-dependent and saturable recycling process, which presents an interesting biological system; (ii) the IgG-FcRn interaction may be exploitable as a means for extending the plasma half-life of therapeutic monoclonal antibodies, which are primarily IgG-based. A less-studied problem is the importance of endogenous IgG metabolism in IgG multiple myeloma. In multiple myeloma, quantification of serum monoclonal immunoglobulin plays an important role in diagnosis, monitoring and response assessment. In order to investigate the dynamics of IgG in this setting, a mathematical model characterizing the metabolism of endogenous IgG in humans is required. A number of authors have proposed a two-compartment nonlinear model of IgG metabolism in which saturable recycling is described using Michaelis-Menten kinetics; however it may be difficult to estimate the model parameters from the limited experimental data that are available. The purpose of this study is to analyse the model alongside the available data from experiments in humans and estimate the model parameters. In order to achieve this aim we linearize the model and use several methods of model and parameter validation: stability analysis, structural identifiability analysis, and sensitivity analysis based on traditional sensitivity functions and generalized sensitivity functions. We find that all model parameters are identifiable, structurally and taking into account parameter correlations, when several types of model output are used for parameter estimation. Based on these analyses we estimate parameter values from the limited available data and compare them with previously published parameter values. Finally we show how the model can be applied in future studies of treatment effectiveness in IgG multiple myeloma with simulations of serum monoclonal Ig

  3. Compartmentation of glycogen metabolism revealed from 13C isotopologue distributions

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    Marin de Mas Igor

    2011-10-01

    Full Text Available Abstract Background Stable isotope tracers are used to assess metabolic flux profiles in living cells. The existing methods of measurement average out the isotopic isomer distribution in metabolites throughout the cell, whereas the knowledge of compartmental organization of analyzed pathways is crucial for the evaluation of true fluxes. That is why we accepted a challenge to create a software tool that allows deciphering the compartmentation of metabolites based on the analysis of average isotopic isomer distribution. Results The software Isodyn, which simulates the dynamics of isotopic isomer distribution in central metabolic pathways, was supplemented by algorithms facilitating the transition between various analyzed metabolic schemes, and by the tools for model discrimination. It simulated 13C isotope distributions in glucose, lactate, glutamate and glycogen, measured by mass spectrometry after incubation of hepatocytes in the presence of only labeled glucose or glucose and lactate together (with label either in glucose or lactate. The simulations assumed either a single intracellular hexose phosphate pool, or also channeling of hexose phosphates resulting in a different isotopic composition of glycogen. Model discrimination test was applied to check the consistency of both models with experimental data. Metabolic flux profiles, evaluated with the accepted model that assumes channeling, revealed the range of changes in metabolic fluxes in liver cells. Conclusions The analysis of compartmentation of metabolic networks based on the measured 13C distribution was included in Isodyn as a routine procedure. The advantage of this implementation is that, being a part of evaluation of metabolic fluxes, it does not require additional experiments to study metabolic compartmentation. The analysis of experimental data revealed that the distribution of measured 13C-labeled glucose metabolites is inconsistent with the idea of perfect mixing of hexose

  4. Compartmentation of glycogen metabolism revealed from 13C isotopologue distributions.

    Science.gov (United States)

    de Mas, Igor Marin; Selivanov, Vitaly A; Marin, Silvia; Roca, Josep; Orešič, Matej; Agius, Loranne; Cascante, Marta

    2011-10-28

    Stable isotope tracers are used to assess metabolic flux profiles in living cells. The existing methods of measurement average out the isotopic isomer distribution in metabolites throughout the cell, whereas the knowledge of compartmental organization of analyzed pathways is crucial for the evaluation of true fluxes. That is why we accepted a challenge to create a software tool that allows deciphering the compartmentation of metabolites based on the analysis of average isotopic isomer distribution. The software Isodyn, which simulates the dynamics of isotopic isomer distribution in central metabolic pathways, was supplemented by algorithms facilitating the transition between various analyzed metabolic schemes, and by the tools for model discrimination. It simulated 13C isotope distributions in glucose, lactate, glutamate and glycogen, measured by mass spectrometry after incubation of hepatocytes in the presence of only labeled glucose or glucose and lactate together (with label either in glucose or lactate). The simulations assumed either a single intracellular hexose phosphate pool, or also channeling of hexose phosphates resulting in a different isotopic composition of glycogen. Model discrimination test was applied to check the consistency of both models with experimental data. Metabolic flux profiles, evaluated with the accepted model that assumes channeling, revealed the range of changes in metabolic fluxes in liver cells. The analysis of compartmentation of metabolic networks based on the measured 13C distribution was included in Isodyn as a routine procedure. The advantage of this implementation is that, being a part of evaluation of metabolic fluxes, it does not require additional experiments to study metabolic compartmentation. The analysis of experimental data revealed that the distribution of measured 13C-labeled glucose metabolites is inconsistent with the idea of perfect mixing of hexose phosphates in cytosol. In contrast, the observed distribution

  5. Rewiring and regulation of cross-compartmentalized metabolism in protists.

    Science.gov (United States)

    Ginger, Michael L; McFadden, Geoffrey I; Michels, Paul A M

    2010-03-12

    Plastid acquisition, endosymbiotic associations, lateral gene transfer, organelle degeneracy or even organelle loss influence metabolic capabilities in many different protists. Thus, metabolic diversity is sculpted through the gain of new metabolic functions and moderation or loss of pathways that are often essential in the majority of eukaryotes. What is perhaps less apparent to the casual observer is that the sub-compartmentalization of ubiquitous pathways has been repeatedly remodelled during eukaryotic evolution, and the textbook pictures of intermediary metabolism established for animals, yeast and plants are not conserved in many protists. Moreover, metabolic remodelling can strongly influence the regulatory mechanisms that control carbon flux through the major metabolic pathways. Here, we provide an overview of how core metabolism has been reorganized in various unicellular eukaryotes, focusing in particular on one near universal catabolic pathway (glycolysis) and one ancient anabolic pathway (isoprenoid biosynthesis). For the example of isoprenoid biosynthesis, the compartmentalization of this process in protists often appears to have been influenced by plastid acquisition and loss, whereas for glycolysis several unexpected modes of compartmentalization have emerged. Significantly, the example of trypanosomatid glycolysis illustrates nicely how mathematical modelling and systems biology can be used to uncover or understand novel modes of pathway regulation.

  6. Compartmental modeling and tracer kinetics

    CERN Document Server

    Anderson, David H

    1983-01-01

    This monograph is concerned with mathematical aspects of compartmental an­ alysis. In particular, linear models are closely analyzed since they are fully justifiable as an investigative tool in tracer experiments. The objective of the monograph is to bring the reader up to date on some of the current mathematical prob­ lems of interest in compartmental analysis. This is accomplished by reviewing mathematical developments in the literature, especially over the last 10-15 years, and by presenting some new thoughts and directions for future mathematical research. These notes started as a series of lectures that I gave while visiting with the Division of Applied ~1athematics, Brown University, 1979, and have developed in­ to this collection of articles aimed at the reader with a beginning graduate level background in mathematics. The text can be used as a self-paced reading course. With this in mind, exercises have been appropriately placed throughout the notes. As an aid in reading the material, the e~d of a ...

  7. Molecular System Bioenergics of the Heart: Experimental Studies of Metabolic Compartmentation and Energy Fluxes versus Computer Modeling

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    Valdur Saks

    2011-12-01

    Full Text Available In this review we analyze the recent important and remarkable advancements in studies of compartmentation of adenine nucleotides in muscle cells due to their binding to macromolecular complexes and cellular structures, which results in non-equilibrium steady state of the creatine kinase reaction. We discuss the problems of measuring the energy fluxes between different cellular compartments and their simulation by using different computer models. Energy flux determinations by 18O transfer method have shown that in heart about 80% of energy is carried out of mitochondrial intermembrane space into cytoplasm by phosphocreatine fluxes generated by mitochondrial creatine kinase from adenosine triphosphate (ATP, produced by ATP Synthasome. We have applied the mathematical model of compartmentalized energy transfer for analysis of experimental data on the dependence of oxygen consumption rate on heart workload in isolated working heart reported by Williamson et al. The analysis of these data show that even at the maximal workloads and respiration rates, equal to 174 µmol O2 per min per g dry weight, phosphocreatine flux, and not ATP, carries about 80–85% percent of energy needed out of mitochondria into the cytosol. We analyze also the reasons of failures of several computer models published in the literature to correctly describe the experimental data.

  8. Compartmental analysis of dynamic nuclear medicine data: models and identifiability

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    Delbary, Fabrice; Garbarino, Sara; Vivaldi, Valentina

    2016-12-01

    Compartmental models based on tracer mass balance are extensively used in clinical and pre-clinical nuclear medicine in order to obtain quantitative information on tracer metabolism in the biological tissue. This paper is the first of a series of two that deal with the problem of tracer coefficient estimation via compartmental modelling in an inverse problem framework. Specifically, here we discuss the identifiability problem for a general n-dimension compartmental system and provide uniqueness results in the case of two-compartment and three-compartment compartmental models. The second paper will utilize this framework in order to show how nonlinear regularization schemes can be applied to obtain numerical estimates of the tracer coefficients in the case of nuclear medicine data corresponding to brain, liver and kidney physiology.

  9. Metabolic flux and compartmentation analysis in the brain in vivo

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    Bernard eLanz

    2013-10-01

    Full Text Available Through significant developments and progresses in the last two decades, in vivo localized nuclear magnetic resonance spectroscopy (MRS became a method of choice to probe brain metabolic pathways in a non-invasive way. Beside the measurement of the total concentration of more than 20 metabolites, 1H MRS can be used to quantify the dynamics of substrate transport across the blood brain barrier (BBB by varying the plasma substrate level. On the other hand, 13C MRS with the infusion of 13C enriched substrates enables the characterization of brain oxidative metabolism and neurotransmission by incorporation of 13C in the different carbon positions of amino acid neurotransmitters. The quantitative determination of the biochemical reactions involved in these processes requires the use of appropriate metabolic models, whose level of details is strongly related to the amount of data accessible with in vivo MRS. In the present work, we present the different steps involved in the elaboration of a mathematical model of a given brain metabolic process and its application to the experimental data in order to extract quantitative brain metabolic rates. We review the recent advances in the localized measurement of brain glucose transport and compartmentalized brain energy metabolism, and how these reveal mechanistic details on glial support to glutamatergic and GABAergic neurons.

  10. Metabolic Flux and Compartmentation Analysis in the Brain In vivo

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    Lanz, Bernard; Gruetter, Rolf; Duarte, João M. N.

    2013-01-01

    Through significant developments and progresses in the last two decades, in vivo localized nuclear magnetic resonance spectroscopy (MRS) became a method of choice to probe brain metabolic pathways in a non-invasive way. Beside the measurement of the total concentration of more than 20 metabolites, 1H MRS can be used to quantify the dynamics of substrate transport across the blood-brain barrier by varying the plasma substrate level. On the other hand, 13C MRS with the infusion of 13C-enriched substrates enables the characterization of brain oxidative metabolism and neurotransmission by incorporation of 13C in the different carbon positions of amino acid neurotransmitters. The quantitative determination of the biochemical reactions involved in these processes requires the use of appropriate metabolic models, whose level of details is strongly related to the amount of data accessible with in vivo MRS. In the present work, we present the different steps involved in the elaboration of a mathematical model of a given brain metabolic process and its application to the experimental data in order to extract quantitative brain metabolic rates. We review the recent advances in the localized measurement of brain glucose transport and compartmentalized brain energy metabolism, and how these reveal mechanistic details on glial support to glutamatergic and GABAergic neurons. PMID:24194729

  11. Rewiring and regulation of cross-compartmentalized metabolism in protists

    OpenAIRE

    Ginger, Michael L.; McFadden, Geoffrey I.; Michels, Paulus

    2010-01-01

    Plastid acquisition, endosymbiotic associations, lateral gene transfer, organelle degeneracy or even organelle loss influence metabolic capabilities in many different protists. Thus, metabolic diversity is sculpted through the gain of new metabolic functions and moderation or loss of pathways that are often essential in the majority of eukaryotes. What is perhaps less apparent to the casual observer is that the sub-compartmentalization of ubiquitous pathways has been repeatedly remodelled dur...

  12. Intracellular compartmentalization of skeletal muscle glycogen metabolism and insulin signalling

    DEFF Research Database (Denmark)

    Prats Gavalda, Clara; Gomez-Cabello, Alba; Vigelsø Hansen, Andreas

    2011-01-01

    The interest in skeletal muscle metabolism and insulin signalling has increased exponentially in recent years as a consequence of their role in the development of type 2 diabetes mellitus. Despite this, the exact mechanisms involved in the regulation of skeletal muscle glycogen metabolism...... and insulin signalling transduction remain elusive. We believe that one of the reasons is that the role of intracellular compartmentalization as a regulator of metabolic pathways and signalling transduction has been rather ignored. This paper briefly reviews the literature to discuss the role of intracellular...... compartmentalization in the regulation of skeletal muscle glycogen metabolism and insulin signalling. As a result, a hypothetical regulatory mechanism is proposed by which cells could direct glycogen resynthesis towards different pools of glycogen particles depending on the metabolic needs. Furthermore, we discuss...

  13. Modelling compartmentalization towards elucidation and engineering of spatial organization in biochemical pathways.

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    Menon, Govind; Okeke, Chinedu; Krishnan, J

    2017-09-21

    Compartmentalization is a fundamental ingredient, central to the functioning of biological systems at multiple levels. At the cellular level, compartmentalization is a key aspect of the functioning of biochemical pathways and an important element used in evolution. It is also being exploited in multiple contexts in synthetic biology. Accurate understanding of the role of compartments and designing compartmentalized systems needs reliable modelling/systems frameworks. We examine a series of building blocks of signalling and metabolic pathways with compartmental organization. We systematically analyze when compartmental ODE models can be used in these contexts, by comparing these models with detailed reaction-transport models, and establishing a correspondence between the two. We build on this to examine additional complexities associated with these pathways, and also examine sample problems in the engineering of these pathways. Our results indicate under which conditions compartmental models can and cannot be used, why this is the case, and what augmentations are needed to make them reliable and predictive. We also uncover other hidden consequences of employing compartmental models in these contexts. Or results contribute a number of insights relevant to the modelling, elucidation, and engineering of biochemical pathways with compartmentalization, at the core of systems and synthetic biology.

  14. Compartmental model of 18F-choline

    Science.gov (United States)

    Janzen, T.; Tavola, F.; Giussani, A.; Cantone, M. C.; Uusijärvi, H.; Mattsson, S.; Zankl, M.; Petoussi-Henß, N.; Hoeschen, C.

    2010-03-01

    The MADEIRA Project (Minimizing Activity and Dose with Enhanced Image quality by Radiopharmaceutical Administrations), aims to improve the efficacy and safety of 3D functional imaging by optimizing, among others, the knowledge of the temporal variation of the radiopharmaceuticals' uptake in and clearance from tumor and healthy tissues. With the help of compartmental modeling it is intended to optimize the time schedule for data collection and improve the evaluation of the organ doses to the patients. Administration of 18F-choline to screen for recurrence or the occurrence of metastases in prostate cancer patients is one of the diagnostic applications under consideration in the frame of the project. PET and CT images have been acquired up to four hours after injection of 18F-choline. Additionally blood and urine samples have been collected and measured in a gamma counter. The radioactivity concentration in different organs and data of plasma clearance and elimination into urine were used to set-up a compartmental model of the biokinetics of the radiopharmaceutical. It features a central compartment (blood) exchanging with organs. The structure describes explicitly liver, kidneys, spleen, plasma and bladder as separate units with a forcing function approach. The model is presented together with an evaluation of the individual and population kinetic parameters, and a revised time schedule for data collection is proposed. This optimized time schedule will be validated in a further set of patient studies.

  15. Compartmentalized metabolic network reconstruction of microbial communities to determine the effect of agricultural intervention on soils.

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    María Camila Alvarez-Silva

    Full Text Available Soil microbial communities are responsible for a wide range of ecological processes and have an important economic impact in agriculture. Determining the metabolic processes performed by microbial communities is crucial for understanding and managing ecosystem properties. Metagenomic approaches allow the elucidation of the main metabolic processes that determine the performance of microbial communities under different environmental conditions and perturbations. Here we present the first compartmentalized metabolic reconstruction at a metagenomics scale of a microbial ecosystem. This systematic approach conceives a meta-organism without boundaries between individual organisms and allows the in silico evaluation of the effect of agricultural intervention on soils at a metagenomics level. To characterize the microbial ecosystems, topological properties, taxonomic and metabolic profiles, as well as a Flux Balance Analysis (FBA were considered. Furthermore, topological and optimization algorithms were implemented to carry out the curation of the models, to ensure the continuity of the fluxes between the metabolic pathways, and to confirm the metabolite exchange between subcellular compartments. The proposed models provide specific information about ecosystems that are generally overlooked in non-compartmentalized or non-curated networks, like the influence of transport reactions in the metabolic processes, especially the important effect on mitochondrial processes, as well as provide more accurate results of the fluxes used to optimize the metabolic processes within the microbial community.

  16. Compartmentalized metabolic network reconstruction of microbial communities to determine the effect of agricultural intervention on soils.

    Science.gov (United States)

    Alvarez-Silva, María Camila; Álvarez-Yela, Astrid Catalina; Gómez-Cano, Fabio; Zambrano, María Mercedes; Husserl, Johana; Danies, Giovanna; Restrepo, Silvia; González-Barrios, Andrés Fernando

    2017-01-01

    Soil microbial communities are responsible for a wide range of ecological processes and have an important economic impact in agriculture. Determining the metabolic processes performed by microbial communities is crucial for understanding and managing ecosystem properties. Metagenomic approaches allow the elucidation of the main metabolic processes that determine the performance of microbial communities under different environmental conditions and perturbations. Here we present the first compartmentalized metabolic reconstruction at a metagenomics scale of a microbial ecosystem. This systematic approach conceives a meta-organism without boundaries between individual organisms and allows the in silico evaluation of the effect of agricultural intervention on soils at a metagenomics level. To characterize the microbial ecosystems, topological properties, taxonomic and metabolic profiles, as well as a Flux Balance Analysis (FBA) were considered. Furthermore, topological and optimization algorithms were implemented to carry out the curation of the models, to ensure the continuity of the fluxes between the metabolic pathways, and to confirm the metabolite exchange between subcellular compartments. The proposed models provide specific information about ecosystems that are generally overlooked in non-compartmentalized or non-curated networks, like the influence of transport reactions in the metabolic processes, especially the important effect on mitochondrial processes, as well as provide more accurate results of the fluxes used to optimize the metabolic processes within the microbial community.

  17. Deciphering neuron-glia compartmentalization in cortical energy metabolism

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    Renaud Jolivet

    2009-07-01

    Full Text Available Energy demand is an important constraint on neural signaling. Several methods have been proposed to assess the energy budget of the brain based on a bottom-up approach in which the energy demand of individual biophysical processes are first estimated independently and then summed up to compute the brain's total energy budget. Here, we address this question using a novel approach that makes use of published datasets that reported average cerebral glucose and oxygen utilization in humans and rodents during different activation states. Our approach allows us (1 to decipher neuron-glia compartmentalization in energy metabolism and (2 to compute a precise state-dependent energy budget for the brain. Under the assumption that the fraction of energy used for signaling is proportional to the cycling of neurotransmitters, we find that in the activated state, most of the energy (~80% is oxidatively produced and consumed by neurons to support neuron-to-neuron signaling. Glial cells, while only contributing for a small fraction to energy production (~6%, actually take up a significant fraction of glucose (50% or more from the blood and provide neurons with glucose-derived energy substrates. Our results suggest that glycolysis occurs for a significant part in astrocytes whereas most of the oxygen is utilized in neurons. As a consequence, a transfer of glucose-derived metabolites from glial cells to neurons has to take place. Furthermore, we find that the amplitude of this transfer is correlated to (1 the activity level of the brain; the larger the activity, the more metabolites are shuttled from glia to neurons and (2 the oxidative activity in astrocytes; with higher glial pyruvate metabolism, less metabolites are shuttled from glia to neurons. While some of the details of a bottom-up biophysical approach have to be simplified, our method allows for a straightforward assessment of the brain's energy budget from macroscopic measurements with minimal

  18. Application of compartmental metabolic models for determination of retention and excretion functions; Aplicacao de modelos metabolicos para a determinacao de funcoes de excrecao e retencao

    Energy Technology Data Exchange (ETDEWEB)

    Rodrigues Junior, O

    1994-07-01

    After an intake of radioactive material, its behaviour in the human body can be described by mathematical models, where organs, tissues or regions of the body are treated as a chain of linked compartments. The mathematical approach for such metabolic models is usually done through a system of differential equations of first order with constant coefficients. The solutions of this system of equations associates the radionuclide intake, with the fraction excreted or retained in the organ of interest. A computer program - called INCORP and for running in PC compatible microcomputers - was developed in order to find the solutions of such system of equations, using an analytical method based on expansion of series of exponential matrices. The metabolic model presented in the ICRP-30 publication was simulated using the INCORP program, in order to find the respective retention and excretion curves for selected radionuclides. (author)

  19. A Calculus for Modelling, Simulating and Analysing Compartmentalized Biological Systems

    DEFF Research Database (Denmark)

    Mardare, Radu Iulian; Ihekwaba, Adoha

    2007-01-01

    A. Ihekwaba, R. Mardare. A Calculus for Modelling, Simulating and Analysing Compartmentalized Biological Systems. Case study: NFkB system. In Proc. of International Conference of Computational Methods in Sciences and Engineering (ICCMSE), American Institute of Physics, AIP Proceedings, N 2...

  20. Metabolism of monoterpenes: evidence for compartmentation of l-menthone metabolism in peppermint (Mentha piperita) leaves

    Energy Technology Data Exchange (ETDEWEB)

    Martinkus, C.; Croteau, R.

    1981-01-01

    Previous studies have shown that the monoterpene ketone l-(G-/sup 3/H)-menthone is reduced to the epimeric alcohols l-menthol and d-neomenthol in leaf discs of flowering peppermint (Mentha piperita L.), and that a portion of the menthol is converted to menthyl acetate while the bulk of the neomenthol is transformed to neomenthyl-..beta..-D-glucoside. When l-(3-/sup 3/H)menthol and d-(3-/sup 3/H)neomenthol are separately administered to leaf discs, both menthyl and neomenthyl acetates and menthyl and neomenthyl glucosides are formed with nearly equal facility, suggesting that the metabolic specificity observed with the ketone precursor was not a function of the specificity of the transglucosylase or transacetylase but rather a result of compartmentation of each stereospecific dehydrogenase with the appropriate transferase. Co-purification of the acceptor-mediated activities, and differential activation and inhibition studies, provided strong evidence that the same UDP-glucose-dependent enzyme could transfer glucose to either l-menthol or d-neometnthol. These results demonstrate that the specific in vivo conversion of l-menthone to l-menthyl acetate and d-neomenthyl-..beta..-D-glucoside cannot be attributed to the selectivity of the transferases, and they clearly indicate that the metabolic specificity observed is a result of compartmentation effects.

  1. Fractional two-compartmental model for articaine serum levels

    Science.gov (United States)

    Petronijevic, Branislava; Sarcev, Ivan; Zorica, Dusan; Janev, Marko; Atanackovic, Teodor M.

    2016-06-01

    Two fractional two-compartmental models are applied to the pharmacokinetics of articaine. Integer order derivatives are replaced by fractional derivatives, either of different, or of same orders. Models are formulated so that the mass balance is preserved. Explicit forms of the solutions are obtained in terms of the Mittag-Leffler functions. Pharmacokinetic parameters are determined by the use of the evolutionary algorithm and trust regions optimization to recover the experimental data.

  2. Products of Compartmental Models in Epidemiology.

    Science.gov (United States)

    Worden, Lee; Porco, Travis C

    2017-01-01

    We show that many structured epidemic models may be described using a straightforward product structure in this paper. Such products, derived from products of directed graphs, may represent useful refinements including geographic and demographic structure, age structure, gender, risk groups, or immunity status. Extension to multistrain dynamics, that is, pathogen heterogeneity, is also shown to be feasible in this framework. Systematic use of such products may aid in model development and exploration, can yield insight, and could form the basis of a systematic approach to numerical structural sensitivity analysis.

  3. Compartmentalization analysis using discrete fracture network models

    Energy Technology Data Exchange (ETDEWEB)

    La Pointe, P.R.; Eiben, T.; Dershowitz, W. [Golder Associates, Redmond, VA (United States); Wadleigh, E. [Marathon Oil Co., Midland, TX (United States)

    1997-08-01

    This paper illustrates how Discrete Fracture Network (DFN) technology can serve as a basis for the calculation of reservoir engineering parameters for the development of fractured reservoirs. It describes the development of quantitative techniques for defining the geometry and volume of structurally controlled compartments. These techniques are based on a combination of stochastic geometry, computational geometry, and graph the theory. The parameters addressed are compartment size, matrix block size and tributary drainage volume. The concept of DFN models is explained and methodologies to compute these parameters are demonstrated.

  4. Analytical properties of a three-compartmental dynamical demographic model

    Science.gov (United States)

    Postnikov, E. B.

    2015-07-01

    The three-compartmental demographic model by Korotaeyv-Malkov-Khaltourina, connecting population size, economic surplus, and education level, is considered from the point of view of dynamical systems theory. It is shown that there exist two integrals of motion, which enables the system to be reduced to one nonlinear ordinary differential equation. The study of its structure provides analytical criteria for the dominance ranges of the dynamics of Malthus and Kremer. Additionally, the particular ranges of parameters enable the derived general ordinary differential equations to be reduced to the models of Gompertz and Thoularis-Wallace.

  5. Poster — Thur Eve — 44: Linearization of Compartmental Models for More Robust Estimates of Regional Hemodynamic, Metabolic and Functional Parameters using DCE-CT/PET Imaging

    Energy Technology Data Exchange (ETDEWEB)

    Blais, AR; Dekaban, M; Lee, T-Y [Department of Medical Biophysics and Robarts Research Institute, Western University, Lawson Health Research Institute, London, ON (Canada)

    2014-08-15

    Quantitative analysis of dynamic positron emission tomography (PET) data usually involves minimizing a cost function with nonlinear regression, wherein the choice of starting parameter values and the presence of local minima affect the bias and variability of the estimated kinetic parameters. These nonlinear methods can also require lengthy computation time, making them unsuitable for use in clinical settings. Kinetic modeling of PET aims to estimate the rate parameter k{sub 3}, which is the binding affinity of the tracer to a biological process of interest and is highly susceptible to noise inherent in PET image acquisition. We have developed linearized kinetic models for kinetic analysis of dynamic contrast enhanced computed tomography (DCE-CT)/PET imaging, including a 2-compartment model for DCE-CT and a 3-compartment model for PET. Use of kinetic parameters estimated from DCE-CT can stabilize the kinetic analysis of dynamic PET data, allowing for more robust estimation of k{sub 3}. Furthermore, these linearized models are solved with a non-negative least squares algorithm and together they provide other advantages including: 1) only one possible solution and they do not require a choice of starting parameter values, 2) parameter estimates are comparable in accuracy to those from nonlinear models, 3) significantly reduced computational time. Our simulated data show that when blood volume and permeability are estimated with DCE-CT, the bias of k{sub 3} estimation with our linearized model is 1.97 ± 38.5% for 1,000 runs with a signal-to-noise ratio of 10. In summary, we have developed a computationally efficient technique for accurate estimation of k{sub 3} from noisy dynamic PET data.

  6. Immunometabolism of obesity and diabetes: microbiota link compartmentalized immunity in the gut to metabolic tissue inflammation.

    Science.gov (United States)

    McPhee, Joseph B; Schertzer, Jonathan D

    2015-12-01

    The bacteria that inhabit us have emerged as factors linking immunity and metabolism. Changes in our microbiota can modify obesity and the immune underpinnings of metabolic diseases such as Type 2 diabetes. Obesity coincides with a low-level systemic inflammation, which also manifests within metabolic tissues such as adipose tissue and liver. This metabolic inflammation can promote insulin resistance and dysglycaemia. However, the obesity and metabolic disease-related immune responses that are compartmentalized in the intestinal environment do not necessarily parallel the inflammatory status of metabolic tissues that control blood glucose. In fact, a permissive immune environment in the gut can exacerbate metabolic tissue inflammation. Unravelling these discordant immune responses in different parts of the body and establishing a connection between nutrients, immunity and the microbiota in the gut is a complex challenge. Recent evidence positions the relationship between host gut barrier function, intestinal T cell responses and specific microbes at the crossroads of obesity and inflammation in metabolic disease. A key problem to be addressed is understanding how metabolite, immune or bacterial signals from the gut are relayed and transferred into systemic or metabolic tissue inflammation that can impair insulin action preceding Type 2 diabetes. © 2015 Authors; published by Portland Press Limited.

  7. Efficient Vaccine Distribution Based on a Hybrid Compartmental Model.

    Directory of Open Access Journals (Sweden)

    Zhiwen Yu

    Full Text Available To effectively and efficiently reduce the morbidity and mortality that may be caused by outbreaks of emerging infectious diseases, it is very important for public health agencies to make informed decisions for controlling the spread of the disease. Such decisions must incorporate various kinds of intervention strategies, such as vaccinations, school closures and border restrictions. Recently, researchers have paid increased attention to searching for effective vaccine distribution strategies for reducing the effects of pandemic outbreaks when resources are limited. Most of the existing research work has been focused on how to design an effective age-structured epidemic model and to select a suitable vaccine distribution strategy to prevent the propagation of an infectious virus. Models that evaluate age structure effects are common, but models that additionally evaluate geographical effects are less common. In this paper, we propose a new SEIR (susceptible-exposed-infectious šC recovered model, named the hybrid SEIR-V model (HSEIR-V, which considers not only the dynamics of infection prevalence in several age-specific host populations, but also seeks to characterize the dynamics by which a virus spreads in various geographic districts. Several vaccination strategies such as different kinds of vaccine coverage, different vaccine releasing times and different vaccine deployment methods are incorporated into the HSEIR-V compartmental model. We also design four hybrid vaccination distribution strategies (based on population size, contact pattern matrix, infection rate and infectious risk for controlling the spread of viral infections. Based on data from the 2009-2010 H1N1 influenza epidemic, we evaluate the effectiveness of our proposed HSEIR-V model and study the effects of different types of human behaviour in responding to epidemics.

  8. Compartmental models for assessing the fishery production in the Indian Ocean

    Digital Repository Service at National Institute of Oceanography (India)

    Dalal, S.G.; Parulekar, A.H.

    Compartmental models for assessing the fishery production in the Indian Ocean is discussed. The article examines the theoretical basis on which modern fishery sciences is built. The model shows that, large changes in energy flux from one pathway...

  9. Metabolic Compartmentation – A System Level Property of Muscle Cells

    Directory of Open Access Journals (Sweden)

    Theo Wallimann

    2008-05-01

    Full Text Available Problems of quantitative investigation of intracellular diffusion and compartmentation of metabolites are analyzed. Principal controversies in recently published analyses of these problems for the living cells are discussed. It is shown that the formal theoretical analysis of diffusion of metabolites based on Fick’s equation and using fixed diffusion coefficients for diluted homogenous aqueous solutions, but applied for biological systems in vivo without any comparison with experimental results, may lead to misleading conclusions, which are contradictory to most biological observations. However, if the same theoretical methods are used for analysis of actual experimental data, the apparent diffusion constants obtained are orders of magnitude lower than those in diluted aqueous solutions. Thus, it can be concluded that local restrictions of diffusion of metabolites in a cell are a system-level properties caused by complex structural organization of the cells, macromolecular crowding, cytoskeletal networks and organization of metabolic pathways into multienzyme complexes and metabolons. This results in microcompartmentation of metabolites, their channeling between enzymes and in modular organization of cellular metabolic networks. The perspectives of further studies of these complex intracellular interactions in the framework of Systems Biology are discussed.

  10. Hypothalamic metabolic compartmentation during appetite regulation as revealed by magnetic resonance imaging and spectroscopy methods

    Directory of Open Access Journals (Sweden)

    Blanca eLizarbe

    2013-06-01

    Full Text Available We review the role of neuroglial compartmentation and transcellular neurotransmitter cycling during hypothalamic appetite regulation as detected by Magnetic Resonance Imaging (MRI and Spectroscopy (MRS methods. We address first the neurochemical basis of neuroendocrine regulation in the hypothalamus and the orexigenic and anorexigenic feed-back loops that control appetite. Then we examine the main Magnetic Resonance Imaging and Spectroscopy strategies that have been used to investigate appetite regulation. Manganese enhanced magnetic resonance imaging (MEMRI, Blood oxygenation level dependent contrast (BOLD and Diffusion weighted magnetic resonance imaging (DWI have revealed Mn2+accumulations, augmented oxygen consumptions and astrocytic swelling in the hypothalamus under fasting conditions, respectively. High field 1H magnetic resonance in vivo, showed increased hypothalamic myo-inositol concentrations as compared to other cerebral structures. 1H and 13C high resolution magic angle spinning (HRMAS revealed increased neuroglial oxidative and glycolytic metabolism, as well as increased hypothalamic glutamatergic and GABAergic neurotransmissions under orexigenic stimulation. We propose here an integrative interpretation of all these findings suggesting that the neuroendocrine regulation of appetite is supported by important ionic and metabolic transcellular fluxes which begin at the tripartite orexigenic clefts and become extended spatially in the hypothalamus through astrocytic networks, becoming eventually MRI and MRS detectable.

  11. Hypothalamic metabolic compartmentation during appetite regulation as revealed by magnetic resonance imaging and spectroscopy methods

    Science.gov (United States)

    Lizarbe, Blanca; Benitez, Ania; Peláez Brioso, Gerardo A.; Sánchez-Montañés, Manuel; López-Larrubia, Pilar; Ballesteros, Paloma; Cerdán, Sebastián

    2013-01-01

    We review the role of neuroglial compartmentation and transcellular neurotransmitter cycling during hypothalamic appetite regulation as detected by Magnetic Resonance Imaging (MRI) and Spectroscopy (MRS) methods. We address first the neurochemical basis of neuroendocrine regulation in the hypothalamus and the orexigenic and anorexigenic feed-back loops that control appetite. Then we examine the main MRI and MRS strategies that have been used to investigate appetite regulation. Manganese-enhanced magnetic resonance imaging (MEMRI), Blood oxygenation level-dependent contrast (BOLD), and Diffusion-weighted magnetic resonance imaging (DWI) have revealed Mn2+ accumulations, augmented oxygen consumptions, and astrocytic swelling in the hypothalamus under fasting conditions, respectively. High field 1H magnetic resonance in vivo, showed increased hypothalamic myo-inositol concentrations as compared to other cerebral structures. 1H and 13C high resolution magic angle spinning (HRMAS) revealed increased neuroglial oxidative and glycolytic metabolism, as well as increased hypothalamic glutamatergic and GABAergic neurotransmissions under orexigenic stimulation. We propose here an integrative interpretation of all these findings suggesting that the neuroendocrine regulation of appetite is supported by important ionic and metabolic transcellular fluxes which begin at the tripartite orexigenic clefts and become extended spatially in the hypothalamus through astrocytic networks becoming eventually MRI and MRS detectable. PMID:23781199

  12. Verification of Compartmental Epidemiological Models using Metamorphic Testing, Model Checking and Visual Analytics

    Energy Technology Data Exchange (ETDEWEB)

    Ramanathan, Arvind [ORNL; Steed, Chad A [ORNL; Pullum, Laura L [ORNL

    2012-01-01

    Compartmental models in epidemiology are widely used as a means to model disease spread mechanisms and understand how one can best control the disease in case an outbreak of a widespread epidemic occurs. However, a significant challenge within the community is in the development of approaches that can be used to rigorously verify and validate these models. In this paper, we present an approach to rigorously examine and verify the behavioral properties of compartmen- tal epidemiological models under several common modeling scenarios including birth/death rates and multi-host/pathogen species. Using metamorphic testing, a novel visualization tool and model checking, we build a workflow that provides insights into the functionality of compartmental epidemiological models. Our initial results indicate that metamorphic testing can be used to verify the implementation of these models and provide insights into special conditions where these mathematical models may fail. The visualization front-end allows the end-user to scan through a variety of parameters commonly used in these models to elucidate the conditions under which an epidemic can occur. Further, specifying these models using a process algebra allows one to automatically construct behavioral properties that can be rigorously verified using model checking. Taken together, our approach allows for detecting implementation errors as well as handling conditions under which compartmental epidemiological models may fail to provide insights into disease spread dynamics.

  13. Aspects of astrocyte energy metabolism, amino acid neurotransmitter homoeostasis and metabolic compartmentation

    DEFF Research Database (Denmark)

    Kreft, Marko; Bak, Lasse Kristoffer; Waagepetersen, Helle S

    2012-01-01

    Astrocytes are key players in brain function; they are intimately involved in neuronal signalling processes and their metabolism is tightly coupled to that of neurons. In the present review, we will be concerned with a discussion of aspects of astrocyte metabolism, including energy...

  14. Global identifiability of linear compartmental models--a computer algebra algorithm.

    Science.gov (United States)

    Audoly, S; D'Angiò, L; Saccomani, M P; Cobelli, C

    1998-01-01

    A priori global identifiability deals with the uniqueness of the solution for the unknown parameters of a model and is, thus, a prerequisite for parameter estimation of biological dynamic models. Global identifiability is however difficult to test, since it requires solving a system of algebraic nonlinear equations which increases both in nonlinearity degree and number of terms and unknowns with increasing model order. In this paper, a computer algebra tool, GLOBI (GLOBal Identifiability) is presented, which combines the topological transfer function method with the Buchberger algorithm, to test global identifiability of linear compartmental models. GLOBI allows for the automatic testing of a priori global identifiability of general structure compartmental models from general multi input-multi output experiments. Examples of usage of GLOBI to analyze a priori global identifiability of some complex biological compartmental models are provided.

  15. A Computational Modeling and Simulation Approach to Investigate Mechanisms of Subcellular cAMP Compartmentation.

    Directory of Open Access Journals (Sweden)

    Pei-Chi Yang

    2016-07-01

    Full Text Available Subcellular compartmentation of the ubiquitous second messenger cAMP has been widely proposed as a mechanism to explain unique receptor-dependent functional responses. How exactly compartmentation is achieved, however, has remained a mystery for more than 40 years. In this study, we developed computational and mathematical models to represent a subcellular sarcomeric space in a cardiac myocyte with varying detail. We then used these models to predict the contributions of various mechanisms that establish subcellular cAMP microdomains. We used the models to test the hypothesis that phosphodiesterases act as functional barriers to diffusion, creating discrete cAMP signaling domains. We also used the models to predict the effect of a range of experimentally measured diffusion rates on cAMP compartmentation. Finally, we modeled the anatomical structures in a cardiac myocyte diad, to predict the effects of anatomical diffusion barriers on cAMP compartmentation. When we incorporated experimentally informed model parameters to reconstruct an in silico subcellular sarcomeric space with spatially distinct cAMP production sites linked to caveloar domains, the models predict that under realistic conditions phosphodiesterases alone were insufficient to generate significant cAMP gradients. This prediction persisted even when combined with slow cAMP diffusion. When we additionally considered the effects of anatomic barriers to diffusion that are expected in the cardiac myocyte dyadic space, cAMP compartmentation did occur, but only when diffusion was slow. Our model simulations suggest that additional mechanisms likely contribute to cAMP gradients occurring in submicroscopic domains. The difference between the physiological and pathological effects resulting from the production of cAMP may be a function of appropriate compartmentation of cAMP signaling. Therefore, understanding the contribution of factors that are responsible for coordinating the spatial and

  16. Compartmental Model For Uptake Of 137cs By Pine In Forest Soil ...

    African Journals Online (AJOL)

    A compartmental model of soil to pine tree transfer of 137Cs following the Chernobyl nuclear accident is presented. The model was validated using data collected in 1996 at five sites in Northern Ukraine. The transfer constants of 137Cs between model compartments are estimated using a semi-empirical method.

  17. A new compartmental method for the analysis of liver FDG kinetics in small animal models.

    Science.gov (United States)

    Garbarino, Sara; Vivaldi, Valentina; Delbary, Fabrice; Caviglia, Giacomo; Piana, Michele; Marini, Cecilia; Capitanio, Selene; Calamia, Iolanda; Buschiazzo, Ambra; Sambuceti, Gianmario

    2015-12-01

    Compartmental analysis is a standard method to quantify metabolic processes using fluorodeoxyglucose-positron emission tomography (FDG-PET). For liver studies, this analysis is complex due to the hepatocyte capability to dephosphorylate and release glucose and FDG into the blood. Moreover, a tracer is supplied to the liver by both the hepatic artery and the portal vein, which is not visible in PET images. This study developed an innovative computational approach accounting for the reversible nature of FDG in the liver and directly computing the portal vein tracer concentration by means of gut radioactivity measurements. Twenty-one mice were subdivided into three groups: the control group 'CTR' (n = 7) received no treatment, the short-term starvation group 'STS' (n = 7) was submitted to food deprivation with free access to water within 48 h before imaging, and the metformin group 'MTF' (n = 7) was treated with metformin (750 mg/Kg per day) for 1 month. All mice underwent a dynamic micro-PET study for 50 min after an (18)F-FDG injection. The compartmental analysis considered two FDG pools (phosphorylated and free) in both the gut and liver. A tracer was carried into the liver by the hepatic artery and the portal vein, and tracer delivery from the gut was considered as the sole input for portal vein tracer concentration. Accordingly, both the liver and gut were characterized by two compartments and two exchange coefficients. Each one of the two two-compartment models was mathematically described by a system of differential equations, and data optimization was performed by applying a Newton algorithm to the inverse problems associated to these differential systems. All rate constants were stable in each group. The tracer coefficient from the free to the metabolized compartment in the liver was increased by STS, while it was unaltered by MTF. By contrast, the tracer coefficient from the metabolized to the free compartment was reduced by MTF and increased by STS. Data

  18. Evaluation of the GastroPlus™ Advanced Compartmental and Transit (ACAT) Model in Early Discovery.

    Science.gov (United States)

    Gobeau, N; Stringer, R; De Buck, S; Tuntland, T; Faller, B

    2016-09-01

    The aim of this study was to evaluate the oral exposure predictions obtained early in drug discovery with a generic GastroPlus Advanced Compartmental And Transit (ACAT) model based on the in vivo intravenous blood concentration-time profile, in silico properties (lipophilicity, pKa) and in vitro high-throughput absorption-distribution-metabolism-excretion (ADME) data (as determined by PAMPA, solubility, liver microsomal stability assays). The model was applied to a total of 623 discovery molecules and their oral exposure was predicted in rats and/or dogs. The predictions of Cmax, AUClast and Tmax were compared against the observations. The generic model proved to make predictions of oral Cmax, AUClast and Tmax within 3-fold of the observations for rats in respectively 65%, 68% and 57% of the 537 cases. For dogs, it was respectively 77%, 79% and 85% of the 124 cases. Statistically, the model was most successful at predicting oral exposure of Biopharmaceutical Classification System (BCS) class 1 compounds compared to classes 2 and 3, and was worst at predicting class 4 compounds oral exposure. The generic GastroPlus ACAT model provided reasonable predictions especially for BCS class 1 compounds. For compounds of other classes, the model may be refined by obtaining more information on solubility and permeability in secondary assays. This increases confidence that such a model can be used in discovery projects to understand the parameters limiting absorption and extrapolate predictions across species. Also, when predictions disagree with the observations, the model can be updated to test hypotheses and understand oral absorption.

  19. Natural isotopic signatures of variations in body nitrogen fluxes: a compartmental model analysis.

    Directory of Open Access Journals (Sweden)

    Nathalie Poupin

    2014-10-01

    Full Text Available Body tissues are generally 15N-enriched over the diet, with a discrimination factor (Δ15N that varies among tissues and individuals as a function of their nutritional and physiopathological condition. However, both 15N bioaccumulation and intra- and inter-individual Δ15N variations are still poorly understood, so that theoretical models are required to understand their underlying mechanisms. Using experimental Δ15N measurements in rats, we developed a multi-compartmental model that provides the first detailed representation of the complex functioning of the body's Δ15N system, by explicitly linking the sizes and Δ15N values of 21 nitrogen pools to the rates and isotope effects of 49 nitrogen metabolic fluxes. We have shown that (i besides urea production, several metabolic pathways (e.g., protein synthesis, amino acid intracellular metabolism, urea recycling and intestinal absorption or secretion are most probably associated with isotope fractionation and together contribute to 15N accumulation in tissues, (ii the Δ15N of a tissue at steady-state is not affected by variations of its P turnover rate, but can vary according to the relative orientation of tissue free amino acids towards oxidation vs. protein synthesis, (iii at the whole-body level, Δ15N variations result from variations in the body partitioning of nitrogen fluxes (e.g., urea production, urea recycling and amino acid exchanges, with or without changes in nitrogen balance, (iv any deviation from the optimal amino acid intake, in terms of both quality and quantity, causes a global rise in tissue Δ15N, and (v Δ15N variations differ between tissues depending on the metabolic changes involved, which can therefore be identified using simultaneous multi-tissue Δ15N measurements. This work provides proof of concept that Δ15N measurements constitute a new promising tool to investigate how metabolic fluxes are nutritionally or physiopathologically reorganized or altered. The

  20. A biphasic compartmentation of neuro-transmission associated metabolic patterns during electro-induced axoplasmic transport in sheep medulla oblongata.

    Science.gov (United States)

    Narasimham, P V; Mohanachari, V; Swami, K S; Indira, K

    1983-01-01

    In the sheep medulla oblongata, on the induction of polarity by the applied voltage gradient of direct current along the length, the enzymes such as acetylcholinesterase and glutamate dehydrogenase showed anodal transport while the enzyme arginase showed cathodal transport indicating the possession of negative and positive charge densities on the enzymes. These studies indicated that the glutamate bound metabolism, one towards ammonia formation and the other towards the energy production and neural transmission, have opposed electro-characteristics. The acetylcholinesterase system had anodal characteristics coupled to the glutamate dehydrogenase patterns. The existence of two charge based compartmentation is envisaged in the neural tissue.

  1. Natural Isotopic Signatures of Variations in Body Nitrogen Fluxes: A Compartmental Model Analysis

    OpenAIRE

    Poupin, Nathalie; Mariotti, François; Huneau, Jean-François; Hermier, Dominique; Fouillet, Hélène

    2014-01-01

    Body tissues are generally N-15-enriched over the diet, with a discrimination factor (Delta N-15) that varies among tissues and individuals as a function of their nutritional and physiopathological condition. However, both N-15 bioaccumulation and intra-and inter-individual D N-15 variations are still poorly understood, so that theoretical models are required to understand their underlying mechanisms. Using experimental D N-15 measurements in rats, we developed a multi-compartmental model tha...

  2. Guaranteed computation methods for compartmental in-series models under uncertainty

    OpenAIRE

    Pereda Sebastián, Diego de; Romero Vivó, Sergio; Ricarte Benedito, Beatriz; Bondía Company, Jorge

    2013-01-01

    The pattern of some real phenomenon can be described by compartmental in-series models. Nevertheless, most of these processes are characterized by their variability, which produces that the exact values of the model parameters are uncertain, although they can be bounded by intervals. The aim of this paper is to compute tight solution envelopes that guarantee the inclusion of all possible behaviors of such processes. Current methods, such as monotonicity analysis, enable us t...

  3. The construction of next-generation matrices for compartmental epidemic models

    OpenAIRE

    Diekmann, O.; Heesterbeek, J.A.P; Roberts, M G

    2009-01-01

    The basic reproduction number ℛ0 is arguably the most important quantity in infectious disease epidemiology. The next-generation matrix (NGM) is the natural basis for the definition and calculation of ℛ0 where finitely many different categories of individuals are recognized. We clear up confusion that has been around in the literature concerning the construction of this matrix, specifically for the most frequently used so-called compartmental models. We present a detailed easy recipe for the ...

  4. A Residual Approach for Balanced Truncation Model Reduction (BTMR of Compartmental Systems

    Directory of Open Access Journals (Sweden)

    William La Cruz

    2014-05-01

    Full Text Available This paper presents a residual approach of the square root balanced truncation algorithm for model order reduction of continuous, linear and time-invariante compartmental systems. Specifically, the new approach uses a residual method to approximate the controllability and observability gramians, whose resolution is an essential step of the square root balanced truncation algorithm, that requires a great computational cost. Numerical experiences are included to highlight the efficacy of the proposed approach.

  5. A compartmental model for computer virus propagation with kill signals

    Science.gov (United States)

    Ren, Jianguo; Xu, Yonghong

    2017-11-01

    Research in the area of kill signals for prevention of computer virus is of significant importance for computer users. The kill signals allow computer users to take precautions beforehand. In this paper, a computer virus propagation model based on the kill signals, called SEIR-KS model, is formulated and full dynamics of the proposed model are theoretically analyzed. An epidemic threshold is obtained and the existence and uniqueness of the virus equilibrium are investigated. It is proved that the virus-free equilibrium and virus equilibrium are locally and globally asymptotically stable by applying Routh-Hurwitz criterion and Lyapunov functional approach. The results of numerical simulations are provided that verifies the theoretical results. The availability of the proposed model has been validated with following observations: (1) the density of infected nodes in the proposed model drops to approximately 75% compared to the model in related literature; and (2) a higher density of KS is conductive to inhibition of virus diffusion.

  6. A generic multistage compartmental model for interpreting gas production profiles

    NARCIS (Netherlands)

    Lopez, S.; Dijkstra, J.; Dhanoa, M.S.; Bannink, A.; Kebreab, E.; France, J.

    2010-01-01

    The gas production technique has become a key tool in feed evaluation and rumen fermentation studies. The value of the technique relies on modelling experimental data to obtain estimates of rumen degradation parameters. One of the first models used to describe gas production profiles was the simple

  7. A comprehensive compartmental model of blood glucose regulation for healthy and type 2 diabetic subjects

    DEFF Research Database (Denmark)

    Vahidi, O; Kwok, K E; Gopaluni, R B

    2016-01-01

    We have expanded a former compartmental model of blood glucose regulation for healthy and type 2 diabetic subjects. The former model was a detailed physiological model which considered the interactions of three substances, glucose, insulin and glucagon on regulating the blood sugar. The main...... obtained during oral glucose tolerance test and isoglycemic intravenous glucose infusion test from both type 2 diabetic and healthy subjects to estimate the model parameters and to validate the model results. The estimation of model parameters is accomplished through solving a nonlinear optimization...

  8. Development of a Novel Oral Cavity Compartmental Absorption and Transit Model for Sublingual Administration: Illustration with Zolpidem

    National Research Council Canada - National Science Library

    Xia, Binfeng; Yang, Zhen; Zhou, Haiying; Lukacova, Viera; Zhu, Wei; Milewski, Mikolaj; Kesisoglou, Filippos

    2015-01-01

    ...™ Oral Cavity Compartmental Absorption and Transit (OCCAT™) model was used to simulate the plasma concentration versus time profiles and the fraction and rate of intraoral drug transit/absorption for Intermezzo...

  9. Bayesian analysis of botanical epidemics using stochastic compartmental models.

    Science.gov (United States)

    Gibson, G J; Kleczkowski, A; Gilligan, C A

    2004-08-17

    A stochastic model for an epidemic, incorporating susceptible, latent, and infectious states, is developed. The model represents primary and secondary infection rates and a time-varying host susceptibility with applications to a wide range of epidemiological systems. A Markov chain Monte Carlo algorithm is presented that allows the model to be fitted to experimental observations within a Bayesian framework. The approach allows the uncertainty in unobserved aspects of the process to be represented in the parameter posterior densities. The methods are applied to experimental observations of damping-off of radish (Raphanus sativus) caused by the fungal pathogen Rhizoctonia solani, in the presence and absence of the antagonistic fungus Trichoderma viride, a biological control agent that has previously been shown to affect the rate of primary infection by using a maximum-likelihood estimate for a simpler model with no allowance for a latent period. Using the Bayesian analysis, we are able to estimate the latent period from population data, even when there is uncertainty in discriminating infectious from latently infected individuals in data collection. We also show that the inference that T. viride can control primary, but not secondary, infection is robust to inclusion of the latent period in the model, although the absolute values of the parameters change. Some refinements and potential difficulties with the Bayesian approach in this context, when prior information on parameters is lacking, are discussed along with broader applications of the methods to a wide range of epidemiological systems.

  10. A compartmentalized solute transport model for redox zones in contaminated aquifers--1, Theory and development

    Science.gov (United States)

    Abrams , Robert H.; Loague, Keith

    2000-01-01

    This paper, the first of two parts [see Abrams and Loague, this issue], takes the compartmentalized approach for the geochemical evolution of redox zones presented by Abrams et al. [1998] and embeds it within a solute transport framework. In this paper the compartmentalized approach is generalized to facilitate the description of its incorporation into a solute transport simulator. An equivalent formulation is developed which removes any discontinuities that may occur when switching compartments. Rate-limited redox reactions are modeled with a modified Monod relationship that allows either the organic substrate or the electron acceptor to be the rate-limiting reactant. Thermodynamic constraints are used to inhibit lower-energy redox reactions from occurring under infeasible geochemical conditions without imposing equilibrium on the lower-energy reactions. The procedure used allows any redox reaction to be simulated as being kinetically limited or thermodynamically limited, depending on local geochemical conditions. Empirical reaction inhibition methods are not needed. The sequential iteration approach (SIA), a technique which allows the number of solute transport equations to be reduced, is adopted to solve the coupled geochemical/solute transport problem. When the compartmentalized approach is embedded within the SIA, with the total analytical concentration of each component as the dependent variable in the transport equation, it is possible to reduce the number of transport equations even further than with the unmodified SIA. A one-dimensional, coupled geochemical/solute transport simulation is presented in which redox zones evolve dynamically in time and space. The compartmentalized solute transport (COMPTRAN) model described in this paper enables the development of redox zones to be simulated under both kinetic and thermodynamic constraints. The modular design of COMPTRAN facilitates the use of many different, preexisting solute transport and geochemical codes

  11. DISTING: A web application for fast algorithmic computation of alternative indistinguishable linear compartmental models.

    Science.gov (United States)

    Davidson, Natalie R; Godfrey, Keith R; Alquaddoomi, Faisal; Nola, David; DiStefano, Joseph J

    2017-05-01

    We describe and illustrate use of DISTING, a novel web application for computing alternative structurally identifiable linear compartmental models that are input-output indistinguishable from a postulated linear compartmental model. Several computer packages are available for analysing the structural identifiability of such models, but DISTING is the first to be made available for assessing indistinguishability. The computational algorithms embedded in DISTING are based on advanced versions of established geometric and algebraic properties of linear compartmental models, embedded in a user-friendly graphic model user interface. Novel computational tools greatly speed up the overall procedure. These include algorithms for Jacobian matrix reduction, submatrix rank reduction, and parallelization of candidate rank computations in symbolic matrix analysis. The application of DISTING to three postulated models with respectively two, three and four compartments is given. The 2-compartment example is used to illustrate the indistinguishability problem; the original (unidentifiable) model is found to have two structurally identifiable models that are indistinguishable from it. The 3-compartment example has three structurally identifiable indistinguishable models. It is found from DISTING that the four-compartment example has five structurally identifiable models indistinguishable from the original postulated model. This example shows that care is needed when dealing with models that have two or more compartments which are neither perturbed nor observed, because the numbering of these compartments may be arbitrary. DISTING is universally and freely available via the Internet. It is easy to use and circumvents tedious and complicated algebraic analysis previously done by hand. Copyright © 2017 Elsevier B.V. All rights reserved.

  12. Subcellular compartmentation of sugar signalling: Links among carbon cellular status, route of sucrolysis, sink-source allocation, and metabolic partitioning

    Directory of Open Access Journals (Sweden)

    Axel eTiessen

    2013-01-01

    Full Text Available Recent findings suggest that both subcellular compartmentation and route of sucrolysis are important for plant development, growth, and yield. Signalling effects are dependent on the tissue, cell type and stage of development. Downstream effects also depend on the amount and localisation of hexoses and disaccharides. All enzymes of sucrose metabolism (e.g. invertase, hexokinase, fructokinase, sucrose synthase, and sucrose 6-phosphate synthase are not produced from single genes, but from paralogue families in plant genomes. Each paralogue has unique expression across plant organs and developmental stages. Multiple isoforms can be targeted to different cellular compartments (e.g. plastids, mitochondria, nuclei, and cytosol. Many of the key enzymes are regulated by post-transcriptional modifications and associate in multimeric protein complexes. Some isoforms have regulatory functions, either in addition to or in replacement of their catalytic activity. This explains why some isozymes are not redundant, but also complicates elucidation of their specific involvement in sugar signalling. The subcellular compartmentation of sucrose metabolism forces refinement of some of the paradigms of sugar signalling during physiological processes. For example, the catalytic and signalling functions of diverse paralogues needs to be more carefully analysed in the context of post-genomic biology. It is important to note that it is the differential localization of both the sugars themselves as well as the sugar-metabolizing enzymes that ultimately led to sugar signalling. We conclude that a combination of subcellular complexity and gene duplication/subfunctionalization gave rise to sugar signalling as a regulatory mechanism in plant cells.

  13. Reconstruction and modeling protein translocation and compartmentalization in Escherichia coli at the genome-scale

    DEFF Research Database (Denmark)

    Liu, Joanne K.; O’Brien, Edward J.; Lerman, Joshua A.

    2014-01-01

    cell morphology. Comparison of computations performed with this expanded ME-model, named iJL1678-ME, against available experimental data reveals that the model accurately describes translocation pathway expression and the functional proteome by compartmentalized mass. Conclusion: iJL1678-ME enables...... the functional content of membranes, cellular compartment-specific composition, and that it can be utilized to examine the effect of perturbing an expanded set of network components. iJL1678-ME takes a notable step towards the inclusion of cellular ultra-structure in genome-scale models....

  14. Modeling Heterogeneity in Direct Infectious Disease Transmission in a Compartmental Model

    Directory of Open Access Journals (Sweden)

    Lingcai Kong

    2016-02-01

    Full Text Available Mathematical models have been used to understand the transmission dynamics of infectious diseases and to assess the impact of intervention strategies. Traditional mathematical models usually assume a homogeneous mixing in the population, which is rarely the case in reality. Here, we construct a new transmission function by using as the probability density function a negative binomial distribution, and we develop a compartmental model using it to model the heterogeneity of contact rates in the population. We explore the transmission dynamics of the developed model using numerical simulations with different parameter settings, which characterize different levels of heterogeneity. The results show that when the reproductive number, R0, is larger than one, a low level of heterogeneity results in dynamics similar to those predicted by the homogeneous mixing model. As the level of heterogeneity increases, the dynamics become more different. As a test case, we calibrated the model with the case incidence data for severe acute respiratory syndrome (SARS in Beijing in 2003, and the estimated parameters demonstrated the effectiveness of the control measures taken during that period.

  15. Multi-scale hierarchical approach for parametric mapping: assessment on multi-compartmental models.

    Science.gov (United States)

    Rizzo, G; Turkheimer, F E; Bertoldo, A

    2013-02-15

    This paper investigates a new hierarchical method to apply basis function to mono- and multi-compartmental models (Hierarchical-Basis Function Method, H-BFM) at a voxel level. This method identifies the parameters of the compartmental model in its nonlinearized version, integrating information derived at the region of interest (ROI) level by segmenting the cerebral volume based on anatomical definition or functional clustering. We present the results obtained by using a two tissue-four rate constant model with two different tracers ([(11)C]FLB457 and [carbonyl-(11)C]WAY100635), one of the most complex models used in receptor studies, especially at the voxel level. H-BFM is robust and its application on both [(11)C]FLB457 and [carbonyl-(11)C]WAY100635 allows accurate and precise parameter estimates, good quality parametric maps and a low percentage of voxels out of physiological bound (modeling at the voxel level. In particular, different from other proposed approaches, this method can also be used when the linearization of the model is not appropriate. We expect that applying it to clinical data will generate reliable parametric maps. Copyright © 2012 Elsevier Inc. All rights reserved.

  16. A compartmental model of the cAMP/PKA/MAPK pathway in Bio-PEPA

    Directory of Open Access Journals (Sweden)

    Federica Ciocchetta

    2009-11-01

    Full Text Available The vast majority of biochemical systems involve the exchange of information between different compartments, either in the form of transportation or via the intervention of membrane proteins which are able to transmit stimuli between bordering compartments. The correct quantitative handling of compartments is, therefore, extremely important when modelling real biochemical systems. The Bio-PEPA process algebra is equipped with the capability of explicitly defining quantitative information such as compartment volumes and membrane surface areas. Furthermore, the recent development of the Bio-PEPA Eclipse Plug-in allows us to perform a correct stochastic simulation of multi-compartmental models. Here we present a Bio-PEPA compartmental model of the cAMP/PKA/MAPK pathway. We analyse the system using the Bio-PEPA Eclipse Plug-in and we show the correctness of our model by comparison with an existing ODE model. Furthermore, we perform computational experiments in order to investigate certain properties of the pathway. Specifically, we focus on the system response to the inhibition and strengthening of feedback loops and to the variation in the activity of key pathway reactions and we observe how these modifications affect the behaviour of the pathway. These experiments are useful to understand the control and regulatory mechanisms of the system.

  17. Personalization of a compartmental physiological model for an artificial pancreas through integration of patient's state estimation.

    Science.gov (United States)

    Jallon, P; Lachal, S; Franco, C; Charpentier, G; Huneker, E; Doron, M; Franc, Sylvia; Benhamou, Pierre-Yves; Borot, Sophie; Guerci, Bruno; Hanaire, He le Ne; Jeandidier, Nathalie; Penfornis, Alfred; Renard, Eric; Reznik, Yves; Schaepelynck, Pauline; Simon, Chantal

    2017-07-01

    Artificial Pancreas (AP) are developed for patients with Type 1 diabetes. This medical device system consists in the association of a subcutaneous continuous glucose monitor (CGM) providing a proxy of the patient's glycaemia and a control algorithm offering the real-time modification of the insulin delivery with an automatic command of the subcutaneous insulin pump. The most complex algorithms are based on a compartmental model of the glucoregulatory system of the patient coupled to an approach of MPC (Model-Predictive-Control) for the command. The automatic and unsupervised control of insulin regulation constitutes a major challenge in AP projects. A given model with its parameterization on the shelf will not directly represent the patient's data behavior and the personalization of the model is a prerequisite before using it in a MPC. The present paper focuses on the personalization of a compartmental showing a method where taking into account the estimation of the patient's state in addition to the parameter estimation improves the results in terms of mean quadratic error.

  18. Metabolic hormones, apolipoproteins, adipokines, and cytokines in the alveolar lining fluid of healthy adults: compartmentalization and physiological correlates.

    Directory of Open Access Journals (Sweden)

    Carlos O Mendivil

    Full Text Available Our current understanding of hormone regulation in lung parenchyma is quite limited. We aimed to quantify a diverse array of biologically relevant protein mediators in alveolar lining fluid (ALF, compared to serum concentrations, and explore factors associated with protein compartmentalization on either side of the air-blood barrier.Participants were 24 healthy adult non-smoker volunteers without respiratory symptoms or significant medical conditions, with normal lung exams and office spirometry. Cell-free bronchoalveolar lavage fluid and serum were analyzed for 24 proteins (including enteric and metabolic hormones, apolipoproteins, adipokines, and cytokines using a highly sensitive multiplex ELISA. Measurements were normalized to ALF concentrations. The ALF:serum concentration ratios were examined in relation to measures of protein size, hydrophobicity, charge, and to participant clinical and spirometric values.ALF measurements from 24 individuals detected 19 proteins, including adiponectin, adipsin, apoA-I, apoA-II, apoB, apoC-II, apoC-III, apoE, C-reactive protein, ghrelin, glucose-dependent insulinotropic peptide (GIP, glucagon-like peptide-1 (GLP-1, glucagon, insulin, leptin, monocyte chemoattractant protein-1, plasminogen activator inhibitor-1, resistin, and visfatin. C-peptide and serpin E1 were not detected in ALF for any individual, and IL-6, IL-10, and TNF-alpha were not detected in either ALF or serum for any individual. In general, ALF levels were similar or lower in concentration for most proteins compared to serum. However, ghrelin, resistin, insulin, visfatin and GLP-1 had ALF concentrations significantly higher compared to serum. Importantly, elevated ALF:serum ratios of ghrelin, visfatin and resistin correlated with protein net charge and isoelectric point, but not with molecular weight or hydrophobicity.Biologically relevant enteric and metabolic hormones, apolipoproteins, adipokines, and cytokines can be detected in the ALF of

  19. Dynamics of the risk of smoking-induced lung cancer : A compartmental hidden markov model for longitudinal analysis

    NARCIS (Netherlands)

    Chadeau-Hyam, Marc; Tubert-Bitter, Pascale; Guihenneuc-Jouyaux, Chantal; Campanella, Gianluca; Richardson, Sylvia; Vermeulen, Roel; De Iorio, Maria; Galea, Sandro; Vineis, Paolo

    2014-01-01

    BACKGROUND:: To account for the dynamic aspects of carcinogenesis, we propose a compartmental hidden Markov model in which each person is healthy, asymptomatically affected, diagnosed, or deceased. Our model is illustrated using the example of smoking-induced lung cancer. METHODS:: The model was

  20. Information Processing in Single Cells and Small Networks: Insights from Compartmental Models

    Science.gov (United States)

    Poirazi, Panayiota

    2009-03-01

    The goal of this paper is to present a set of predictions generated by detailed compartmental models regarding the ways in which information may be processed, encoded and propagated by single cells and neural assemblies. Towards this goal, I will review a number of modelling studies from our lab that investigate how single pyramidal neurons and small neural networks in different brain regions process incoming signals that are associated with learning and memory. I will first discuss the computational capabilities of individual pyramidal neurons in the hippocampus [1-3] and how these properties may allow a single cell to discriminate between different memories [4]. I will then present biophysical models of prefrontal layer V neurons and small networks that exhibit sustained activity under realistic synaptic stimulation and discuss their potential role in working memory [5].

  1. Assessment of spatial heterogeneity in continuous twin screw wet granulation process using three-compartmental population balance model.

    Science.gov (United States)

    Liu, Huolong; Galbraith, Shaun C; Park, Seo-Young; Cha, Bumjoon; Huang, Zhuangrong; Meyer, Robert Frederick; Flamm, Matthew H; O'Connor, Thomas; Lee, Sau; Yoon, Seongkyu

    2018-01-25

    In this study, a novel three-compartmental population balance model (PBM) for a continuous twin screw wet granulation process is developed, combining the techniques of PBM and regression process modeling. The developed model links screw configuration, screw speed, and blend throughput with granule properties to predict the granule size distribution (GSD) and volume-average granule diameter. The granulator screw barrel was divided into three compartments along barrel length: wetting compartment, mixing compartment, and steady growth compartment. Different granulation mechanisms are assumed in each compartment. The proposed model therefore considers spatial heterogeneity, improving model prediction accuracy. An industrial data set containing 14 experiments is applied for model development. Three validation experiments show that the three-compartmental PBM can accurately predict granule diameter and size distribution at randomly selected operating conditions. Sixteen combinations of aggregation and breakage kernels are investigated in predicting the experimental GSD to best judge the granulation mechanism. The three-compartmental model is compared with a one-compartmental model in predicting granule diameter at different experimental conditions to demonstrate its advantage. The influence of the screw configuration, screw speed and blend throughput on the volume-average granule diameter is analyzed based on the developed model.

  2. The construction of next-generation matrices for compartmental epidemic models.

    Science.gov (United States)

    Diekmann, O; Heesterbeek, J A P; Roberts, M G

    2010-06-06

    The basic reproduction number (0) is arguably the most important quantity in infectious disease epidemiology. The next-generation matrix (NGM) is the natural basis for the definition and calculation of (0) where finitely many different categories of individuals are recognized. We clear up confusion that has been around in the literature concerning the construction of this matrix, specifically for the most frequently used so-called compartmental models. We present a detailed easy recipe for the construction of the NGM from basic ingredients derived directly from the specifications of the model. We show that two related matrices exist which we define to be the NGM with large domain and the NGM with small domain. The three matrices together reflect the range of possibilities encountered in the literature for the characterization of (0). We show how they are connected and how their construction follows from the basic model ingredients, and establish that they have the same non-zero eigenvalues, the largest of which is the basic reproduction number (0). Although we present formal recipes based on linear algebra, we encourage the construction of the NGM by way of direct epidemiological reasoning, using the clear interpretation of the elements of the NGM and of the model ingredients. We present a selection of examples as a practical guide to our methods. In the appendix we present an elementary but complete proof that (0) defined as the dominant eigenvalue of the NGM for compartmental systems and the Malthusian parameter r, the real-time exponential growth rate in the early phase of an outbreak, are connected by the properties that (0) > 1 if and only if r > 0, and (0) = 1 if and only if r = 0.

  3. Compartmental and noncompartmental modeling of ¹³C-lycopene absorption, isomerization, and distribution kinetics in healthy adults.

    Science.gov (United States)

    Moran, Nancy E; Cichon, Morgan J; Riedl, Kenneth M; Grainger, Elizabeth M; Schwartz, Steven J; Novotny, Janet A; Erdman, John W; Clinton, Steven K

    2015-12-01

    Lycopene, which is a red carotenoid in tomatoes, has been hypothesized to mediate disease-preventive effects associated with tomato consumption. Lycopene is consumed primarily as the all-trans geometric isomer in foods, whereas human plasma and tissues show greater proportions of cis isomers. With the use of compartmental modeling and stable isotope technology, we determined whether endogenous all-trans-to-cis-lycopene isomerization or isomeric-bioavailability differences underlie the greater proportion of lycopene cis isomers in human tissues than in tomato foods. Healthy men (n = 4) and women (n = 4) consumed (13)C-lycopene (10.2 mg; 82% all-trans and 18% cis), and plasma was collected over 28 d. Unlabeled and (13)C-labeled total lycopene and lycopene-isomer plasma concentrations, which were measured with the use of high-performance liquid chromatography-mass spectrometry, were fit to a 7-compartment model. Subjects absorbed a mean ± SEM of 23% ± 6% of the lycopene. The proportion of plasma cis-(13)C-lycopene isomers increased over time, and all-trans had a shorter half-life than that of cis isomers (5.3 ± 0.3 and 8.8 ± 0.6 d, respectively; P lycopene bioavailability and endogenous trans-to-cis-lycopene isomerization was predictive of plasma (13)C and unlabeled cis- and all-trans-lycopene concentrations. Although the bioavailability of cis (24.5% ± 6%) and all-trans (23.2% ± 8%) isomers did not differ, endogenous isomerization (0.97 ± 0.25 μmol/d in the fast-turnover tissue lycopene pool) drove tissue and plasma isomeric profiles. (13)C-Lycopene combined with physiologic compartmental modeling provides a strategy for following complex in vivo metabolic processes in humans and reveals that postabsorptive trans-to-cis-lycopene isomerization, and not the differential bioavailability of isomers, drives tissue and plasma enrichment of cis-lycopene. This trial was registered at clinicaltrials.gov as NCT01692340. © 2015 American Society for Nutrition.

  4. Compartmental and noncompartmental modeling of 13C-lycopene absorption, isomerization, and distribution kinetics in healthy adults123

    Science.gov (United States)

    Moran, Nancy E; Cichon, Morgan J; Riedl, Kenneth M; Grainger, Elizabeth M; Schwartz, Steven J; Novotny, Janet A; Erdman, John W; Clinton, Steven K

    2015-01-01

    Background: Lycopene, which is a red carotenoid in tomatoes, has been hypothesized to mediate disease-preventive effects associated with tomato consumption. Lycopene is consumed primarily as the all-trans geometric isomer in foods, whereas human plasma and tissues show greater proportions of cis isomers. Objective: With the use of compartmental modeling and stable isotope technology, we determined whether endogenous all-trans-to-cis-lycopene isomerization or isomeric-bioavailability differences underlie the greater proportion of lycopene cis isomers in human tissues than in tomato foods. Design: Healthy men (n = 4) and women (n = 4) consumed 13C-lycopene (10.2 mg; 82% all-trans and 18% cis), and plasma was collected over 28 d. Unlabeled and 13C-labeled total lycopene and lycopene-isomer plasma concentrations, which were measured with the use of high-performance liquid chromatography–mass spectrometry, were fit to a 7-compartment model. Results: Subjects absorbed a mean ± SEM of 23% ± 6% of the lycopene. The proportion of plasma cis-13C-lycopene isomers increased over time, and all-trans had a shorter half-life than that of cis isomers (5.3 ± 0.3 and 8.8 ± 0.6 d, respectively; P lycopene bioavailability and endogenous trans-to-cis-lycopene isomerization was predictive of plasma 13C and unlabeled cis- and all-trans-lycopene concentrations. Although the bioavailability of cis (24.5% ± 6%) and all-trans (23.2% ± 8%) isomers did not differ, endogenous isomerization (0.97 ± 0.25 μmol/d in the fast-turnover tissue lycopene pool) drove tissue and plasma isomeric profiles. Conclusion: 13C-Lycopene combined with physiologic compartmental modeling provides a strategy for following complex in vivo metabolic processes in humans and reveals that postabsorptive trans-to-cis-lycopene isomerization, and not the differential bioavailability of isomers, drives tissue and plasma enrichment of cis-lycopene. This trial was registered at clinicaltrials.gov as NCT01692340. PMID

  5. Compartmental modelling of the pharmacokinetics of a breast cancer resistance protein.

    Science.gov (United States)

    Grandjean, Thomas R B; Chappell, Mike J; Yates, James T W; Jones, Kevin; Wood, Gemma; Coleman, Tanya

    2011-11-01

    A mathematical model for the pharmacokinetics of Hoechst 33342 following administration into a culture medium containing a population of transfected cells (HEK293 hBCRP) with a potent breast cancer resistance protein inhibitor, Fumitremorgin C (FTC), present is described. FTC is reported to almost completely annul resistance mediated by BCRP in vitro. This non-linear compartmental model has seven macroscopic sub-units, with 14 rate parameters. It describes the relationship between the concentration of Hoechst 33342 and FTC, initially spiked in the medium, and the observed change in fluorescence due to Hoechst 33342 binding to DNA. Structural identifiability analysis has been performed using two methods, one based on the similarity transformation/exhaustive modelling approach and the other based on the differential algebra approach. The analyses demonstrated that all models derived are uniquely identifiable for the experiments/observations available. A kinetic modelling software package, namely FACSIMILE (MPCA Software, UK), was used for parameter fitting and to obtain numerical solutions for the system equations. Model fits gave very good agreement with in vitro data provided by AstraZeneca across a variety of experimental scenarios. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

  6. Integrated compartmental model for describing the transport of solute in a fractured porous medium. [FRACPORT

    Energy Technology Data Exchange (ETDEWEB)

    DeAngelis, D.L.; Yeh, G.T.; Huff, D.D.

    1984-10-01

    This report documents a model, FRACPORT, that simulates the transport of a solute through a fractured porous matrix. The model should be useful in analyzing the possible transport of radionuclides from shallow-land burial sites in humid environments. The use of the model is restricted to transport through saturated zones. The report first discusses the general modeling approach used, which is based on the Integrated Compartmental Method. The basic equations of solute transport are then presented. The model, which assumes a known water velocity field, solves these equations on two different time scales; one related to rapid transport of solute along fractures and the other related to slower transport through the porous matrix. FRACPORT is validated by application to a simple example of fractured porous medium transport that has previously been analyzed by other methods. Then its utility is demonstrated in analyzing more complex cases of pulses of solute into a fractured matrix. The report serves as a user's guide to FRACPORT. A detailed description of data input, along with a listing of input for a sample problem, is provided. 16 references, 18 figures, 3 tables.

  7. Proposing a Compartmental Model for Leprosy and Parameterizing Using Regional Incidence in Brazil.

    Directory of Open Access Journals (Sweden)

    Rebecca Lee Smith

    2016-08-01

    Full Text Available Hansen's disease (HD, or leprosy, is still considered a public health risk in much of Brazil. Understanding the dynamics of the infection at a regional level can aid in identification of targets to improve control. A compartmental continuous-time model for leprosy dynamics was designed based on understanding of the biology of the infection. The transmission coefficients for the model and the rate of detection were fit for each region using Approximate Bayesian Computation applied to paucibacillary and multibacillary incidence data over the period of 2000 to 2010, and model fit was validated on incidence data from 2011 to 2012. Regional variation was noted in detection rate, with cases in the Midwest estimated to be infectious for 10 years prior to detection compared to 5 years for most other regions. Posterior predictions for the model estimated that elimination of leprosy as a public health risk would require, on average, 44-45 years in the three regions with the highest prevalence. The model is easily adaptable to other settings, and can be studied to determine the efficacy of improved case finding on leprosy control.

  8. Proposing a Compartmental Model for Leprosy and Parameterizing Using Regional Incidence in Brazil.

    Science.gov (United States)

    Smith, Rebecca Lee

    2016-08-01

    Hansen's disease (HD), or leprosy, is still considered a public health risk in much of Brazil. Understanding the dynamics of the infection at a regional level can aid in identification of targets to improve control. A compartmental continuous-time model for leprosy dynamics was designed based on understanding of the biology of the infection. The transmission coefficients for the model and the rate of detection were fit for each region using Approximate Bayesian Computation applied to paucibacillary and multibacillary incidence data over the period of 2000 to 2010, and model fit was validated on incidence data from 2011 to 2012. Regional variation was noted in detection rate, with cases in the Midwest estimated to be infectious for 10 years prior to detection compared to 5 years for most other regions. Posterior predictions for the model estimated that elimination of leprosy as a public health risk would require, on average, 44-45 years in the three regions with the highest prevalence. The model is easily adaptable to other settings, and can be studied to determine the efficacy of improved case finding on leprosy control.

  9. The Green's function formalism as a bridge between single- and multi-compartmental modeling.

    Science.gov (United States)

    Wybo, Willem A M; Stiefel, Klaus M; Torben-Nielsen, Benjamin

    2013-12-01

    Neurons are spatially extended structures that receive and process inputs on their dendrites. It is generally accepted that neuronal computations arise from the active integration of synaptic inputs along a dendrite between the input location and the location of spike generation in the axon initial segment. However, many application such as simulations of brain networks use point-neurons-neurons without a morphological component-as computational units to keep the conceptual complexity and computational costs low. Inevitably, these applications thus omit a fundamental property of neuronal computation. In this work, we present an approach to model an artificial synapse that mimics dendritic processing without the need to explicitly simulate dendritic dynamics. The model synapse employs an analytic solution for the cable equation to compute the neuron's membrane potential following dendritic inputs. Green's function formalism is used to derive the closed version of the cable equation. We show that by using this synapse model, point-neurons can achieve results that were previously limited to the realms of multi-compartmental models. Moreover, a computational advantage is achieved when only a small number of simulated synapses impinge on a morphologically elaborate neuron. Opportunities and limitations are discussed.

  10. Development of a Novel Oral Cavity Compartmental Absorption and Transit Model for Sublingual Administration: Illustration with Zolpidem

    OpenAIRE

    Xia, Binfeng; Yang, Zhen; Zhou, Haiying; Lukacova, Viera; Zhu, Wei; Milewski, Mikolaj; Kesisoglou, Filippos

    2015-01-01

    Intraoral (IO) delivery is an alternative administration route to deliver a drug substance via the mouth that provides several advantages over conventional oral dosage forms. The purpose of this work was to develop and evaluate a novel, physiologically based oral cavity model for projection and mechanistic analysis of the clinical pharmacokinetics of intraoral formulations. The GastroPlus™ Oral Cavity Compartmental Absorption and Transit (OCCAT™) model was used to simulate the plasma concentr...

  11. Plant Metabolic Modeling: Achieving New Insight into Metabolism and Metabolic Engineering

    Science.gov (United States)

    Baghalian, Kambiz; Hajirezaei, Mohammad-Reza; Schreiber, Falk

    2014-01-01

    Models are used to represent aspects of the real world for specific purposes, and mathematical models have opened up new approaches in studying the behavior and complexity of biological systems. However, modeling is often time-consuming and requires significant computational resources for data development, data analysis, and simulation. Computational modeling has been successfully applied as an aid for metabolic engineering in microorganisms. But such model-based approaches have only recently been extended to plant metabolic engineering, mainly due to greater pathway complexity in plants and their highly compartmentalized cellular structure. Recent progress in plant systems biology and bioinformatics has begun to disentangle this complexity and facilitate the creation of efficient plant metabolic models. This review highlights several aspects of plant metabolic modeling in the context of understanding, predicting and modifying complex plant metabolism. We discuss opportunities for engineering photosynthetic carbon metabolism, sucrose synthesis, and the tricarboxylic acid cycle in leaves and oil synthesis in seeds and the application of metabolic modeling to the study of plant acclimation to the environment. The aim of the review is to offer a current perspective for plant biologists without requiring specialized knowledge of bioinformatics or systems biology. PMID:25344492

  12. Analysis of metabolic flux using dynamic labelling and metabolic modelling.

    Science.gov (United States)

    Fernie, A R; Morgan, J A

    2013-09-01

    Metabolic fluxes and the capacity to modulate them are a crucial component of the ability of the plant cell to react to environmental perturbations. Our ability to quantify them and to attain information concerning the regulatory mechanisms that control them is therefore essential to understand and influence metabolic networks. For all but the simplest of flux measurements labelling methods have proven to be the most informative. Both steady-state and dynamic labelling approaches have been adopted in the study of plant metabolism. Here the conceptual basis of these complementary approaches, as well as their historical application in microbial, mammalian and plant sciences, is reviewed, and an update on technical developments in label distribution analyses is provided. This is supported by illustrative cases studies involving the kinetic modelling of secondary metabolism. One issue that is particularly complex in the analysis of plant fluxes is the extensive compartmentation of the plant cell. This problem is discussed from both theoretical and experimental perspectives, and the current approaches used to address it are assessed. Finally, current limitations and future perspectives of kinetic modelling of plant metabolism are discussed. © 2013 John Wiley & Sons Ltd.

  13. Linear regressive model structures for estimation and prediction of compartmental diffusive systems

    NARCIS (Netherlands)

    Vries, D.; Keesman, K.J.; Zwart, H.

    2006-01-01

    Abstract In input-output relations of (compartmental) diffusive systems, physical parameters appear non-linearly, resulting in the use of (constrained) non-linear parameter estimation techniques with its short-comings regarding global optimality and computational effort. Given a LTI system in state

  14. Linear regressive model structures for estimation and prediction of compartmental diffusive systems

    NARCIS (Netherlands)

    Vries, D; Keesman, K.J.; Zwart, Heiko J.

    In input-output relations of (compartmental) diffusive systems, physical parameters appear non-linearly, resulting in the use of (constrained) non-linear parameter estimation techniques with its short-comings regarding global optimality and computational effort. Given a LTI system in state space

  15. Computer Modeling of Sand Transport on Mars Using a Compart-Mentalized Fluids Algorithm (CFA)

    Science.gov (United States)

    Marshall, J.; Stratton, D.

    1999-01-01

    of sand comminution on Mars. A multiple-grain transport model using just the equations of grain motion describing lift and drag is impossible to develop owing to stochastic effects --the very effects we wish to model. Also, unless we were to employ supercomputing techniques and extremely complex computer codes that could deal with millions of grains simultaneously, it would also be difficult to model grain transport if we attempted to consider every grain in motion. No existing computer models were found that satisfactorily used the equations of motion to arrive at transport flux numbers for the different populations of saltation and reptation. Modeling all the grains in a transport system was an intractable problem within our resources, and thus we developed what we believe to be a new modeling approach to simulating grain transport. The CFA deals with grain populations, but considers them to belong to various compartmentalized fluid units in the boundary layer. In this way, the model circumvents the multigrain problem by dealing primarily with the consequences of grain transport --momentum transfer between air and grains, which is the physical essence of a dynamic grain-fluid mixture. We thus chose to model the aeolian transport process as a superposition of fluids. These fluids include the air as well as particle populations of various properties. The prime property distinguishing these fluids is upward and downward grain motion. In a normal saltation trajectory, a grain's downwind velocity increases with time, so a rising grain will have a smaller downwind velocity than a failing grain. Because of this disparity in rising and falling grain proper-ties, it seemed appropriate to track these as two separate grain populations within the same physical space. The air itself can be considered a separate fluid superimposed within and interacting with the various grain-cloud "fluids". Additional informaiton is contained in the original.

  16. Perfusion–diffusion compartmental models describe cerebral helium kinetics at high and low cerebral blood flows in sheep

    Science.gov (United States)

    Doolette, David J; Upton, Richard N; Grant, Cliff

    2005-01-01

    This study evaluated the relative importance of perfusion and diffusion mechanisms in compartmental models of blood:tissue helium exchange in the brain. Helium has different physiochemical properties from previously studied gases, and is a common diluent gas in underwater diving where decompression schedules are based on theoretical models of inert gas kinetics. Helium kinetics across the cerebrum were determined during and after 15 min of helium inhalation, at separate low and high steady states of cerebral blood flow in seven sheep under isoflurane anaesthesia. Helium concentrations in arterial and sagittal sinus venous blood were determined using gas chromatographic analysis, and sagittal sinus blood flow was monitored continuously. Parameters and model selection criteria of various perfusion-limited or perfusion–diffusion compartmental models of the brain were estimated by simultaneous fitting of the models to the sagittal sinus helium concentrations for both blood flow states. Purely perfusion-limited models fitted the data poorly. Models that allowed a diffusion-limited exchange of helium between a perfusion-limited tissue compartment and an unperfused deep compartment provided better overall fit of the data and credible parameter estimates. Fit to the data was also improved by allowing countercurrent diffusion shunt of helium between arterial and venous blood. These results suggest a role of diffusion in blood:tissue helium equilibration in brain. PMID:15649976

  17. Radical pathway in catecholase activity with zinc-based model complexes of compartmental ligands.

    Science.gov (United States)

    Guha, Averi; Chattopadhyay, Tanmay; Paul, Nanda Dulal; Mukherjee, Madhuparna; Goswami, Somen; Mondal, Tapan Kumar; Zangrando, Ennio; Das, Debasis

    2012-08-20

    Four dinuclear and three mononuclear Zn(II) complexes of phenol-based compartmental ligands (HL(1)-HL(7)) have been synthesized with the aim to investigate the viability of a radical pathway in catecholase activity. The complexes have been characterized by routine physicochemical studies as well as X-ray single-crystal structure analysis: [Zn(2)(H(2)L(1))(OH)(H(2)O)(NO(3))](NO(3))(3) (1), [Zn(2)L(2)Cl(3)] (2), [Zn(2)L(3)Cl(3)] (3), [Zn(2)(L(4))(2)(CH(3)COO)(2)] (4), [Zn(HL(5))Cl(2)] (5), [Zn(HL(6))Cl(2)] (6), and [Zn(HL(7))Cl(2)] (7) [L(1)-L(3) and L(5)-L(7) = 2,6-bis(R-iminomethyl)-4-methylphenolato, where R= N-ethylpiperazine for L(1), R = 2-(N-ethyl)pyridine for L(2), R = N-ethylpyrrolidine for L(3), R = N-methylbenzene for L(5), R = 2-(N-methyl)thiophene for L(6), R = 2-(N-ethyl)thiophene for L(7), and L(4) = 2-formyl-4-methyl-6-N-methylbenzene-iminomethyl-phenolato]. Catecholase-like activity of the complexes has been investigated in methanol medium by UV-vis spectrophotometric study using 3,5-di-tert-butylcatechol as model substrate. All complexes are highly active in catalyzing the aerobic oxidation of 3,5-di-tert-butylcatechol (3,5-DTBC) to 3,5-di-tert-butylbenzoquinone (3,5-DTBQ). Conversion of 3,5-DTBC to 3,5-DTBQ catalyzed by mononuclear complexes (5-7) is observed to proceed via formation of two enzyme-substrate adducts, ES1 and ES2, detected spectroscopically, a finding reported for the first time in any Zn(II) complex catalyzed oxidation of catechol. On the other hand, no such enzyme-substrate adduct has been identified, and 3,5-DTBC to 3,5-DTBQ conversion is observed to be catalyzed by the dinuclear complexes (1-4) very smoothly. EPR experiment suggests generation of radicals in the presence of 3,5-DTBC, and that finding has been strengthened by cyclic voltammetric study. Thus, it may be proposed that the radical pathway is probably responsible for conversion of 3,5-DTBC to 3,5-DTBQ promoted by complexes of redox-innocent Zn(II) ion. The ligand

  18. Dynamics of the risk of smoking-induced lung cancer: a compartmental hidden Markov model for longitudinal analysis.

    Science.gov (United States)

    Chadeau-Hyam, Marc; Tubert-Bitter, Pascale; Guihenneuc-Jouyaux, Chantal; Campanella, Gianluca; Richardson, Sylvia; Vermeulen, Roel; De Iorio, Maria; Galea, Sandro; Vineis, Paolo

    2014-01-01

    To account for the dynamic aspects of carcinogenesis, we propose a compartmental hidden Markov model in which each person is healthy, asymptomatically affected, diagnosed, or deceased. Our model is illustrated using the example of smoking-induced lung cancer. The model was fitted on a case-control study nested in the European Prospective Investigation into Cancer and Nutrition study, including 757 incident cases and 1524 matched controls. Estimation was done through a Markov Chain Monte Carlo algorithm, and simulations based on the posterior estimates of the parameters were used to provide measures of model fit. We performed sensitivity analyses to assess robustness of our findings. After adjusting for its impact on exposure duration, age was not found to independently drive the risk of lung carcinogenesis, whereas age at starting smoking in ever-smokers and time since cessation in former smokers were found to be influential. Our data did not support an age-dependent time to diagnosis. The estimated time between onset of malignancy and clinical diagnosis ranged from 2 to 4 years. Our approach yielded good performance in reconstructing individual trajectories in both cases (sensitivity >90%) and controls (sensitivity >80%). Our compartmental model enabled us to identify time-varying predictors of risk and provided us with insights into the dynamics of smoking-induced lung carcinogenesis. Its flexible and general formulation enables the future incorporation of disease states, as measured by intermediate markers, into the modeling of the natural history of cancer, suggesting a large range of applications in chronic disease epidemiology.

  19. An empirically adjusted approach to reproductive number estimation for stochastic compartmental models: A case study of two Ebola outbreaks.

    Science.gov (United States)

    Brown, Grant D; Oleson, Jacob J; Porter, Aaron T

    2016-06-01

    The various thresholding quantities grouped under the "Basic Reproductive Number" umbrella are often confused, but represent distinct approaches to estimating epidemic spread potential, and address different modeling needs. Here, we contrast several common reproduction measures applied to stochastic compartmental models, and introduce a new quantity dubbed the "empirically adjusted reproductive number" with several advantages. These include: more complete use of the underlying compartmental dynamics than common alternatives, use as a potential diagnostic tool to detect the presence and causes of intensity process underfitting, and the ability to provide timely feedback on disease spread. Conceptual connections between traditional reproduction measures and our approach are explored, and the behavior of our method is examined under simulation. Two illustrative examples are developed: First, the single location applications of our method are established using data from the 1995 Ebola outbreak in the Democratic Republic of the Congo and a traditional stochastic SEIR model. Second, a spatial formulation of this technique is explored in the context of the ongoing Ebola outbreak in West Africa with particular emphasis on potential use in model selection, diagnosis, and the resulting applications to estimation and prediction. Both analyses are placed in the context of a newly developed spatial analogue of the traditional SEIR modeling approach. © 2015, The International Biometric Society.

  20. Mathematical modelling of metabolism

    DEFF Research Database (Denmark)

    Gombert, Andreas Karoly; Nielsen, Jens

    2000-01-01

    Mathematical models of the cellular metabolism have a special interest within biotechnology. Many different kinds of commercially important products are derived from the cell factory, and metabolic engineering can be applied to improve existing production processes, as well as to make new processes...... availability of genomic information and powerful analytical techniques, mathematical models also serve as a tool for understanding the cellular metabolism and physiology....... available. Both stoichiometric and kinetic models have been used to investigate the metabolism, which has resulted in defining the optimal fermentation conditions, as well as in directing the genetic changes to be introduced in order to obtain a good producer strain or cell line. With the increasing...

  1. Bayesian network analysis of multi-compartmentalized immune responses in a murine model of sepsis and direct lung injury.

    Science.gov (United States)

    Nemzek, Jean A; Hodges, Andrew P; He, Yongqun

    2015-09-30

    Inflammatory disease processes involve complex and interrelated systems of mediators. Determining the causal relationships among these mediators becomes more complicated when two, concurrent inflammatory conditions occur. In those cases, the outcome may also be dependent upon the timing, severity and compartmentalization of the insults. Unfortunately, standard methods of experimentation and analysis of data sets may investigate a single scenario without uncovering many potential associations among mediators. However, Bayesian network analysis is able to model linear, nonlinear, combinatorial, and stochastic relationships among variables to explore complex inflammatory disease systems. In these studies, we modeled the development of acute lung injury from an indirect insult (sepsis induced by cecal ligation and puncture) complicated by a direct lung insult (aspiration). To replicate multiple clinical situations, the aspiration injury was delivered at different severities and at different time intervals relative to the septic insult. For each scenario, we measured numerous inflammatory cell types and cytokines in samples from the local compartments (peritoneal and bronchoalveolar lavage fluids) and the systemic compartment (plasma). We then analyzed these data by Bayesian networks and standard methods. Standard data analysis demonstrated that the lung injury was actually reduced when two insults were involved as compared to one lung injury alone. Bayesian network analysis determined that both the severity of lung insult and presence of sepsis influenced neutrophil recruitment and the amount of injury to the lung. However, the levels of chemoattractant cytokines responsible for neutrophil recruitment were more strongly linked to the timing and severity of the lung insult compared to the presence of sepsis. This suggests that something other than sepsis-driven exacerbation of chemokine levels was influencing the lung injury, contrary to previous theories. To our

  2. Combining a Complex Network Approach and a SEIR Compartmental Model to link Fast Spreading of Infectious Diseases with Climate Change

    Science.gov (United States)

    Brenner, F.; Hoffmann, P.; Marwan, N.

    2016-12-01

    Infectious diseases are a major threat to human health. The spreading of airborne diseases has become fast and hard to predict. Global air travelling created a network which allows a pathogen to migrate worldwide in only a few days. Pandemics of SARS (2002/03) and H1N1 (2009) have impressively shown the epidemiological danger in a strongly connected world. In this study we simulate the outbreak of an airborne infectious disease that is directly transmitted from human to human. We use a regular Susceptible-Infected-Recovered (SIR) model and a modified Susceptible-Exposed-Infected-Recovered (SEIR) compartmental approach with the basis of a complex network built by global air traffic data (from openflights.org). Local Disease propagation is modeled with a global population dataset (from SEDAC and MaxMind) and parameterizations of human behavior regarding mobility, contacts and awareness. As a final component we combine the worldwide outbreak simulation with daily averaged climate data from WATCH-Forcing-Data-ERA-Interim (WFDEI) and Coupled Model Intercomparison Project Phase 5 (CMIP5). Here we focus on Influenza-like illnesses (ILI), whose transmission rate has a dependency on relative humidity and temperature. Even small changes in relative humidity are sufficient to trigger significant differences in the global outbreak behavior. Apart from the direct effect of climate change on the transmission of airborne diseases, there are indirect ramifications that alter spreading patterns. For example seasonal changing human mobility is influenced by climate settings.

  3. Two-compartmental population balance modeling of a pulsed spray fluidized bed granulation based on computational fluid dynamics (CFD) analysis.

    Science.gov (United States)

    Liu, Huolong; Li, Mingzhong

    2014-11-20

    In this work a two-compartmental population balance model (TCPBM) was proposed to model a pulsed top-spray fluidized bed granulation. The proposed TCPBM considered the spatially heterogeneous granulation mechanisms of the granule growth by dividing the granulator into two perfectly mixed zones of the wetting compartment and drying compartment, in which the aggregation mechanism was assumed in the wetting compartment and the breakage mechanism was considered in the drying compartment. The sizes of the wetting and drying compartments were constant in the TCPBM, in which 30% of the bed was the wetting compartment and 70% of the bed was the drying compartment. The exchange rate of particles between the wetting and drying compartments was determined by the details of the flow properties and distribution of particles predicted by the computational fluid dynamics (CFD) simulation. The experimental validation has shown that the proposed TCPBM can predict evolution of the granule size and distribution within the granulator under different binder spray operating conditions accurately. Copyright © 2014 Elsevier B.V. All rights reserved.

  4. Adequacy of compartmental model for positron emission tomography examinations; Adequacao de modelo compartimental para exames de tomografia por emissao de positrons

    Energy Technology Data Exchange (ETDEWEB)

    Silva, Joao Eduardo Maeda Moreira da; Furuie, Sergio Shiguemi [Universidade de Sao Paulo (USP), SP (Brazil). Dept. de Engenharia de Telecomunicacoes e Controle. Lab. de Engenharia Biomedica

    2011-12-15

    The objective of this work is the determination of the most adequate compartmental model for the study of physiological dynamics based on positron emission tomography exams. We propose the use of Akaike's information criterion for the optimal model selection, and Levenberg-Marquardt algorithm with sensitivity equations for the task of estimating the characteristic parameters of the differential equations describing the models. We have considered three compartmental structures represented, respectively, by two compartments and two characteristic constants, three compartments and four characteristic constants and four compartments and six characteristics constants. The data considered in this work were synthesized taking into account key features of a real tomography exam, such as type and level of noise and morphology of the input function of the system. Applying the proposed methodology with three noise levels (low, medium and high), we obtained agreement of the best model with strong and considerable degrees (with Kappa indexes equal to 0.95, 0.93 and 0.63, respectively). It was observed that, with high noise level and more complex models (four compartments), the classification is deteriorated due to lack of data for the decision. Programs have been developed and a graphical interface that can be used in research, development, simulation and parameter identification of compartmental models, supporting analysis of clinical diagnostics and scientific practices. (author)

  5. Metabolomics, Standards, and Metabolic Modeling for Synthetic Biology in Plants.

    Science.gov (United States)

    Hill, Camilla Beate; Czauderna, Tobias; Klapperstück, Matthias; Roessner, Ute; Schreiber, Falk

    2015-01-01

    Life on earth depends on dynamic chemical transformations that enable cellular functions, including electron transfer reactions, as well as synthesis and degradation of biomolecules. Biochemical reactions are coordinated in metabolic pathways that interact in a complex way to allow adequate regulation. Biotechnology, food, biofuel, agricultural, and pharmaceutical industries are highly interested in metabolic engineering as an enabling technology of synthetic biology to exploit cells for the controlled production of metabolites of interest. These approaches have only recently been extended to plants due to their greater metabolic complexity (such as primary and secondary metabolism) and highly compartmentalized cellular structures and functions (including plant-specific organelles) compared with bacteria and other microorganisms. Technological advances in analytical instrumentation in combination with advances in data analysis and modeling have opened up new approaches to engineer plant metabolic pathways and allow the impact of modifications to be predicted more accurately. In this article, we review challenges in the integration and analysis of large-scale metabolic data, present an overview of current bioinformatics methods for the modeling and visualization of metabolic networks, and discuss approaches for interfacing bioinformatics approaches with metabolic models of cellular processes and flux distributions in order to predict phenotypes derived from specific genetic modifications or subjected to different environmental conditions.

  6. Comparison of linear and nonlinear implementation of the compartmental tissue uptake model for dynamic contrast‐enhanced MRI

    Science.gov (United States)

    Sourbron, Steven; Irving, Benjamin; Tanderup, Kari; Schnabel, Julia A.; Chappell, Michael A.

    2016-01-01

    Purpose Fitting tracer kinetic models using linear methods is much faster than using their nonlinear counterparts, although this comes often at the expense of reduced accuracy and precision. The aim of this study was to derive and compare the performance of the linear compartmental tissue uptake (CTU) model with its nonlinear version with respect to their percentage error and precision. Theory and Methods The linear and nonlinear CTU models were initially compared using simulations with varying noise and temporal sampling. Subsequently, the clinical applicability of the linear model was demonstrated on 14 patients with locally advanced cervical cancer examined with dynamic contrast‐enhanced magnetic resonance imaging. Results Simulations revealed equal percentage error and precision when noise was within clinical achievable ranges (contrast‐to‐noise ratio >10). The linear method was significantly faster than the nonlinear method, with a minimum speedup of around 230 across all tested sampling rates. Clinical analysis revealed that parameters estimated using the linear and nonlinear CTU model were highly correlated (ρ ≥ 0.95). Conclusion The linear CTU model is computationally more efficient and more stable against temporal downsampling, whereas the nonlinear method is more robust to variations in noise. The two methods may be used interchangeably within clinical achievable ranges of temporal sampling and noise. Magn Reson Med 77:2414–2423, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. PMID:27605429

  7. Comparison of linear and nonlinear implementation of the compartmental tissue uptake model for dynamic contrast-enhanced MRI.

    Science.gov (United States)

    Kallehauge, Jesper F; Sourbron, Steven; Irving, Benjamin; Tanderup, Kari; Schnabel, Julia A; Chappell, Michael A

    2017-06-01

    Fitting tracer kinetic models using linear methods is much faster than using their nonlinear counterparts, although this comes often at the expense of reduced accuracy and precision. The aim of this study was to derive and compare the performance of the linear compartmental tissue uptake (CTU) model with its nonlinear version with respect to their percentage error and precision. The linear and nonlinear CTU models were initially compared using simulations with varying noise and temporal sampling. Subsequently, the clinical applicability of the linear model was demonstrated on 14 patients with locally advanced cervical cancer examined with dynamic contrast-enhanced magnetic resonance imaging. Simulations revealed equal percentage error and precision when noise was within clinical achievable ranges (contrast-to-noise ratio >10). The linear method was significantly faster than the nonlinear method, with a minimum speedup of around 230 across all tested sampling rates. Clinical analysis revealed that parameters estimated using the linear and nonlinear CTU model were highly correlated (ρ ≥ 0.95). The linear CTU model is computationally more efficient and more stable against temporal downsampling, whereas the nonlinear method is more robust to variations in noise. The two methods may be used interchangeably within clinical achievable ranges of temporal sampling and noise. Magn Reson Med 77:2414-2423, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.

  8. Generalized framework for context-specific metabolic model extraction methods

    Directory of Open Access Journals (Sweden)

    Semidán eRobaina Estévez

    2014-09-01

    Full Text Available Genome-scale metabolic models are increasingly applied to investigate the physiology not only of simple prokaryotes, but also eukaryotes, such as plants, characterized with compartmentalized cells of multiple types. While genome-scale models aim at including the entirety of known metabolic reactions, mounting evidence has indicated that only a subset of these reactions is active in a given context, including: developmental stage, cell type, or environment. As a result, several methods have been proposed to reconstruct context-specific models from existing genome-scale models by integrating various types of high-throughput data. Here we present a mathematical framework that puts all existing methods under one umbrella and provides the means to better understand their functioning, highlight similarities and differences, and to help users in selecting a most suitable method for an application.

  9. Compartmental analysis of dynamic nuclear medicine data: regularization procedure and application to physiology

    CERN Document Server

    Fabrice, Delbary

    2016-01-01

    Compartmental models based on tracer mass balance are extensively used in clinical and pre-clinical nuclear medicine in order to obtain quantitative information on tracer metabolism in the biological tissue. This paper is the second of a series of two that deal with the problem of tracer coefficient estimation via compartmental modelling in an inverse problem framework. While the previous work was devoted to the discussion of identifiability issues for 2, 3 and n-dimension compartmental systems, here we discuss the problem of numerically determining the tracer coefficients by means of a general regularized Multivariate Gauss Newton scheme. In this paper, applications concerning cerebral, hepatic and renal functions are considered, involving experimental measurements on FDG-PET data on different set of murine models.

  10. Compartmental syndromes in children.

    Science.gov (United States)

    Matsen, F A; Veith, R G

    1981-01-01

    Compartmental syndromes are reported in 24 children after injuries and surgery. In these cases, increased tissue pressure compromised local perfusion and neuromuscular function. Compartmental syndromes occurred in the interosseous compartments of the hand, the volar and dorsal compartments of the forearm, and the four compartments of the leg. The most common etiologies were fracture, vascular injury, and tibial osteotomy. In many instances, clinical data were sufficient to establish the diagnosis. However, in young patients or in patients with neurologic or vascular injuries, tissue pressure measurement helped to resolve otherwise ambiguous findings. The most significant determinant of the quality of the end result was the duration of the compartmental syndrome prior to surgical decompression. We conclude that prompt diagnosis and decompression of compartmental syndromes can minimize the sequela from these conditions.

  11. A workflow for mathematical modeling of subcellular metabolic pathways in leaf metabolism of Arabidopsis thaliana

    Directory of Open Access Journals (Sweden)

    Thomas eNägele

    2013-12-01

    Full Text Available During the last decade genome sequencing has experienced a rapid technological development resulting in numerous sequencing projects and applications in life science. In plant molecular biology, the availability of sequence data on whole genomes has enabled the reconstruction of metabolic networks. Enzymatic reactions are predicted by the sequence information. Pathways arise due to the participation of chemical compounds as substrates and products in these reactions. Although several of these comprehensive networks have been reconstructed for the genetic model plant Arabidopsis thaliana, the integration of experimental data is still challenging. Particularly the analysis of subcellular organization of plant cells limits the understanding of regulatory instances in these metabolic networks in vivo. In this study, we develop an approach for the functional integration of experimental high-throughput data into such large-scale networks. We present a subcellular metabolic network model comprising 524 metabolic intermediates and 548 metabolic interactions derived from a total of 2769 reactions. We demonstrate how to link the metabolite covariance matrix of different Arabidopsis thaliana accessions with the subcellular metabolic network model for the inverse calculation of the biochemical Jacobian, finally resulting in the calculation of a matrix which satisfies a Lyaponov equation involving a covariance matrix. In this way, differential strategies of metabolite compartmentation and involved reactions were identified in the accessions when exposed to low temperature.

  12. A compartmentalized mathematical model of the β1-adrenergic signaling system in mouse ventricular myocytes.

    Directory of Open Access Journals (Sweden)

    Vladimir E Bondarenko

    Full Text Available The β1-adrenergic signaling system plays an important role in the functioning of cardiac cells. Experimental data shows that the activation of this system produces inotropy, lusitropy, and chronotropy in the heart, such as increased magnitude and relaxation rates of [Ca(2+]i transients and contraction force, and increased heart rhythm. However, excessive stimulation of β1-adrenergic receptors leads to heart dysfunction and heart failure. In this paper, a comprehensive, experimentally based mathematical model of the β1-adrenergic signaling system for mouse ventricular myocytes is developed, which includes major subcellular functional compartments (caveolae, extracaveolae, and cytosol. The model describes biochemical reactions that occur during stimulation of β1-adrenoceptors, changes in ionic currents, and modifications of Ca(2+ handling system. Simulations describe the dynamics of major signaling molecules, such as cyclic AMP and protein kinase A, in different subcellular compartments; the effects of inhibition of phosphodiesterases on cAMP production; kinetics and magnitudes of phosphorylation of ion channels, transporters, and Ca(2+ handling proteins; modifications of action potential shape and duration; magnitudes and relaxation rates of [Ca(2+]i transients; changes in intracellular and transmembrane Ca(2+ fluxes; and [Na(+]i fluxes and dynamics. The model elucidates complex interactions of ionic currents upon activation of β1-adrenoceptors at different stimulation frequencies, which ultimately lead to a relatively modest increase in action potential duration and significant increase in [Ca(2+]i transients. In particular, the model includes two subpopulations of the L-type Ca(2+ channels, in caveolae and extracaveolae compartments, and their effects on the action potential and [Ca(2+]i transients are investigated. The presented model can be used by researchers for the interpretation of experimental data and for the developments of

  13. Explicit linear regressive model structures for estimation, prediction and experimental design in compartmental diffusive systems

    NARCIS (Netherlands)

    Vries, Dirk; Keesman, Karel; Zwart, Heiko J.

    2006-01-01

    A linear regressive model structure and output predictor, both in algebraic form, are deduced from an LTI state space system with certain properties without the need of direct matrix inversion. On the basis of this, explicit expressions of parametric sensitivities are given. As an example, a

  14. A Proof of the Occupancy Principle and the Mean-Transit-Time Theorem for Compartmental Models

    Science.gov (United States)

    RAMAKRISHNAN, RAJASEKHAR; LEONARD, EDWARD F.; DELL, RALPH B.

    2012-01-01

    The occupancy principle and the mean-transit-time theorem are derived for the passage of a tracer through a system that can be described by a general pool model. It is proved, using matrix theory, that if (and only if) tracer entering the system labels equally all tracee fluxes into the system, then the integral of the tracer concentration is the same in all the pools. It is also proved that if, in addition, all flow out of the system is through the observation point, the first moment of the tracer concentration at the observation point can be used to calculate the total amount of trace in the system. The necessity of this condition is analyzed. Examples are given of models in which the occupancy principle and the mean-transit-time theorem hold or do not hold. PMID:22328793

  15. A compartmental model analysis of integrative and self-regulatory ion dynamics in pollen tube growth.

    Directory of Open Access Journals (Sweden)

    Junli Liu

    2010-10-01

    Full Text Available Sexual reproduction in higher plants relies upon the polarised growth of pollen tubes. The growth-site at the pollen tube tip responds to signalling processes to successfully steer the tube to an ovule. Essential features of pollen tube growth are polarisation of ion fluxes, intracellular ion gradients, and oscillating dynamics. However, little is known about how these features are generated and how they are causally related. We propose that ion dynamics in biological systems should be studied in an integrative and self-regulatory way. Here we have developed a two-compartment model by integrating major ion transporters at both the tip and shank of pollen tubes. We demonstrate that the physiological features of polarised growth in the pollen tube can be explained by the localised distribution of transporters at the tip and shank. Model analysis reveals that the tip and shank compartments integrate into a self-regulatory dynamic system, however the oscillatory dynamics at the tip do not play an important role in maintaining ion gradients. Furthermore, an electric current travelling along the pollen tube contributes to the regulation of ion dynamics. Two candidate mechanisms for growth-induced oscillations are proposed: the transition of tip membrane into shank membrane, and growth-induced changes in kinetic parameters of ion transporters. The methodology and principles developed here are applicable to the study of ion dynamics and their interactions with other functional modules in any plant cellular system.

  16. A multi-task learning approach for compartmental model parameter estimation in DCE-CT sequences.

    Science.gov (United States)

    Romain, Blandine; Letort, Véronique; Lucidarme, Olivier; Rouet, Laurence; Dalché-Buc, Florence

    2013-01-01

    Today's follow-up of patients presenting abdominal tumors is generally performed through acquisition of dynamic sequences of contrast-enhanced CT. Estimating parameters of appropriate models of contrast intake diffusion through tissues should help characterizing the tumor physiology, but is impeded by the high level of noise inherent to the acquisition conditions. To improve the quality of estimation, we consider parameter estimation in voxels as a multi-task learning problem (one task per voxel) that takes advantage from the similarity between two tasks. We introduce a temporal similarity between tasks based on a robust distance between observed contrast-intake profiles of intensity. Using synthetic images, we compare multi-task learning using this temporal similarity, a spatial similarity and a single-task learning. The similarities based on temporal profiles are shown to bring significant improvements compared to the spatial one. Results on real CT sequences also confirm the relevance of the approach.

  17. Modelagem por compartimentos para integrar e comunicar conhecimento em nutrição Compartmental modeling to integrate and communicate nutritional knowledge

    Directory of Open Access Journals (Sweden)

    Edgar O. Oviedo-Rondón

    2007-07-01

    Full Text Available Esta palestra tem o objetivo de apresentar e discutir metodologias utilizadas para modelar e integrar o conhecimento clássico em nutrição animal, e o produzido por novas ciências moleculares como nutrigenoma, proteoma e metaboloma. Estas ciências e a bioinformática estão ajudando a expandir rapidamente o conhecimento dos sistemas biológicos de interesse em nutrição animal. Na palestra discutirei como é importante dedicar parte de nosso tempo a integrar o conhecimento existente para esclarecer os problemas em pesquisa, utilizando as ferramentas mais adequadas para evitar duplicação de pesquisas, que causam desperdício de recursos humanos, econômicos, e de tempo. A modelagem matemática por compartimentos utilizando programas de computador pode ser a melhor maneira de acumular estas informações, integrar diferentes descobertas, e comunicar o conhecimento atual dos sistemas, e do metabolismo de nutrientes às novas gerações, e avançar na determinação mais adequada das exigências nutricionais.This presentation aims to present and discuss methodologies used to model and integrate classical knowledge in animal nutrition, and new discoveries produced by new molecular sciences like nutrigenomics, proteomics and metabolomics. These sciences and bioinformatics are helping to expand >very quickly the knowledge of the biological systems of interest in animal nutrition. I will discuss the importance of dedicating part of our efforts to integrate current knowledge to prioritize research problems using the most adequate tools. This will help to avoid research duplication that causes waste of valuable resources. Compartmental mathematical modeling using computer software could be one of the best ways to accumulate this information. It can help to integrate new discoveries, communicate the knowledge about animal systems and nutrient metabolism to a new generation of scientists, and advance to more accurate determination of nutrient

  18. Gadoxetate-enhanced MR imaging and compartmental modelling to assess hepatocyte bidirectional transport function in rats with advanced liver fibrosis

    Energy Technology Data Exchange (ETDEWEB)

    Giraudeau, Celine; Leporq, Benjamin; Doblas, Sabrina [University Paris Diderot, Sorbonne Paris Cite, Hopital Beaujon, Laboratory of Imaging Biomarkers, UMR1149 Inserm, Clichy (France); Lagadec, Matthieu; Daire, Jean-Luc; Van Beers, Bernard E. [University Paris Diderot, Sorbonne Paris Cite, Hopital Beaujon, Laboratory of Imaging Biomarkers, UMR1149 Inserm, Clichy (France); Beaujon University Hospital Paris Nord, Department of Radiology, Clichy (France); Pastor, Catherine M. [University Paris Diderot, Sorbonne Paris Cite, Hopital Beaujon, Laboratory of Imaging Biomarkers, UMR1149 Inserm, Clichy (France); Hopitaux Universitaires de Geneve, Departement d' Imagerie et des Sciences de l' Information Medicale, Geneva (Switzerland)

    2017-05-15

    Changes in the expression of hepatocyte membrane transporters in advanced fibrosis decrease the hepatic transport function of organic anions. The aim of our study was to assess if these changes can be evaluated with pharmacokinetic analysis of the hepatobiliary transport of the MR contrast agent gadoxetate. Dynamic gadoxetate-enhanced MRI was performed in 17 rats with advanced fibrosis and 8 normal rats. After deconvolution, hepatocyte three-compartmental analysis was performed to calculate the hepatocyte influx, biliary efflux and sinusoidal backflux rates. The expression of Oatp1a1, Mrp2 and Mrp3 organic anion membrane transporters was assessed with reverse transcription polymerase chain reaction. In the rats with advanced fibrosis, the influx and efflux rates of gadoxetate decreased and the backflux rate increased significantly (p = 0.003, 0.041 and 0.010, respectively). Significant correlations were found between influx and Oatp1a1 expression (r = 0.78, p < 0.001), biliary efflux and Mrp2 (r = 0.50, p = 0.016) and sinusoidal backflux and Mrp3 (r = 0.61, p = 0.002). These results show that changes in the bidirectional organic anion hepatocyte transport function in rats with advanced liver fibrosis can be assessed with compartmental analysis of gadoxetate-enhanced MRI. (orig.)

  19. RNA catalysis through compartmentalization

    Science.gov (United States)

    Strulson, Christopher A.; Molden, Rosalynn C.; Keating, Christine D.; Bevilacqua, Philip C.

    2012-11-01

    RNA performs important cellular functions in contemporary life forms. Its ability to act both as a catalyst and a storage mechanism for genetic information is also an important part of the RNA world hypothesis. Compartmentalization within modern cells allows the local concentration of RNA to be controlled and it has been suggested that this was also important in early life forms. Here, we mimic intracellular compartmentalization and macromolecular crowding by partitioning RNA in an aqueous two-phase system (ATPS). We show that the concentration of RNA is enriched by up to 3,000-fold in the dextran-rich phase of a polyethylene glycol/dextran ATPS and demonstrate that this can lead to approximately 70-fold increase in the rate of ribozyme cleavage. This rate enhancement can be tuned by the relative volumes of the two phases in the ATPS. Our observations support the importance of compartmentalization in the attainment of function in an RNA World as well as in modern biology.

  20. epiModel: A system to build automatically systems of differential equations of compartmental type-epidemiological models

    OpenAIRE

    Cortés López, Juan Carlos; Sánchez-Sánchez, Almudena; Santonja, Francisco; Villanueva Micó, Rafael Jacinto

    2011-01-01

    In this paper we describe epiModel, a code developed in Mathematica that facilitates the building of systems of differential equations corresponding to type-epidemiological linear or quadratic models whose characteristics are defined in text files following an easy syntax. It includes the possibility of obtaining the equations of models involving age and/or sex groups. © 2011. This work has been partially supported by the FIS PI-10/01433, the Spanish Government MICINN and FEDER Grant MTM20...

  1. PET-based compartmental modeling of {sup 124}I-A33 antibody: quantitative characterization of patient-specific tumor targeting in colorectal cancer

    Energy Technology Data Exchange (ETDEWEB)

    Zanzonico, Pat; O' Donoghue, Joseph A.; Humm, John L. [Memorial Sloan Kettering Cancer Center, Department of Medical Physics, New York, NY (United States); Carrasquillo, Jorge A.; Pandit-Taskar, Neeta; Ruan, Shutian; Larson, Steven M. [Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, NY (United States); Smith-Jones, Peter [Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, NY (United States); Stony Brook School of Medicine, Departments of Psychiatry and Radiology, Stony Brook, NY (United States); Divgi, Chaitanya [Columbia University Medical Center, New York, NY (United States); Scott, Andrew M. [La Trobe University, Olivia Newton-John Cancer Research Institute, Melbourne (Australia); Kemeny, Nancy E.; Wong, Douglas; Scheinberg, David [Memorial Sloan Kettering Cancer Center, Department of Medicine, New York, NY (United States); Fong, Yuman [Memorial Sloan Kettering Cancer Center, Department of Surgery, New York, NY (United States); City of Hope, Department of Surgery, Duarte, CA (United States); Ritter, Gerd; Jungbluth, Achem; Old, Lloyd J. [Memorial Sloan Kettering Cancer Center, Ludwig Institute for Cancer Research, New York, NY (United States)

    2015-10-15

    The molecular specificity of monoclonal antibodies (mAbs) directed against tumor antigens has proven effective for targeted therapy of human cancers, as shown by a growing list of successful antibody-based drug products. We describe a novel, nonlinear compartmental model using PET-derived data to determine the ''best-fit'' parameters and model-derived quantities for optimizing biodistribution of intravenously injected {sup 124}I-labeled antitumor antibodies. As an example of this paradigm, quantitative image and kinetic analyses of anti-A33 humanized mAb (also known as ''A33'') were performed in 11 colorectal cancer patients. Serial whole-body PET scans of {sup 124}I-labeled A33 and blood samples were acquired and the resulting tissue time-activity data for each patient were fit to a nonlinear compartmental model using the SAAM II computer code. Excellent agreement was observed between fitted and measured parameters of tumor uptake, ''off-target'' uptake in bowel mucosa, blood clearance, tumor antigen levels, and percent antigen occupancy. This approach should be generally applicable to antibody-antigen systems in human tumors for which the masses of antigen-expressing tumor and of normal tissues can be estimated and for which antibody kinetics can be measured with PET. Ultimately, based on each patient's resulting ''best-fit'' nonlinear model, a patient-specific optimum mAb dose (in micromoles, for example) may be derived. (orig.)

  2. Cancer metabolism: a modeling perspective

    Directory of Open Access Journals (Sweden)

    Pouyan eGhaffari Nouran

    2015-12-01

    Full Text Available Tumor cells alter their metabolism to maintain unregulated cellular proliferation and survival, but this transformation leaves them reliant on constant supply of nutrients and energy. In addition to the widely studied dysregulated glucose metabolism to fuel tumor cell growth, accumulating evidences suggest that utilization of amino acids and lipids contributes significantly to cancer cell metabolism. Also recent progresses in our understanding of carcinogenesis have revealed that cancer is a complex disease and cannot be understood through simple investigation of genetic mutations of cancerous cells. Cancer cells present in complex tumor tissues communicate with the surrounding microenvironment and develop traits which promote their growth, survival and metastasis. Decoding the full scope and targeting dysregulated metabolic pathways that support neoplastic transformations and their preservation requires both the advancement of experimental technologies for more comprehensive measurement of omics as well as the advancement of robust computational methods for accurate analysis of the generated data. Here, we review cancer-associated reprogramming of metabolism and highlight the capability of genome-scale metabolic modeling approaches in perceiving a system-level perspective of cancer metabolism and in detecting novel selective drug targets

  3. A topological map of the compartmentalized Arabidopsis thaliana leaf metabolome.

    Directory of Open Access Journals (Sweden)

    Stephan Krueger

    Full Text Available BACKGROUND: The extensive subcellular compartmentalization of metabolites and metabolism in eukaryotic cells is widely acknowledged and represents a key factor of metabolic activity and functionality. In striking contrast, the knowledge of actual compartmental distribution of metabolites from experimental studies is surprisingly low. However, a precise knowledge of, possibly all, metabolites and their subcellular distributions remains a key prerequisite for the understanding of any cellular function. METHODOLOGY/PRINCIPAL FINDINGS: Here we describe results for the subcellular distribution of 1,117 polar and 2,804 lipophilic mass spectrometric features associated to known and unknown compounds from leaves of the model plant Arabidopsis thaliana. Using an optimized non-aqueous fractionation protocol in conjunction with GC/MS- and LC/MS-based metabolite profiling, 81.5% of the metabolic data could be associated to one of three subcellular compartments: the cytosol (including the mitochondria, vacuole, or plastids. Statistical analysis using a marker-'free' approach revealed that 18.5% of these metabolites show intermediate distributions, which can either be explained by transport processes or by additional subcellular compartments. CONCLUSION/SIGNIFICANCE: Next to a functional and conceptual workflow for the efficient, highly resolved metabolite analysis of the fractionated Arabidopsis thaliana leaf metabolome, a detailed survey of the subcellular distribution of several compounds, in the graphical format of a topological map, is provided. This complex data set therefore does not only contain a rich repository of metabolic information, but due to thorough validation and testing by statistical methods, represents an initial step in the analysis of metabolite dynamics and fluxes within and between subcellular compartments.

  4. A Simple Plasma Retinol Isotope Ratio Method for Estimating β-Carotene Relative Bioefficacy in Humans: Validation with the Use of Model-Based Compartmental Analysis.

    Science.gov (United States)

    Ford, Jennifer Lynn; Green, Joanne Balmer; Lietz, Georg; Oxley, Anthony; Green, Michael H

    2017-09-01

    Background: Provitamin A carotenoids are an important source of dietary vitamin A for many populations. Thus, accurate and simple methods for estimating carotenoid bioefficacy are needed to evaluate the vitamin A value of test solutions and plant sources. β-Carotene bioefficacy is often estimated from the ratio of the areas under plasma isotope response curves after subjects ingest labeled β-carotene and a labeled retinyl acetate reference dose [isotope reference method (IRM)], but to our knowledge, the method has not yet been evaluated for accuracy. Objectives: Our objectives were to develop and test a physiologically based compartmental model that includes both absorptive and postabsorptive β-carotene bioconversion and to use the model to evaluate the accuracy of the IRM and a simple plasma retinol isotope ratio [(RIR), labeled β-carotene-derived retinol/labeled reference-dose-derived retinol in one plasma sample] for estimating relative bioefficacy. Methods: We used model-based compartmental analysis (Simulation, Analysis and Modeling software) to develop and apply a model that provided known values for β-carotene bioefficacy. Theoretical data for 10 subjects were generated by the model and used to determine bioefficacy by RIR and IRM; predictions were compared with known values. We also applied RIR and IRM to previously published data. Results: Plasma RIR accurately predicted β-carotene relative bioefficacy at 14 d or later. IRM also accurately predicted bioefficacy by 14 d, except that, when there was substantial postabsorptive bioconversion, IRM underestimated bioefficacy. Based on our model, 1-d predictions of relative bioefficacy include absorptive plus a portion of early postabsorptive conversion. Conclusion: The plasma RIR is a simple tracer method that accurately predicts β-carotene relative bioefficacy based on analysis of one blood sample obtained at ≥14 d after co-ingestion of labeled β-carotene and retinyl acetate. The method also provides

  5. Genome scale metabolic modeling of cancer

    DEFF Research Database (Denmark)

    Nilsson, Avlant; Nielsen, Jens

    2017-01-01

    Cancer cells reprogram metabolism to support rapid proliferation and survival. Energy metabolism is particularly important for growth and genes encoding enzymes involved in energy metabolism are frequently altered in cancer cells. A genome scale metabolic model (GEM) is a mathematical formalization...... of metabolism which allows simulation and hypotheses testing of metabolic strategies. It has successfully been applied to many microorganisms and is now used to study cancer metabolism. Generic models of human metabolism have been reconstructed based on the existence of metabolic genes in the human genome....... Cancer specific models of metabolism have also been generated by reducing the number of reactions in the generic model based on high throughput expression data, e.g. transcriptomics and proteomics. Targets for drugs and bio markers for diagnostics have been identified using these models. They have also...

  6. Compartmental analysis approach to fluorescence anisotropy: Perylene in viscous solvents

    OpenAIRE

    Piston, DW; Bilash, T; Gratton, E

    1989-01-01

    The fluorescence and polarization anisotropy decays of perylene in viscous solvents are investigated at several temperatures between -20 and 35 °C by using the technique of multifrequency phase and modulation fluorometry. The anisotropy decay data are globally analyzed over all temperatures studied and fit directly to physical quantities by using a compartmental model. We present a generalized compartmental model that can be used to calculate anisotropy decay arising from any type of intercon...

  7. Bioequivalence tests based on individual estimates using non-compartmental or model-based analyses: evaluation of estimates of sample means and type I error for different designs

    Science.gov (United States)

    Dubois, Anne; Gsteiger, Sandro; Pigeolet, Etienne; Mentré, France

    2010-01-01

    The main objective of this work is to compare the standard bioequivalence tests based on individual estimates of the area under the curve and the maximal concentration obtained by non compartmental analysis (NCA) to those based on individual empirical Bayes estimates (EBE) obtained by nonlinear mixed effects models. We evaluate by simulation the precision of sample means estimates and the type I error of bioequivalence tests for both approaches. Crossover trials are simulated under H0 using different numbers of subjects (N) and of samples per subject (n). We simulate concentration-time profiles with different variability settings for the between-subject and within-subject variabilities and for the variance of the residual error. Bioequivalence tests based on NCA show satisfactory properties with low and high variabilities, except when the residual error is high which leads to a very poor type I error or when n is small which leads to biased estimates. Tests based on EBE lead to an increase of the type I error when the shrinkage is above 20% which occurs notably when NCA fails. In those cases, tests based on individual estimates cannot be used. PMID:19876723

  8. A Generic Multi-Compartmental CNS Distribution Model Structure for 9 Drugs Allows Prediction of Human Brain Target Site Concentrations.

    Science.gov (United States)

    Yamamoto, Yumi; Välitalo, Pyry A; van den Berg, Dirk-Jan; Hartman, Robin; van den Brink, Willem; Wong, Yin Cheong; Huntjens, Dymphy R; Proost, Johannes H; Vermeulen, An; Krauwinkel, Walter; Bakshi, Suruchi; Aranzana-Climent, Vincent; Marchand, Sandrine; Dahyot-Fizelier, Claire; Couet, William; Danhof, Meindert; van Hasselt, Johan G C; de Lange, Elizabeth C M

    2017-02-01

    Predicting target site drug concentration in the brain is of key importance for the successful development of drugs acting on the central nervous system. We propose a generic mathematical model to describe the pharmacokinetics in brain compartments, and apply this model to predict human brain disposition. A mathematical model consisting of several physiological brain compartments in the rat was developed using rich concentration-time profiles from nine structurally diverse drugs in plasma, brain extracellular fluid, and two cerebrospinal fluid compartments. The effect of active drug transporters was also accounted for. Subsequently, the model was translated to predict human concentration-time profiles for acetaminophen and morphine, by scaling or replacing system- and drug-specific parameters in the model. A common model structure was identified that adequately described the rat pharmacokinetic profiles for each of the nine drugs across brain compartments, with good precision of structural model parameters (relative standard error human concentration-time profiles in different brain compartments well (symmetric mean absolute percentage error brain pharmacokinetic model was developed and its structure could adequately describe data across nine different drugs. The model could be successfully translated to predict human brain concentrations.

  9. In or out? On the tightness of glycosomal compartmentalization of metabolites and enzymes in Trypanosoma brucei

    NARCIS (Netherlands)

    Haanstra, Jurgen R.; Bakker, Barbara M.; Michels, Paul A. M.

    2014-01-01

    Trypanosomatids sequester large parts of glucose metabolism inside specialised peroxisomes, called glycosomes. Many studies have shown that correct glycosomal compartmentalization of glycolytic enzymes is essential for bloodstream-form Trypanosoma brucel. The recent finding of pore-forming

  10. Mathematical models of folate-mediated one-carbon metabolism.

    Science.gov (United States)

    Nijhout, H F; Reed, M C; Ulrich, C M

    2008-01-01

    Folate-mediated one-carbon metabolism is an unusually complex metabolic network, consisting of several interlocking cycles, and compartmentation between cytosol and mitochondria. The cycles have diverse functions, the primary being thymidylate synthesis (the rate limiting step in DNA synthesis), the initial steps in purine synthesis, glutathione synthesis, and a host of methyl transfer reactions that include DNA and histone methylation. Regulation within the network is accomplished by numerous allosteric interactions in which metabolites in one part of the network affect the activity of enzymes elsewhere in the network. Although a large body of experimental work has elucidated the details of the mechanisms in every part of the network, the multitude of complex and non-linear interactions within the network makes it difficult to deduce how the network as a whole operates. Understanding the operation of this network is further complicated by the fact that human populations maintain functional polymorphisms for several enzymes in the network, and that the network is subject to continual short and long-term fluctuations in its inputs as well as in demands on its various outputs. Understanding how such a complex system operates is possible only by means of mathematical models that take account of all the reactions and interactions. Simulations with such models can be used as an adjunct to laboratory experimentation to test ideas and alternative hypotheses and interpretations quickly and inexpensively. A number of mathematical models have been developed over the years, largely motivated by the need to understand the complex mechanisms by which anticancer drugs like methotrexate inhibit nucleotide synthesis and thus limit the ability of cells to divide. More recently, mathematical models have been used to investigate the regulatory and homeostatic mechanisms that allow the system to accommodate large fluctuations in one part of the network without affecting critical

  11. Nonnegative and Compartmental Dynamical Systems

    CERN Document Server

    Haddad, Wassim M; Hui, Qing

    2010-01-01

    This comprehensive book provides the first unified framework for stability and dissipativity analysis and control design for nonnegative and compartmental dynamical systems, which play a key role in a wide range of fields, including engineering, thermal sciences, biology, ecology, economics, genetics, chemistry, medicine, and sociology. Using the highest standards of exposition and rigor, the authors explain these systems and advance the state of the art in their analysis and active control design. Nonnegative and Compartmental Dynamical Systems presents the most complete treatment available o

  12. Metabolic model of central carbon and energy metabolisms of growing Arabidopsis thaliana in relation to sucrose translocation.

    Science.gov (United States)

    Zakhartsev, Maksim; Medvedeva, Irina; Orlov, Yury; Akberdin, Ilya; Krebs, Olga; Schulze, Waltraud X

    2016-12-28

    Sucrose translocation between plant tissues is crucial for growth, development and reproduction of plants. Systemic analysis of these metabolic and underlying regulatory processes allow a detailed understanding of carbon distribution within the plant and the formation of associated phenotypic traits. Sucrose translocation from 'source' tissues (e.g. mesophyll) to 'sink' tissues (e.g. root) is tightly bound to the proton gradient across the membranes. The plant sucrose transporters are grouped into efflux exporters (SWEET family) and proton-symport importers (SUC, STP families). To better understand regulation of sucrose export from source tissues and sucrose import into sink tissues, there is a need for a metabolic model that takes in account the tissue organisation of Arabidopsis thaliana with corresponding metabolic specificities of respective tissues in terms of sucrose and proton production/utilization. An ability of the model to operate under different light modes ('light' and 'dark') and correspondingly in different energy producing modes is particularly important in understanding regulatory modules. Here, we describe a multi-compartmental model consisting of a mesophyll cell with plastid and mitochondrion, a phloem cell, as well as a root cell with mitochondrion. In this model, the phloem was considered as a non-growing transport compartment, the mesophyll compartment was considered as both autotrophic (growing on CO2 under light) and heterotrophic (growing on starch in darkness), and the root was always considered as heterotrophic tissue dependent on sucrose supply from the mesophyll compartment. In total, the model includes 413 balanced compounds interconnected by 400 transformers. The structured metabolic model accounts for central carbon metabolism, photosynthesis, photorespiration, carbohydrate metabolism, energy and redox metabolisms, proton metabolism, biomass growth, nutrients uptake, proton gradient generation and sucrose translocation between

  13. Transit times and mean ages for nonautonomous and autonomous compartmental systems

    Energy Technology Data Exchange (ETDEWEB)

    Rasmussen, Martin; Hastings, Alan; Smith, Matthew J.; Agusto, Folashade B.; Chen-Charpentier, Benito M.; Hoffman, Forrest M.; Jiang, Jiang; Todd-Brown, Katherine E. O.; Wang, Ying; Wang, Ying-Ping; Luo, Yiqi

    2016-04-01

    We develop a theory for residence times and mean ages for nonautonomous compartmental systems. Using the McKendrick–von Forster equation, we show that the mean ages of mass in a compartmental system satisfy a linear nonautonomous ordinary differential equation that is exponentially stable. We then define a nonautonomous version of residence time as the mean age of mass leaving the compartmental system at a particular time and show that our nonautonomous theory is consistent with the autonomous case. We apply these results to study a nine-dimensional nonautonomous compartmental system modeling the carbon cycle, which is a simplified version of the Carnegie–Ames–Stanford approach (CASA) model.

  14. Cancer Metabolism: A Modeling Perspective

    DEFF Research Database (Denmark)

    Ghaffari, Pouyan; Mardinoglu, Adil; Nielsen, Jens

    2015-01-01

    Tumor cells alter their metabolism to maintain unregulated cellular proliferation and survival, but this transformation leaves them reliant on constant supply of nutrients and energy. In addition to the widely studied dysregulated glucose metabolism to fuel tumor cell growth, accumulating evidenc...

  15. Inferring ancient metabolism using ancestral core metabolic models of enterobacteria.

    Science.gov (United States)

    Baumler, David J; Ma, Bing; Reed, Jennifer L; Perna, Nicole T

    2013-06-11

    Enterobacteriaceae diversified from an ancestral lineage ~300-500 million years ago (mya) into a wide variety of free-living and host-associated lifestyles. Nutrient availability varies across niches, and evolution of metabolic networks likely played a key role in adaptation. Here we use a paleo systems biology approach to reconstruct and model metabolic networks of ancestral nodes of the enterobacteria phylogeny to investigate metabolism of ancient microorganisms and evolution of the networks. Specifically, we identified orthologous genes across genomes of 72 free-living enterobacteria (16 genera), and constructed core metabolic networks capturing conserved components for ancestral lineages leading to E. coli/Shigella (~10 mya), E. coli/Shigella/Salmonella (~100 mya), and all enterobacteria (~300-500 mya). Using these models we analyzed the capacity for carbon, nitrogen, phosphorous, sulfur, and iron utilization in aerobic and anaerobic conditions, identified conserved and differentiating catabolic phenotypes, and validated predictions by comparison to experimental data from extant organisms. This is a novel approach using quantitative ancestral models to study metabolic network evolution and may be useful for identification of new targets to control infectious diseases caused by enterobacteria.

  16. Genome-scale modeling for metabolic engineering.

    Science.gov (United States)

    Simeonidis, Evangelos; Price, Nathan D

    2015-03-01

    We focus on the application of constraint-based methodologies and, more specifically, flux balance analysis in the field of metabolic engineering, and enumerate recent developments and successes of the field. We also review computational frameworks that have been developed with the express purpose of automatically selecting optimal gene deletions for achieving improved production of a chemical of interest. The application of flux balance analysis methods in rational metabolic engineering requires a metabolic network reconstruction and a corresponding in silico metabolic model for the microorganism in question. For this reason, we additionally present a brief overview of automated reconstruction techniques. Finally, we emphasize the importance of integrating metabolic networks with regulatory information-an area which we expect will become increasingly important for metabolic engineering-and present recent developments in the field of metabolic and regulatory integration.

  17. Metabolic-hydaulic model Data

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — The data contained in this workbook were compiled to investigate the relationship between hydrology of the Colorado River and ecosystem metabolism parameters (i.e.,...

  18. Network Thermodynamic Curation of Human and Yeast Genome-Scale Metabolic Models

    Science.gov (United States)

    Martínez, Verónica S.; Quek, Lake-Ee; Nielsen, Lars K.

    2014-01-01

    Genome-scale models are used for an ever-widening range of applications. Although there has been much focus on specifying the stoichiometric matrix, the predictive power of genome-scale models equally depends on reaction directions. Two-thirds of reactions in the two eukaryotic reconstructions Homo sapiens Recon 1 and Yeast 5 are specified as irreversible. However, these specifications are mainly based on biochemical textbooks or on their similarity to other organisms and are rarely underpinned by detailed thermodynamic analysis. In this study, a to our knowledge new workflow combining network-embedded thermodynamic and flux variability analysis was used to evaluate existing irreversibility constraints in Recon 1 and Yeast 5 and to identify new ones. A total of 27 and 16 new irreversible reactions were identified in Recon 1 and Yeast 5, respectively, whereas only four reactions were found with directions incorrectly specified against thermodynamics (three in Yeast 5 and one in Recon 1). The workflow further identified for both models several isolated internal loops that require further curation. The framework also highlighted the need for substrate channeling (in human) and ATP hydrolysis (in yeast) for the essential reaction catalyzed by phosphoribosylaminoimidazole carboxylase in purine metabolism. Finally, the framework highlighted differences in proline metabolism between yeast (cytosolic anabolism and mitochondrial catabolism) and humans (exclusively mitochondrial metabolism). We conclude that network-embedded thermodynamics facilitates the specification and validation of irreversibility constraints in compartmentalized metabolic models, at the same time providing further insight into network properties. PMID:25028891

  19. Use of a "Super-child" Approach to Assess the Vitamin A Equivalence of Moringa oleifera Leaves, Develop a Compartmental Model for Vitamin A Kinetics, and Estimate Vitamin A Total Body Stores in Young Mexican Children.

    Science.gov (United States)

    Lopez-Teros, Veronica; Ford, Jennifer Lynn; Green, Michael H; Tang, Guangwen; Grusak, Michael A; Quihui-Cota, Luis; Muzhingi, Tawanda; Paz-Cassini, Mariela; Astiazaran-Garcia, Humberto

    2017-09-20

    Background: Worldwide, an estimated 250 million children <5 y old are vitamin A (VA) deficient. In Mexico, despite ongoing efforts to reduce VA deficiency, it remains an important public health problem; thus, food-based interventions that increase the availability and consumption of provitamin A-rich foods should be considered.Objective: The objectives were to assess the VA equivalence of (2)H-labeled Moringa oleifera (MO) leaves and to estimate both total body stores (TBS) of VA and plasma retinol kinetics in young Mexican children.Methods: β-Carotene was intrinsically labeled by growing MO plants in a (2)H2O nutrient solution. Fifteen well-nourished children (17-35 mo old) consumed puréed MO leaves (1 mg β-carotene) and a reference dose of [(13)C10]retinyl acetate (1 mg) in oil. Blood (2 samples/child) was collected 10 times (2 or 3 children each time) over 35 d. The bioefficacy of MO leaves was calculated from areas under the composite "super-child" plasma isotope response curves, and MO VA equivalence was estimated through the use of these values; a compartmental model was developed to predict VA TBS and retinol kinetics through the use of composite plasma [(13)C10]retinol data. TBS were also estimated with isotope dilution.Results: The relative bioefficacy of β-carotene retinol activity equivalents from MO was 28%; VA equivalence was 3.3:1 by weight (0.56 μmol retinol:1 μmol β-carotene). Kinetics of plasma retinol indicate more rapid plasma appearance and turnover and more extensive recycling in these children than are observed in adults. Model-predicted mean TBS (823 μmol) was similar to values predicted using a retinol isotope dilution equation applied to data from 3 to 6 d after dosing (mean ± SD: 832 ± 176 μmol; n = 7).Conclusions: The super-child approach can be used to estimate population carotenoid bioefficacy and VA equivalence, VA status, and parameters of retinol metabolism from a composite data set. Our results provide initial estimates

  20. Advanced compositional gradient and compartmentalization analysis

    Energy Technology Data Exchange (ETDEWEB)

    Canas, Jesus A.; Petti, Daniela; Mullins, Oliver [Schlumberger Servicos de Petroleo Ltda., Rio de Janeiro, RJ (Brazil)

    2008-07-01

    acting as compartmentalization elements. Using this method we present field DFA and pressure gradient data acquisitions and integrate into numerical simulation modeling to conceptually evaluate the impact of fluid composition / properties gradation and compartmentalization in the productivity of some reservoirs. (author)

  1. von Bertalanffy 1.0: a COBRA toolbox extension to thermodynamically constrain metabolic models.

    Science.gov (United States)

    Fleming, Ronan M T; Thiele, Ines

    2011-01-01

    In flux balance analysis of genome scale stoichiometric models of metabolism, the principal constraints are uptake or secretion rates, the steady state mass conservation assumption and reaction directionality. Here, we introduce an algorithmic pipeline for quantitative assignment of reaction directionality in multi-compartmental genome scale models based on an application of the second law of thermodynamics to each reaction. Given experimental or computationally estimated standard metabolite species Gibbs energy and metabolite concentrations, the algorithms bounds reaction Gibbs energy, which is transformed to in vivo pH, temperature, ionic strength and electrical potential. This cross-platform MATLAB extension to the COnstraint-Based Reconstruction and Analysis (COBRA) toolbox is computationally efficient, extensively documented and open source. http://opencobra.sourceforge.net.

  2. Drivers of compartmentalization in a Mediterranean pollination network

    DEFF Research Database (Denmark)

    Gonzalez, Ana M. Martin; Allesina, Stefano; Rodrigo, Anselm

    2012-01-01

    of several species attributes related to pollination syndromes, species phenology, abundance and connectivity in structuring compartmentalization. BM could not identify compartments in our network. By contrast, UM revealed four modules composed of plants and pollinators, and GM four groups of plants and five...... syndrome-related features in compartmentalization mostly emerged with UM. Only UM compartments differed in corolla length and pollen production. Both UM and GM compartments differed in their pollinator spectra. We found inconsistent reciprocity between plant attributes and pollinator spectra, thus......We study compartmentalization in a Mediterranean pollination network using three different analytical approaches: unipartite modularity (UM), bipartite modularity (BM) and the group model (GM). Our objectives are to compare compartments obtained with these three approaches and to explore the role...

  3. Ensemble Kinetic Modeling of Metabolic Networks from Dynamic Metabolic Profiles

    Directory of Open Access Journals (Sweden)

    Gengjie Jia

    2012-11-01

    Full Text Available Kinetic modeling of metabolic pathways has important applications in metabolic engineering, but significant challenges still remain. The difficulties faced vary from finding best-fit parameters in a highly multidimensional search space to incomplete parameter identifiability. To meet some of these challenges, an ensemble modeling method is developed for characterizing a subset of kinetic parameters that give statistically equivalent goodness-of-fit to time series concentration data. The method is based on the incremental identification approach, where the parameter estimation is done in a step-wise manner. Numerical efficacy is achieved by reducing the dimensionality of parameter space and using efficient random parameter exploration algorithms. The shift toward using model ensembles, instead of the traditional “best-fit” models, is necessary to directly account for model uncertainty during the application of such models. The performance of the ensemble modeling approach has been demonstrated in the modeling of a generic branched pathway and the trehalose pathway in Saccharomyces cerevisiae using generalized mass action (GMA kinetics.

  4. Computational model of cellular metabolic dynamics

    DEFF Research Database (Denmark)

    Li, Yanjun; Solomon, Thomas; Haus, Jacob M

    2010-01-01

    , by application of mechanism M.3, the model predicts metabolite concentration changes and glucose partitioning patterns consistent with experimental data. The reaction rate fluxes quantified by this detailed model of insulin/glucose metabolism provide information that can be used to evaluate the development......, pyruvate dehydrogenase); or M.3, parallel activation by a phenomenological insulin-mediated intracellular signal that modifies reaction rate coefficients. These simulations indicated that models M.1 and M.2 were not sufficient to explain the experimentally measured metabolic responses. However...... of the cytosol and mitochondria. The model simulated skeletal muscle metabolic responses to insulin corresponding to human hyperinsulinemic-euglycemic clamp studies. Insulin-mediated rate of glucose disposal was the primary model input. For model validation, simulations were compared with experimental data...

  5. Editorial: metabolic modeling in biotechnology and medical research.

    Science.gov (United States)

    Mattanovich, Diethard; Hatzimanikatis, Vassily

    2013-09-01

    Metabolic Modeling and Simulation: This special issue of Biotechnology Journal is edited by Diethard Mattanovich and Vassily Hatzimanikatis and covers the state-of-the-art in metabolic modeling, including the major themes of methods in metabolic modeling, modeling of human and microbial metabolism, and modeling of bioprocesses. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  6. Reconstruction and analysis of a genome-scale metabolic model for Scheffersomyces stipitis

    Directory of Open Access Journals (Sweden)

    Balagurunathan Balaji

    2012-02-01

    Full Text Available Abstract Background Fermentation of xylose, the major component in hemicellulose, is essential for economic conversion of lignocellulosic biomass to fuels and chemicals. The yeast Scheffersomyces stipitis (formerly known as Pichia stipitis has the highest known native capacity for xylose fermentation and possesses several genes for lignocellulose bioconversion in its genome. Understanding the metabolism of this yeast at a global scale, by reconstructing the genome scale metabolic model, is essential for manipulating its metabolic capabilities and for successful transfer of its capabilities to other industrial microbes. Results We present a genome-scale metabolic model for Scheffersomyces stipitis, a native xylose utilizing yeast. The model was reconstructed based on genome sequence annotation, detailed experimental investigation and known yeast physiology. Macromolecular composition of Scheffersomyces stipitis biomass was estimated experimentally and its ability to grow on different carbon, nitrogen, sulphur and phosphorus sources was determined by phenotype microarrays. The compartmentalized model, developed based on an iterative procedure, accounted for 814 genes, 1371 reactions, and 971 metabolites. In silico computed growth rates were compared with high-throughput phenotyping data and the model could predict the qualitative outcomes in 74% of substrates investigated. Model simulations were used to identify the biosynthetic requirements for anaerobic growth of Scheffersomyces stipitis on glucose and the results were validated with published literature. The bottlenecks in Scheffersomyces stipitis metabolic network for xylose uptake and nucleotide cofactor recycling were identified by in silico flux variability analysis. The scope of the model in enhancing the mechanistic understanding of microbial metabolism is demonstrated by identifying a mechanism for mitochondrial respiration and oxidative phosphorylation. Conclusion The genome

  7. Rodent Models for Metabolic Syndrome Research

    Directory of Open Access Journals (Sweden)

    Sunil K. Panchal

    2011-01-01

    Full Text Available Rodents are widely used to mimic human diseases to improve understanding of the causes and progression of disease symptoms and to test potential therapeutic interventions. Chronic diseases such as obesity, diabetes and hypertension, together known as the metabolic syndrome, are causing increasing morbidity and mortality. To control these diseases, research in rodent models that closely mimic the changes in humans is essential. This review will examine the adequacy of the many rodent models of metabolic syndrome to mimic the causes and progression of the disease in humans. The primary criterion will be whether a rodent model initiates all of the signs, especially obesity, diabetes, hypertension and dysfunction of the heart, blood vessels, liver and kidney, primarily by diet since these are the diet-induced signs in humans with metabolic syndrome. We conclude that the model that comes closest to fulfilling this criterion is the high carbohydrate, high fat-fed male rodent.

  8. Modeling cancer metabolism on a genome scale

    Science.gov (United States)

    Yizhak, Keren; Chaneton, Barbara; Gottlieb, Eyal; Ruppin, Eytan

    2015-01-01

    Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genome-scale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network-level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field. PMID:26130389

  9. A mathematical model of glutathione metabolism

    Directory of Open Access Journals (Sweden)

    James S Jill

    2008-04-01

    Full Text Available Abstract Background Glutathione (GSH plays an important role in anti-oxidant defense and detoxification reactions. It is primarily synthesized in the liver by the transsulfuration pathway and exported to provide precursors for in situ GSH synthesis by other tissues. Deficits in glutathione have been implicated in aging and a host of diseases including Alzheimer's disease, Parkinson's disease, cardiovascular disease, cancer, Down syndrome and autism. Approach We explore the properties of glutathione metabolism in the liver by experimenting with a mathematical model of one-carbon metabolism, the transsulfuration pathway, and glutathione synthesis, transport, and breakdown. The model is based on known properties of the enzymes and the regulation of those enzymes by oxidative stress. We explore the half-life of glutathione, the regulation of glutathione synthesis, and its sensitivity to fluctuations in amino acid input. We use the model to simulate the metabolic profiles previously observed in Down syndrome and autism and compare the model results to clinical data. Conclusion We show that the glutathione pools in hepatic cells and in the blood are quite insensitive to fluctuations in amino acid input and offer an explanation based on model predictions. In contrast, we show that hepatic glutathione pools are highly sensitive to the level of oxidative stress. The model shows that overexpression of genes on chromosome 21 and an increase in oxidative stress can explain the metabolic profile of Down syndrome. The model also correctly simulates the metabolic profile of autism when oxidative stress is substantially increased and the adenosine concentration is raised. Finally, we discuss how individual variation arises and its consequences for one-carbon and glutathione metabolism.

  10. Protocell design through modular compartmentalization.

    Science.gov (United States)

    Miller, David; Booth, Paula J; Seddon, John M; Templer, Richard H; Law, Robert V; Woscholski, Rudiger; Ces, Oscar; Barter, Laura M C

    2013-10-06

    De novo synthetic biological design has the potential to significantly impact upon applications such as energy generation and nanofabrication. Current designs for constructing organisms from component parts are typically limited in scope, as they utilize a cut-and-paste ideology to create simple stepwise engineered protein-signalling pathways. We propose the addition of a new design element that segregates components into lipid-bound 'proto-organelles', which are interfaced with response elements and housed within a synthetic protocell. This design is inspired by living cells, which utilize multiple types of signalling molecules to facilitate communication between isolated compartments. This paper presents our design and validation of the components required for a simple multi-compartment protocell machine, for coupling a light transducer to a gene expression system. This represents a general design concept for the compartmentalization of different types of artificial cellular machinery and the utilization of non-protein signal molecules for signal transduction.

  11. Neuro-fuzzy model of homocysteine metabolism

    Indian Academy of Sciences (India)

    In view of well-documented association of hyperhomocysteinaemia with a wide spectrum of diseases and higher incidence of vitamin deficiencies in Indians, we proposed a mathematical model to forecast the role of demographic and geneticvariables in influencing homocysteine metabolism and investigated the influence ...

  12. Derivative processes for modelling metabolic fluxes

    Science.gov (United States)

    Žurauskienė, Justina; Kirk, Paul; Thorne, Thomas; Pinney, John; Stumpf, Michael

    2014-01-01

    Motivation: One of the challenging questions in modelling biological systems is to characterize the functional forms of the processes that control and orchestrate molecular and cellular phenotypes. Recently proposed methods for the analysis of metabolic pathways, for example, dynamic flux estimation, can only provide estimates of the underlying fluxes at discrete time points but fail to capture the complete temporal behaviour. To describe the dynamic variation of the fluxes, we additionally require the assumption of specific functional forms that can capture the temporal behaviour. However, it also remains unclear how to address the noise which might be present in experimentally measured metabolite concentrations. Results: Here we propose a novel approach to modelling metabolic fluxes: derivative processes that are based on multiple-output Gaussian processes (MGPs), which are a flexible non-parametric Bayesian modelling technique. The main advantages that follow from MGPs approach include the natural non-parametric representation of the fluxes and ability to impute the missing data in between the measurements. Our derivative process approach allows us to model changes in metabolite derivative concentrations and to characterize the temporal behaviour of metabolic fluxes from time course data. Because the derivative of a Gaussian process is itself a Gaussian process, we can readily link metabolite concentrations to metabolic fluxes and vice versa. Here we discuss how this can be implemented in an MGP framework and illustrate its application to simple models, including nitrogen metabolism in Escherichia coli. Availability and implementation: R code is available from the authors upon request. Contact: j.norkunaite@imperial.ac.uk; m.stumpf@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24578401

  13. A Novel Method for Performance Analysis of Compartmentalized Reservoirs

    Directory of Open Access Journals (Sweden)

    Shahamat Mohammad Sadeq

    2016-05-01

    Full Text Available This paper presents a simple analytical model for performance analysis of compartmentalized reservoirs producing under Constant Terminal Rate (CTR and Constant Terminal Pressure (CTP. The model is based on the well-known material balance and boundary dominated flow equations and is written in terms of capacitance and resistance of a production and a support compartment. These capacitance and resistance terms account for a combination of reservoir parameters which enable the developed model to be used for characterizing such systems. In addition to considering the properties contrast between the two reservoir compartments, the model takes into account existence of transmissibility barriers with the use of resistance terms. The model is used to analyze production performance of unconventional reservoirs, where the multistage fracturing of horizontal wells effectively creates a Stimulated Reservoir Volume (SRV with an enhanced permeability surrounded by a non-stimulated region. It can also be used for analysis of compartmentalized conventional reservoirs. The analytical solutions provide type curves through which the controlling reservoirs parameters of a compartmentalized system can be estimated. The contribution of the supporting compartment is modeled based on a boundary dominated flow assumption. The transient behaviour of the support compartment is captured by application of “distance of investigation” concept. The model shows that depletion of the production and support compartments exhibit two unit slopes on a log-log plot of pressure versus time for CTR. For CTP, however, the depletions display two exponential declines. The depletion signatures are separated by transition periods, which depend on the contribution of the support compartment (i.e. transient or boundary dominated flow. The developed equations can be implemented easily in a spreadsheet application, and are corroborated with the use of a numerical simulation. The study

  14. Network thermodynamic curation of human and yeast genome-scale metabolic models.

    Science.gov (United States)

    Martínez, Verónica S; Quek, Lake-Ee; Nielsen, Lars K

    2014-07-15

    Genome-scale models are used for an ever-widening range of applications. Although there has been much focus on specifying the stoichiometric matrix, the predictive power of genome-scale models equally depends on reaction directions. Two-thirds of reactions in the two eukaryotic reconstructions Homo sapiens Recon 1 and Yeast 5 are specified as irreversible. However, these specifications are mainly based on biochemical textbooks or on their similarity to other organisms and are rarely underpinned by detailed thermodynamic analysis. In this study, a to our knowledge new workflow combining network-embedded thermodynamic and flux variability analysis was used to evaluate existing irreversibility constraints in Recon 1 and Yeast 5 and to identify new ones. A total of 27 and 16 new irreversible reactions were identified in Recon 1 and Yeast 5, respectively, whereas only four reactions were found with directions incorrectly specified against thermodynamics (three in Yeast 5 and one in Recon 1). The workflow further identified for both models several isolated internal loops that require further curation. The framework also highlighted the need for substrate channeling (in human) and ATP hydrolysis (in yeast) for the essential reaction catalyzed by phosphoribosylaminoimidazole carboxylase in purine metabolism. Finally, the framework highlighted differences in proline metabolism between yeast (cytosolic anabolism and mitochondrial catabolism) and humans (exclusively mitochondrial metabolism). We conclude that network-embedded thermodynamics facilitates the specification and validation of irreversibility constraints in compartmentalized metabolic models, at the same time providing further insight into network properties. Copyright © 2014 Biophysical Society. Published by Elsevier Inc. All rights reserved.

  15. Modelling of FDG metabolism in liver voxels.

    Science.gov (United States)

    Baker, C; Dowson, N; Thomas, P; Rose, S

    2015-01-21

    Kinetic analysis is a tool used to glean additional information from positron emission tomography (PET) data by exploiting the dynamics of tissue metabolism. The standard irreversible and reversible two compartment models used in kinetic analysis were initially developed to analyse brain PET data. The application of kinetic analysis to PET of the liver presents the opportunity to move beyond the generic standard models and develop physiologically informed pharmacokinetic models that incorporate structural and functional features in particular to the liver. In this paper, we develop a new compartment model, called the tubes model, which is informed by the liver׳s sinusoidal architecture, high fractional blood volume, high perfusion rate, and large hepatocyte surface area facing the space of Disse. The tubes model distributes tracer between the blood and intracellular compartments in more physiologically faithful proportions than the standard model, producing parametric images with improved contrast between healthy and neoplastic tissue. Crown Copyright © 2014. Published by Elsevier Ltd. All rights reserved.

  16. Lyophilized kits of diamino dithiol compounds for labelling with {sup 99m}-technetium. Pharmacokinetics studies and distribution compartmental models of the related complexes; Conjuntos de reativos liofilizados de compostos diaminoditiolicos para marcacao com tecnecio-99m. Estudo farmacocinetico e elaboracao de modelos compartimentalizados dos respectivos complexos

    Energy Technology Data Exchange (ETDEWEB)

    Araujo, Elaine Bortoleti de

    1995-07-01

    for enough period, permitting the acquisition of scintigraphic cerebral images. The cerebral retention of the complex, and also its relative fast elimination are related to the metabolization of the compound to more polar species, identified in the urine and bile HPLC. The lipophilicity of the complex determine high liver uptake and intestinal elimination of the compound. Biological distribution of the complex {sup 99m}Tc-L.L-ECD was adjusted to a compartmental distribution model composed by seven compartments, characterized by a fast blood clearance, an unidirectional renal depuration and intestinal elimination, determined by intense hepato-biliary transit. (author)

  17. Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions1

    Science.gov (United States)

    Zuñiga, Cristal; Li, Chien-Ting; Zielinski, Daniel C.; Guarnieri, Michael T.; Antoniewicz, Maciek R.; Zengler, Karsten

    2016-01-01

    The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine. PMID:27372244

  18. Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions.

    Science.gov (United States)

    Zuñiga, Cristal; Li, Chien-Ting; Huelsman, Tyler; Levering, Jennifer; Zielinski, Daniel C; McConnell, Brian O; Long, Christopher P; Knoshaug, Eric P; Guarnieri, Michael T; Antoniewicz, Maciek R; Betenbaugh, Michael J; Zengler, Karsten

    2016-09-01

    The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine. © 2016 American Society of Plant Biologists. All rights reserved.

  19. Preventing Age-Related Decline of Gut Compartmentalization Limits Microbiota Dysbiosis and Extends Lifespan

    OpenAIRE

    Li, Hongjie; Qi, Yanyan; Jasper, Heinrich

    2016-01-01

    Compartmentalization of the gastrointestinal (GI) tract of metazoans is critical for health. GI compartments contain specific microbiota, and microbiota dysbiosis is associated with intestinal dysfunction. Dysbiosis develops in aging intestines, yet how this relates to changes in GI compartmentalization remains unclear. The Drosophila GI tract is an accessible model to address this question. Here we show that the stomach-like copper cell region (CCR) in the middle midgut controls distribution...

  20. Dynamic Metabolic Model Building Based on the Ensemble Modeling Approach

    Energy Technology Data Exchange (ETDEWEB)

    Liao, James C. [Univ. of California, Los Angeles, CA (United States)

    2016-10-01

    Ensemble modeling of kinetic systems addresses the challenges of kinetic model construction, with respect to parameter value selection, and still allows for the rich insights possible from kinetic models. This project aimed to show that constructing, implementing, and analyzing such models is a useful tool for the metabolic engineering toolkit, and that they can result in actionable insights from models. Key concepts are developed and deliverable publications and results are presented.

  1. Krebs cycle metabolon formation: metabolite concentration gradient enhanced compartmentation of sequential enzymes.

    Science.gov (United States)

    Wu, Fei; Pelster, Lindsey N; Minteer, Shelley D

    2015-01-25

    Dynamics of metabolon formation in mitochondria was probed by studying diffusional motion of two sequential Krebs cycle enzymes in a microfluidic channel. Enhanced directional co-diffusion of both enzymes against a substrate concentration gradient was observed in the presence of intermediate generation. This reveals a metabolite directed compartmentation of metabolic pathways.

  2. Analysis of metabolic flux using dynamic labeling and metabolic modeling

    OpenAIRE

    Fernie, A.; Morgan, J.

    2013-01-01

    Metabolic fluxes and the capacity to modulate them are a crucial component of the ability of the plant cell to react to environmental perturbations. Our ability to quantify them and to attain information concerning the regulatory mechanisms which control them is therefore essential to understand and influence metabolic networks. For all but the simplest of flux measurements labelling methods have proven to be the most informative. Both steady-state and dynamic labelling approaches having been...

  3. Mathematical Modelling of Metabolic Regulation in Aging

    Directory of Open Access Journals (Sweden)

    Mark T. Mc Auley

    2015-04-01

    Full Text Available The underlying cellular mechanisms that characterize aging are complex and multifaceted. However, it is emerging that aging could be regulated by two distinct metabolic hubs. These hubs are the pathway defined by the mammalian target of rapamycin (mTOR and that defined by the NAD+-dependent deacetylase enzyme, SIRT1. Recent experimental evidence suggests that there is crosstalk between these two important pathways; however, the mechanisms underpinning their interaction(s remains poorly understood. In this review, we propose using computational modelling in tandem with experimentation to delineate the mechanism(s. We briefly discuss the main modelling frameworks that could be used to disentangle this relationship and present a reduced reaction pathway that could be modelled. We conclude by outlining the limitations of computational modelling and by discussing opportunities for future progress in this area.

  4. Improved annotation through genome-scale metabolic modeling of Aspergillus oryzae

    Directory of Open Access Journals (Sweden)

    Hansen Kim

    2008-05-01

    Full Text Available Abstract Background Since ancient times the filamentous fungus Aspergillus oryzae has been used in the fermentation industry for the production of fermented sauces and the production of industrial enzymes. Recently, the genome sequence of A. oryzae with 12,074 annotated genes was released but the number of hypothetical proteins accounted for more than 50% of the annotated genes. Considering the industrial importance of this fungus, it is therefore valuable to improve the annotation and further integrate genomic information with biochemical and physiological information available for this microorganism and other related fungi. Here we proposed the gene prediction by construction of an A. oryzae Expressed Sequence Tag (EST library, sequencing and assembly. We enhanced the function assignment by our developed annotation strategy. The resulting better annotation was used to reconstruct the metabolic network leading to a genome scale metabolic model of A. oryzae. Results Our assembled EST sequences we identified 1,046 newly predicted genes in the A. oryzae genome. Furthermore, it was possible to assign putative protein functions to 398 of the newly predicted genes. Noteworthy, our annotation strategy resulted in assignment of new putative functions to 1,469 hypothetical proteins already present in the A. oryzae genome database. Using the substantially improved annotated genome we reconstructed the metabolic network of A. oryzae. This network contains 729 enzymes, 1,314 enzyme-encoding genes, 1,073 metabolites and 1,846 (1,053 unique biochemical reactions. The metabolic reactions are compartmentalized into the cytosol, the mitochondria, the peroxisome and the extracellular space. Transport steps between the compartments and the extracellular space represent 281 reactions, of which 161 are unique. The metabolic model was validated and shown to correctly describe the phenotypic behavior of A. oryzae grown on different carbon sources. Conclusion A much

  5. Prevalence of metabolic syndrome in HIV-infected patients receiving highly active antiretroviral therapy using International Diabetes Foundation and Adult Treatment Panel III criteria: associations with insulin resistance, disturbed body fat compartmentalization, elevated C-reactive protein, and [corrected] hypoadiponectinemia.

    Science.gov (United States)

    Samaras, Katherine; Wand, Handan; Law, Matthew; Emery, Sean; Cooper, David; Carr, Andrew

    2007-01-01

    Metabolic syndrome is a cluster of risk factors for cardiovascular disease and type 2 diabetes. Definitions exist to identify those "at risk." Treatment of HIV infection with highly active antiretroviral therapy can induce severe metabolic complications including lipodystrophy, dyslipidemia, and insulin resistance. The purpose of this study was to report the prevalence of metabolic syndrome in HIV-infected patients and compare insulin resistance and total body, limb, and visceral fat and adipokines in those with and without metabolic syndrome. This was an international cross-sectional study of a well-characterized cohort of 788 HIV-infected adults recruited at 32 centers. Metabolic syndrome prevalence was examined using International Diabetes Federation (IDF) and U.S. National Cholesterol Education Program Adult Treatment Panel III (ATPIII) criteria, relative to body composition (whole-body dual-energy X-ray absorptiometry and abdominal computed tomography), lipids, glycemic parameters, insulin resistance, leptin, adiponectin, and C-reactive protein (CRP). The prevalence of metabolic syndrome was 14% (n = 114; 83 men) by IDF criteria and 18% (n = 139; 118 men) by ATPIII criteria; the concordance was significant but only moderate (kappa = 0.46, P adults was lower than that reported for the general population. Metabolic syndrome was associated with a substantially increased prevalence of type 2 diabetes in this specific cohort. Many subjects without metabolic syndrome had at least two metabolic syndrome components (particularly elevated lipid levels) but did not meet waist circumference or waist-to-hip ratio cutoff metabolic syndrome criteria in this group with high rates of body fat partitioning disturbances.

  6. iOD907, the first genome-scale metabolic model for the milk yeast Kluyveromyces lactis.

    Science.gov (United States)

    Dias, Oscar; Pereira, Rui; Gombert, Andreas K; Ferreira, Eugénio C; Rocha, Isabel

    2014-06-01

    We describe here the first genome-scale metabolic model of Kluyveromyces lactis, iOD907. It is partially compartmentalized (four compartments), composed of 1867 reactions and 1476 metabolites. The iOD907 model performed well when comparing the positive growth of K. lactis to Biolog experiments and to an online catalogue of strains that provides information on carbon sources in which K. lactis is able to grow. Chemostat experiments were used to adjust non-growth-associated energy requirements, and the model proved accurate when predicting the biomass, oxygen and carbon dioxide yields. When compared to published experiments, in silico knockouts accurately predicted in vivo phenotypes. The iOD907 genome-scale metabolic model complies with the MIRIAM (minimum information required for the annotation of biochemical models) standards for the annotation of enzymes, transporters, metabolites and reactions. Moreover, it contains direct links to Kyoto encyclopedia of genes and genomes (KEGG; for enzymes, metabolites and reactions) and to the Transporters Classification Database (TCDB) for transporters, allowing easy comparisons to other models. Furthermore, this model is provided in the well-established systems biology markup language (SBML) format, which means that it can be used in most metabolic engineering platforms, such as OptFlux or Cobra. The model is able to predict the behavior of K. lactis under different environmental conditions and genetic perturbations. Furthermore, by performing simulations and optimizations, it can be important in the design of minimal media and will allow insights on the milk yeast's metabolism, as well as identifying metabolic engineering targets for improving the production of products of interest. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  7. Diagnosis and management of compartmental syndromes.

    Science.gov (United States)

    Matsen, F A; Winquist, R A; Krugmire, R B

    1980-03-01

    Patients at risk for compartmental syndromes challenge both the diagnostic and the therapeutic abilities of the physician. Suboptimum results may be due to delays in diagnosis and treatment, to incomplete surgical decompression, and to difficulties in the management of the limb after decompression. Although careful clinical assessment permits the diagnosis of a compartmental syndrome in most patients, we have found measurement of tissue pressure and direct nerve stimulation to be helpful for resolving ambiguous or equivocal cases. In our experience, the four-compartment parafibular approach to the leg and the ulnar approach to the volar compartments of the forearm provide efficient and complete decompression of potentially involved compartments. The skeletal stabilization of fractures associated with compartmental syndromes may facilitate management of the limb after surgical decompression.

  8. Transgenic mouse models of metabolic bone disease.

    Science.gov (United States)

    McCauley, L K

    2001-07-01

    The approach of gene-targeted animal models is likely the most important experimental tool contributing to recent advances in skeletal biology. Modifying the expression of a gene in vivo, and the analysis of the consequences of the mutation, are central to the understanding of gene function during development and physiology, and therefore to our understanding of the gene's role in disease states. Researchers had been limited to animal models primarily involving pharmaceutical manipulations and spontaneous mutations. With the advent of gene targeting, however, animal models that impact our understanding of metabolic bone disease have evolved dramatically. Interestingly, some genes that were expected to yield dramatic phenotypes in bone, such as estrogen receptor-alpha or osteopontin, proved to have subtle phenotypes, whereas other genes, such as interleukin-5 or osteoprotegerin, were initially identified as having a role in bone metabolism via the analysis of their phenotype after gene ablation or overexpression. Particularly important has been the advance in knowledge of osteoblast and osteoclast independent and dependent roles via the selective targeting of genes and the consequent disruption of bone formation, bone resorption, or both. Our understanding of interactions of the skeletal system with other systems, ie, the vascular system and homeostatic controls of adipogenesis, has evolved via animal models such as the matrix gla protein, knock-out, and the targeted overexpression of Delta FosB. Challenging transgenic models such as the osteopontin-deficient mice with mediators of bone remodeling like parathyroid hormone and mechanical stimuli and extending phenotype characterization to mechanistic in vitro studies of primary bone cells is providing additional insight into the mechanisms involved in pathologic states and their potentials for therapeutic strategies. This review segregates characterization of transgenic models based on the category of gene altered

  9. Toward metabolic engineering in the context of system biology and synthetic biology: advances and prospects.

    Science.gov (United States)

    Liu, Yanfeng; Shin, Hyun-dong; Li, Jianghua; Liu, Long

    2015-02-01

    Metabolic engineering facilitates the rational development of recombinant bacterial strains for metabolite overproduction. Building on enormous advances in system biology and synthetic biology, novel strategies have been established for multivariate optimization of metabolic networks in ensemble, spatial, and dynamic manners such as modular pathway engineering, compartmentalization metabolic engineering, and metabolic engineering guided by genome-scale metabolic models, in vitro reconstitution, and systems and synthetic biology. Herein, we summarize recent advances in novel metabolic engineering strategies. Combined with advancing kinetic models and synthetic biology tools, more efficient new strategies for improving cellular properties can be established and applied for industrially important biochemical production.

  10. TNF and IL-10 are major factors in modulation of the phagocytic cell environment in lung and lymph node in tuberculosis: a next-generation two-compartmental model.

    Science.gov (United States)

    Marino, Simeone; Myers, Amy; Flynn, JoAnne L; Kirschner, Denise E

    2010-08-21

    Tuberculosis (TB) is one of the earliest recorded human diseases and still one of the deadliest worldwide. Its causative agent is the bacteria Mycobacterium tuberculosis (Mtb). Cytokine-mediated macrophage activation is a necessary step in control of bacterial growth, and early immunologic events in lymph node and lung are crucial to the outcome of infection, although the factors that influence these environments and the immune response are poorly understood. Our goal is to build the next-generation two-compartmental model of the immune response to provide a gateway to more spatial and mechanistic investigations of M. tuberculosis infection in the LN and lung. Crucial immune factors emerge that affect macrophage populations and inflammation, namely TNF-dependent recruitment and apoptosis, and IL-10 levels. Surprisingly, bacterial load plays a less important role than TNF in increasing the population of infected macrophages and inflammation. Using a mathematical model, it is possible to distinguish the effects of pro-inflammatory (TNF) and anti-inflammatory (IL-10) cytokines on the spectrum of phagocyte populations (macrophages and dendritic cells) in the lung and lymph node. Our results suggest that TNF is a major mediator of recruitment of phagocytes to the lungs. In contrast, IL-10 plays a role in balancing the dominant macrophage phenotype in LN and lung. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

  11. The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism

    DEFF Research Database (Denmark)

    Birkel, Garrett W.; Ghosh, Amit; Kumar, Vinay S.

    2017-01-01

    analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics, proteomics and metabolomics) are urgently needed.Results: The jQMM library presented here provides an open-source, Python-based framework for modeling internal metabolic fluxes...

  12. Lipid metabolism, adipocyte depot physiology and utilization of meat animals as experimental models for metabolic research.

    Science.gov (United States)

    Dodson, Michael V; Hausman, Gary J; Guan, Leluo; Du, Min; Rasmussen, Theodore P; Poulos, Sylvia P; Mir, Priya; Bergen, Werner G; Fernyhough, Melinda E; McFarland, Douglas C; Rhoads, Robert P; Soret, Beatrice; Reecy, James M; Velleman, Sandra G; Jiang, Zhihua

    2010-11-22

    Meat animals are unique as experimental models for both lipid metabolism and adipocyte studies because of their direct economic value for animal production. This paper discusses the principles that regulate adipogenesis in major meat animals (beef cattle, dairy cattle, and pigs), the definition of adipose depot-specific regulation of lipid metabolism or adipogenesis, and introduces the potential value of these animals as models for metabolic research including mammary biology and the ontogeny of fatty livers.

  13. Lipid metabolism, adipocyte depot physiology and utilization of meat animals as experimental models for metabolic research

    OpenAIRE

    Michael V. Dodson, Gary J. Hausman, LeLuo Guan, Min Du, Theodore P. Rasmussen, Sylvia P. Poulos, Priya Mir, Werner G. Bergen, Melinda E. Fernyhough, Douglas C. McFarland, Robert P. Rhoads, Beatrice Soret, James M. Reecy, Sandra G. Velleman, Zhihua Jiang

    2010-01-01

    Meat animals are unique as experimental models for both lipid metabolism and adipocyte studies because of their direct economic value for animal production. This paper discusses the principles that regulate adipogenesis in major meat animals (beef cattle, dairy cattle, and pigs), the definition of adipose depot-specific regulation of lipid metabolism or adipogenesis, and introduces the potential value of these animals as models for metabolic research including mammary biology and the ontogeny...

  14. Metabolic modeling to understand and redesign microbial systems

    NARCIS (Netherlands)

    Heck, van Ruben G.A.

    2017-01-01

    The goals of this thesis are to increase the understanding of microbial metabolism and to functionally (re-)design microbial systems using Genome- Scale Metabolic models (GSMs). GSMs are species-specific knowledge repositories that can be used to predict metabolic activities for wildtype and

  15. Model for metabolic resistance against ALS inhibitors

    Directory of Open Access Journals (Sweden)

    Richter, Otto

    2014-02-01

    Full Text Available Due to herbicide selection pressure metabolic resistance has evolved in many weed species. In this paper we analyse the interaction between the branched chain amino acid (BBC pathway and detoxifying pathways for herbicide breakdown. The four phase detoxification pathway of herbicides comprising the action of P450, GST, glycosyltransferase and ABC transporter is modelled by a system of coupled enzyme kinetic reactions represented by nonlinear differential equations. The herbicide under consideration inhibits the enzyme ALS, which is the key enzyme for the biosynthesis of branched amino acids. For the kinetics of ALS a Monod approach is employed with a binding site for the inhibitor. Synthetic and detoxification pathways are coupled. The model is used to study the production of branched amino acids under the action of ALS inhibitors for different structures and modes of action of the detoxification pathway. The model is capable of generating typical dose response curves and their shift in dependence of the activity pattern of the enzymes of the detoxification pathway of the inhibitor.

  16. Calibration and validation of an air-displacement plethysmography method for estimating percentage body fat in an elderly population: a comparison among compartmental models.

    Science.gov (United States)

    Yee, A J; Fuerst, T; Salamone, L; Visser, M; Dockrell, M; Van Loan, M; Kern, M

    2001-11-01

    The use of hydrostatic weighing (HW) to measure body composition in the elderly can be difficult and is based on the assumption of constancy of body compartments. We calibrated and validated a new air-displacement plethysmography (AP) method for measuring body composition in the elderly. A 4-compartment equation for calculating percentage body fat (%BF) that used body density (D(b)), total body water, and bone mineral content was used as the criterion for evaluating %BF estimated by the 2- and 3-compartment models. D(b) was measured by HW [D(b(HW))] and by use of the AP instrument [D(b(AP))] in 30 elderly men and 28 elderly women aged 70-79 y. D(b(AP)) was not significantly different from D(b(HW)). However, analysis of variance showed a significant two-way interaction between sex and compartment model (P < 0.02), indicating that the comparisons between the sexes were different across all compartment models. The %BF calculated for the women was significantly higher than that calculated for the men by both HW and AP and for all compartment models. Our data indicate that D(b(AP)) was not significantly different from D(b(HW)). Although differences were seen in %BF between the sexes, we observed no significant differences among the compartment models within each sex for this group of older individuals.

  17. The architecture of antagonistic networks: Node degree distribution, compartmentalization and nestedness

    Directory of Open Access Journals (Sweden)

    Savannah Nuwagaba

    2015-12-01

    Full Text Available Describing complex ecosystems as networks of interacting components has proved fruitful - revealing many distinctive patterns and dynamics of ecological systems. Of these patterns, three have often been brought up in literature, including species degree distribution, compartmentalization and nestedness, due largely to their implications for the functionality and stability of communities. Here, using 61 empirical antagonistic networks, we aim to settle the inconsistency in literature by (i fitting their node degree distributions to five different parametric models and identifying the one fits the best, (ii measuring the levels of nestedness and compartmentalization of these 61 networks and testing their significance using different null models, and (iii exploring how network connectance affects these three network architecture metrics. This research showed that most antagonistic networks do not display power law degree distributions and that resource species are generally uniformly distributed. We also clearly showed that the conclusion of whether a network is significantly compartmentalized or nested depends largely on the null model used.

  18. Transcriptional regulation and steady-state modeling of metabolic networks

    DEFF Research Database (Denmark)

    Zelezniak, Aleksej

    . In nature, microorganisms do not exist as pure cultures, but evolve and co-exist with other species. Microbial communities have a variety of potential applications, including metabolic disease therapies and biotechnology. For example, microbial consortia consisting of various bacteria and fungi are known...... to identify metabolic properties that shape the community structures. The analysis based on a global metagenomic dataset and genome-scale metabolic models suggested that species within coexisting communities have higher potential of metabolic cooperation compared to random controls. This work yielded a novel...... methodology (termed species metabolic coupling analysis) for studying metabolic interaction and interdependencies within microbial communities. Species metabolic coupling analysis has a spectrum of applications to real-world problems, including investigation of metabolic interactions within the human...

  19. Metabolic dynamics in skeletal muscle during acute reduction in blood flow and oxygen supply to mitochondria: in-silico studies using a multi-scale, top-down integrated model.

    Directory of Open Access Journals (Sweden)

    Ranjan K Dash

    Full Text Available Control mechanisms of cellular metabolism and energetics in skeletal muscle that may become evident in response to physiological stresses such as reduction in blood flow and oxygen supply to mitochondria can be quantitatively understood using a multi-scale computational model. The analysis of dynamic responses from such a model can provide insights into mechanisms of metabolic regulation that may not be evident from experimental studies. For the purpose, a physiologically-based, multi-scale computational model of skeletal muscle cellular metabolism and energetics was developed to describe dynamic responses of key chemical species and reaction fluxes to muscle ischemia. The model, which incorporates key transport and metabolic processes and subcellular compartmentalization, is based on dynamic mass balances of 30 chemical species in both capillary blood and tissue cells (cytosol and mitochondria domains. The reaction fluxes in cytosol and mitochondria are expressed in terms of a general phenomenological Michaelis-Menten equation involving the compartmentalized energy controller ratios ATP/ADP and NADH/NAD(+. The unknown transport and reaction parameters in the model are estimated simultaneously by minimizing the differences between available in vivo experimental data on muscle ischemia and corresponding model outputs in coupled with the resting linear flux balance constraints using a robust, nonlinear, constrained-based, reduced gradient optimization algorithm. With the optimal parameter values, the model is able to simulate dynamic responses to reduced blood flow and oxygen supply to mitochondria associated with muscle ischemia of several key metabolite concentrations and metabolic fluxes in the subcellular cytosolic and mitochondrial compartments, some that can be measured and others that can not be measured with the current experimental techniques. The model can be applied to test complex hypotheses involving dynamic regulation of cellular

  20. Development and Evaluation of a Compartmental Picture Archiving and Communications System Model for Integration and Visualization of Multidisciplinary Biomedical Data to Facilitate Student Learning in an Integrative Health Clinic

    Science.gov (United States)

    Chow, Meyrick; Chan, Lawrence

    2010-01-01

    Information technology (IT) has the potential to improve the clinical learning environment. The extent to which IT enhances or detracts from healthcare professionals' role performance can be expected to affect both student learning and patient outcomes. This study evaluated nursing students' satisfaction with a novel compartmental Picture…

  1. Dissecting the energy metabolism in Mycoplasma pneumoniae through genome-scale metabolic modeling

    NARCIS (Netherlands)

    Wodke, J.A.; Puchalka, J.; Lluch-Senar, M.; Marcos, J.; Yus, E.; Godinho, M.; Gutierrez-Gallego, R.; Martins Dos Santos, V.A.P.; Serrano, L.; Klipp, E.; Maier, T.

    2013-01-01

    Mycoplasma pneumoniae, a threatening pathogen with a minimal genome, is a model organism for bacterial systems biology for which substantial experimental information is available. With the goal of understanding the complex interactions underlying its metabolism, we analyzed and characterized the

  2. Next-generation genome-scale models for metabolic engineering

    DEFF Research Database (Denmark)

    King, Zachary A.; Lloyd, Colton J.; Feist, Adam M.

    2015-01-01

    Constraint-based reconstruction and analysis (COBRA) methods have become widely used tools for metabolic engineering in both academic and industrial laboratories. By employing a genome-scale in silico representation of the metabolic network of a host organism, COBRA methods can be used to predict...... examples of applying COBRA methods to strain optimization are presented and discussed. Then, an outlook is provided on the next generation of COBRA models and the new types of predictions they will enable for systems metabolic engineering....

  3. Metabolic Flux Analysis -application in plant metabolic modelling for advanced life support systems

    Science.gov (United States)

    Sasidharan L, Swathy; Hezard, Pauline; Poughon, Laurent; Dussap, Claude-Gilles

    Plants have an important role in providing food and fresh oxygen for humans in a closed environment during long duration missions to Mars or Moon. Also, plants play an important role for recycling water. Thus, plant modelling (crop composition, yield prediction and the responses to its environment within the closed loop) gets much attention in the development of closed ecological life support systems. In order to achieve this, metabolic flux computation methods accounting for reactions stoichiometry and chemical energy conservation obtained from metabolic pathways description of different plant parts are required. The basic ideas of metabolic modelling and their application to various plant parts will be discussed. Metabolic systems consist of a set of metabolites and reactions that consume or produce them. The metabolic pathways within a metabolic network for each plant part or sub level are characterised and the metabolic fluxes, defined as the amount of converted metabolite per unit time and per unit mass of tissue (or per plant part), can be calculated. MBA (Metabolic flux analysis) which is a constraint based approach is effective at calculating flux distributions through bio-chemical networks. This methodology can be applied to several plants' growth situations. In terms of space appli-cations, it is shown how this approach could bring valuable tools for assessing and quantifying the effects of the environment of a close system on growth rate and conversion yields.

  4. Integrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic network.

    Science.gov (United States)

    Pacheco, Maria Pires; John, Elisabeth; Kaoma, Tony; Heinäniemi, Merja; Nicot, Nathalie; Vallar, Laurent; Bueb, Jean-Luc; Sinkkonen, Lasse; Sauter, Thomas

    2015-10-19

    The reconstruction of context-specific metabolic models from easily and reliably measurable features such as transcriptomics data will be increasingly important in research and medicine. Current reconstruction methods suffer from high computational effort and arbitrary threshold setting. Moreover, understanding the underlying epigenetic regulation might allow the identification of putative intervention points within metabolic networks. Genes under high regulatory load from multiple enhancers or super-enhancers are known key genes for disease and cell identity. However, their role in regulation of metabolism and their placement within the metabolic networks has not been studied. Here we present FASTCORMICS, a fast and robust workflow for the creation of high-quality metabolic models from transcriptomics data. FASTCORMICS is devoid of arbitrary parameter settings and due to its low computational demand allows cross-validation assays. Applying FASTCORMICS, we have generated models for 63 primary human cell types from microarray data, revealing significant differences in their metabolic networks. To understand the cell type-specific regulation of the alternative metabolic pathways we built multiple models during differentiation of primary human monocytes to macrophages and performed ChIP-Seq experiments for histone H3 K27 acetylation (H3K27ac) to map the active enhancers in macrophages. Focusing on the metabolic genes under high regulatory load from multiple enhancers or super-enhancers, we found these genes to show the most cell type-restricted and abundant expression profiles within their respective pathways. Importantly, the high regulatory load genes are associated to reactions enriched for transport reactions and other pathway entry points, suggesting that they are critical regulatory control points for cell type-specific metabolism. By integrating metabolic modelling and epigenomic analysis we have identified high regulatory load as a common feature of metabolic

  5. Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.

    Science.gov (United States)

    Cuperlovic-Culf, Miroslava

    2018-01-11

    Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

  6. Designing metabolic engineering strategies with genome-scale metabolic flux modeling

    Directory of Open Access Journals (Sweden)

    Yen JY

    2015-01-01

    Full Text Available Jiun Y Yen,1,2 Imen Tanniche,1 Amanda K Fisher,1–3 Glenda E Gillaspy,2 David R Bevan,2,3 Ryan S Senger1 1Department of Biological Systems Engineering, 2Department of Biochemistry, 3Genomics, Bioinformatics, and Computational Biology Interdisciplinary Program, Virginia Tech, Blacksburg, VA, USA Abstract: New in silico tools that make use of genome-scale metabolic flux modeling are improving the design of metabolic engineering strategies. This review highlights the latest developments in this area, explains the interface between these in silico tools and the experimental implementation tools of metabolic engineers, and provides a way forward so that in silico predictions can better mimic reality and more experimental methods can be considered in simulation studies. The several methodologies for solving genome-scale models (eg, flux balance analysis [FBA], parsimonious FBA, flux variability analysis, and minimization of metabolic adjustment all have unique advantages and applications. There are two basic approaches to designing metabolic engineering strategies in silico, and both have demonstrated success in the literature. The first involves: 1 making a genetic manipulation in a model; 2 testing for improved performance through simulation; and 3 iterating the process. The second approach has been used in more recently designed in silico tools and involves: 1 comparing metabolic flux profiles of a wild-type and ideally engineered state and 2 designing engineering strategies based on the differences in these flux profiles. Improvements in genome-scale modeling are anticipated in areas such as the inclusion of all relevant cellular machinery, the ability to understand and anticipate the results of combinatorial enrichment experiments, and constructing dynamic and flexible biomass equations that can respond to environmental and genetic manipulations. Keywords: genome-scale modeling, genome-scale modeling, flux balance analysis, flux variability

  7. Systems metabolic engineering: Genome-scale models and beyond

    Science.gov (United States)

    Blazeck, John; Alper, Hal

    2010-01-01

    The advent of high throughput genome-scale bioinformatics has led to an exponential increase in available cellular system data. Systems metabolic engineering attempts to use data-driven approaches – based on the data collected with high throughput technologies – to identify gene targets and optimize phenotypical properties on a systems level. Current systems metabolic engineering tools are limited for predicting and defining complex phenotypes such as chemical tolerances and other global, multigenic traits. The most pragmatic systems-based tool for metabolic engineering to arise is the in silico genome-scale metabolic reconstruction. This tool has seen wide adoption for modeling cell growth and predicting beneficial gene knockouts, and we examine here how this approach can be expanded for novel organisms. This review will highlight advances of the systems metabolic engineering approach with a focus on de novo development and use of genome-scale metabolic reconstructions for metabolic engineering applications. We will then discuss the challenges and prospects for this emerging field to enable model-based metabolic engineering. Specifically, we argue that current state-of-the-art systems metabolic engineering techniques represent a viable first step for improving product yield that still must be followed by combinatorial techniques or random strain mutagenesis to achieve optimal cellular systems. PMID:20151446

  8. Systems metabolic engineering: genome-scale models and beyond.

    Science.gov (United States)

    Blazeck, John; Alper, Hal

    2010-07-01

    The advent of high throughput genome-scale bioinformatics has led to an exponential increase in available cellular system data. Systems metabolic engineering attempts to use data-driven approaches--based on the data collected with high throughput technologies--to identify gene targets and optimize phenotypical properties on a systems level. Current systems metabolic engineering tools are limited for predicting and defining complex phenotypes such as chemical tolerances and other global, multigenic traits. The most pragmatic systems-based tool for metabolic engineering to arise is the in silico genome-scale metabolic reconstruction. This tool has seen wide adoption for modeling cell growth and predicting beneficial gene knockouts, and we examine here how this approach can be expanded for novel organisms. This review will highlight advances of the systems metabolic engineering approach with a focus on de novo development and use of genome-scale metabolic reconstructions for metabolic engineering applications. We will then discuss the challenges and prospects for this emerging field to enable model-based metabolic engineering. Specifically, we argue that current state-of-the-art systems metabolic engineering techniques represent a viable first step for improving product yield that still must be followed by combinatorial techniques or random strain mutagenesis to achieve optimal cellular systems.

  9. Comparative Analysis of Yeast Metabolic Network Models Highlights Progress, Opportunities for Metabolic Reconstruction

    Science.gov (United States)

    Heavner, Benjamin D.; Price, Nathan D.

    2015-01-01

    We have compared 12 genome-scale models of the Saccharomyces cerevisiae metabolic network published since 2003 to evaluate progress in reconstruction of the yeast metabolic network. We compared the genomic coverage, overlap of annotated metabolites, predictive ability for single gene essentiality with a selection of model parameters, and biomass production predictions in simulated nutrient-limited conditions. We have also compared pairwise gene knockout essentiality predictions for 10 of these models. We found that varying approaches to model scope and annotation reflected the involvement of multiple research groups in model development; that single-gene essentiality predictions were affected by simulated medium, objective function, and the reference list of essential genes; and that predictive ability for single-gene essentiality did not correlate well with predictive ability for our reference list of synthetic lethal gene interactions (R = 0.159). We conclude that the reconstruction of the yeast metabolic network is indeed gradually improving through the iterative process of model development, and there remains great opportunity for advancing our understanding of biology through continued efforts to reconstruct the full biochemical reaction network that constitutes yeast metabolism. Additionally, we suggest that there is opportunity for refining the process of deriving a metabolic model from a metabolic network reconstruction to facilitate mechanistic investigation and discovery. This comparative study lays the groundwork for developing improved tools and formalized methods to quantitatively assess metabolic network reconstructions independently of any particular model application, which will facilitate ongoing efforts to advance our understanding of the relationship between genotype and cellular phenotype. PMID:26566239

  10. Comparative Analysis of Yeast Metabolic Network Models Highlights Progress, Opportunities for Metabolic Reconstruction.

    Directory of Open Access Journals (Sweden)

    Benjamin D Heavner

    2015-11-01

    Full Text Available We have compared 12 genome-scale models of the Saccharomyces cerevisiae metabolic network published since 2003 to evaluate progress in reconstruction of the yeast metabolic network. We compared the genomic coverage, overlap of annotated metabolites, predictive ability for single gene essentiality with a selection of model parameters, and biomass production predictions in simulated nutrient-limited conditions. We have also compared pairwise gene knockout essentiality predictions for 10 of these models. We found that varying approaches to model scope and annotation reflected the involvement of multiple research groups in model development; that single-gene essentiality predictions were affected by simulated medium, objective function, and the reference list of essential genes; and that predictive ability for single-gene essentiality did not correlate well with predictive ability for our reference list of synthetic lethal gene interactions (R = 0.159. We conclude that the reconstruction of the yeast metabolic network is indeed gradually improving through the iterative process of model development, and there remains great opportunity for advancing our understanding of biology through continued efforts to reconstruct the full biochemical reaction network that constitutes yeast metabolism. Additionally, we suggest that there is opportunity for refining the process of deriving a metabolic model from a metabolic network reconstruction to facilitate mechanistic investigation and discovery. This comparative study lays the groundwork for developing improved tools and formalized methods to quantitatively assess metabolic network reconstructions independently of any particular model application, which will facilitate ongoing efforts to advance our understanding of the relationship between genotype and cellular phenotype.

  11. A small metabolic flux model to identify transient metabolic regulations in Saccaromucs cerevisiae

    Energy Technology Data Exchange (ETDEWEB)

    Herwig, C.; Stockar, U. von [Lab. of Chemical and Biochemical Engineering, Swiss Federal Inst. of Technology, Lausanne (Switzerland); 1

    2002-03-01

    The understanding of dynamic metabolic regulations is important for physiological studies and strain characterization tasks. The present study combined transient experiments with online metabolic flux analysis (MFA) in order to quantify metabolic regulations, namely carbon catabolite repression of respiration and transient acetic-acid production, in Saccharomyces cerevisiae during aerobic growth on glucose. The aim was to investigate which additional information can be gained from using a small metabolic flux model to study transient growth provoked by shift-up and shift-down experiments, compared to online monitoring alone. The MFA model allowed us to propose new correlations between pathways of the central metabolism. A linear correlation between glycolytic flux and respiratory capacity holds for shift-down and shift-up experiments. This confirmed that respiratory functions were subjected to carbon catabolite repression and suggested that respiratory capacity is controlled by the glycolytic flux rather than the glucose influx. Furthermore, the model showed that control of repression of respiration by the glycolytic flux was a dynamic phenomenon. Co-factor balancing within the MFA model showed that transient acetic-acid production indicated a transient limitation in another part of the central metabolism but not in oxidative phosphorylation. However, at super-critical growth rates and when coupling of anabolism and catabolism is resumed, the limitation shifts to oxidative phosphorylation, with the consequence that ethanol is formed. The online application of small metabolic flux models to transient experiments enhanced the physiological insight into transient growth and opens up the use of transient experiments as an efficient tool to understand dynamic metabolic regulations. (orig.)

  12. Compartmentation prevents a lethal turbo-explosion of glycolysis in trypanosomes

    NARCIS (Netherlands)

    J.R. Haanstra (Jurgen); A. van Tuijl (Arjen); P. Kessler (Peter); W. Reijnders (Willem); P.A.M. Michels (Paul); H.V. Westerhoff (Hans); M. Parsons (Marilyn); B.M. Bakker (Barbara)

    2008-01-01

    textabstractATP generation by both glycolysis and glycerol catabolism is autocatalytic, because the first kinases of these pathways are fuelled by ATP produced downstream. Previous modeling studies predicted that either feedback inhibition or compartmentation of glycolysis can protect cells from

  13. Modeling of the metabolic energy dissipation for restricted tumor growth.

    Science.gov (United States)

    Pajic-Lijakovic, Ivana; Milivojevic, Milan

    2017-10-01

    Energy dissipation mostly represents unwanted outcome but in the biochemical processes it may alter the biochemical pathways. However, it is rarely considered in the literature although energy dissipation and its alteration due to the changes in cell microenvironment may improve methods for guiding chemical and biochemical processes in the desired directions. Deeper insight into the changes of metabolic activity of tumor cells exposed to osmotic stress or irradiation may offer the possibility of tumor growth reduction. In this work effects of the osmotic stress and irradiation on the thermodynamical affinity of tumor cells and their damping effects on metabolic energy dissipation were investigated and modeled. Although many various models were applied to consider the tumor restrictive growth they have not considered the metabolic energy dissipation. In this work a pseudo rheological model in the form of "the metabolic spring-pot element" is formulated to describe theoretically the metabolic susceptibility of tumor spheroid. This analog model relates the thermodynamical affinity of cell growth with the volume expansion of tumor spheroid under isotropic loading conditions. Spheroid relaxation induces anomalous nature of the metabolic energy dissipation which causes the damping effects on cell growth. The proposed model can be used for determining the metabolic energy "structure" in the context of restrictive cell growth as well as for predicting optimal doses for cancer curing in order to tailor the clinical treatment for each person and each type of cancer.

  14. Neuro-fuzzy model of homocysteine metabolism

    Indian Academy of Sciences (India)

    SHAIK Mohammad Naushad

    2017-12-08

    Dec 8, 2017 ... Parkinson's disease (Kumudini et al. 2014; Kirbas et al. 2016), etc. Hyperhomocysteinaemia could occur due to genetic polymorphisms/mutations or cofactor deficiencies of the folate metabolic pathway. The dietary source of folate is in the form of folyl polyglutamate and is converted to monoglutamates with.

  15. Evaluating computational models of cholesterol metabolism

    NARCIS (Netherlands)

    Paalvast, Yared; Kuivenhoven, Jan Albert; Groen, Albert K.

    2015-01-01

    Regulation of cholesterol homeostasis has been studied extensively during the last decades. Many of the metabolic pathways involved have been discovered. Yet important gaps in our knowledge remain. For example, knowledge on intracellular cholesterol traffic and its relation to the regulation of

  16. Reactive oxygen species and redox compartmentalization

    Directory of Open Access Journals (Sweden)

    Nina eKaludercic

    2014-08-01

    Full Text Available Reactive oxygen species (ROS formation and signaling are of major importance and regulate a number of processes in physiological conditions. A disruption in redox status regulation, however, has been associated with numerous pathological conditions. In recent years it has become increasingly clear that oxidative and reductive modifications are confined in a spatiotemporal manner. This makes ROS signaling similar to that of Ca2+ or other second messengers. Some subcellular compartments are more oxidizing (such as lysosomes or peroxisomes whereas others are more reducing (mitochondria, nuclei. Moreover, although more reducing, mitochondria are especially susceptible to oxidation, most likely due to the high number of exposed thiols present in that compartment. Recent advances in the development of redox probes allow specific measurement of defined ROS in different cellular compartments in intact living cells or organisms. The availability of these tools now allows simultaneous spatiotemporal measurements and correlation between ROS generation and organelle and/or cellular function. The study of ROS compartmentalization and microdomains will help elucidate their role in physiology and disease. Here we will examine redox probes currently available and how ROS generation may vary between subcellular compartments. Furthermore, we will discuss ROS compartmentalization in physiological and pathological conditions focusing our attention on mitochondria, since their vulnerability to oxidative stress is likely at the basis of several diseases.

  17. Partitioning Variability of a Compartmentalized In Vitro Transcriptional Thresholding Circuit.

    Science.gov (United States)

    Kapsner, Korbinian; Simmel, Friedrich C

    2015-10-16

    Encapsulation of in vitro biochemical reaction circuits into small, cell-sized compartments can result in considerable variations in the dynamical properties of the circuits. As a model system, we here investigate a simple in vitro transcriptional reaction circuit, which generates an ultrasensitive fluorescence response when the concentration of an RNA transcript reaches a preset threshold. The reaction circuit is compartmentalized into spherical water-in-oil microemulsion droplets, and the reaction progress is monitored by fluorescence microscopy. A quantitative statistical analysis of thousands of individual droplets ranging in size from a few up to 20 μm reveals a strong variability in effective RNA production rates, which by computational modeling is traced back to a larger-than-Poisson variability in RNAP activities in the droplets. The noise level in terms of the noise strength (the Fano factor) is strongly dependent on the ratio between transcription templates and polymerases, and increases for higher template concentrations.

  18. Repurposing the Saccharomyces cerevisiae peroxisome for compartmentalizing multi-enzyme pathways

    Energy Technology Data Exchange (ETDEWEB)

    DeLoache, William [Univ. of California, Berkeley, CA (United States); Russ, Zachary [Univ. of California, Berkeley, CA (United States); Samson, Jennifer [Univ. of California, Berkeley, CA (United States); Dueber, John [Univ. of California, Berkeley, CA (United States)

    2017-09-25

    The peroxisome of Saccharomyces cerevisiae was targeted for repurposing in order to create a synthetic organelle that provides a generalizable compartment for engineered metabolic pathways. Compartmentalization of enzymes into organelles is a promising strategy for limiting metabolic crosstalk, improving pathway efficiency, and ultimately modifying the chemical environment to be distinct from that of the cytoplasm. We focused on the Saccharomyces cerevisiae peroxisome, as this organelle is not required for viability when grown on conventional media. We identified an enhanced peroxisomal targeting signal type 1 (PTS1) for rapidly importing non-native cargo proteins. Additionally, we performed the first systematic in vivo measurements of nonspecific metabolite permeability across the peroxisomal membrane using a polymer exclusion assay and characterized the size dependency of metabolite trafficking. Finally, we applied these new insights to compartmentalize a two-enzyme pathway in the peroxisome and characterize the expression regimes where compartmentalization leads to improved product titer. This work builds a foundation for using the peroxisome as a synthetic organelle, highlighting both promise and future challenges on the way to realizing this goal.

  19. Applications of computational modeling in metabolic engineering of yeast.

    Science.gov (United States)

    Kerkhoven, Eduard J; Lahtvee, Petri-Jaan; Nielsen, Jens

    2015-02-01

    Generally, a microorganism's phenotype can be described by its pattern of metabolic fluxes. Although fluxes cannot be measured directly, inference of fluxes is well established. In biotechnology the aim is often to increase the capacity of specific fluxes. For this, metabolic engineering methods have been developed and applied extensively. Many of these rely on balancing of intracellular metabolites, redox, and energy fluxes, using genome-scale models (GEMs) that in combination with appropriate objective functions and constraints can be used to predict potential gene targets for obtaining a preferred flux distribution. These methods point to strategies for altering gene expression; however, fluxes are often controlled by post-transcriptional events. Moreover, GEMs are usually not taking into account metabolic regulation, thermodynamics and enzyme kinetics. To facilitate metabolic engineering, tools from synthetic biology have emerged, enabling integration and assembly of naturally nonexistent, but well-characterized components into a living organism. To describe these systems kinetic models are often used and to integrate these systems with the standard metabolic engineering approach, it is necessary to expand the modeling of metabolism to consider kinetics of individual processes. This review will give an overview about models available for metabolic engineering of yeast and discusses their applications. © FEMS 2015. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.

  20. Use of genome-scale microbial models for metabolic engineering

    DEFF Research Database (Denmark)

    Patil, Kiran Raosaheb; Åkesson, M.; Nielsen, Jens

    2004-01-01

    network structures. The major challenge for metabolic engineering in the post-genomic era is to broaden its design methodologies to incorporate genome-scale biological data. Genome-scale stoichiometric models of microorganisms represent a first step in this direction.......Metabolic engineering serves as an integrated approach to design new cell factories by providing rational design procedures and valuable mathematical and experimental tools. Mathematical models have an important role for phenotypic analysis, but can also be used for the design of optimal metabolic...

  1. Compartmentalized human immunodeficiency virus type 1 originates from long-lived cells in some subjects with HIV-1-associated dementia.

    Science.gov (United States)

    Schnell, Gretja; Spudich, Serena; Harrington, Patrick; Price, Richard W; Swanstrom, Ronald

    2009-04-01

    Human immunodeficiency virus type 1 (HIV-1) invades the central nervous system (CNS) shortly after systemic infection and can result in the subsequent development of HIV-1-associated dementia (HAD) in a subset of infected individuals. Genetically compartmentalized virus in the CNS is associated with HAD, suggesting autonomous viral replication as a factor in the disease process. We examined the source of compartmentalized HIV-1 in the CNS of subjects with HIV-1-associated neurological disease and in asymptomatic subjects who were initiating antiretroviral therapy. The heteroduplex tracking assay (HTA), targeting the variable regions of env, was used to determine which HIV-1 genetic variants in the cerebrospinal fluid (CSF) were compartmentalized and which variants were shared with the blood plasma. We then measured the viral decay kinetics of individual variants after the initiation of antiretroviral therapy. Compartmentalized HIV-1 variants in the CSF of asymptomatic subjects decayed rapidly after the initiation of antiretroviral therapy, with a mean half-life of 1.57 days. Rapid viral decay was also measured for CSF-compartmentalized variants in four HAD subjects (t(1/2) mean = 2.27 days). However, slow viral decay was measured for CSF-compartmentalized variants from an additional four subjects with neurological disease (t(1/2) range = 9.85 days to no initial decay). The slow decay detected for CSF-compartmentalized variants was not associated with poor CNS drug penetration, drug resistant virus in the CSF, or the presence of X4 virus genotypes. We found that the slow decay measured for CSF-compartmentalized variants in subjects with neurological disease was correlated with low peripheral CD4 cell count and reduced CSF pleocytosis. We propose a model in which infiltrating macrophages replace CD4(+) T cells as the primary source of productive viral replication in the CNS to maintain high viral loads in the CSF in a substantial subset of subjects with HAD.

  2. Compartmentalized human immunodeficiency virus type 1 originates from long-lived cells in some subjects with HIV-1-associated dementia.

    Directory of Open Access Journals (Sweden)

    Gretja Schnell

    2009-04-01

    Full Text Available Human immunodeficiency virus type 1 (HIV-1 invades the central nervous system (CNS shortly after systemic infection and can result in the subsequent development of HIV-1-associated dementia (HAD in a subset of infected individuals. Genetically compartmentalized virus in the CNS is associated with HAD, suggesting autonomous viral replication as a factor in the disease process. We examined the source of compartmentalized HIV-1 in the CNS of subjects with HIV-1-associated neurological disease and in asymptomatic subjects who were initiating antiretroviral therapy. The heteroduplex tracking assay (HTA, targeting the variable regions of env, was used to determine which HIV-1 genetic variants in the cerebrospinal fluid (CSF were compartmentalized and which variants were shared with the blood plasma. We then measured the viral decay kinetics of individual variants after the initiation of antiretroviral therapy. Compartmentalized HIV-1 variants in the CSF of asymptomatic subjects decayed rapidly after the initiation of antiretroviral therapy, with a mean half-life of 1.57 days. Rapid viral decay was also measured for CSF-compartmentalized variants in four HAD subjects (t(1/2 mean = 2.27 days. However, slow viral decay was measured for CSF-compartmentalized variants from an additional four subjects with neurological disease (t(1/2 range = 9.85 days to no initial decay. The slow decay detected for CSF-compartmentalized variants was not associated with poor CNS drug penetration, drug resistant virus in the CSF, or the presence of X4 virus genotypes. We found that the slow decay measured for CSF-compartmentalized variants in subjects with neurological disease was correlated with low peripheral CD4 cell count and reduced CSF pleocytosis. We propose a model in which infiltrating macrophages replace CD4(+ T cells as the primary source of productive viral replication in the CNS to maintain high viral loads in the CSF in a substantial subset of subjects with HAD.

  3. Dual regulation of muscle glycogen synthase during exercise by activation and compartmentalization

    DEFF Research Database (Denmark)

    Prats, Clara; Helge, Jørn W; Nordby, Pernille

    2009-01-01

    , C., Cadefau, J. A., Cussó, R., Qvortrup, K., Nielsen, J. N., Wojtaszewki, J. F., Wojtaszewki, J. F., Hardie, D. G., Stewart, G., Hansen, B. F., and Ploug, T. (2005) J. Biol. Chem. 280, 23165-23172). In the present study we investigate the regulation of human muscle GS activity by glycogen, exercise......, and insulin. Using immunocytochemistry we investigate the existence and relevance of GS intracellular compartmentalization during exercise and during glycogen re-synthesis. The results show that GS intrinsic activity is strongly dependent on glycogen levels and that such regulation involves associated...... dephosphorylation at sites 2+2a, 3a, and 3a + 3b. Furthermore, we report the existence of several glycogen metabolism regulatory mechanisms based on GS intracellular compartmentalization. After exhausting exercise, epinephrine-induced protein kinase A activation leads to GS site 1b phosphorylation targeting...

  4. PSAMM: A Portable System for the Analysis of Metabolic Models.

    Directory of Open Access Journals (Sweden)

    Jon Lund Steffensen

    2016-02-01

    Full Text Available The genome-scale models of metabolic networks have been broadly applied in phenotype prediction, evolutionary reconstruction, community functional analysis, and metabolic engineering. Despite the development of tools that support individual steps along the modeling procedure, it is still difficult to associate mathematical simulation results with the annotation and biological interpretation of metabolic models. In order to solve this problem, here we developed a Portable System for the Analysis of Metabolic Models (PSAMM, a new open-source software package that supports the integration of heterogeneous metadata in model annotations and provides a user-friendly interface for the analysis of metabolic models. PSAMM is independent of paid software environments like MATLAB, and all its dependencies are freely available for academic users. Compared to existing tools, PSAMM significantly reduced the running time of constraint-based analysis and enabled flexible settings of simulation parameters using simple one-line commands. The integration of heterogeneous, model-specific annotation information in PSAMM is achieved with a novel format of YAML-based model representation, which has several advantages, such as providing a modular organization of model components and simulation settings, enabling model version tracking, and permitting the integration of multiple simulation problems. PSAMM also includes a number of quality checking procedures to examine stoichiometric balance and to identify blocked reactions. Applying PSAMM to 57 models collected from current literature, we demonstrated how the software can be used for managing and simulating metabolic models. We identified a number of common inconsistencies in existing models and constructed an updated model repository to document the resolution of these inconsistencies.

  5. Automation on the generation of genome-scale metabolic models.

    Science.gov (United States)

    Reyes, R; Gamermann, D; Montagud, A; Fuente, D; Triana, J; Urchueguía, J F; de Córdoba, P Fernández

    2012-12-01

    Nowadays, the reconstruction of genome-scale metabolic models is a nonautomatized and interactive process based on decision making. This lengthy process usually requires a full year of one person's work in order to satisfactory collect, analyze, and validate the list of all metabolic reactions present in a specific organism. In order to write this list, one manually has to go through a huge amount of genomic, metabolomic, and physiological information. Currently, there is no optimal algorithm that allows one to automatically go through all this information and generate the models taking into account probabilistic criteria of unicity and completeness that a biologist would consider. This work presents the automation of a methodology for the reconstruction of genome-scale metabolic models for any organism. The methodology that follows is the automatized version of the steps implemented manually for the reconstruction of the genome-scale metabolic model of a photosynthetic organism, Synechocystis sp. PCC6803. The steps for the reconstruction are implemented in a computational platform (COPABI) that generates the models from the probabilistic algorithms that have been developed. For validation of the developed algorithm robustness, the metabolic models of several organisms generated by the platform have been studied together with published models that have been manually curated. Network properties of the models, like connectivity and average shortest mean path of the different models, have been compared and analyzed.

  6. New paradigms for metabolic modeling of human cells

    DEFF Research Database (Denmark)

    Mardinoglu, Adil; Nielsen, Jens

    2015-01-01

    review recent work on reconstruction of GEMs for human cell/tissue types and cancer, and the use of GEMs for identification of metabolic changes occurring in response to disease development. We further discuss how GEMs can be used for the development of efficient therapeutic strategies. Finally......Abnormalities in cellular functions are associated with the progression of human diseases, often resulting in metabolic reprogramming. GEnome-scale metabolic Models (GEMs) have enabled studying global metabolic reprogramming in connection with disease development in a systematic manner. Here we......, challenges in integration of cell/tissue models for simulation of whole body functions as well as integration of GEMs with other biological networks for generating complete cell/tissue models are presented....

  7. Applications of computational modeling in metabolic engineering of yeast

    DEFF Research Database (Denmark)

    Kerkhoven, Eduard J.; Lahtvee, Petri-Jaan; Nielsen, Jens

    2015-01-01

    Generally, a microorganism's phenotype can be described by its pattern of metabolic fluxes. Although fluxes cannot be measured directly, inference of fluxes is well established. In biotechnology the aim is often to increase the capacity of specific fluxes. For this, metabolic engineering methods...... a preferred flux distribution. These methods point to strategies for altering gene expression; however, fluxes are often controlled by post-transcriptional events. Moreover, GEMs are usually not taking into account metabolic regulation, thermodynamics and enzyme kinetics. To facilitate metabolic engineering......, tools from synthetic biology have emerged, enabling integration and assembly of naturally nonexistent, but well-characterized components into a living organism. To describe these systems kinetic models are often used and to integrate these systems with the standard metabolic engineering approach...

  8. Acute compartmental syndrome from hematogenous osteomyelitis of the ulna.

    Science.gov (United States)

    Lundy, D W; Lourie, G M; Morrissy, R T

    1998-08-01

    Compartmental syndrome of the forearm in children is usually caused by fractures, soft-tissue damage, burns, or arterial injury. This report presents the case of a child who had compartmental syndrome of the forearm resulting from acute hematogenous osteomyelitis of the ulna.

  9. Enzyme compartmentalization during biphasic enamel matrix processing.

    Science.gov (United States)

    Brookes, S J; Kirkham, J; Shore, R C; Bonass, W A; Robinson, C

    1998-01-01

    Processing of enamel matrix proteins is essentially biphasic. Secretory stage metalloprotease activity generates a discrete, presumably functional, spectrum of molecules which may also undergo dephosphorylation. Maturation stage serine proteases almost completely destroy the matrix. The present aim was to examine the tissue compartmentalization of these enzyme activities in relation to their possible function. A sequential extraction using synthetic enamel fluid, phosphate buffer and SDS was used to identify enzymes free in the enamel fluid, crystal bound or aggregated with the bulk matrix respectively. Results indicated that the metallo-proteases and alkaline phosphatase were free in the secretory stage enamel fluid while the serine proteases appeared to be largely bound to the maturation stage crystals. The mobility of the metallo-proteases and alkaline phosphatase would ensure efficient initial processing of secretory matrix, while the largely mineral bound serine proteases would ensure retention of protease activity despite massive destruction and protein removal.

  10. Sustainability of a Compartmentalized Host-Parasite Replicator System under Periodic Washout-Mixing Cycles

    Directory of Open Access Journals (Sweden)

    Taro Furubayashi

    2018-01-01

    Full Text Available The emergence and dominance of parasitic replicators are among the major hurdles for the proliferation of primitive replicators. Compartmentalization of replicators is proposed to relieve the parasite dominance; however, it remains unclear under what conditions simple compartmentalization uncoupled with internal reaction secures the long-term survival of a population of primitive replicators against incessant parasite emergence. Here, we investigate the sustainability of a compartmentalized host-parasite replicator (CHPR system undergoing periodic washout-mixing cycles, by constructing a mathematical model and performing extensive simulations. We describe sustainable landscapes of the CHPR system in the parameter space and elucidate the mechanism of phase transitions between sustainable and extinct regions. Our findings revealed that a large population size of compartments, a high mixing intensity, and a modest amount of nutrients are important factors for the robust survival of replicators. We also found two distinctive sustainable phases with different mixing intensities. These results suggest that a population of simple host–parasite replicators assumed before the origin of life can be sustained by a simple compartmentalization with periodic washout-mixing processes.

  11. Cooperative Metabolism in a Three-Partner Insect-Bacterial Symbiosis Revealed by Metabolic Modeling.

    Science.gov (United States)

    Ankrah, Nana Y D; Luan, Junbo; Douglas, Angela E

    2017-08-01

    An important factor determining the impact of microbial symbionts on their animal hosts is the balance between the cost of nutrients consumed by the symbionts and the benefit of nutrients released back to the host, but the quantitative significance of nutrient exchange in symbioses involving multiple microbial partners has rarely been addressed. In this study on the association between two intracellular bacterial symbionts, "Candidatus Portiera aleyrodidarum" and "Candidatus Hamiltonella defensa," and their animal host, the whitefly Bemisia tabaci, we apply metabolic modeling to investigate host-symbiont nutrient exchange. Our in silico analysis revealed that >60% of the essential amino acids and related metabolites synthesized by "Candidatus Portiera aleyrodidarum" are utilized by the host, including a substantial contribution of nitrogen recycled from host nitrogenous waste, and that these interactions are required for host growth. In contrast, "Candidatus Hamiltonella defensa" retains most or all of the essential amino acids and B vitamins that it is capable of synthesizing. Furthermore, "Candidatus Hamiltonella defensa" suppresses host growth in silico by competition with "Candidatus Portiera aleyrodidarum" for multiple host nutrients, by suppressing "Candidatus Portiera aleyrodidarum" growth and metabolic function, and also by consumption of host nutrients that would otherwise be allocated to host growth. The interpretation from these modeling outputs that "Candidatus Hamiltonella defensa" is a nutritional parasite could not be inferred reliably from gene content alone but requires consideration of constraints imposed by the structure of the metabolic network. Furthermore, these quantitative models offer precise predictions for future experimental study and the opportunity to compare the functional organization of metabolic networks in different symbioses.IMPORTANCE The metabolic functions of unculturable intracellular bacteria with much reduced genomes are

  12. Comparative genome-scale metabolic modeling of actinomycetes : The topology of essential core metabolism

    NARCIS (Netherlands)

    Alam, Mohammad Tauqeer; Medema, Marnix H.; Takano, Eriko; Breitling, Rainer; Gojobori, Takashi

    2011-01-01

    Actinomycetes are highly important bacteria. On one hand, some of them cause severe human and plant diseases, on the other hand, many species are known for their ability to produce antibiotics. Here we report the results of a comparative analysis of genome-scale metabolic models of 37 species of

  13. Systematic assignment of thermodynamic constraints in metabolic network models

    NARCIS (Netherlands)

    Kümmel, Anne; Panke, Sven; Heinemann, Matthias

    2006-01-01

    Background: The availability of genome sequences for many organisms enabled the reconstruction of several genome-scale metabolic network models. Currently, significant efforts are put into the automated reconstruction of such models. For this, several computational tools have been developed that

  14. Modeling metabolic response to changes of enzyme amount in ...

    African Journals Online (AJOL)

    Based on the work of Hynne et al. (2001), in an in silico model of glycolysis, Saccharomyces cerevisiae is established by introducing an enzyme amount multiple factor (.) into the kinetic equations. The model is aimed to predict the metabolic response to the change of enzyme amount. With the help of .α, the amounts of ...

  15. Kinetic modelling of central carbon metabolism in Escherichia coli.

    Science.gov (United States)

    Peskov, Kirill; Mogilevskaya, Ekaterina; Demin, Oleg

    2012-09-01

    In the present study, we developed a detailed kinetic model of Escherichia coli central carbon metabolism. The main model assumptions were based on the results of metabolic and regulatory reconstruction of the system and thorough model verification with experimental data. The development and verification of the model included several stages, which allowed us to take into account both in vitro and in vivo experimental data and avoid the ambiguity that frequently occurs in detailed models of biochemical pathways. The choice of the level of detail for the mathematical description of enzymatic reaction rates and the evaluation of parameter values were based on available published data. Validation of the complete model of the metabolic pathway describing specific physiological states was based on fluxomics and metabolomics data. In particular, we developed a model that describes aerobic growth of E. coli in continuous culture with a limiting concentration of glucose. Such modification of the model was used to integrate experimental metabolomics data obtained in steady-state conditions for wild-type E. coli and genetically modified strains, e.g. knockout of the pyruvate kinase gene (pykA). Following analysis of the model behaviour, and comparison of the coincidence between predicted and experimental data, it was possible to investigate the functional and regulatory properties of E. coli central carbon metabolism. For example, a novel metabolic regulatory mechanism for 6-phosphogluconate dehydrogenase inhibition by phosphoenolpyruvate was hypothesized, and the flux ratios between the reactions catalysed by enzyme isoforms were predicted. The mathematical model described here has been submitted to the JWS Online Cellular Systems Modelling Database and can be accessed at http://jjj.biochem.sun.ac.za/database/peskov/index.html © 2012 The Authors Journal compilation © 2012 FEBS.

  16. Metabolic and Blood Pressure Effects of Walnut Supplementation in a Mouse Model of the Metabolic Syndrome

    OpenAIRE

    Scott, Nicola J. A.; Ellmers, Leigh. J.; Pilbrow, Anna P; Lotte Thomsen; Arthur Mark Richards; Chris M Frampton; Cameron, Vicky A.

    2017-01-01

    There is extensive evidence that walnut consumption is protective against cardiovascular disease and diabetes in the healthy population, but the beneficial effects of walnut consumption in individuals with the metabolic syndrome (MetS) remain uncertain. We compared a range of cardio-metabolic traits and related tissue gene expression associated with 21 weeks of dietary walnut supplementation in a mouse model of MetS (MetS-Tg) and wild-type (WT) mice (n = 10 per genotype per diet, equal males ...

  17. Lipid transfer proteins and the tuning of compartmental identity in the Golgi apparatus.

    Science.gov (United States)

    McDermott, Mark I; Mousley, Carl J

    2016-10-01

    The Golgi complex constitutes a central way station of the eukaryotic endomembrane system, an intricate network of organelles engaged in control of membrane trafficking and the processing of various cellular components. Previous ideas of compartmental stability within this network are gradually being reshaped by concepts describing a biochemical continuum of hybrid organelles whose constitution is regulated by compartmental maturation. Membrane lipid composition and lipid signaling processes make fundamental contributions to compartmentalization strategies that are themselves critical for organizing cellular architecture and biochemical activities. Phosphatidylinositol transfer proteins (PITPs) are increasingly recognized as key regulators of membrane trafficking through the secretory pathway. They do so by coordinating lipid metabolism with lipid signaling, translating this information to core protein components of the membrane trafficking machinery. In this capacity, PITPs can be viewed as regulators of an essential lipid-protein interface of cisternal maturation. It is also now becoming appreciated, for the first time, that such an interface plays important roles in larger systems processes that link secretory pathway function with cell proliferation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  18. Compartmentation of sucrose during radial transfer in mature sorghum culm

    Directory of Open Access Journals (Sweden)

    Vietor Donald M

    2007-06-01

    Full Text Available Abstract Background The sucrose that accumulates in the culm of sorghum (Sorghum bicolor (L. Moench and other large tropical andropogonoid grasses can be of commercial value, and can buffer assimilate supply during development. Previous study conducted with intact plants showed that sucrose can be radially transferred to the intracellular compartment of mature ripening sorghum internode without being hydrolysed. In this study, culm-infused radiolabelled sucrose was traced between cellular compartments and among related metabolites to determine if the compartmental path of sucrose during radial transfer in culm tissue was symplasmic or included an apoplasmic step. This transfer path was evaluated for elongating and ripening culm tissue of intact plants of two semidwarf grain sorghums. The metabolic path in elongating internode tissue was also evaluated. Results On the day after culm infusion of the tracer sucrose, the specific radioactivity of sucrose recovered from the intracellular compartment of growing axillary-branch tissue was greater (nearly twice than that in the free space, indicating that sucrose was preferentially transferred through symplasmic routes. In contrast, the sucrose specific radioactivity in the intracellular compartment of the mature (ripening culm tissue was probably less (about 3/4's than that in free space indicating that sucrose was preferentially transferred through routes that included an apoplasmic step. In growing internodes of the axillary branch of sorghum, the tritium label initially provided in the fructose moiety of sucrose molecules was largely (81% recovered in the fructose moiety, indicating that a large portion of sucrose molecules is not hydrolysed and resynthesized during radial transfer. Conclusion During radial transfer of sucrose in ripening internodes of intact sorghum plants, much of the sucrose is transferred intact (without hydrolysis and resynthesis and primarily through a path that includes an

  19. Next-generation genome-scale models for metabolic engineering.

    Science.gov (United States)

    King, Zachary A; Lloyd, Colton J; Feist, Adam M; Palsson, Bernhard O

    2015-12-01

    Constraint-based reconstruction and analysis (COBRA) methods have become widely used tools for metabolic engineering in both academic and industrial laboratories. By employing a genome-scale in silico representation of the metabolic network of a host organism, COBRA methods can be used to predict optimal genetic modifications that improve the rate and yield of chemical production. A new generation of COBRA models and methods is now being developed--encompassing many biological processes and simulation strategies-and next-generation models enable new types of predictions. Here, three key examples of applying COBRA methods to strain optimization are presented and discussed. Then, an outlook is provided on the next generation of COBRA models and the new types of predictions they will enable for systems metabolic engineering. Copyright © 2014 Elsevier Ltd. All rights reserved.

  20. Modelling the metabolic shift of polyphosphate-accumulating organisms.

    Science.gov (United States)

    Acevedo, B; Borrás, L; Oehmen, A; Barat, R

    2014-11-15

    Enhanced biological phosphorus removal (EBPR) is one of the most important methods of phosphorus removal in municipal wastewater treatment plants, having been described by different modelling approaches. In this process, the PAOs (polyphosphate accumulating organisms) and GAOs (glycogen accumulating organisms) compete for volatile fatty acids uptake under anaerobic conditions. Recent studies have revealed that the metabolic pathways used by PAOs in order to obtain the energy and the reducing power needed for polyhydroxyalkanoates synthesis could change depending on the amount of polyphosphate stored in the cells. The model presented in this paper extends beyond previously developed metabolic models by including the ability of PAO to change their metabolic pathways according to the content of poly-P available. The processes of the PAO metabolic model were adapted to new formulations enabling the change from P-driven VFA uptake to glycogen-driven VFA uptake using the same process equations. The stoichiometric parameters were changed from a typical PAO coefficient to a typical GAO coefficient depending on the internal poly-P with Monod-type expressions. The model was calibrated and validated with seven experiments under different internal poly-P concentrations, showing the ability to correctly represent the PAO metabolic shift at low poly-P concentrations. The sensitivity and error analysis showed that the model is robust and has the ability to describe satisfactorily the change from one metabolic pathway to the other one, thereby encompassing a wider range of process conditions found in EBPR plants. Copyright © 2014 Elsevier Ltd. All rights reserved.

  1. Field Testing of Compartmentalization Methods for Multifamily Construction

    Energy Technology Data Exchange (ETDEWEB)

    Ueno, K. [Building Science Corporation, Westford, MA (United States); Lstiburek, J. W. [Building Science Corporation, Westford, MA (United States)

    2015-03-01

    The 2012 International Energy Conservation Code (IECC) has an airtightness requirement of 3 air changes per hour at 50 Pascals test pressure (3 ACH50) for single-family and multifamily construction (in climate zones 3–8). The Leadership in Energy & Environmental Design certification program and ASHRAE Standard 189 have comparable compartmentalization requirements. ASHRAE Standard 62.2 will soon be responsible for all multifamily ventilation requirements (low rise and high rise); it has an exceptionally stringent compartmentalization requirement. These code and program requirements are driving the need for easier and more effective methods of compartmentalization in multifamily buildings.

  2. Metabolic alterations in experimental models of depression

    Directory of Open Access Journals (Sweden)

    Maria G. Puiu

    2016-10-01

    Full Text Available Introduction: Major depressive disorder is one of the most prevalent psychiatric disorders and is associated with a severe impact on the personal functioning, thus with incurring significant direct and indirect costs. The presence of depression in patients with medical comorbidities increases the risks of myocardial infarction and decreases diabetes control, and adherence to treatment. The mechanism through which these effects are produced is still uncertain. Objectives of this study were to evaluate the metabolic alterations in female Wistar rats with induced depression, with and without administration of Agomelatine. The methods included two experiments. All data were analyzed by comparison with group I (control, and with each other. In the first experiment we induced depression by: exposure to chronic mild stress-group II; olfactory bulbectomy-group III; and exposure to chronic mild stress and hyperlipidic/ hyper caloric dietgroup IV. The second experiment was similar with the first but the rats received Agomelatine (0.16mg/ animal: group V (depression induced through exposure to chronic mild stress, VI (depression induced through olfactory bulbectomy and VII (depression induced through exposure to chronic mild stressing hyperlipidic/ hypercaloric diet. Weight, cholesterol, triglycerides and glycaemia were measured at day 0 and 28, and leptin value was measured at day 28. The results in the 1st experiment revealed significant differences (p<0.01 for weight and cholesterol in Group IV, for triglycerides in groups III and IV (p<0.001, and for glycaemia in group II. The 2nd experiment revealed significant differences (p<0.001 in group VII for weight and triglycerides, and in groups V and VI for triglycerides (p<0.01. In conclusion, significant correlations were found between high level of triglycerides and depression induced by chronic stress and olfactory bulbectomy. Agomelatine groups had a lower increase of triglycerides levels.

  3. Metabolic impact assessment for heterologous protein production in Streptomyces lividans based on genome-scale metabolic network modeling.

    Science.gov (United States)

    Lule, Ivan; D'Huys, Pieter-Jan; Van Mellaert, Lieve; Anné, Jozef; Bernaerts, Kristel; Van Impe, Jan

    2013-11-01

    The metabolic impact exerted on a microorganism due to heterologous protein production is still poorly understood in Streptomyces lividans. In this present paper, based on exometabolomic data, a proposed genome-scale metabolic network model is used to assess this metabolic impact in S. lividans. Constraint-based modeling results obtained in this work revealed that the metabolic impact due to heterologous protein production is widely distributed in the genome of S. lividans, causing both slow substrate assimilation and a shift in active pathways. Exchange fluxes that are critical for model performance have been identified for metabolites of mouse tumor necrosis factor, histidine, valine and lysine, as well as biomass. Our results unravel the interaction of heterologous protein production with intracellular metabolism of S. lividans, thus, a possible basis for further studies in relieving the metabolic burden via metabolic or bioprocess engineering. Copyright © 2013. Published by Elsevier Inc.

  4. Improving Metabolic Pathway Efficiency by Statistical Model-Based Multivariate Regulatory Metabolic Engineering.

    Science.gov (United States)

    Xu, Peng; Rizzoni, Elizabeth Anne; Sul, Se-Yeong; Stephanopoulos, Gregory

    2017-01-20

    Metabolic engineering entails target modification of cell metabolism to maximize the production of a specific compound. For empowering combinatorial optimization in strain engineering, tools and algorithms are needed to efficiently sample the multidimensional gene expression space and locate the desirable overproduction phenotype. We addressed this challenge by employing design of experiment (DoE) models to quantitatively correlate gene expression with strain performance. By fractionally sampling the gene expression landscape, we statistically screened the dominant enzyme targets that determine metabolic pathway efficiency. An empirical quadratic regression model was subsequently used to identify the optimal gene expression patterns of the investigated pathway. As a proof of concept, our approach yielded the natural product violacein at 525.4 mg/L in shake flasks, a 3.2-fold increase from the baseline strain. Violacein production was further increased to 1.31 g/L in a controlled benchtop bioreactor. We found that formulating discretized gene expression levels into logarithmic variables (Linlog transformation) was essential for implementing this DoE-based optimization procedure. The reported methodology can aid multivariate combinatorial pathway engineering and may be generalized as a standard procedure for accelerating strain engineering and improving metabolic pathway efficiency.

  5. Metabolic robustness and network modularity: a model study.

    Science.gov (United States)

    Holme, Petter

    2011-02-02

    Several studies have mentioned network modularity-that a network can easily be decomposed into subgraphs that are densely connected within and weakly connected between each other-as a factor affecting metabolic robustness. In this paper we measure the relation between network modularity and several aspects of robustness directly in a model system of metabolism. By using a model for generating chemical reaction systems where one can tune the network modularity, we find that robustness increases with modularity for changes in the concentrations of metabolites, whereas it decreases with changes in the expression of enzymes. The same modularity scaling is true for the speed of relaxation after the perturbations. Modularity is not a general principle for making metabolism either more or less robust; this question needs to be addressed specifically for different types of perturbations of the system.

  6. Cardiovascular Changes in Animal Models of Metabolic Syndrome

    Directory of Open Access Journals (Sweden)

    Alexandre M. Lehnen

    2013-01-01

    Full Text Available Metabolic syndrome has been defined as a group of risk factors that directly contribute to the development of cardiovascular disease and/or type 2 diabetes. Insulin resistance seems to have a fundamental role in the genesis of this syndrome. Over the past years to the present day, basic and translational research has used small animal models to explore the pathophysiology of metabolic syndrome and to develop novel therapies that might slow the progression of this prevalent condition. In this paper we discuss the animal models used for the study of metabolic syndrome, with particular focus on cardiovascular changes, since they are the main cause of death associated with the condition in humans.

  7. Compartmentalized Cytokine Responses in Hidradenitis Suppurativa.

    Directory of Open Access Journals (Sweden)

    Theodora Kanni

    Full Text Available Favorable treatment outcomes with TNF blockade led us to explore cytokine responses in hidradenitis suppurativa (HS.Blood monocytes of 120 patients and 24 healthy volunteers were subtyped by flow cytometry. Isolated blood mononuclear cells (PBMCs were stimulated for cytokine production; this was repeated in 13 severe patients during treatment with etanercept. Cytokines in pus were measured.CD14brightCD16dim inflammatory monocytes and patrolling monocytes were increased in Hurley III patients. Cytokine production by stimulated PBMCs was low compared to controls but the cytokine gene copies did not differ, indicating post-translational inhibition. The low production of IL-17 was restored, when cells were incubated with adalimumab. In pus, high concentrations of pro-inflammatory cytokines were detected. Based on the patterns, six different cytokine profiles were discerned, which are potentially relevant for the choice of treatment. Clinical improvement with etanercept was predicted by increased production of IL-1β and IL-17 by PBMCs at week 8.Findings indicate compartmentalized cytokine expression in HS; high in pus but suppressed in PBMCs. This is modulated through blockade of TNF.

  8. Compartmentalization and Transport in Synthetic Vesicles

    Directory of Open Access Journals (Sweden)

    Christine eSchmitt

    2016-02-01

    Full Text Available Nano-scale vesicles have become a popular tool in life sciences. Besides liposomes that are generated from phospholipids of natural origin, polymersomes fabricated of synthetic block copolymers enjoy increasing popularity, as they represent more versatile membrane building blocks that can be selected based on their specific physicochemical properties, like permeability, stability or chemical reactivity.In this review, we focus on the application of simple and nested artificial vesicles in synthetic biology. First, we provide an introduction into the utilization of multi-compartmented vesosomes as compartmentalized nano-scale bioreactors. In the bottom-up development of protocells from vesicular nano-reactors, the specific exchange of pathway intermediates across compartment boundaries represents a bottleneck for future studies. To date, most compartmented bioreactors rely on unspecific exchange of substrates and products. This is either based on changes in permeability of the coblock polymer shell by physicochemical triggers or by the incorporation of unspecific porin proteins into the vesicle membrane. Since the incorporation of membrane transport proteins into simple and nested artificial vesicles offers the potential for specific exchange of substances between subcompartments, it opens new vistas in the design of protocells. Therefore we devote the main part of the review to summarize the technical advances in the use of phospholipids and block copolymers for the reconstitution of membrane proteins.

  9. Dynamic modeling of lactic acid fermentation metabolism with Lactococcus lactis.

    Science.gov (United States)

    Oh, Euhlim; Lu, Mingshou; Park, Changhun; Park, Changhun; Oh, Han Bin; Lee, Sang Yup; Lee, Jinwon

    2011-02-01

    A dynamic model of lactic acid fermentation using Lactococcus lactis was constructed, and a metabolic flux analysis (MFA) and metabolic control analysis (MCA) were performed to reveal an intensive metabolic understanding of lactic acid bacteria (LAB). The parameter estimation was conducted with COPASI software to construct a more accurate metabolic model. The experimental data used in the parameter estimation were obtained from an LC-MS/ MS analysis and time-course simulation study. The MFA results were a reasonable explanation of the experimental data. Through the parameter estimation, the metabolic system of lactic acid bacteria can be thoroughly understood through comparisons with the original parameters. The coefficients derived from the MCA indicated that the reaction rate of L-lactate dehydrogenase was activated by fructose 1,6-bisphosphate and pyruvate, and pyruvate appeared to be a stronger activator of L-lactate dehydrogenase than fructose 1,6-bisphosphate. Additionally, pyruvate acted as an inhibitor to pyruvate kinase and the phosphotransferase system. Glucose 6-phosphate and phosphoenolpyruvate showed activation effects on pyruvate kinase. Hexose transporter was the strongest effector on the flux through L-lactate dehydrogenase. The concentration control coefficient (CCC) showed similar results to the flux control coefficient (FCC).

  10. Fast reconstruction of compact context-specific metabolic network models.

    Directory of Open Access Journals (Sweden)

    Nikos Vlassis

    2014-01-01

    Full Text Available Systemic approaches to the study of a biological cell or tissue rely increasingly on the use of context-specific metabolic network models. The reconstruction of such a model from high-throughput data can routinely involve large numbers of tests under different conditions and extensive parameter tuning, which calls for fast algorithms. We present fastcore, a generic algorithm for reconstructing context-specific metabolic network models from global genome-wide metabolic network models such as Recon X. fastcore takes as input a core set of reactions that are known to be active in the context of interest (e.g., cell or tissue, and it searches for a flux consistent subnetwork of the global network that contains all reactions from the core set and a minimal set of additional reactions. Our key observation is that a minimal consistent reconstruction can be defined via a set of sparse modes of the global network, and fastcore iteratively computes such a set via a series of linear programs. Experiments on liver data demonstrate speedups of several orders of magnitude, and significantly more compact reconstructions, over a rival method. Given its simplicity and its excellent performance, fastcore can form the backbone of many future metabolic network reconstruction algorithms.

  11. Modelling the metabolic versatility of a microbial trichome.

    NARCIS (Netherlands)

    van den Berg, H.A.

    1998-01-01

    A microbial trichome extracts nutrients from its immediate surroundings. It may also oxidize electron donors, reduce electron acceptors, and exude the 'waste' products of endogenous redox metabolism. Finally, it may effect light harvesting. These exchange fluxes are summed up in a generic model,

  12. Integrating cellular metabolism into a multiscale whole-body model.

    Directory of Open Access Journals (Sweden)

    Markus Krauss

    Full Text Available Cellular metabolism continuously processes an enormous range of external compounds into endogenous metabolites and is as such a key element in human physiology. The multifaceted physiological role of the metabolic network fulfilling the catalytic conversions can only be fully understood from a whole-body perspective where the causal interplay of the metabolic states of individual cells, the surrounding tissue and the whole organism are simultaneously considered. We here present an approach relying on dynamic flux balance analysis that allows the integration of metabolic networks at the cellular scale into standardized physiologically-based pharmacokinetic models at the whole-body level. To evaluate our approach we integrated a genome-scale network reconstruction of a human hepatocyte into the liver tissue of a physiologically-based pharmacokinetic model of a human adult. The resulting multiscale model was used to investigate hyperuricemia therapy, ammonia detoxification and paracetamol-induced toxication at a systems level. The specific models simultaneously integrate multiple layers of biological organization and offer mechanistic insights into pathology and medication. The approach presented may in future support a mechanistic understanding in diagnostics and drug development.

  13. Mechanistic modeling of aberrant energy metabolism in human disease

    Directory of Open Access Journals (Sweden)

    Vineet eSangar

    2012-10-01

    Full Text Available Dysfunction in energy metabolism—including in pathways localized to the mitochondria—has been implicated in the pathogenesis of a wide array of disorders, ranging from cancer to neurodegenerative diseases to type II diabetes. The inherent complexities of energy and mitochondrial metabolism present a significant obstacle in the effort to understand the role that these molecular processes play in the development of disease. To help unravel these complexities, systems biology methods have been applied to develop an array of computational metabolic models, ranging from mitochondria-specific processes to genome-scale cellular networks. These constraint-based models can efficiently simulate aspects of normal and aberrant metabolism in various genetic and environmental conditions. Development of these models leverages—and also provides a powerful means to integrate and interpret—information from a wide range of sources including genomics, proteomics, metabolomics, and enzyme kinetics. Here, we review a variety of mechanistic modeling studies that explore metabolic functions, deficiency disorders, and aberrant biochemical pathways in mitochondria and related regions in the cell.

  14. Compartmentalization, resource allocation, and wood quality

    Science.gov (United States)

    Kevin T. Smith

    2015-01-01

    The concept of a trade-off of tree resources between growth and defense is readily grasped. The most detailed development of the concept is for the growth-differentiation balance hypothesis that predicts that resources for normal growth and primary metabolism are diverted to support plant defense and secondary or stress metabolism. This hypothesis has been applied to...

  15. Diet-induced metabolic syndrome model in rats

    Directory of Open Access Journals (Sweden)

    Reza Homayounfar

    2013-03-01

    Full Text Available Background & Objective: Risk for heart disease, diabetes, and stroke increases with the number of the metabolic risk factors. In general, a person who has the metabolic syndrome is twice as likely to develop heart disease and five times as likely to develop diabetes as someone who does not have the metabolic syndrome. High-calorie-diet rodent models have contributed significantly to the analysis of the pathophysiology of the metabolic syndrome, but their phenotype varies distinctly between different studies and maybe is not very similar to a model of the metabolic syndrome in humans. We sought to create a model in this study close to the disease in humans.   Materials & Methods: Twenty male, Wistar rats were randomly assigned to the high-calorie diet group with 416 calories per 100 grams (researcher made or the control diet group for 12 weeks. Weight changes, lipid profile, glucose, insulin levels, and QUICKI index (an indicator of insulin sensitivity were measured. Weight changes were compared using the repeated measures and the independent t-test, and serum factors were compared using the independent t-test.   Results: There was a significant change in weight, glucose, insulin, and lipid profile except for HDL at the end of the study. The QUICKI index (0.34 ± 0.02 vs. 0.40 ± 0.01; p value <0.0001 suggested that insulin resistance had been created in the high-calorie diet group.   Conclusion: The present study demonstrates the ability to make diet-induced metabolic syndrome domestically.

  16. Applications of genome-scale metabolic network model in metabolic engineering.

    Science.gov (United States)

    Kim, Byoungjin; Kim, Won Jun; Kim, Dong In; Lee, Sang Yup

    2015-03-01

    Genome-scale metabolic network model (GEM) is a fundamental framework in systems metabolic engineering. GEM is built upon extensive experimental data and literature information on gene annotation and function, metabolites and enzymes so that it contains all known metabolic reactions within an organism. Constraint-based analysis of GEM enables the identification of phenotypic properties of an organism and hypothesis-driven engineering of cellular functions to achieve objectives. Along with the advances in omics, high-throughput technology and computational algorithms, the scope and applications of GEM have substantially expanded. In particular, various computational algorithms have been developed to predict beneficial gene deletion and amplification targets and used to guide the strain development process for the efficient production of industrially important chemicals. Furthermore, an Escherichia coli GEM was integrated with a pathway prediction algorithm and used to evaluate all possible routes for the production of a list of commodity chemicals in E. coli. Combined with the wealth of experimental data produced by high-throughput techniques, much effort has been exerted to add more biological contexts into GEM through the integration of omics data and regulatory network information for the mechanistic understanding and improved prediction capabilities. In this paper, we review the recent developments and applications of GEM focusing on the GEM-based computational algorithms available for microbial metabolic engineering.

  17. Metabolic modeling of synthesis gas fermentation in bubble column reactors.

    Science.gov (United States)

    Chen, Jin; Gomez, Jose A; Höffner, Kai; Barton, Paul I; Henson, Michael A

    2015-01-01

    A promising route to renewable liquid fuels and chemicals is the fermentation of synthesis gas (syngas) streams to synthesize desired products such as ethanol and 2,3-butanediol. While commercial development of syngas fermentation technology is underway, an unmet need is the development of integrated metabolic and transport models for industrially relevant syngas bubble column reactors. We developed and evaluated a spatiotemporal metabolic model for bubble column reactors with the syngas fermenting bacterium Clostridium ljungdahlii as the microbial catalyst. Our modeling approach involved combining a genome-scale reconstruction of C. ljungdahlii metabolism with multiphase transport equations that govern convective and dispersive processes within the spatially varying column. The reactor model was spatially discretized to yield a large set of ordinary differential equations (ODEs) in time with embedded linear programs (LPs) and solved using the MATLAB based code DFBAlab. Simulations were performed to analyze the effects of important process and cellular parameters on key measures of reactor performance including ethanol titer, ethanol-to-acetate ratio, and CO and H2 conversions. Our computational study demonstrated that mathematical modeling provides a complementary tool to experimentation for understanding, predicting, and optimizing syngas fermentation reactors. These model predictions could guide future cellular and process engineering efforts aimed at alleviating bottlenecks to biochemical production in syngas bubble column reactors.

  18. Building Single-Cell Models of Planktonic Metabolism Using PSAMM

    Science.gov (United States)

    Dufault-Thompson, K.; Zhang, Y.; Steffensen, J. L.

    2016-02-01

    The Genome-scale models (GEMs) of metabolic networks simulate the metabolic activities of individual cells by integrating omics data with biochemical and physiological measurements. GEMs were applied in the simulation of various photo-, chemo-, and heterotrophic organisms and provide significant insights into the function and evolution of planktonic cells. Despite the quick accumulation of GEMs, challenges remain in assembling the individual cell-based models into community-level models. Among various problems, the lack of consistencies in model representation and model quality checking has hindered the integration of individual GEMs and can lead to erroneous conclusions in the development of new modeling algorithms. Here, we present a Portable System for the Analysis of Metabolic Models (PSAMM). Along with the software a novel format of model representation was developed to enhance the readability of model files and permit the inclusion of heterogeneous, model-specific annotation information. A number of quality checking procedures was also implemented in PSAMM to ensure stoichiometric balance and to identify unused reactions. Using a case study of Shewanella piezotolerans WP3, we demonstrated the application of PSAMM in simulating the coupling of carbon utilization and energy production pathways under low-temperature and high-pressure stress. Applying PSAMM, we have also analyzed over 50 GEMs in the current literature and released an updated collection of the models with corrections on a number of common inconsistencies. Overall, PSAMM opens up new opportunities for integrating individual GEMs for the construction and mathematical simulation of community-level models in the scope of entire ecosystems.

  19. Reconstructing genome-scale metabolic models with merlin.

    Science.gov (United States)

    Dias, Oscar; Rocha, Miguel; Ferreira, Eugénio C; Rocha, Isabel

    2015-04-30

    The Metabolic Models Reconstruction Using Genome-Scale Information (merlin) tool is a user-friendly Java application that aids the reconstruction of genome-scale metabolic models for any organism that has its genome sequenced. It performs the major steps of the reconstruction process, including the functional genomic annotation of the whole genome and subsequent construction of the portfolio of reactions. Moreover, merlin includes tools for the identification and annotation of genes encoding transport proteins, generating the transport reactions for those carriers. It also performs the compartmentalisation of the model, predicting the organelle localisation of the proteins encoded in the genome and thus the localisation of the metabolites involved in the reactions promoted by such enzymes. The gene-proteins-reactions (GPR) associations are automatically generated and included in the model. Finally, merlin expedites the transition from genomic data to draft metabolic models reconstructions exported in the SBML standard format, allowing the user to have a preliminary view of the biochemical network, which can be manually curated within the environment provided by merlin. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

  20. A joint model of regulatory and metabolic networks

    Directory of Open Access Journals (Sweden)

    Vingron Martin

    2006-07-01

    Full Text Available Abstract Background Gene regulation and metabolic reactions are two primary activities of life. Although many works have been dedicated to study each system, the coupling between them is less well understood. To bridge this gap, we propose a joint model of gene regulation and metabolic reactions. Results We integrate regulatory and metabolic networks by adding links specifying the feedback control from the substrates of metabolic reactions to enzyme gene expressions. We adopt two alternative approaches to build those links: inferring the links between metabolites and transcription factors to fit the data or explicitly encoding the general hypotheses of feedback control as links between metabolites and enzyme expressions. A perturbation data is explained by paths in the joint network if the predicted response along the paths is consistent with the observed response. The consistency requirement for explaining the perturbation data imposes constraints on the attributes in the network such as the functions of links and the activities of paths. We build a probabilistic graphical model over the attributes to specify these constraints, and apply an inference algorithm to identify the attribute values which optimally explain the data. The inferred models allow us to 1 identify the feedback links between metabolites and regulators and their functions, 2 identify the active paths responsible for relaying perturbation effects, 3 computationally test the general hypotheses pertaining to the feedback control of enzyme expressions, 4 evaluate the advantage of an integrated model over separate systems. Conclusion The modeling results provide insight about the mechanisms of the coupling between the two systems and possible "design rules" pertaining to enzyme gene regulation. The model can be used to investigate the less well-probed systems and generate consistent hypotheses and predictions for further validation.

  1. Chronic hyperglycemia affects bone metabolism in adult zebrafish scale model.

    Science.gov (United States)

    Carnovali, Marta; Luzi, Livio; Banfi, Giuseppe; Mariotti, Massimo

    2016-12-01

    Type II diabetes mellitus is a metabolic disease characterized by chronic hyperglycemia that induce other pathologies including diabetic retinopathy and bone disease. The mechanisms implicated in bone alterations induced by type II diabetes mellitus have been debated for years and are not yet clear because there are other factors involved that hide bone mineral density alterations. Despite this, it is well known that chronic hyperglycemia affects bone health causing fragility, mechanical strength reduction and increased propensity of fractures because of impaired bone matrix microstructure and aberrant bone cells function. Adult Danio rerio (zebrafish) represents a powerful model to study glucose and bone metabolism. Then, the aim of this study was to evaluate bone effects of chronic hyperglycemia in a new type II diabetes mellitus zebrafish model created by glucose administration in the water. Fish blood glucose levels have been monitored in time course experiments and basal glycemia was found increased. After 1 month treatment, the morphology of the retinal blood vessels showed abnormalities resembling to the human diabetic retinopathy. The adult bone metabolism has been evaluated in fish using the scales as read-out system. The scales of glucose-treated fish didn't depose new mineralized matrix and shown bone resorption lacunae associated with an intense osteoclast activity. In addition, hyperglycemic fish scales have shown a significant decrease of alkaline phosphatase activity and increase of tartrate-resistant acid phosphatase activity, in association with alterations in other bone-specific markers. These data indicates an imbalance in bone metabolism, which leads to the osteoporotic-like phenotype visualized through scale mineral matrix staining. The zebrafish model of hyperglycemic damage can contribute to elucidate in vivo the molecular mechanisms of metabolic changes, which influence the bone tissues regulation in human diabetic patients.

  2. Subcellular compartmentation of glutathione in dicotyledonous plants

    Science.gov (United States)

    Müller, Maria

    2010-01-01

    This study describes the subcellular distribution of glutathione in roots and leaves of different plant species (Arabidopsis, Cucurbita, and Nicotiana). Glutathione is an important antioxidant and redox buffer which is involved in many metabolic processes including plant defense. Thus information on the subcellular distribution in these model plants especially during stress situations provides a deeper insight into compartment specific defense reactions and reflects the occurrence of compartment specific oxidative stress. With immunogold cytochemistry and computer-supported transmission electron microscopy glutathione could be localized in highest contents in mitochondria, followed by nuclei, peroxisomes, the cytosol, and plastids. Within chloroplasts and mitochondria, glutathione was restricted to the stroma and matrix, respectively, and did not occur in the lumen of cristae and thylakoids. Glutathione was also found at the membrane and in the lumen of the endoplasmic reticulum. It was also associated with the trans and cis side of dictyosomes. None or only very little glutathione was detected in vacuoles and the apoplast of mesophyll and root cells. Additionally, glutathione was found in all cell compartments of phloem vessels, vascular parenchyma cells (including vacuoles) but was absent in xylem vessels. The specificity of this method was supported by the reduction of glutathione labeling in all cell compartments (up to 98%) of the glutathione-deficient Arabidopsis thaliana rml1 mutant. Additionally, we found a similar distribution of glutathione in samples after conventional fixation and rapid microwave-supported fixation. Thus, indicating that a redistribution of glutathione does not occur during sample preparation. Summing up, this study gives a detailed insight into the subcellular distribution of glutathione in plants and presents solid evidence for the accuracy and specificity of the applied method. PMID:20186447

  3. [Regulation of terpene metabolism

    Energy Technology Data Exchange (ETDEWEB)

    Croteau, R.

    1989-11-09

    Terpenoid oils, resins, and waxes from plants are important renewable resources. The objective of this project is to understand the regulation of terpenoid metabolism using the monoterpenes (C[sub 10]) as a model. The pathways of monoterpene biosynthesis and catabolism have been established, and the relevant enzymes characterized. Developmental studies relating enzyme levels to terpene accumulation within the oil gland sites of synthesis, and work with bioregulators, indicate that monoterpene production is controlled by terpene cyclases, the enzymes catalyzing the first step of the monoterpene pathway. As the leaf oil glands mature, cyclase levels decline and monoterpene biosynthesis ceases. Yield then decreases as the monoterpenes undergo catabolism by a process involving conversion to a glycoside and transport from the leaf glands to the root. At this site, the terpenoid is oxidatively degraded to acetate that is recycled into other lipid metabolites. During the transition from terpene biosynthesis to catabolism, the oil glands undergo dramatic ultrastructural modification. Degradation of the producing cells results in mixing of previously compartmentized monoterpenes with the catabolic enzymes, ultimately leading to yield decline. This regulatory model is being applied to the formation of other terpenoid classes (C[sub 15] C[sub 20], C[sub 30], C[sub 40]) within the oil glands. Preliminary investigations on the formation of sesquiterpenes (C[sub 15]) suggest that the corresponding cyclases may play a lesser role in determining yield of these products, but that compartmentation effects are important. From these studies, a comprehensive scheme for the regulation of terpene metabolism is being constructed. Results from this project wail have important consequences for the yield and composition of terpenoid natural products that can be made available for industrial exploitation.

  4. Adaptive and neuroadaptive control for nonnegative and compartmental dynamical systems

    Science.gov (United States)

    Volyanskyy, Kostyantyn Y.

    Neural networks have been extensively used for adaptive system identification as well as adaptive and neuroadaptive control of highly uncertain systems. The goal of adaptive and neuroadaptive control is to achieve system performance without excessive reliance on system models. To improve robustness and the speed of adaptation of adaptive and neuroadaptive controllers several controller architectures have been proposed in the literature. In this dissertation, we develop a new neuroadaptive control architecture for nonlinear uncertain dynamical systems. The proposed framework involves a novel controller architecture with additional terms in the update laws that are constructed using a moving window of the integrated system uncertainty. These terms can be used to identify the ideal system weights of the neural network as well as effectively suppress system uncertainty. Linear and nonlinear parameterizations of the system uncertainty are considered and state and output feedback neuroadaptive controllers are developed. Furthermore, we extend the developed framework to discrete-time dynamical systems. To illustrate the efficacy of the proposed approach we apply our results to an aircraft model with wing rock dynamics, a spacecraft model with unknown moment of inertia, and an unmanned combat aerial vehicle undergoing actuator failures, and compare our results with standard neuroadaptive control methods. Nonnegative systems are essential in capturing the behavior of a wide range of dynamical systems involving dynamic states whose values are nonnegative. A sub-class of nonnegative dynamical systems are compartmental systems. These systems are derived from mass and energy balance considerations and are comprised of homogeneous interconnected microscopic subsystems or compartments which exchange variable quantities of material via intercompartmental flow laws. In this dissertation, we develop direct adaptive and neuroadaptive control framework for stabilization, disturbance

  5. Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13C Metabolic Flux Analysis

    Directory of Open Access Journals (Sweden)

    Tyler W. H. Backman

    2018-01-01

    Full Text Available Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function. 13 C Metabolic Flux Analysis ( 13 C MFA and Two-Scale 13 C Metabolic Flux Analysis (2S- 13 C MFA are two techniques used to determine such fluxes. Both operate on the simplifying approximation that metabolic flux from peripheral metabolism into central “core” carbon metabolism is minimal, and can be omitted when modeling isotopic labeling in core metabolism. The validity of this “two-scale” or “bow tie” approximation is supported both by the ability to accurately model experimental isotopic labeling data, and by experimentally verified metabolic engineering predictions using these methods. However, the boundaries of core metabolism that satisfy this approximation can vary across species, and across cell culture conditions. Here, we present a set of algorithms that (1 systematically calculate flux bounds for any specified “core” of a genome-scale model so as to satisfy the bow tie approximation and (2 automatically identify an updated set of core reactions that can satisfy this approximation more efficiently. First, we leverage linear programming to simultaneously identify the lowest fluxes from peripheral metabolism into core metabolism compatible with the observed growth rate and extracellular metabolite exchange fluxes. Second, we use Simulated Annealing to identify an updated set of core reactions that allow for a minimum of fluxes into core metabolism to satisfy these experimental constraints. Together, these methods accelerate and automate the identification of a biologically reasonable set of core reactions for use with 13 C MFA or 2S- 13 C MFA, as well as provide for a substantially lower set of flux bounds for fluxes into the core as compared with previous methods. We provide an open source Python implementation of these algorithms at https://github.com/JBEI/limitfluxtocore.

  6. Metabolic and Blood Pressure Effects of Walnut Supplementation in a Mouse Model of the Metabolic Syndrome.

    Science.gov (United States)

    Scott, Nicola J A; Ellmers, Leigh J; Pilbrow, Anna P; Thomsen, Lotte; Richards, Arthur Mark; Frampton, Chris M; Cameron, Vicky A

    2017-07-07

    There is extensive evidence that walnut consumption is protective against cardiovascular disease and diabetes in the healthy population, but the beneficial effects of walnut consumption in individuals with the metabolic syndrome (MetS) remain uncertain. We compared a range of cardio-metabolic traits and related tissue gene expression associated with 21 weeks of dietary walnut supplementation in a mouse model of MetS (MetS-Tg) and wild-type (WT) mice (n = 10 per genotype per diet, equal males and females). Compared to standard diet, walnuts did not significantly alter food consumption or body weight trajectory of either MetS-Tg or WT mice. In MetS-Tg mice, walnuts were associated with reductions in oral glucose area under the curve (gAUC, standard diet 1455 ± 54, walnut 1146 ± 91, p = 0.006) and mean arterial blood pressure (MAP, standard diet 100.6 ± 1.9, walnut 73.2 ± 1.8 mmHg, p liver expression of genes associated with metabolism (Fabp1, Insr), cell stress (Atf6, Ddit3, Eif2ak3), fibrosis (Hgf, Sp1, Timp1) and inflammation (Tnf, Ptpn22, Pparg). In conclusion, dietary walnuts were associated with modest favourable effects in WT mice, but a combination of beneficial and adverse effects in MetS-Tg mice, and up-regulation of hepatic pro-fibrotic and pro-inflammatory genes in both mouse strains.

  7. A Zebrafish Model of Diabetes Mellitus and Metabolic Memory

    Science.gov (United States)

    Intine, Robert V.; Olsen, Ansgar S.; Sarras, Michael P.

    2013-01-01

    Diabetes mellitus currently affects 346 million individuals and this is projected to increase to 400 million by 2030. Evidence from both the laboratory and large scale clinical trials has revealed that diabetic complications progress unimpeded via the phenomenon of metabolic memory even when glycemic control is pharmaceutically achieved. Gene expression can be stably altered through epigenetic changes which not only allow cells and organisms to quickly respond to changing environmental stimuli but also confer the ability of the cell to "memorize" these encounters once the stimulus is removed. As such, the roles that these mechanisms play in the metabolic memory phenomenon are currently being examined. We have recently reported the development of a zebrafish model of type I diabetes mellitus and characterized this model to show that diabetic zebrafish not only display the known secondary complications including the changes associated with diabetic retinopathy, diabetic nephropathy and impaired wound healing but also exhibit impaired caudal fin regeneration. This model is unique in that the zebrafish is capable to regenerate its damaged pancreas and restore a euglycemic state similar to what would be expected in post-transplant human patients. Moreover, multiple rounds of caudal fin amputation allow for the separation and study of pure epigenetic effects in an in vivo system without potential complicating factors from the previous diabetic state. Although euglycemia is achieved following pancreatic regeneration, the diabetic secondary complication of fin regeneration and skin wound healing persists indefinitely. In the case of impaired fin regeneration, this pathology is retained even after multiple rounds of fin regeneration in the daughter fin tissues. These observations point to an underlying epigenetic process existing in the metabolic memory state. Here we present the methods needed to successfully generate the diabetic and metabolic memory groups of fish and

  8. A new model for the body size-metabolism relationship.

    Science.gov (United States)

    Roberts, Michael F; Lightfoot, Edwin N; Porter, Warren P

    2010-01-01

    The allometric 3/4 power relation, initially used for describing the relation between mammalian basal metabolic rate and body size, is often used as a general model for organismal design. The use of allometric regression as a model has important limitations: it is not mechanistic, it combines all physiological variables into one correlate of body size, and it combines data from several physiological states. In reassessing the use of allometric equations, we first describe problems with their use in studies of organismal design and then use a formulation for distributed net heat production and temperature distribution within the body to derive an alternative equation for the relation between basal metabolism and body size. Tests of the heat flow equation against data reported in the literature indicate that it is an accurate estimator of basal metabolism under thermoneutral conditions and suggest that the allometric equation is a special case of this mechanistic and more general model. We propose that our method is more meaningful and widely applicable for thermoneutral conditions than is a purely allometric approach.

  9. Preventing Age-Related Decline of Gut Compartmentalization Limits Microbiota Dysbiosis and Extends Lifespan.

    Science.gov (United States)

    Li, Hongjie; Qi, Yanyan; Jasper, Heinrich

    2016-02-10

    Compartmentalization of the gastrointestinal (GI) tract of metazoans is critical for health. GI compartments contain specific microbiota, and microbiota dysbiosis is associated with intestinal dysfunction. Dysbiosis develops in aging intestines, yet how this relates to changes in GI compartmentalization remains unclear. The Drosophila GI tract is an accessible model to address this question. Here we show that the stomach-like copper cell region (CCR) in the middle midgut controls distribution and composition of the microbiota. We find that chronic activation of JAK/Stat signaling in the aging gut induces a metaplasia of the gastric epithelium, CCR decline, and subsequent commensal dysbiosis and epithelial dysplasia along the GI tract. Accordingly, inhibition of JAK/Stat signaling in the CCR specifically prevents age-related metaplasia, commensal dysbiosis and functional decline in old guts, and extends lifespan. Our results establish a mechanism by which age-related chronic inflammation causes the decline of intestinal compartmentalization and microbiota dysbiosis, limiting lifespan. Copyright © 2016 Elsevier Inc. All rights reserved.

  10. Recent advances in compartmentalized synthetic architectures as drug carriers, cell mimics and artificial organelles.

    Science.gov (United States)

    York-Duran, M J; Godoy-Gallardo, M; Labay, C; Urquhart, A J; Andresen, T L; Hosta-Rigau, L

    2017-04-01

    Compartmentalization is a key feature of biological cells which conduct their metabolic activity in individual steps isolated in distinct, separated compartments. The creation of architectures containing multiple compartments with a structure that resembles that of a biological cell has generated significant research attention and these assemblies are proposed as candidate materials for a range of biomedical applications. In this Review article, the recent successes of multicompartment architectures as carriers for the delivery of therapeutic cargo or the creation of micro- and nanoreactors that mimic metabolic activities, thus acting as artificial cells or organelles, are discussed. The developed technologies to assemble such complex architectures are outlined, the multicompartment carriers' properties which contribute to their performance in diverse applications are discussed, and their successful applications are highlighted. Finally, future directions and developments in the field are suggested. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. Construction of a Genome-Scale Metabolic Model of Arthrospira platensis NIES-39 and Metabolic Design for Cyanobacterial Bioproduction.

    Directory of Open Access Journals (Sweden)

    Katsunori Yoshikawa

    Full Text Available Arthrospira (Spirulina platensis is a promising feedstock and host strain for bioproduction because of its high accumulation of glycogen and superior characteristics for industrial production. Metabolic simulation using a genome-scale metabolic model and flux balance analysis is a powerful method that can be used to design metabolic engineering strategies for the improvement of target molecule production. In this study, we constructed a genome-scale metabolic model of A. platensis NIES-39 including 746 metabolic reactions and 673 metabolites, and developed novel strategies to improve the production of valuable metabolites, such as glycogen and ethanol. The simulation results obtained using the metabolic model showed high consistency with experimental results for growth rates under several trophic conditions and growth capabilities on various organic substrates. The metabolic model was further applied to design a metabolic network to improve the autotrophic production of glycogen and ethanol. Decreased flux of reactions related to the TCA cycle and phosphoenolpyruvate reaction were found to improve glycogen production. Furthermore, in silico knockout simulation indicated that deletion of genes related to the respiratory chain, such as NAD(PH dehydrogenase and cytochrome-c oxidase, could enhance ethanol production by using ammonium as a nitrogen source.

  12. Upper extremity compartmental anatomy: clinical relevance to radiologists

    Energy Technology Data Exchange (ETDEWEB)

    Toomayan, Glen A.; Robertson, Fabienne; Major, Nancy M. [Duke University Medical Center, Department of Radiology, P.O. Box 3808, Durham, NC (United States); Duke University Medical Center, Division of Orthopaedic Surgery, Department of Surgery, P.O. Box 3808, Durham, NC (United States); Brigman, Brian E. [Duke University Medical Center, Division of Orthopaedic Surgery, Department of Surgery, P.O. Box 3808, Durham, NC (United States)

    2006-04-15

    Malignant tumors of the upper extremity are uncommon, and their care should be referred to specialized facilities with experience treating these lesions. The Musculoskeletal Tumor Society (MSTS) staging system is used by the surgeon to determine appropriate surgical management, assess prognosis, and communicate with other healthcare providers. Magnetic resonance imaging (MRI) is employed pre-operatively to identify a lesion's compartment of origin, determine extent of spread, and plan biopsy and resection approaches. Involvement of neurovascular structures may result in devastating loss of upper extremity function, requiring amputation. Violation of high-resistance compartmental barriers necessitates more extensive surgical resection. Biopsy may be performed by the radiologist using imaging guidance. Knowledge of compartmental anatomy allows the radiologist or surgeon to use an easily excisable biopsy approach and prevent iatrogenic spread to unaffected compartments. Case examples are presented to illustrate the importance of compartmental anatomy in the management of benign and malignant upper extremity tumors. (orig.)

  13. Transgenic Mouse Models for Alcohol Metabolism, Toxicity and Cancer

    Science.gov (United States)

    Heit, Claire; Dong, Hongbin; Chen, Ying; Shah, Yatrik M.; Thompson, David C.; Vasiliou, Vasilis

    2015-01-01

    Alcohol abuse leads to tissue damage including a variety of cancers; however, the molecular mechanisms by which this damage occurs remains to be fully understood. The primary enzymes involved in ethanol metabolism include alcohol dehydrogenase (ADH), cytochrome P450 isoform 2E1, (CYP2E1), catalase (CAT), and aldehyde dehydrogenases (ALDH). Genetic polymorphisms in human genes encoding these enzymes are associated with increased risks of alcohol-related tissue damage, as well as differences in alcohol consumption and dependence. Oxidative stress resulting from ethanol oxidation is one established pathogenic event in alcohol-induced toxicity. Ethanol metabolism generates free radicals, such as reactive oxygen species (ROS) and reactive nitrogen species (RNS), and has been associated with diminished glutathione (GSH) levels as well as changes in other antioxidant mechanisms. In addition, the formation of protein and DNA adducts associated with the accumulation of ethanol-derived aldehydes can adversely affect critical biological functions and thereby promote cellular and tissue pathology. Animal models have proven to be valuable tools for investigating mechanisms underlying pathogenesis caused by alcohol. In this review, we provide a brief discussion on several animal models with genetic defects in alcohol metabolizing enzymes and GSH synthesizing enzymes and their relevance to alcohol research. PMID:25427919

  14. Transgenic mouse models for alcohol metabolism, toxicity, and cancer.

    Science.gov (United States)

    Heit, Claire; Dong, Hongbin; Chen, Ying; Shah, Yatrik M; Thompson, David C; Vasiliou, Vasilis

    2015-01-01

    Alcohol abuse leads to tissue damage including a variety of cancers; however, the molecular mechanisms by which this damage occurs remain to be fully understood. The primary enzymes involved in ethanol metabolism include alcohol dehydrogenase (ADH), cytochrome P450 isoform 2E1, (CYP2E1), catalase (CAT), and aldehyde dehydrogenases (ALDH). Genetic polymorphisms in human genes encoding these enzymes are associated with increased risks of alcohol-related tissue damage, as well as differences in alcohol consumption and dependence. Oxidative stress resulting from ethanol oxidation is one established pathogenic event in alcohol-induced toxicity. Ethanol metabolism generates free radicals, such as reactive oxygen species (ROS) and reactive nitrogen species (RNS), and has been associated with diminished glutathione (GSH) levels as well as changes in other antioxidant mechanisms. In addition, the formation of protein and DNA adducts associated with the accumulation of ethanol-derived aldehydes can adversely affect critical biological functions and thereby promote cellular and tissue pathology. Animal models have proven to be valuable tools for investigating mechanisms underlying pathogenesis caused by alcohol. In this review, we provide a brief discussion on several animal models with genetic defects in alcohol-metabolizing enzymes and GSH-synthesizing enzymes and their relevance to alcohol research.

  15. Compartmentalized Human Immunodeficiency Virus Type 1 Originates from Long-Lived Cells in Some Subjects with HIV-1–Associated Dementia

    Science.gov (United States)

    Schnell, Gretja; Spudich, Serena; Harrington, Patrick; Price, Richard W.; Swanstrom, Ronald

    2009-01-01

    Human immunodeficiency virus type 1 (HIV-1) invades the central nervous system (CNS) shortly after systemic infection and can result in the subsequent development of HIV-1–associated dementia (HAD) in a subset of infected individuals. Genetically compartmentalized virus in the CNS is associated with HAD, suggesting autonomous viral replication as a factor in the disease process. We examined the source of compartmentalized HIV-1 in the CNS of subjects with HIV-1–associated neurological disease and in asymptomatic subjects who were initiating antiretroviral therapy. The heteroduplex tracking assay (HTA), targeting the variable regions of env, was used to determine which HIV-1 genetic variants in the cerebrospinal fluid (CSF) were compartmentalized and which variants were shared with the blood plasma. We then measured the viral decay kinetics of individual variants after the initiation of antiretroviral therapy. Compartmentalized HIV-1 variants in the CSF of asymptomatic subjects decayed rapidly after the initiation of antiretroviral therapy, with a mean half-life of 1.57 days. Rapid viral decay was also measured for CSF-compartmentalized variants in four HAD subjects (t1/2 mean = 2.27 days). However, slow viral decay was measured for CSF-compartmentalized variants from an additional four subjects with neurological disease (t1/2 range = 9.85 days to no initial decay). The slow decay detected for CSF-compartmentalized variants was not associated with poor CNS drug penetration, drug resistant virus in the CSF, or the presence of X4 virus genotypes. We found that the slow decay measured for CSF-compartmentalized variants in subjects with neurological disease was correlated with low peripheral CD4 cell count and reduced CSF pleocytosis. We propose a model in which infiltrating macrophages replace CD4+ T cells as the primary source of productive viral replication in the CNS to maintain high viral loads in the CSF in a substantial subset of subjects with HAD

  16. Human lead metabolism: Chronic exposure, bone lead and physiological models

    Science.gov (United States)

    Fleming, David Eric Berkeley

    Exposure to lead is associated with a variety of detrimental health effects. After ingestion or inhalation, lead may be taken up from the bloodstream and retained by bone tissue. X-ray fluorescence was used to make in vivo measurements of bone lead concentration at the tibia and calcaneus for 367 active and 14 retired lead smelter workers. Blood lead levels following a labour disruption were used in conjunction with bone lead readings to examine the endogenous release of lead from bone. Relations between bone lead and a cumulative blood lead index differed depending on time of hiring. This suggests that the transfer of lead from blood to bone has changed over time, possibly as a result of varying exposure conditions. A common polymorphism in the δ-aminolevulinate dehydratase (ALAD) enzyme may influence the distribution of lead in humans. Blood lead levels were higher for smelter workers expressing the more rare ALAD2 allele. Bone lead concentrations, however, were not significantly different. This implies that a smaller proportion of lead in blood is distributed to tissue for individuals expressing the ALAD2 allele. The O'Flaherty physiological model of lead metabolism was modified slightly and tested with input from the personal exposure histories of smelter workers. The model results were consistent with observation in tern of endogenous exposure to lead and accumulation of lead in cortical bone. Modelling the calcaneus as a trabecular bone site did not reproduce observed trends. variations in lead metabolism between different trabecular sites may therefore be significant. The model does not incorporate a genetic component, and its output did not reflect observed differences in this respect. This result provides further support for the influence of the ALAD polymorphism on lead metabolism. Experimental trials with a digital spectrometer revealed superior energy resolution and count throughput relative to the conventional X-ray fluorescence system. The associated

  17. A continuum model for metabolic gas exchange in pear fruit.

    Directory of Open Access Journals (Sweden)

    Q Tri Ho

    2008-03-01

    Full Text Available Exchange of O(2 and CO(2 of plants with their environment is essential for metabolic processes such as photosynthesis and respiration. In some fruits such as pears, which are typically stored under a controlled atmosphere with reduced O(2 and increased CO(2 levels to extend their commercial storage life, anoxia may occur, eventually leading to physiological disorders. In this manuscript we have developed a mathematical model to predict the internal gas concentrations, including permeation, diffusion, and respiration and fermentation kinetics. Pear fruit has been selected as a case study. The model has been used to perform in silico experiments to evaluate the effect of, for example, fruit size or ambient gas concentration on internal O(2 and CO(2 levels. The model incorporates the actual shape of the fruit and was solved using fluid dynamics software. Environmental conditions such as temperature and gas composition have a large effect on the internal distribution of oxygen and carbon dioxide in fruit. Also, the fruit size has a considerable effect on local metabolic gas concentrations; hence, depending on the size, local anaerobic conditions may result, which eventually may lead to physiological disorders. The model developed in this manuscript is to our knowledge the most comprehensive model to date to simulate gas exchange in plant tissue. It can be used to evaluate the effect of environmental stresses on fruit via in silico experiments and may lead to commercial applications involving long-term storage of fruit under controlled atmospheres.

  18. Stockhome: A Spreadsheet Model of Urban Heavy Metal Metabolism

    Energy Technology Data Exchange (ETDEWEB)

    Hedbrant, J. [Linkoeping University, Department of Water and Environmental Studies (Sweden)], E-mail: johhe@ikp.liu.se

    2001-05-15

    Computer models for analysis,visualising and decision support in environmental research have become increasingly popular. The Stockhome project, where the urban metabolism of heavy metals in Stockholm was studied, resulted in a database with historical data of the use of goods containing cadmium (Cd), chromium (Cr),copper (Cu), lead (Pb), mercury (Hg), nickel (Ni)and zinc (Zn). A spreadsheet model was developed to study flows and stocks of the metal consumption process and emissions. The model indicates uncertainties of the data, societal aspects such as field of use and rights of disposition of the goods. By considering goods as the drivers of the emissions, the model would be well suited for policy support.

  19. An Ovine Model of Hyperdynamic Endotoxemia and Vital Organ Metabolism.

    Science.gov (United States)

    Byrne, Liam; Obonyo, Nchafatso G; Diab, Sara; Dunster, Kimble; Passmore, Margaret; Boon, Ai Ching; Hoe, Louise See; Hay, Karen; Van Haren, Frank; Tung, John-Paul; Cullen, Louise; Shekar, Kiran; Maitland, Kathryn; Fraser, John F

    2017-05-17

    Animal models of endotoxemia are frequently used to understand the pathophysiology of sepsis and test new therapies. However, important differences exist between commonly used experimental models of endotoxemia and clinical sepsis. Animal models of endotoxemia frequently produce hypodynamic shock in contrast to clinical hyperdynamic shock. This difference may exaggerate the importance of hypoperfusion as a causative factor in organ dysfunction. This study sought to develop an ovine model of hyperdynamic endotoxemia and assess if there is evidence of impaired oxidative metabolism in the vital organs. Eight sheep had microdialysis catheters implanted into the brain, heart, liver, kidney and arterial circulation. Shock was induced with a 4hr escalating dose infusion of endotoxin. After 3hrs vasopressor support was initiated with noradrenaline and vasopressin. Animals were monitored for 12hrs after endotoxemia. Blood samples were recovered for haemoglobin, white blood cell count, creatinine and proinflammatory cytokines (IL-1Beta, IL-6 & IL-8). The endotoxin infusion was successful in producing distributive shock with the mean arterial pressure decreasing from 84.5 ± 12.8 mmHg to 49 ± 8.03 mmHg (p shock. There was evidence of impaired oxidative metabolism in the liver suggesting impaired splanchnic perfusion. This may be a modifiable factor in the progression to multiple organ dysfunction and death.

  20. Understanding the causes and implications of endothelial metabolic variation in cardiovascular disease through genome scale metabolic modeling

    Directory of Open Access Journals (Sweden)

    Sarah eMcGarrity

    2016-04-01

    Full Text Available High-throughput biochemical profiling has led to a requirement for advanced data interpretation techniques capable of integrating the analysis of gene, protein, and metabolic profiles to shed light on genotype-phenotype relationships. Herein, we consider the current state of knowledge of endothelial cell (EC metabolism and its connections to cardiovascular disease, and explore the use of genome scale metabolic models (GEMs for integrating metabolic and genomic data. GEMs combine gene expression and metabolic data acting as frameworks for their analysis and, ultimately, afford mechanistic understanding of how genetic variation impacts metabolism. We demonstrate how GEMs can be used to investigate cardiovascular disease-related genetic variation, drug resistance mechanisms, and novel metabolic pathways, in ECs. The application of GEMs in personalized medicine is also highlighted. Particularly, we focus on the potential of GEMs to identify metabolic biomarkers of endothelial dysfunction and to discover methods of stratifying treatments for cardiovascular diseases based on individual genetic markers. Recent advances in systems biology methodology, and how these methodologies can be applied to understand EC metabolism in both health and disease, are thus highlighted.

  1. Metabolic modeling and analysis of the metabolic switch in Streptomyces coelicolor

    NARCIS (Netherlands)

    Alam, Mohammad T.; Merlo, Maria E.; Hodgson, David A.; Wellington, Elizabeth M. H.; Takano, Eriko; Breitling, Rainer

    2010-01-01

    Background: The transition from exponential to stationary phase in Streptomyces coelicolor is accompanied by a major metabolic switch and results in a strong activation of secondary metabolism. Here we have explored the underlying reorganization of the metabolome by combining computational

  2. Metabolic phenotype in the mouse model of osteogenesis imperfecta.

    Science.gov (United States)

    Boraschi-Diaz, Iris; Tauer, Josephine T; El-Rifai, Omar; Guillemette, Delphine; Lefebvre, Geneviève; Rauch, Frank; Ferron, Mathieu; Komarova, Svetlana V

    2017-09-01

    Osteogenesis imperfecta (OI) is the most common heritable bone fragility disorder, usually caused by dominant mutations in genes coding for collagen type I alpha chains, COL1A1 or COL1A2 Osteocalcin (OCN) is now recognized as a bone-derived regulator of insulin secretion and sensitivity and glucose homeostasis. Since OI is associated with increased rates of bone formation and resorption, we hypothesized that the levels of undercarboxylated OCN are increased in OI. The objective of this study was to determine changes in OCN and to elucidate the metabolic phenotype in the Col1a1 Jrt/+ mouse, a model of dominant OI caused by a Col1a1 mutation. Circulating levels of undercarboxylated OCN were higher in 4-week-old OI mice and normal by 8 weeks of age. Young OI animals exhibited a sex-dependent metabolic phenotype, including increased insulin levels in males, improved glucose tolerance in females, lower levels of random glucose and low adiposity in both sexes. The rates of O 2 consumption and CO 2 production, as well as energy expenditure assessed using indirect calorimetry were significantly increased in OI animals of both sexes, whereas respiratory exchange ratio was significantly higher in OI males only. Although OI mice have significant physical impairment that may contribute to metabolic differences, we specifically accounted for movement and compared OI and WT animals during the periods of similar activity levels. Taken together, our data strongly suggest that OI animals have alterations in whole body energy metabolism that are consistent with the action of undercarboxylated osteocalcin. © 2017 Society for Endocrinology.

  3. DRUM: A New Framework for Metabolic Modeling under Non-Balanced Growth. Application to the Carbon Metabolism of Unicellular Microalgae

    Science.gov (United States)

    Baroukh, Caroline; Muñoz-Tamayo, Rafael; Steyer, Jean-Philippe; Bernard, Olivier

    2014-01-01

    Metabolic modeling is a powerful tool to understand, predict and optimize bioprocesses, particularly when they imply intracellular molecules of interest. Unfortunately, the use of metabolic models for time varying metabolic fluxes is hampered by the lack of experimental data required to define and calibrate the kinetic reaction rates of the metabolic pathways. For this reason, metabolic models are often used under the balanced growth hypothesis. However, for some processes such as the photoautotrophic metabolism of microalgae, the balanced-growth assumption appears to be unreasonable because of the synchronization of their circadian cycle on the daily light. Yet, understanding microalgae metabolism is necessary to optimize the production yield of bioprocesses based on this microorganism, as for example production of third-generation biofuels. In this paper, we propose DRUM, a new dynamic metabolic modeling framework that handles the non-balanced growth condition and hence accumulation of intracellular metabolites. The first stage of the approach consists in splitting the metabolic network into sub-networks describing reactions which are spatially close, and which are assumed to satisfy balanced growth condition. The left metabolites interconnecting the sub-networks behave dynamically. Then, thanks to Elementary Flux Mode analysis, each sub-network is reduced to macroscopic reactions, for which simple kinetics are assumed. Finally, an Ordinary Differential Equation system is obtained to describe substrate consumption, biomass production, products excretion and accumulation of some internal metabolites. DRUM was applied to the accumulation of lipids and carbohydrates of the microalgae Tisochrysis lutea under day/night cycles. The resulting model describes accurately experimental data obtained in day/night conditions. It efficiently predicts the accumulation and consumption of lipids and carbohydrates. PMID:25105494

  4. DRUM: a new framework for metabolic modeling under non-balanced growth. Application to the carbon metabolism of unicellular microalgae.

    Directory of Open Access Journals (Sweden)

    Caroline Baroukh

    Full Text Available Metabolic modeling is a powerful tool to understand, predict and optimize bioprocesses, particularly when they imply intracellular molecules of interest. Unfortunately, the use of metabolic models for time varying metabolic fluxes is hampered by the lack of experimental data required to define and calibrate the kinetic reaction rates of the metabolic pathways. For this reason, metabolic models are often used under the balanced growth hypothesis. However, for some processes such as the photoautotrophic metabolism of microalgae, the balanced-growth assumption appears to be unreasonable because of the synchronization of their circadian cycle on the daily light. Yet, understanding microalgae metabolism is necessary to optimize the production yield of bioprocesses based on this microorganism, as for example production of third-generation biofuels. In this paper, we propose DRUM, a new dynamic metabolic modeling framework that handles the non-balanced growth condition and hence accumulation of intracellular metabolites. The first stage of the approach consists in splitting the metabolic network into sub-networks describing reactions which are spatially close, and which are assumed to satisfy balanced growth condition. The left metabolites interconnecting the sub-networks behave dynamically. Then, thanks to Elementary Flux Mode analysis, each sub-network is reduced to macroscopic reactions, for which simple kinetics are assumed. Finally, an Ordinary Differential Equation system is obtained to describe substrate consumption, biomass production, products excretion and accumulation of some internal metabolites. DRUM was applied to the accumulation of lipids and carbohydrates of the microalgae Tisochrysis lutea under day/night cycles. The resulting model describes accurately experimental data obtained in day/night conditions. It efficiently predicts the accumulation and consumption of lipids and carbohydrates.

  5. DRUM: a new framework for metabolic modeling under non-balanced growth. Application to the carbon metabolism of unicellular microalgae.

    Science.gov (United States)

    Baroukh, Caroline; Muñoz-Tamayo, Rafael; Steyer, Jean-Philippe; Bernard, Olivier

    2014-01-01

    Metabolic modeling is a powerful tool to understand, predict and optimize bioprocesses, particularly when they imply intracellular molecules of interest. Unfortunately, the use of metabolic models for time varying metabolic fluxes is hampered by the lack of experimental data required to define and calibrate the kinetic reaction rates of the metabolic pathways. For this reason, metabolic models are often used under the balanced growth hypothesis. However, for some processes such as the photoautotrophic metabolism of microalgae, the balanced-growth assumption appears to be unreasonable because of the synchronization of their circadian cycle on the daily light. Yet, understanding microalgae metabolism is necessary to optimize the production yield of bioprocesses based on this microorganism, as for example production of third-generation biofuels. In this paper, we propose DRUM, a new dynamic metabolic modeling framework that handles the non-balanced growth condition and hence accumulation of intracellular metabolites. The first stage of the approach consists in splitting the metabolic network into sub-networks describing reactions which are spatially close, and which are assumed to satisfy balanced growth condition. The left metabolites interconnecting the sub-networks behave dynamically. Then, thanks to Elementary Flux Mode analysis, each sub-network is reduced to macroscopic reactions, for which simple kinetics are assumed. Finally, an Ordinary Differential Equation system is obtained to describe substrate consumption, biomass production, products excretion and accumulation of some internal metabolites. DRUM was applied to the accumulation of lipids and carbohydrates of the microalgae Tisochrysis lutea under day/night cycles. The resulting model describes accurately experimental data obtained in day/night conditions. It efficiently predicts the accumulation and consumption of lipids and carbohydrates.

  6. Differential gating of dendritic spikes by compartmentalized inhibition

    Directory of Open Access Journals (Sweden)

    Katharina Anna Wilmes

    2014-03-01

    Full Text Available Different types of local inhibitory interneurons innervate different dendritic sites of pyramidal neurons in cortex and hippocampus (Klausberger 2009. What could be the functional role of compartmentalized inhibition? Pyramidal cell dendrites support different forms of active signal propagation, which are important not only for dendritic and neuronal signal processing (Smith et al. 2013, but also for synaptic plasticity. While back-propagating action potentials signal post-synaptic activity to synapses in apical oblique and basal dendrites (Markram et al. 1997, Cho et al. 2006, calcium spikes cause plasticity of distal apical tuft synapses (Golding et al. 2002. Suspiciously, the associated regions of the dendrite are targeted by different interneuron populations. Parvalbumin-positive interneurons typically target the proximal dendritic and somatic parts of the neuron, while somatostatin-positive interneurons target the apical dendrite. The matching compartmentalization in terms of dendritic spikes and inhibitory control suggests that inhibition could differentially regulate different dendritic spikes and thereby introduce a compartment-specific modulation of synaptic plasticity. We evaluate this hypothesis in a biophysical multi-compartment model of a pyramidal neuron, receiving shunting inhibition at different locations on the dendrite. The model shows that, first, inhibition can gate dendritic spikes in an all-or-none manner. Second, spatially selective inhibition can individually suppress back-propagating action potentials and calcium spikes, thereby allowing a compartment-specific switch for synaptic plasticity. In our model, proximal inhibition on the apical dendrite eliminated both the back-propagating action potential and the calcium spike, thus influencing plasticity in the whole apical dendrite. Distal apical inhibition could selectively affect calcium spikes and thus distal plasticity, without suppressing back­propagation of action

  7. Metabolic modeling and comparative biochemistry in glyoxylate cycle

    Directory of Open Access Journals (Sweden)

    Itamar Luís Gonçalves

    2016-07-01

    Full Text Available Glyoxylate cycle in fatty acid catabolism enhances net production of oxaloacetate, a substrate for gluconeogenesis, in certain bacteria, invertebrates and oilseed in the growth stage. A theoretical model was developed to calculate ATP amount produced in each step of the catabolic pathway, taking into account the fatty acid’s hydrocarbon chain size. Results showed a decrease in energy efficiency in glyoxylate cycle when compared to animal metabolism. Although the glyoxylate cycle provides evolutionary adaptations, it determines a smaller amount of energy produced per carbon atom when compared to animal catabolism of fatty acids.

  8. Modeling Clinical States and Metabolic Rhythms in Bioarcheology

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    Clifford Qualls

    2015-01-01

    Full Text Available Bioarcheology is cross disciplinary research encompassing the study of human remains. However, life’s activities have, up till now, eluded bioarcheological investigation. We hypothesized that growth lines in hair might archive the biologic rhythms, growth rate, and metabolism during life. Computational modeling predicted the physical appearance, derived from hair growth rate, biologic rhythms, and mental state for human remains from the Roman period. The width of repeat growth intervals (RI’s on the hair, shown by confocal microscopy, allowed computation of time series of periodicities of the RI’s to model growth rates of the hairs. Our results are based on four hairs from controls yielding 212 data points and the RI’s of six cropped hairs from Zweeloo woman’s scalp yielding 504 data points. Hair growth was, ten times faster than normal consistent with hypertrichosis. Cantú syndrome consists of hypertrichosis, dyschondrosteosis, short stature, and cardiomegaly. Sympathetic activation and enhanced metabolic state suggesting arousal was also present. Two-photon microscopy visualized preserved portions of autonomic nerve fibers surrounding the hair bulb. Scanning electron microscopy found evidence that a knife was used to cut the hair three to five days before death. Thus computational modeling enabled the elucidation of life’s activities 2000 years after death in this individual with Cantu syndrome. This may have implications for archeology and forensic sciences.

  9. Model-Assisted Analysis of Sugar Metabolism throughout Tomato Fruit Development Reveals Enzyme and Carrier Properties in Relation to Vacuole Expansion[W

    Science.gov (United States)

    Beauvoit, Bertrand P.; Colombié, Sophie; Monier, Antoine; Andrieu, Marie-Hélène; Biais, Benoit; Bénard, Camille; Chéniclet, Catherine; Dieuaide-Noubhani, Martine; Nazaret, Christine; Mazat, Jean-Pierre; Gibon, Yves

    2014-01-01

    A kinetic model combining enzyme activity measurements and subcellular compartmentation was parameterized to fit the sucrose, hexose, and glucose-6-P contents of pericarp throughout tomato (Solanum lycopersicum) fruit development. The model was further validated using independent data obtained from domesticated and wild tomato species and on transgenic lines. A hierarchical clustering analysis of the calculated fluxes and enzyme capacities together revealed stage-dependent features. Cell division was characterized by a high sucrolytic activity of the vacuole, whereas sucrose cleavage during expansion was sustained by both sucrose synthase and neutral invertase, associated with minimal futile cycling. Most importantly, a tight correlation between flux rate and enzyme capacity was found for fructokinase and PPi-dependent phosphofructokinase during cell division and for sucrose synthase, UDP-glucopyrophosphorylase, and phosphoglucomutase during expansion, thus suggesting an adaptation of enzyme abundance to metabolic needs. In contrast, for most enzymes, flux rates varied irrespectively of enzyme capacities, and most enzymes functioned at <5% of their maximal catalytic capacity. One of the major findings with the model was the high accumulation of soluble sugars within the vacuole together with organic acids, thus enabling the osmotic-driven vacuole expansion that was found during cell division. PMID:25139005

  10. Genetic models of PGC-1 and glucose metabolism and homeostasis.

    Science.gov (United States)

    Rowe, Glenn C; Arany, Zoltan

    2014-03-01

    Type II diabetes and its complications are a tremendous health burden throughout the world. Our understanding of the changes that lead to glucose imbalance and insulin resistance and ultimately diabetes remain incompletely understood. Many signaling and transcriptional pathways have been identified as being important to maintain normal glucose balance, including that of the peroxisome proliferator activated receptor gamma coactivator (PGC-1) family. This family of transcriptional coactivators strongly regulates mitochondrial and metabolic biology in numerous organs. The use of genetic models of PGC-1s, including both tissue-specific overexpression and knock-out models, has helped to reveal the specific roles that these coactivators play in each tissue. This review will thus focus on the PGC-1s and recently developed genetic rodent models that have highlighted the importance of these molecules in maintaining normal glucose homeostasis.

  11. Quantum Mechanics/Molecular Mechanics Modeling of Drug Metabolism

    DEFF Research Database (Denmark)

    Lonsdale, Richard; Fort, Rachel M; Rydberg, Patrik

    2016-01-01

    )-mexiletine in CYP1A2 with hybrid quantum mechanics/molecular mechanics (QM/MM) methods, providing a more detailed and realistic model. Multiple reaction barriers have been calculated at the QM(B3LYP-D)/MM(CHARMM27) level for the direct N-oxidation and H-abstraction/rebound mechanisms. Our calculated barriers......The mechanism of cytochrome P450(CYP)-catalyzed hydroxylation of primary amines is currently unclear and is relevant to drug metabolism; previous small model calculations have suggested two possible mechanisms: direct N-oxidation and H-abstraction/rebound. We have modeled the N-hydroxylation of (R...... indicate that the direct N-oxidation mechanism is preferred and proceeds via the doublet spin state of Compound I. Molecular dynamics simulations indicate that the presence of an ordered water molecule in the active site assists in the binding of mexiletine in the active site...

  12. Macroanatomy of compartmentalization in fire scars of three western conifers

    Science.gov (United States)

    Kevin T. Smith; Elaine Sutherland; Estelle Arbellay; Markus Stoffel; Donald. Falk

    2013-01-01

    Fire scars are visible evidence of compartmentalization and closure processes that contribute to tree survival after fire injury. Preliminary observations of dissected fire scars from trees injured within the last decade showed centripetal development of wound-initiated discoloration (WID) through 2-3 decades of former sapwood in Larix occidentalis and Pseudotsuga...

  13. Structure of the archaeal pab87 peptidase reveals a novel self-compartmentalizing protease family.

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    Vanessa Delfosse

    Full Text Available Self-compartmentalizing proteases orchestrate protein turnover through an original architecture characterized by a central catalytic chamber. Here we report the first structure of an archaeal member of a new self-compartmentalizing protease family forming a cubic-shaped octamer with D(4 symmetry and referred to as CubicO. We solved the structure of the Pyrococcus abyssi Pab87 protein at 2.2 A resolution using the anomalous signal of the high-phasing-power lanthanide derivative Lu-HPDO3A. A 20 A wide channel runs through this supramolecular assembly of 0.4 MDa, giving access to a 60 A wide central chamber holding the eight active sites. Surprisingly, activity assays revealed that Pab87 degrades specifically d-amino acid containing peptides, which have never been observed in archaea. Genomic context of the Pab87 gene showed that it is surrounded by genes involved in the amino acid/peptide transport or metabolism. We propose that CubicO proteases are involved in the processing of d-peptides from environmental origins.

  14. Reconstruction of genome-scale human metabolic models using omics data

    DEFF Research Database (Denmark)

    Ryu, Jae Yong; Kim, Hyun Uk; Lee, Sang Yup

    2015-01-01

    used to describe metabolic phenotypes of healthy and diseased human tissues and cells, and to predict therapeutic targets. Here we review recent trends in genome-scale human metabolic modeling, including various generic and tissue/cell type-specific human metabolic models developed to date, and methods...... refined through gap filling, reaction directionality assignment and the subcellular localization of metabolic reactions. We review relevant tools for this model refinement procedure as well. Finally, we suggest the direction of further studies on reconstructing an improved human metabolic model....

  15. redGEM: Systematic reduction and analysis of genome-scale metabolic reconstructions for development of consistent core metabolic models.

    Directory of Open Access Journals (Sweden)

    Meric Ataman

    2017-07-01

    Full Text Available Genome-scale metabolic reconstructions have proven to be valuable resources in enhancing our understanding of metabolic networks as they encapsulate all known metabolic capabilities of the organisms from genes to proteins to their functions. However the complexity of these large metabolic networks often hinders their utility in various practical applications. Although reduced models are commonly used for modeling and in integrating experimental data, they are often inconsistent across different studies and laboratories due to different criteria and detail, which can compromise transferability of the findings and also integration of experimental data from different groups. In this study, we have developed a systematic semi-automatic approach to reduce genome-scale models into core models in a consistent and logical manner focusing on the central metabolism or subsystems of interest. The method minimizes the loss of information using an approach that combines graph-based search and optimization methods. The resulting core models are shown to be able to capture key properties of the genome-scale models and preserve consistency in terms of biomass and by-product yields, flux and concentration variability and gene essentiality. The development of these "consistently-reduced" models will help to clarify and facilitate integration of different experimental data to draw new understanding that can be directly extendable to genome-scale models.

  16. Mathematical modelling of microbes: metabolism, gene expression and growth

    Science.gov (United States)

    Cinquemani, Eugenio; Ropers, Delphine; Gouzé, Jean-Luc

    2017-01-01

    The growth of microorganisms involves the conversion of nutrients in the environment into biomass, mostly proteins and other macromolecules. This conversion is accomplished by networks of biochemical reactions cutting across cellular functions, such as metabolism, gene expression, transport and signalling. Mathematical modelling is a powerful tool for gaining an understanding of the functioning of this large and complex system and the role played by individual constituents and mechanisms. This requires models of microbial growth that provide an integrated view of the reaction networks and bridge the scale from individual reactions to the growth of a population. In this review, we derive a general framework for the kinetic modelling of microbial growth from basic hypotheses about the underlying reaction systems. Moreover, we show that several families of approximate models presented in the literature, notably flux balance models and coarse-grained whole-cell models, can be derived with the help of additional simplifying hypotheses. This perspective clearly brings out how apparently quite different modelling approaches are related on a deeper level, and suggests directions for further research. PMID:29187637

  17. Integrated stoichiometric, thermodynamic and kinetic modelling of steady state metabolism

    Science.gov (United States)

    Fleming, R.M.T.; Thiele, I.; Provan, G.; Nasheuer, H.P.

    2010-01-01

    The quantitative analysis of biochemical reactions and metabolites is at frontier of biological sciences. The recent availability of high-throughput technology data sets in biology has paved the way for new modelling approaches at various levels of complexity including the metabolome of a cell or an organism. Understanding the metabolism of a single cell and multi-cell organism will provide the knowledge for the rational design of growth conditions to produce commercially valuable reagents in biotechnology. Here, we demonstrate how equations representing steady state mass conservation, energy conservation, the second law of thermodynamics, and reversible enzyme kinetics can be formulated as a single system of linear equalities and inequalities, in addition to linear equalities on exponential variables. Even though the feasible set is non-convex, the reformulation is exact and amenable to large-scale numerical analysis, a prerequisite for computationally feasible genome scale modelling. Integrating flux, concentration and kinetic variables in a unified constraint-based formulation is aimed at increasing the quantitative predictive capacity of flux balance analysis. Incorporation of experimental and theoretical bounds on thermodynamic and kinetic variables ensures that the predicted steady state fluxes are both thermodynamically and biochemically feasible. The resulting in silico predictions are tested against fluxomic data for central metabolism in E. coli and compare favourably with in silico prediction by flux balance analysis. PMID:20230840

  18. In silico strain optimization by adding reactions to metabolic models.

    Science.gov (United States)

    Correia, Sara; Rocha, Miguel

    2012-12-01

    Nowadays, the concerns about the environment and the needs to increase the productivity at low costs, demand for the search of new ways to produce compounds with industrial interest. Based on the increasing knowledge of biological processes, through genome sequencing projects, and high-throughput experimental techniques as well as the available computational tools, the use of microorganisms has been considered as an approach to produce desirable compounds. However, this usually requires to manipulate these organisms by genetic engineering and/ or changing the enviromental conditions to make the production of these compounds possible. In many cases, it is necessary to enrich the genetic material of those microbes with hereologous pathways from other species and consequently adding the potential to produce novel compounds. This paper introduces a new plug-in for the OptFlux Metabolic Engineering platform, aimed at finding suitable sets of reactions to add to the genomes of selected microbes (wild type strain), as well as finding complementary sets of deletions, so that the mutant becomes able to overproduce compounds with industrial interest, while preserving their viability. The necessity of adding reactions to the metabolic model arises from existing gaps in the original model or motivated by the productions of new compounds by the organism. The optimization methods used are metaheuristics such as Evolutionary Algorithms and Simulated Annealing. The usefulness of this plug-in is demonstrated by a case study, regarding the production of vanillin by the bacterium E. coli.

  19. Continuous Dissolved Oxygen Measurements and Modelling Metabolism in Peatland Streams.

    Science.gov (United States)

    Dick, Jonathan J; Soulsby, Chris; Birkel, Christian; Malcolm, Iain; Tetzlaff, Doerthe

    2016-01-01

    Stream water dissolved oxygen was monitored in a 3.2km2 moorland headwater catchment in the Scottish Highlands. The stream consists of three 1st order headwaters and a 2nd order main stem. The stream network is fringed by peat soils with no riparian trees, though dwarf shrubs provide shading in the lower catchment. Dissolved oxygen (DO) is regulated by the balance between atmospheric re-aeration and the metabolic processes of photosynthesis and respiration. DO was continuously measured for >1 year and the data used to calibrate a mass balance model, to estimate primary production, respiration and re-aeration for a 1st order site and in the 2nd order main stem. Results showed that the stream was always heterotrophic at both sites. Sites were most heterotrophic in the summer reflecting higher levels of stream metabolism. The 1st order stream appeared more heterotrophic which was consistent with the evident greater biomass of macrophytes in the 2nd order stream, with resulting higher primary productivity. Comparison between respiration, primary production, re-aeration and potential physical controls revealed only weak relationships. However, the most basic model parameters (e.g. the parameter linking light and photosynthesis) controlling ecosystem processes resulted in significant differences between the sites which seem related to the stream channel geometry.

  20. Metabolic modelling of polyhydroxyalkanoate copolymers production by mixed microbial cultures

    Directory of Open Access Journals (Sweden)

    Reis Maria AM

    2008-07-01

    Full Text Available Abstract Background This paper presents a metabolic model describing the production of polyhydroxyalkanoate (PHA copolymers in mixed microbial cultures, using mixtures of acetic and propionic acid as carbon source material. Material and energetic balances were established on the basis of previously elucidated metabolic pathways. Equations were derived for the theoretical yields for cell growth and PHA production on mixtures of acetic and propionic acid as functions of the oxidative phosphorylation efficiency, P/O ratio. The oxidative phosphorylation efficiency was estimated from rate measurements, which in turn allowed the estimation of the theoretical yield coefficients. Results The model was validated with experimental data collected in a sequencing batch reactor (SBR operated under varying feeding conditions: feeding of acetic and propionic acid separately (control experiments, and the feeding of acetic and propionic acid simultaneously. Two different feast and famine culture enrichment strategies were studied: (i either with acetate or (ii with propionate as carbon source material. Metabolic flux analysis (MFA was performed for the different feeding conditions and culture enrichment strategies. Flux balance analysis (FBA was used to calculate optimal feeding scenarios for high quality PHA polymers production, where it was found that a suitable polymer would be obtained when acetate is fed in excess and the feeding rate of propionate is limited to ~0.17 C-mol/(C-mol.h. The results were compared with published pure culture metabolic studies. Conclusion Acetate was more conducive toward the enrichment of a microbial culture with higher PHA storage fluxes and yields as compared to propionate. The P/O ratio was not only influenced by the selected microbial culture, but also by the carbon substrate fed to each culture, where higher P/O ratio values were consistently observed for acetate than propionate. MFA studies suggest that when mixtures of

  1. Metabolic and Blood Pressure Effects of Walnut Supplementation in a Mouse Model of the Metabolic Syndrome

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    Nicola J. A. Scott

    2017-07-01

    Full Text Available There is extensive evidence that walnut consumption is protective against cardiovascular disease and diabetes in the healthy population, but the beneficial effects of walnut consumption in individuals with the metabolic syndrome (MetS remain uncertain. We compared a range of cardio-metabolic traits and related tissue gene expression associated with 21 weeks of dietary walnut supplementation in a mouse model of MetS (MetS-Tg and wild-type (WT mice (n = 10 per genotype per diet, equal males and females. Compared to standard diet, walnuts did not significantly alter food consumption or body weight trajectory of either MetS-Tg or WT mice. In MetS-Tg mice, walnuts were associated with reductions in oral glucose area under the curve (gAUC, standard diet 1455 ± 54, walnut 1146 ± 91, p = 0.006 and mean arterial blood pressure (MAP, standard diet 100.6 ± 1.9, walnut 73.2 ± 1.8 mmHg, p < 0.001, with neutral effects on gAUC and MAP in WT mice. However, in MetS-Tg mice, walnuts were also associated with trends for higher plasma cholesterol (standard diet 4.73 ± 0.18, walnut 7.03 ± 1.99 mmol/L, p = 0.140 and triglyceride levels (standard diet 2.4 ± 0.5, walnut 5.4 ± 1.6 mmol/L, p = 0.061, despite lowering cholesterol and having no effect on triglycerides in WT mice. Moreover, in both MetS-Tg and WT mice, walnuts were associated with significantly increased liver expression of genes associated with metabolism (Fabp1, Insr, cell stress (Atf6, Ddit3, Eif2ak3, fibrosis (Hgf, Sp1, Timp1 and inflammation (Tnf, Ptpn22, Pparg. In conclusion, dietary walnuts were associated with modest favourable effects in WT mice, but a combination of beneficial and adverse effects in MetS-Tg mice, and up-regulation of hepatic pro-fibrotic and pro-inflammatory genes in both mouse strains.

  2. Impact of prebiotics on metabolic and behavioral alterations in a mouse model of metabolic syndrome.

    Science.gov (United States)

    de Cossío, Lourdes Fernández; Fourrier, Célia; Sauvant, Julie; Everard, Amandine; Capuron, Lucile; Cani, Patrice D; Layé, Sophie; Castanon, Nathalie

    2017-08-01

    Mounting evidence shows that the gut microbiota, an important player within the gut-brain communication axis, can affect metabolism, inflammation, brain function and behavior. Interestingly, gut microbiota composition is known to be altered in patients with metabolic syndrome (MetS), who also often display neuropsychiatric symptoms. The use of prebiotics, which beneficially alters the microbiota, may therefore be a promising way to potentially improve physical and mental health in MetS patients. This hypothesis was tested in a mouse model of MetS, namely the obese and type-2 diabetic db/db mice, which display emotional and cognitive alterations associated with changes in gut microbiota composition and hippocampal inflammation compared to their lean db/+ littermates. We assessed the impact of chronic administration (8weeks) of prebiotics (oligofructose) on both metabolic (body weight, food intake, glucose homeostasis) and behavioral (increased anxiety-like behavior and impaired spatial memory) alterations characterizing db/db mice, as well as related neurobiological correlates, with particular attention to neuroinflammatory processes. Prebiotic administration improved excessive food intake and glycemic dysregulations (glucose tolerance and insulin resistance) in db/db mice. This was accompanied by an increase of plasma anti-inflammatory cytokine IL-10 levels and hypothalamic mRNA expression of the anorexigenic cytokine IL-1β, whereas unbalanced mRNA expression of hypothalamic orexigenic (NPY) and anorexigenic (CART, POMC) peptides was unchanged. We also detected signs of improved blood-brain-barrier integrity in the hypothalamus of oligofructose-treated db/db mice (normalized expression of tight junction proteins ZO-1 and occludin). On the contrary, prebiotic administration did not improve behavioral alterations and associated reduction of hippocampal neurogenesis displayed by db/db mice, despite normalization of increased hippocampal IL-6 mRNA expression. Of note

  3. Genome-scale modeling using flux ratio constraints to enable metabolic engineering of clostridial metabolism in silico

    Directory of Open Access Journals (Sweden)

    McAnulty Michael J

    2012-05-01

    Full Text Available Abstract Background Genome-scale metabolic networks and flux models are an effective platform for linking an organism genotype to its phenotype. However, few modeling approaches offer predictive capabilities to evaluate potential metabolic engineering strategies in silico. Results A new method called “flux balance analysis with flux ratios (FBrAtio” was developed in this research and applied to a new genome-scale model of Clostridium acetobutylicum ATCC 824 (iCAC490 that contains 707 metabolites and 794 reactions. FBrAtio was used to model wild-type metabolism and metabolically engineered strains of C. acetobutylicum where only flux ratio constraints and thermodynamic reversibility of reactions were required. The FBrAtio approach allowed solutions to be found through standard linear programming. Five flux ratio constraints were required to achieve a qualitative picture of wild-type metabolism for C. acetobutylicum for the production of: (i acetate, (ii lactate, (iii butyrate, (iv acetone, (v butanol, (vi ethanol, (vii CO2 and (viii H2. Results of this simulation study coincide with published experimental results and show the knockdown of the acetoacetyl-CoA transferase increases butanol to acetone selectivity, while the simultaneous over-expression of the aldehyde/alcohol dehydrogenase greatly increases ethanol production. Conclusions FBrAtio is a promising new method for constraining genome-scale models using internal flux ratios. The method was effective for modeling wild-type and engineered strains of C. acetobutylicum.

  4. Genome-scale modeling using flux ratio constraints to enable metabolic engineering of clostridial metabolism in silico

    Science.gov (United States)

    2012-01-01

    Background Genome-scale metabolic networks and flux models are an effective platform for linking an organism genotype to its phenotype. However, few modeling approaches offer predictive capabilities to evaluate potential metabolic engineering strategies in silico. Results A new method called “flux balance analysis with flux ratios (FBrAtio)” was developed in this research and applied to a new genome-scale model of Clostridium acetobutylicum ATCC 824 (iCAC490) that contains 707 metabolites and 794 reactions. FBrAtio was used to model wild-type metabolism and metabolically engineered strains of C. acetobutylicum where only flux ratio constraints and thermodynamic reversibility of reactions were required. The FBrAtio approach allowed solutions to be found through standard linear programming. Five flux ratio constraints were required to achieve a qualitative picture of wild-type metabolism for C. acetobutylicum for the production of: (i) acetate, (ii) lactate, (iii) butyrate, (iv) acetone, (v) butanol, (vi) ethanol, (vii) CO2 and (viii) H2. Results of this simulation study coincide with published experimental results and show the knockdown of the acetoacetyl-CoA transferase increases butanol to acetone selectivity, while the simultaneous over-expression of the aldehyde/alcohol dehydrogenase greatly increases ethanol production. Conclusions FBrAtio is a promising new method for constraining genome-scale models using internal flux ratios. The method was effective for modeling wild-type and engineered strains of C. acetobutylicum. PMID:22583864

  5. Comprehensive Mapping of Pluripotent Stem Cell Metabolism Using Dynamic Genome-Scale Network Modeling

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    Sriram Chandrasekaran

    2017-12-01

    Full Text Available Summary: Metabolism is an emerging stem cell hallmark tied to cell fate, pluripotency, and self-renewal, yet systems-level understanding of stem cell metabolism has been limited by the lack of genome-scale network models. Here, we develop a systems approach to integrate time-course metabolomics data with a computational model of metabolism to analyze the metabolic state of naive and primed murine pluripotent stem cells. Using this approach, we find that one-carbon metabolism involving phosphoglycerate dehydrogenase, folate synthesis, and nucleotide synthesis is a key pathway that differs between the two states, resulting in differential sensitivity to anti-folates. The model also predicts that the pluripotency factor Lin28 regulates this one-carbon metabolic pathway, which we validate using metabolomics data from Lin28-deficient cells. Moreover, we identify and validate metabolic reactions related to S-adenosyl-methionine production that can differentially impact histone methylation in naive and primed cells. Our network-based approach provides a framework for characterizing metabolic changes influencing pluripotency and cell fate. : Chandrasekaran et al. use computational modeling, metabolomics, and metabolic inhibitors to discover metabolic differences between various pluripotent stem cell states and infer their impact on stem cell fate decisions. Keywords: systems biology, stem cell biology, metabolism, genome-scale modeling, pluripotency, histone methylation, naive (ground state, primed state, cell fate, metabolic network

  6. Flux balance analysis of genome-scale metabolic model of rice ...

    Indian Academy of Sciences (India)

    Here, we analyse a genome-scale metabolic model of rice leaf using Flux Balance Analysis to investigate whether it has potential metabolic flexibility to increase the biosynthesis of any of the biomass components. We initially simulate the metabolic responses under an objective to maximize the biomass components.

  7. A Modeling and Simulation Approach to the Study of Metabolic Control Analysis

    Science.gov (United States)

    Rodriguez-Caso, Carlos; Sanchez-Jimenez, Francisca; Medina, Miguel Angel

    2002-01-01

    Metabolic control analysis has contributed to the rapid advance in our understanding of metabolic regulation. However, up to now this topic has not been covered properly in biochemistry courses. This work reports the development and implementation of a practical lesson on metabolic control analysis (MCA) using modeling and simulation. The…

  8. Basic concepts and principles of stoichiometric modeling of metabolic networks

    NARCIS (Netherlands)

    T.R. Maarleveld (Timo); R.A. Khandelwal; B.E. Olivier; B. Teusink (Bas); F.J. Bruggeman (Frank)

    2013-01-01

    htmlabstractMetabolic networks supply the energy and building blocks for cell growth and maintenance. Cells continuously rewire their metabolic networks in response to changes in environmental conditions to sustain fitness. Studies of the systemic properties of metabolic networks give insight into

  9. Compartmental analysis and dosimetric aspects applied to cholesterol with {sup 3}H labeled; Analise compartimental e aspectos dosimetricos aplicados ao colesterol marcado com {sup 3}H

    Energy Technology Data Exchange (ETDEWEB)

    Oliveira, Adriano dos Santos

    2015-07-01

    Cardiovascular diseases (CVDs) are one of the major reasons of death around the world according to the World Health Organization (WHO). It is well known that changes in levels of plasma lipoproteins, which are responsible for the transport of cholesterol into the bloodstream, are associated with cardiovascular diseases. For this reason to know the biokinetic parameters of plasma lipoproteins and quantifies them is important to correct and deep understanding about the diseases associated with these disorders. The main aim of this study is to provide a biokinetic model and estimate the radiometric doses for {sup 3}H-Cholesterol, a radioactive tracer widely used in physiological and metabolic studies. The model was based on [Schwartz et al. 2004] about the distribution of cholesterol by the lipoprotein and gastrointestinal model [ICRP 30, 1979]. The doses distribution in compartments of the model and other organs and tissues of a standard adult described in [ICRP 106, 2008] was calculated using MIRD method (Medical Internal Radiation Dose) and compartmental analysis using the computer program Matlab®. The dose coefficients were estimated for a standard phantom man (73 kg) described in [ICRP 60, 1991]. The estimated doses for both model and for other organs were low and did not exceed the highest dose obtained that was in the upper large intestine, as 44,8 μGy these parameters will assist in ethics committee's opinions on the use of works that use the {sup 3}H-cholesterol which radioactive tracer. (author)

  10. A coupled metabolic-hydraulic model and calibration scheme for estimating of whole-river metabolism during dynamic flow conditions

    Science.gov (United States)

    Payn, Robert A.; Hall, Robert O Jr.; Kennedy, Theodore; Poole, Geoff C; Marshall, Lucy A.

    2017-01-01

    Conventional methods for estimating whole-stream metabolic rates from measured dissolved oxygen dynamics do not account for the variation in solute transport times created by dynamic flow conditions. Changes in flow at hourly time scales are common downstream of hydroelectric dams (i.e. hydropeaking), and hydrologic limitations of conventional metabolic models have resulted in a poor understanding of the controls on biological production in these highly managed river ecosystems. To overcome these limitations, we coupled a two-station metabolic model of dissolved oxygen dynamics with a hydrologic river routing model. We designed calibration and parameter estimation tools to infer values for hydrologic and metabolic parameters based on time series of water quality data, achieving the ultimate goal of estimating whole-river gross primary production and ecosystem respiration during dynamic flow conditions. Our case study data for model design and calibration were collected in the tailwater of Glen Canyon Dam (Arizona, USA), a large hydropower facility where the mean discharge was 325 m3 s 1 and the average daily coefficient of variation of flow was 0.17 (i.e. the hydropeaking index averaged from 2006 to 2016). We demonstrate the coupled model’s conceptual consistency with conventional models during steady flow conditions, and illustrate the potential bias in metabolism estimates with conventional models during unsteady flow conditions. This effort contributes an approach to solute transport modeling and parameter estimation that allows study of whole-ecosystem metabolic regimes across a more diverse range of hydrologic conditions commonly encountered in streams and rivers.

  11. Compartmental syndrome of the forearm: report of an unusual case.

    Science.gov (United States)

    Pollock, M S; Morris, S B; Sadeghpour, E

    1984-02-01

    Compartmental syndrome of the forearm is a well-recognized complication of upper extremity injuries. An unusual case of the syndrome is presented, which developed after a "sprained" wrist. The etiology of the syndrome in this case was possibly tearing of anterior compartment muscle fibers when the wrist was forcibly extended in the fall, or a reflex sympathetic arc set up by the wrist trauma. Copyright 2013, SLACK Incorporated.

  12. MultiMetEval : Comparative and Multi-Objective Analysis of Genome-Scale Metabolic Models

    NARCIS (Netherlands)

    Zakrzewski, Piotr; Medema, Marnix H.; Gevorgyan, Albert; Kierzek, Andrzej M.; Breitling, Rainer; Takano, Eriko; Fong, Stephen S.

    2012-01-01

    Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the

  13. A Numerical Simulation for a Deterministic Compartmental ...

    African Journals Online (AJOL)

    In this work, an earlier deterministic mathematical model of HIV/AIDS is revisited and numerical solutions obtained using Eulers numerical method. Using hypothetical values for the parameters, a program was written in VISUAL BASIC programming language to generate series for the system of difference equations from the ...

  14. Field Testing of Compartmentalization Methods for Multifamily Construction

    Energy Technology Data Exchange (ETDEWEB)

    Ueno, K. [Building Science Corporation, Westford, MA (United States); Lstiburek, J. [Building Science Corporation, Westford, MA (United States)

    2015-03-01

    The 2012 IECC has an airtightness requirement of 3 air changes per hour at 50 Pascals test pressure for both single-family and multifamily construction in Climate Zones 3-8. Other programs (LEED, ASHRAE 189, ASHRAE 62.2) have similar or tighter compartmentalization requirements, driving the need for easier and more effective methods of compartmentalization in multifamily buildings. Builders and practitioners have found that fire-resistance rated wall assemblies are a major source of difficulty in air sealing/compartmentalization, particularly in townhouse construction. This problem is exacerbated when garages are “tucked in” to the units and living space is located over the garages. In this project, Building Science Corporation examined the taping of exterior sheathing details to improve air sealing results in townhouse and multifamily construction, when coupled with a better understanding of air leakage pathways. Current approaches are cumbersome, expensive, time consuming, and ineffective; these details were proposed as a more effective and efficient method. The effectiveness of these air sealing methods was tested with blower door testing, including “nulled” or “guarded” testing (adjacent units run at equal test pressure to null out inter-unit air leakage, or “pressure neutralization”). Pressure diagnostics were used to evaluate unit-to-unit connections and series leakage pathways (i.e., air leakage from exterior, into the fire-resistance rated wall assembly, and to the interior).

  15. Template-Assisted Metabolic Reconstruction and Assembly of Hybrid Bacterial Models.

    Science.gov (United States)

    Vignolini, Tiziano; Mengoni, Alessio; Fondi, Marco

    2018-01-01

    Intraspecific genomic exchanges happen frequently between bacteria living in the same natural environment and can also be performed artificially in the laboratory for basic research or genetic/metabolic engineering purposes. In silico metabolic reconstruction and simulation of the metabolism of the hybrid strains that result from these processes can be used to predict the phenotypic outcome of such genomic rearrangements; this can be especially helpful as a designing tool in the purview of synthetic biology. However, reconstructing the metabolism of a bacterium with a hybrid genome through in silico approaches is not a trivial task, as it requires taking into account the complex relationships existing between metabolic genes and how they change (or remain unchanged) when new genes are placed in a different genomic context. Furthermore, in order to "mix" the metabolic models of different bacterial strains one needs at least two different metabolic models to begin with, and reconstructing a genome-scale model from the ground up is a challenging task itself, requiring an intensive manual effort and a great deal of information. In this chapter, we propose two general protocols to address the aforementioned issues of: (1) quickly generating strain-specific metabolic models, given the relevant genomic sequence and an already existing, high-quality metabolic model of a different strain belonging to the same species, and (2) reconstructing the metabolic model of a hybrid strain containing genomic elements from two different parental strains.

  16. Compartmental architecture and dynamics of hematopoiesis.

    Directory of Open Access Journals (Sweden)

    David Dingli

    Full Text Available BACKGROUND: Blood cell formation is maintained by the replication of hematopoietic stem cells (HSC that continuously feed downstream "compartments" where amplification and differentiation of cells occurs, giving rise to all blood lineages. Whereas HSC replicate slowly, committed cells replicate faster as they become more differentiated. METHODOLOGY/SIGNIFICANT FINDING: We propose a multi-compartment model of hematopoiesis, designed on the principle of cell flow conservation under stationary conditions. Cells lost from one compartment due to differentiation are replaced by cells from the upstream compartment. We assume that there is a constant relationship between cell input and output in each compartment and fix the single parameter of the model using data available for granulocyte maturation. We predict that approximately 31 mitotic events separate the HSC from the mature cells observed in the circulation. Besides estimating the number of compartments, our model allows us to estimate the size of each compartment, the rate of cell replication within each compartment, the mean time a given cell type contributes to hematopoiesis, the amplification rate in each compartment, as well as the mean time separating stem-cell replication and mature blood-cell formation. CONCLUSIONS: Despite its simplicity, the model agrees with the limited in vivo data available and can make testable predictions. In particular, our prediction of the average lifetime of a PIG-A mutated clone agrees closely with the experimental results available for the PIG-A gene mutation in healthy adults. The present elucidation of the compartment structure and dynamics of hematopoiesis may prove insightful in further understanding a variety of hematopoietic disorders.

  17. Computational Modeling of Human Metabolism and Its Application to Systems Biomedicine.

    Science.gov (United States)

    Aurich, Maike K; Thiele, Ines

    2016-01-01

    Modern high-throughput techniques offer immense opportunities to investigate whole-systems behavior, such as those underlying human diseases. However, the complexity of the data presents challenges in interpretation, and new avenues are needed to address the complexity of both diseases and data. Constraint-based modeling is one formalism applied in systems biology. It relies on a genome-scale reconstruction that captures extensive biochemical knowledge regarding an organism. The human genome-scale metabolic reconstruction is increasingly used to understand normal cellular and disease states because metabolism is an important factor in many human diseases. The application of human genome-scale reconstruction ranges from mere querying of the model as a knowledge base to studies that take advantage of the model's topology and, most notably, to functional predictions based on cell- and condition-specific metabolic models built based on omics data.An increasing number and diversity of biomedical questions are being addressed using constraint-based modeling and metabolic models. One of the most successful biomedical applications to date is cancer metabolism, but constraint-based modeling also holds great potential for inborn errors of metabolism or obesity. In addition, it offers great prospects for individualized approaches to diagnostics and the design of disease prevention and intervention strategies. Metabolic models support this endeavor by providing easy access to complex high-throughput datasets. Personalized metabolic models have been introduced. Finally, constraint-based modeling can be used to model whole-body metabolism, which will enable the elucidation of metabolic interactions between organs and disturbances of these interactions as either causes or consequence of metabolic diseases. This chapter introduces constraint-based modeling and describes some of its contributions to systems biomedicine.

  18. Genome-scale modeling of human metabolism - a systems biology approach.

    Science.gov (United States)

    Mardinoglu, Adil; Gatto, Francesco; Nielsen, Jens

    2013-09-01

    Altered metabolism is linked to the appearance of various human diseases and a better understanding of disease-associated metabolic changes may lead to the identification of novel prognostic biomarkers and the development of new therapies. Genome-scale metabolic models (GEMs) have been employed for studying human metabolism in a systematic manner, as well as for understanding complex human diseases. In the past decade, such metabolic models - one of the fundamental aspects of systems biology - have started contributing to the understanding of the mechanistic relationship between genotype and phenotype. In this review, we focus on the construction of the Human Metabolic Reaction database, the generation of healthy cell type- and cancer-specific GEMs using different procedures, and the potential applications of these developments in the study of human metabolism and in the identification of metabolic changes associated with various disorders. We further examine how in silico genome-scale reconstructions can be employed to simulate metabolic flux distributions and how high-throughput omics data can be analyzed in a context-dependent fashion. Insights yielded from this mechanistic modeling approach can be used for identifying new therapeutic agents and drug targets as well as for the discovery of novel biomarkers. Finally, recent advancements in genome-scale modeling and the future challenge of developing a model of whole-body metabolism are presented. The emergent contribution of GEMs to personalized and translational medicine is also discussed. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  19. Development of a kinetic metabolic model: application to Catharanthus roseus hairy root

    Science.gov (United States)

    Leduc, M.; Tikhomiroff, C.; Cloutier, M.; Perrier, M.

    2006-01-01

    A kinetic metabolic model describing Catharanthus roseus hairy root growth and nutrition was developed. The metabolic network includes glycolysis, pentose-phosphate pathway, TCA cycle and the catabolic reactions leading to cell building blocks such as amino acids, organic acids, organic phosphates, lipids and structural hexoses. The central primary metabolic network was taken at pseudo-steady state and metabolic flux analysis technique allowed reducing from 31 metabolic fluxes to 20 independent pathways. Hairy root specific growth rate was described as a function of intracellular concentration in cell building blocks. Intracellular transport and accumulation kinetics for major nutrients were included. The model uses intracellular nutrients as well as energy shuttles to describe metabolic regulation. Model calibration was performed using experimental data obtained from batch and medium exchange liquid cultures of C. roseus hairy root using a minimal medium in Petri dish. The model is efficient in estimating the growth rate. PMID:16453114

  20. Metabolic models to investigate energy limited anaerobic ecosystems.

    Science.gov (United States)

    Rodríguez, J; Premier, G C; Guwy, A J; Dinsdale, R; Kleerebezem, R

    2009-01-01

    Anaerobic wastewater treatment is shifting from a philosophy of solely pollutants removal to a philosophy of combined resource recovery and waste treatment. Simultaneous wastewater treatment with energy recovery in the form of energy rich products, brings renewed interest to non-methanogenic anaerobic bioprocesses such as the anaerobic production of hydrogen, ethanol, solvents, VFAs, bioplastics and even electricity from microbial fuel cells. The existing kinetic-based modelling approaches, widely used in aerobic and methanogenic wastewater treatment processes, do not seem adequate in investigating such energy limited microbial ecosystems. The great diversity of similar microbial species, which share many of the fermentative reaction pathways, makes quantify microbial groups very difficult and causes identifiability problems. A modelling approach based on the consideration of metabolic reaction networks instead of on separated microbial groups is suggested as an alternative to describe anaerobic microbial ecosystems and in particular for the prediction of product formation as a function of environmental conditions imposed. The limited number of existing relevant fermentative pathways in conjunction with the fact that anaerobic reactions proceed very close to thermodynamic equilibrium reduces the complexity of such approach and the degrees of freedom in terms of product formation fluxes. In addition, energy limitation in these anaerobic microbial ecosystems makes plausible that selective forces associated with energy further define the system activity by favouring those conversions/microorganisms which provide the most energy for growth under the conditions imposed.

  1. Human-induced pluripotent stem cell approaches to model inborn and acquired metabolic heart diseases.

    Science.gov (United States)

    Chanana, Anita M; Rhee, June-Wha; Wu, Joseph C

    2016-05-01

    The article provides an overview of advances in the induced pluripotent stem cell field to model cardiomyopathies of inherited inborn errors of metabolism and acquired metabolic syndromes in vitro. Several inborn errors of metabolism have been studied using 'disease in a dish' models, including Pompe disease, Danon disease, Fabry disease, and Barth syndrome. Disease phenotypes of complex metabolic syndromes, such as diabetes mellitus and aldehyde dehydrogenase 2 deficiency, have also been observed. Differentiation of patient and disease-specific induced pluripotent stem cell-derived cardiomyocytes has provided the capacity to model deleterious cardiometabolic diseases to understand molecular mechanisms, perform drug screens, and identify novel drug targets.

  2. MFAML: a standard data structure for representing and exchanging metabolic flux models.

    Science.gov (United States)

    Yun, Hongseok; Lee, Dong-Yup; Jeong, Joonwoo; Lee, Seunghyun; Lee, Sang Yup

    2005-08-01

    MFAML is a standard data structure designed for the formal representation and effective exchange of metabolic flux models. It allows for the explicit description of stationary states of a metabolic system by defining environmental/genetic conditions of the system, e.g. flux measurements, balancing constraints and physiological objectives as well as basic information on metabolites and reactions. In addition, a library of MFAML comprising a model parser and a converter provides an open framework for establishing the pipeline from metabolic modeling to metabolic flux analysis. MFAML (version 1) is fully described and available at http://mbel.kaist.ac.kr/mfaml/.

  3. Development and experimental verification of a genome-scale metabolic model for Corynebacterium glutamicum

    Directory of Open Access Journals (Sweden)

    Hirasawa Takashi

    2009-08-01

    Full Text Available Abstract Background In silico genome-scale metabolic models enable the analysis of the characteristics of metabolic systems of organisms. In this study, we reconstructed a genome-scale metabolic model of Corynebacterium glutamicum on the basis of genome sequence annotation and physiological data. The metabolic characteristics were analyzed using flux balance analysis (FBA, and the results of FBA were validated using data from culture experiments performed at different oxygen uptake rates. Results The reconstructed genome-scale metabolic model of C. glutamicum contains 502 reactions and 423 metabolites. We collected the reactions and biomass components from the database and literatures, and made the model available for the flux balance analysis by filling gaps in the reaction networks and removing inadequate loop reactions. Using the framework of FBA and our genome-scale metabolic model, we first simulated the changes in the metabolic flux profiles that occur on changing the oxygen uptake rate. The predicted production yields of carbon dioxide and organic acids agreed well with the experimental data. The metabolic profiles of amino acid production phases were also investigated. A comprehensive gene deletion study was performed in which the effects of gene deletions on metabolic fluxes were simulated; this helped in the identification of several genes whose deletion resulted in an improvement in organic acid production. Conclusion The genome-scale metabolic model provides useful information for the evaluation of the metabolic capabilities and prediction of the metabolic characteristics of C. glutamicum. This can form a basis for the in silico design of C. glutamicum metabolic networks for improved bioproduction of desirable metabolites.

  4. The obese Göttingen minipig as a model of the metabolic syndrome

    DEFF Research Database (Denmark)

    Johansen, T.; Malmlöf, K.; Hansen, Harald S.

    2001-01-01

    The objective of the study reported here was to induce obesity in the female Göttingen minipig to establish a model of the human metabolic syndrome. Nine- to ten-month-old female Göttingen minipigs received a high-fat high-energy (HFE) diet or a low-fat, low-energy (LFE) diet. The energy contents...... of the metabolic impairments seen in obese humans, and may thus serve as a model of the metabolic syndrome....

  5. Metabolic network reconstruction and flux variability analysis of storage synthesis in developing oilseed rape (Brassica napus L.) embryos

    Energy Technology Data Exchange (ETDEWEB)

    Hay, J.; Schwender, J.

    2011-08-01

    Computational simulation of large-scale biochemical networks can be used to analyze and predict the metabolic behavior of an organism, such as a developing seed. Based on the biochemical literature, pathways databases and decision rules defining reaction directionality we reconstructed bna572, a stoichiometric metabolic network model representing Brassica napus seed storage metabolism. In the highly compartmentalized network about 25% of the 572 reactions are transport reactions interconnecting nine subcellular compartments and the environment. According to known physiological capabilities of developing B. napus embryos, four nutritional conditions were defined to simulate heterotrophy or photoheterotrophy, each in combination with the availability of inorganic nitrogen (ammonia, nitrate) or amino acids as nitrogen sources. Based on mathematical linear optimization the optimal solution space was comprehensively explored by flux variability analysis, thereby identifying for each reaction the range of flux values allowable under optimality. The range and variability of flux values was then categorized into flux variability types. Across the four nutritional conditions, approximately 13% of the reactions have variable flux values and 10-11% are substitutable (can be inactive), both indicating metabolic redundancy given, for example, by isoenzymes, subcellular compartmentalization or the presence of alternative pathways. About one-third of the reactions are never used and are associated with pathways that are suboptimal for storage synthesis. Fifty-seven reactions change flux variability type among the different nutritional conditions, indicating their function in metabolic adjustments. This predictive modeling framework allows analysis and quantitative exploration of storage metabolism of a developing B. napus oilseed.

  6. Integration of a constraint-based metabolic model of Brassica napus developing seeds with 13C-metabolic flux analysis

    Science.gov (United States)

    Hay, Jordan O.; Shi, Hai; Heinzel, Nicolas; Hebbelmann, Inga; Rolletschek, Hardy; Schwender, Jorg

    2014-01-01

    The use of large-scale or genome-scale metabolic reconstructions for modeling and simulation of plant metabolism and integration of those models with large-scale omics and experimental flux data is becoming increasingly important in plant metabolic research. Here we report an updated version of bna572, a bottom-up reconstruction of oilseed rape (Brassica napus L.; Brassicaceae) developing seeds with emphasis on representation of biomass-component biosynthesis. New features include additional seed-relevant pathways for isoprenoid, sterol, phenylpropanoid, flavonoid, and choline biosynthesis. Being now based on standardized data formats and procedures for model reconstruction, bna572+ is available as a COBRA-compliant Systems Biology Markup Language (SBML) model and conforms to the Minimum Information Requested in the Annotation of Biochemical Models (MIRIAM) standards for annotation of external data resources. Bna572+ contains 966 genes, 671 reactions, and 666 metabolites distributed among 11 subcellular compartments. It is referenced to the Arabidopsis thaliana genome, with gene-protein-reaction (GPR) associations resolving subcellular localization. Detailed mass and charge balancing and confidence scoring were applied to all reactions. Using B. napus seed specific transcriptome data, expression was verified for 78% of bna572+ genes and 97% of reactions. Alongside bna572+ we also present a revised carbon centric model for 13C-Metabolic Flux Analysis (13C-MFA) with all its reactions being referenced to bna572+ based on linear projections. By integration of flux ratio constraints obtained from 13C-MFA and by elimination of infinite flux bounds around thermodynamically infeasible loops based on COBRA loopless methods, we demonstrate improvements in predictive power of Flux Variability Analysis (FVA). Using this combined approach we characterize the difference in metabolic flux of developing seeds of two B. napus genotypes contrasting in starch and oil content. PMID

  7. Integration of a constraint-based metabolic model of Brassica napus developing seeds with 13C-Metabolic Flux Analysis

    Directory of Open Access Journals (Sweden)

    Jordan eHay

    2014-12-01

    Full Text Available The use of large-scale or genome-scale metabolic reconstructions for modeling and simulation of plant metabolism and integration of those models with large-scale omics and experimental flux data is becoming increasingly important in plant metabolic research. Here we report an updated version of bna572, a bottom-up reconstruction of oilseed rape (Brassica napus L.; Brassicaceae developing seeds with emphasis on representation of biomass-component biosynthesis. New features include additional seed-relevant pathways for isoprenoid, sterol, phenylpropanoid, flavonoid, and choline biosynthesis. Being now based on standardized data formats and procedures for model reconstruction, bna572+ is available as a COBRA-compliant Systems Biology Markup Language (SBML model and conforms to the Minimum Information Requested in the Annotation of Biochemical Models (MIRIAM standards for annotation of external data resources. Bna572+ contains 966 genes, 671 reactions, and 666 metabolites distributed among 11 subcellular compartments. It is referenced to the Arabidopsis thaliana genome, with gene-protein-reaction associations resolving subcellular localization. Detailed mass and charge balancing and confidence scoring were applied to all reactions. Using Brassica napus seed specific transcriptome data, expression was verified for 78% of bna572+ genes and 97% of reactions. Alongside bna572+ we also present a revised carbon centric model for 13C-Metabolic Flux Analysis (13C-MFA with all its reactions being referenced to bna572+ based on linear projections. By integration of flux ratio constraints obtained from 13C-MFA and by elimination of infinite flux bounds around thermodynamically infeasible loops based on COBRA loopless methods, we demonstrate improvements in predictive power of Flux Variability Analysis (FVA. Using this combined approach we characterize the difference in metabolic flux of developing seeds of two Brassica napus genotypes contrasting in starch and

  8. Integration of a constraint-based metabolic model of Brassica napus developing seeds with (13)C-metabolic flux analysis.

    Science.gov (United States)

    Hay, Jordan O; Shi, Hai; Heinzel, Nicolas; Hebbelmann, Inga; Rolletschek, Hardy; Schwender, Jorg

    2014-01-01

    The use of large-scale or genome-scale metabolic reconstructions for modeling and simulation of plant metabolism and integration of those models with large-scale omics and experimental flux data is becoming increasingly important in plant metabolic research. Here we report an updated version of bna572, a bottom-up reconstruction of oilseed rape (Brassica napus L.; Brassicaceae) developing seeds with emphasis on representation of biomass-component biosynthesis. New features include additional seed-relevant pathways for isoprenoid, sterol, phenylpropanoid, flavonoid, and choline biosynthesis. Being now based on standardized data formats and procedures for model reconstruction, bna572+ is available as a COBRA-compliant Systems Biology Markup Language (SBML) model and conforms to the Minimum Information Requested in the Annotation of Biochemical Models (MIRIAM) standards for annotation of external data resources. Bna572+ contains 966 genes, 671 reactions, and 666 metabolites distributed among 11 subcellular compartments. It is referenced to the Arabidopsis thaliana genome, with gene-protein-reaction (GPR) associations resolving subcellular localization. Detailed mass and charge balancing and confidence scoring were applied to all reactions. Using B. napus seed specific transcriptome data, expression was verified for 78% of bna572+ genes and 97% of reactions. Alongside bna572+ we also present a revised carbon centric model for (13)C-Metabolic Flux Analysis ((13)C-MFA) with all its reactions being referenced to bna572+ based on linear projections. By integration of flux ratio constraints obtained from (13)C-MFA and by elimination of infinite flux bounds around thermodynamically infeasible loops based on COBRA loopless methods, we demonstrate improvements in predictive power of Flux Variability Analysis (FVA). Using this combined approach we characterize the difference in metabolic flux of developing seeds of two B. napus genotypes contrasting in starch and oil content.

  9. Metabolic Model-Based Integration of Microbiome Taxonomic and Metabolomic Profiles Elucidates Mechanistic Links between Ecological and Metabolic Variation

    Energy Technology Data Exchange (ETDEWEB)

    Noecker, Cecilia; Eng, Alexander; Srinivasan, Sujatha; Theriot, Casey M.; Young, Vincent B.; Jansson, Janet K.; Fredricks, David N.; Borenstein, Elhanan; Sanchez, Laura M.

    2015-12-22

    ABSTRACT

    Multiple molecular assays now enable high-throughput profiling of the ecology, metabolic capacity, and activity of the human microbiome. However, to date, analyses of such multi-omic data typically focus on statistical associations, often ignoring extensive prior knowledge of the mechanisms linking these various facets of the microbiome. Here, we introduce a comprehensive framework to systematically link variation in metabolomic data with community composition by utilizing taxonomic, genomic, and metabolic information. Specifically, we integrate available and inferred genomic data, metabolic network modeling, and a method for predicting community-wide metabolite turnover to estimate the biosynthetic and degradation potential of a given community. Our framework then compares variation in predicted metabolic potential with variation in measured metabolites’ abundances to evaluate whether community composition can explain observed shifts in the community metabolome, and to identify key taxa and genes contributing to the shifts. Focusing on two independent vaginal microbiome data sets, each pairing 16S community profiling with large-scale metabolomics, we demonstrate that our framework successfully recapitulates observed variation in 37% of metabolites. Well-predicted metabolite variation tends to result from disease-associated metabolism. We further identify several disease-enriched species that contribute significantly to these predictions. Interestingly, our analysis also detects metabolites for which the predicted variation negatively correlates with the measured variation, suggesting environmental control points of community metabolism. Applying this framework to gut microbiome data sets reveals similar trends, including prediction of bile acid metabolite shifts. This framework is an important first step toward a system-level multi-omic integration and an improved mechanistic understanding of the microbiome activity and dynamics in

  10. Considerations on pig models for appetite, metabolic syndrome and obese type 2 diabetes: Form food intake to metabolic disease

    NARCIS (Netherlands)

    Koopmans, S.J.; Schuurman, T.

    2015-01-01

    (Mini)pigs have proven to be a valuable animal model in nutritional, metabolic and cardiovascular research and in some other biomedical research areas (toxicology, neurobiology). The large resemblance of (neuro)anatomy, the gastro-intestinal tract, body size, body composition, and the omnivorous

  11. Metabolic Engineering and Modeling of Metabolic Pathways to Improve Hydrogen Production by Photosynthetic Bacteria

    Energy Technology Data Exchange (ETDEWEB)

    Jiao, Y. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Navid, A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2014-12-19

    traits act as the biocatalysts of the process designed to both enhance the system efficiency of CO2 fixation and the net hydrogen production rate. Additionally we applied metabolic engineering approaches guided by computational modeling for the chosen model microorganisms to enable efficient hydrogen production.

  12. Integration of gene expression data into genome-scale metabolic models

    DEFF Research Database (Denmark)

    Åkesson, M.; Förster, Jochen; Nielsen, Jens

    2004-01-01

    of gene expression from chemostat and batch cultures of Saccharomyces cerevisiae were combined with a recently developed genome-scale model, and the computed metabolic flux distributions were compared to experimental values from carbon labeling experiments and metabolic network analysis. The integration......A framework for integration of transcriptome data into stoichiometric metabolic models to obtain improved flux predictions is presented. The key idea is to exploit the regulatory information in the expression data to give additional constraints on the metabolic fluxes in the model. Measurements...... of expression data resulted in improved predictions of metabolic behavior in batch cultures, enabling quantitative predictions of exchange fluxes as well as qualitative estimations of changes in intracellular fluxes. A critical discussion of correlation between gene expression and metabolic fluxes is given....

  13. Genome-Scale Model Reveals Metabolic Basis of Biomass Partitioning in a Model Diatom.

    Directory of Open Access Journals (Sweden)

    Jennifer Levering

    Full Text Available Diatoms are eukaryotic microalgae that contain genes from various sources, including bacteria and the secondary endosymbiotic host. Due to this unique combination of genes, diatoms are taxonomically and functionally distinct from other algae and vascular plants and confer novel metabolic capabilities. Based on the genome annotation, we performed a genome-scale metabolic network reconstruction for the marine diatom Phaeodactylum tricornutum. Due to their endosymbiotic origin, diatoms possess a complex chloroplast structure which complicates the prediction of subcellular protein localization. Based on previous work we implemented a pipeline that exploits a series of bioinformatics tools to predict protein localization. The manually curated reconstructed metabolic network iLB1027_lipid accounts for 1,027 genes associated with 4,456 reactions and 2,172 metabolites distributed across six compartments. To constrain the genome-scale model, we determined the organism specific biomass composition in terms of lipids, carbohydrates, and proteins using Fourier transform infrared spectrometry. Our simulations indicate the presence of a yet unknown glutamine-ornithine shunt that could be used to transfer reducing equivalents generated by photosynthesis to the mitochondria. The model reflects the known biochemical composition of P. tricornutum in defined culture conditions and enables metabolic engineering strategies to improve the use of P. tricornutum for biotechnological applications.

  14. Genome-Scale Model Reveals Metabolic Basis of Biomass Partitioning in a Model Diatom.

    Science.gov (United States)

    Levering, Jennifer; Broddrick, Jared; Dupont, Christopher L; Peers, Graham; Beeri, Karen; Mayers, Joshua; Gallina, Alessandra A; Allen, Andrew E; Palsson, Bernhard O; Zengler, Karsten

    2016-01-01

    Diatoms are eukaryotic microalgae that contain genes from various sources, including bacteria and the secondary endosymbiotic host. Due to this unique combination of genes, diatoms are taxonomically and functionally distinct from other algae and vascular plants and confer novel metabolic capabilities. Based on the genome annotation, we performed a genome-scale metabolic network reconstruction for the marine diatom Phaeodactylum tricornutum. Due to their endosymbiotic origin, diatoms possess a complex chloroplast structure which complicates the prediction of subcellular protein localization. Based on previous work we implemented a pipeline that exploits a series of bioinformatics tools to predict protein localization. The manually curated reconstructed metabolic network iLB1027_lipid accounts for 1,027 genes associated with 4,456 reactions and 2,172 metabolites distributed across six compartments. To constrain the genome-scale model, we determined the organism specific biomass composition in terms of lipids, carbohydrates, and proteins using Fourier transform infrared spectrometry. Our simulations indicate the presence of a yet unknown glutamine-ornithine shunt that could be used to transfer reducing equivalents generated by photosynthesis to the mitochondria. The model reflects the known biochemical composition of P. tricornutum in defined culture conditions and enables metabolic engineering strategies to improve the use of P. tricornutum for biotechnological applications.

  15. Construction and analysis of the model of energy metabolism in E. coli.

    Science.gov (United States)

    Xu, Zixiang; Sun, Xiao; Sun, Jibin

    2013-01-01

    Genome-scale models of metabolism have only been analyzed with the constraint-based modelling philosophy and there have been several genome-scale gene-protein-reaction models. But research on the modelling for energy metabolism of organisms just began in recent years and research on metabolic weighted complex network are rare in literature. We have made three research based on the complete model of E. coli's energy metabolism. We first constructed a metabolic weighted network using the rates of free energy consumption within metabolic reactions as the weights. We then analyzed some structural characters of the metabolic weighted network that we constructed. We found that the distribution of the weight values was uneven, that most of the weight values were zero while reactions with abstract large weight values were rare and that the relationship between w (weight values) and v (flux values) was not of linear correlation. At last, we have done some research on the equilibrium of free energy for the energy metabolism system of E. coli. We found that E(out) (free energy rate input from the environment) can meet the demand of E(ch)(in) (free energy rate dissipated by chemical process) and that chemical process plays a great role in the dissipation of free energy in cells. By these research and to a certain extend, we can understand more about the energy metabolism of E. coli.

  16. Construction and analysis of the model of energy metabolism in E. coli.

    Directory of Open Access Journals (Sweden)

    Zixiang Xu

    Full Text Available Genome-scale models of metabolism have only been analyzed with the constraint-based modelling philosophy and there have been several genome-scale gene-protein-reaction models. But research on the modelling for energy metabolism of organisms just began in recent years and research on metabolic weighted complex network are rare in literature. We have made three research based on the complete model of E. coli's energy metabolism. We first constructed a metabolic weighted network using the rates of free energy consumption within metabolic reactions as the weights. We then analyzed some structural characters of the metabolic weighted network that we constructed. We found that the distribution of the weight values was uneven, that most of the weight values were zero while reactions with abstract large weight values were rare and that the relationship between w (weight values and v (flux values was not of linear correlation. At last, we have done some research on the equilibrium of free energy for the energy metabolism system of E. coli. We found that E(out (free energy rate input from the environment can meet the demand of E(ch(in (free energy rate dissipated by chemical process and that chemical process plays a great role in the dissipation of free energy in cells. By these research and to a certain extend, we can understand more about the energy metabolism of E. coli.

  17. Modulation in Wistar rats of blood corticosterone compartmentation by sex and a cafeteria diet.

    Directory of Open Access Journals (Sweden)

    María del Mar Romero

    Full Text Available In the metabolic syndrome, glucocorticoid activity is increased, but circulating levels show little change. Most of blood glucocorticoids are bound to corticosteroid-binding globulin (CBG, which liver expression and circulating levels are higher in females than in males. Since blood hormones are also bound to blood cells, and the size of this compartment is considerable for androgens and estrogens, we analyzed whether sex or eating a cafeteria diet altered the compartmentation of corticosterone in rat blood. The main corticosterone compartment in rat blood is that specifically bound to plasma proteins, with smaller compartments bound to blood cells or free. Cafeteria diet increased the expression of liver CBG gene, binding plasma capacity and the proportion of blood cell-bound corticosterone. There were marked sex differences in blood corticosterone compartmentation in rats, which were unrelated to testosterone. The use of a monoclonal antibody ELISA and a polyclonal Western blot for plasma CBG compared with both specific plasma binding of corticosterone and CBG gene expression suggested the existence of different forms of CBG, with varying affinities for corticosterone in males and females, since ELISA data showed higher plasma CBG for males, but binding and Western blot analyses (plus liver gene expression and higher physiological effectiveness for females. Good cross-reactivity to the antigen for polyclonal CBG antibody suggests that in all cases we were measuring CBG. The different immunoreactivity and binding affinity may help explain the marked sex-related differences in plasma hormone binding as sex-linked different proportions of CBG forms.

  18. Kinetics and intracellular compartmentalization of HTLV-1 gene expression: nuclear retention of HBZ mRNAs.

    Science.gov (United States)

    Rende, Francesca; Cavallari, Ilaria; Corradin, Alberto; Silic-Benussi, Micol; Toulza, Frederic; Toffolo, Gianna M; Tanaka, Yuetsu; Jacobson, Steven; Taylor, Graham P; D'Agostino, Donna M; Bangham, Charles R M; Ciminale, Vincenzo

    2011-05-05

    Human T-cell leukemia virus type 1 (HTLV-1) codes for 9 alternatively spliced transcripts and 2 major regulatory proteins named Tax and Rex that function at the transcriptional and posttranscriptional levels, respectively. We investigated the temporal sequence of HTLV-1 gene expression in primary cells from infected patients using splice site-specific quantitative RT-PCR. The results indicated a two-phase kinetics with the tax/rex mRNA preceding expression of other viral transcripts. Analysis of mRNA compartmentalization in cells transfected with HTLV-1 molecular clones demonstrated the strict Rex-dependency of the two-phase kinetics and revealed strong nuclear retention of HBZ mRNAs, supporting their function as noncoding transcripts. Mathematical modeling underscored the importance of a delay between the functions of Tax and Rex, which was supported by experimental evidence of the longer half-life of Rex. These data provide evidence for a temporal pattern of HTLV-1 expression and reveal major differences in the intracellular compartmentalization of HTLV-1 transcripts.

  19. Neonatal Resuscitation Training: Implications of Course Construct and Discipline Compartmentalization on Role Confusion and Role Ambiguity.

    Science.gov (United States)

    Jnah, Amy J; Newberry, Desi M; Trembath, Andrea N; Robertson, Tracey; Downing, April; Greene, Miriam; Sewell, Kerry

    2016-06-01

    The Neonatal Resuscitation Program's (NRP's) Sixth Edition introduced simulation-based training (SBT) into neonatal life support training. SBT offers neonatal emergency response teams a safe, secure environment to rehearse coordinated neonatal resuscitations. Teamwork and communication training can reduce tension and anxiety during neonatal medical emergencies. To discuss the implications of variability in number and type of simulation scenario, number and type of learners who comprise a course, and their influence upon scope of practice, role confusion, and role ambiguity. Relevant articles from MEDLINE, CINAHL, EMBASE, Google Scholar, the World Health Organization, the American Heart Association, and NRP were included in this integrative review of the literature. Purposeful synergy of optimal SBT course construct with teamwork and communication can resist discipline compartmentalization, role confusion, and role ambiguity. Five key themes were identified and coined the "5 Rights" of NRP SBT. These "5 Rights" can guide healthcare institutions with planning, implementation, and evaluation of NRP SBT courses. NRP SBT can facilitate optimal team function and reduce errors when teams of learners and varied scenarios are woven into the course construct. The simulated environment must be realistic and fully equipped to encourage knowledge transfer and attainment of the NRP's key behavioral outcomes. Investigation of teamwork and communication training with NRP SBT, course construct, discipline compartmentalization, and behavioral and clinical outcomes is indicated. Investigation of outcomes of SBT using a team-teaching model, combining basic and advanced practice NRP instructors, is indicated.

  20. Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction

    Science.gov (United States)

    Li, Haiyan; Sun, Jin; Fan, Xiaowen; Sui, Xiaofan; Zhang, Lan; Wang, Yongjun; He, Zhonggui

    2008-11-01

    Quantitative structure-activity relationships (QSAR) methods are urgently needed for predicting ADME/T (absorption, distribution, metabolism, excretion and toxicity) properties to select lead compounds for optimization at the early stage of drug discovery, and to screen drug candidates for clinical trials. Use of suitable QSAR models ultimately results in lesser time-cost and lower attrition rate during drug discovery and development. In the case of ADME/T parameters, drug metabolism is a key determinant of metabolic stability, drug-drug interactions, and drug toxicity. QSAR models for predicting drug metabolism have undergone significant advances recently. However, most of the models used lack sufficient interpretability and offer poor predictability for novel drugs. In this review, we describe some considerations to be taken into account by QSAR for modeling drug metabolism, such as the accuracy/consistency of the entire data set, representation and diversity of the training and test sets, and variable selection. We also describe some novel statistical techniques (ensemble methods, multivariate adaptive regression splines and graph machines), which are not yet used frequently to develop QSAR models for drug metabolism. Subsequently, rational recommendations for developing predictable and interpretable QSAR models are made. Finally, the recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction, including in vivo hepatic clearance, in vitro metabolic stability, inhibitors and substrates of cytochrome P450 families, are briefly summarized.

  1. TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks

    Directory of Open Access Journals (Sweden)

    Jensen Paul A

    2011-09-01

    Full Text Available Abstract Background Several methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation. Many of these methods, such as Flux Balance Analysis, use constrained optimization to predict relationships between metabolic flux and the genes that encode and regulate enzyme activity. Recently, mixed integer programming has been used to encode these gene-protein-reaction (GPR relationships into a single optimization problem, but these techniques are often of limited generality and lack a tool for automating the conversion of rules to a coupled regulatory/metabolic model. Results We present TIGER, a Toolbox for Integrating Genome-scale Metabolism, Expression, and Regulation. TIGER converts a series of generalized, Boolean or multilevel rules into a set of mixed integer inequalities. The package also includes implementations of existing algorithms to integrate high-throughput expression data with genome-scale models of metabolism and transcriptional regulation. We demonstrate how TIGER automates the coupling of a genome-scale metabolic model with GPR logic and models of transcriptional regulation, thereby serving as a platform for algorithm development and large-scale metabolic analysis. Additionally, we demonstrate how TIGER's algorithms can be used to identify inconsistencies and improve existing models of transcriptional regulation with examples from the reconstructed transcriptional regulatory network of Saccharomyces cerevisiae. Conclusion The TIGER package provides a consistent platform for algorithm development and extending existing genome-scale metabolic models with regulatory networks and high-throughput data.

  2. MicrobesFlux: a web platform for drafting metabolic models from the KEGG database.

    Science.gov (United States)

    Feng, Xueyang; Xu, You; Chen, Yixin; Tang, Yinjie J

    2012-08-02

    Concurrent with the efforts currently underway in mapping microbial genomes using high-throughput sequencing methods, systems biologists are building metabolic models to characterize and predict cell metabolisms. One of the key steps in building a metabolic model is using multiple databases to collect and assemble essential information about genome-annotations and the architecture of the metabolic network for a specific organism. To speed up metabolic model development for a large number of microorganisms, we need a user-friendly platform to construct metabolic networks and to perform constraint-based flux balance analysis based on genome databases and experimental results. We have developed a semi-automatic, web-based platform (MicrobesFlux) for generating and reconstructing metabolic models for annotated microorganisms. MicrobesFlux is able to automatically download the metabolic network (including enzymatic reactions and metabolites) of ~1,200 species from the KEGG database (Kyoto Encyclopedia of Genes and Genomes) and then convert it to a metabolic model draft. The platform also provides diverse customized tools, such as gene knockouts and the introduction of heterologous pathways, for users to reconstruct the model network. The reconstructed metabolic network can be formulated to a constraint-based flux model to predict and analyze the carbon fluxes in microbial metabolisms. The simulation results can be exported in the SBML format (The Systems Biology Markup Language). Furthermore, we also demonstrated the platform functionalities by developing an FBA model (including 229 reactions) for a recent annotated bioethanol producer, Thermoanaerobacter sp. strain X514, to predict its biomass growth and ethanol production. MicrobesFlux is an installation-free and open-source platform that enables biologists without prior programming knowledge to develop metabolic models for annotated microorganisms in the KEGG database. Our system facilitates users to reconstruct

  3. MicrobesFlux: a web platform for drafting metabolic models from the KEGG database

    Directory of Open Access Journals (Sweden)

    Feng Xueyang

    2012-08-01

    Full Text Available Abstract Background Concurrent with the efforts currently underway in mapping microbial genomes using high-throughput sequencing methods, systems biologists are building metabolic models to characterize and predict cell metabolisms. One of the key steps in building a metabolic model is using multiple databases to collect and assemble essential information about genome-annotations and the architecture of the metabolic network for a specific organism. To speed up metabolic model development for a large number of microorganisms, we need a user-friendly platform to construct metabolic networks and to perform constraint-based flux balance analysis based on genome databases and experimental results. Results We have developed a semi-automatic, web-based platform (MicrobesFlux for generating and reconstructing metabolic models for annotated microorganisms. MicrobesFlux is able to automatically download the metabolic network (including enzymatic reactions and metabolites of ~1,200 species from the KEGG database (Kyoto Encyclopedia of Genes and Genomes and then convert it to a metabolic model draft. The platform also provides diverse customized tools, such as gene knockouts and the introduction of heterologous pathways, for users to reconstruct the model network. The reconstructed metabolic network can be formulated to a constraint-based flux model to predict and analyze the carbon fluxes in microbial metabolisms. The simulation results can be exported in the SBML format (The Systems Biology Markup Language. Furthermore, we also demonstrated the platform functionalities by developing an FBA model (including 229 reactions for a recent annotated bioethanol producer, Thermoanaerobacter sp. strain X514, to predict its biomass growth and ethanol production. Conclusion MicrobesFlux is an installation-free and open-source platform that enables biologists without prior programming knowledge to develop metabolic models for annotated microorganisms in the KEGG

  4. The iDuo Bi-compartmental Knee Replacement: Our Early Experience.

    Directory of Open Access Journals (Sweden)

    Peter Jemmett

    2016-12-01

    Our early results suggest that the iDuo knee is a good option for those with isolated bi-compartmental disease and outcome scores are comparable with those reported for the BKA. This bi-compartmental design may bridge the gap between the uni-compartmental and total knee replacement. The choice between monolithic or modular designs remains in debate. We will continue to use this prosthesis for a carefully selected group of patients.

  5. Effects of Microbial Metabolic Lag in Contaminant Transport and Biodegradation Modeling

    Science.gov (United States)

    Wood, Brian D.; Ginn, Timothy R.; Dawson, Clint N.

    1995-03-01

    A model is introduced for microbial kinetics in porous media that includes effects of transients in the metabolic activity of subsurface microorganisms. The model represents the microbial metabolic activity as a functional of the history of aqueous phase substrates; this dependence is represented as a temporally nonlocal convolution integral. Conceptually, this convolution represents the activity of a microbial component as a fraction of its maximum activity, and it is conventionally known as the metabolic potential. The metabolic potential is used to scale the kinetic expressions to account for the metabolic state of the organisms and allows the representation of delayed response in the microbial kinetic equations. Calculation of the convolution requires the definition of a memory (or kernel) function that upon integration over the substrate history represents the microbial metabolic response. A simple piecewise-linear metabolic potential functional is developed here; however, the approach can be generalized to fit the observed behavior of specific systems of interest. The convolution that results from the general form of this model is nonlinear; these nonlinearities are handled by using two separate memory functions and by scaling the domains of the convolution integrals. The model is applied to describe the aerobic degradation of benzene in saturated porous media. Comparative simulations show that metabolic lag can be used to consistently describe observations and that a convolution form can effectively represent microbial lag for this system. Simulations also show that disregarding metabolic lag when it exists can lead to overestimation of the amount of substrate degraded.

  6. Fractional kinetics of compartmental systems: first approach with use digraph-based method

    Science.gov (United States)

    Markowski, Konrad Andrzej

    2017-08-01

    In the last two decades, integral and differential calculus of a fractional order has become a subject of great interest in different areas of physics, biology, economics and other sciences. The idea of such a generalization was mentioned in 1695 by Leibniz and L'Hospital. The first definition of the fractional derivative was introduced by Liouville and Riemann at the end of the 19th century. Fractional calculus was found to be a very useful tool for modelling the behaviour of many materials and systems. In this paper fractional calculus was applied to pharmacokinetic compartmental model. For introduced model determine all possible quasi-positive realisation based on one-dimensional digraph theory. The proposed method was discussed and illustrated in detail with some numerical examples.

  7. Fermented Red Ginseng Potentiates Improvement of Metabolic Dysfunction in Metabolic Syndrome Rat Models

    Directory of Open Access Journals (Sweden)

    Min Chul Kho

    2016-06-01

    Full Text Available Metabolic syndrome including obesity, dyslipidemia and hypertension is a cluster of risk factors of cardiovascular disease. Fermentation of medicinal herbs improves their pharmacological efficacy. Red ginseng (RG, a widely used traditional herbal medicine, was reported with anti-inflammatory and anti-oxidant activity. Aim in the present study was to investigate that the effects of fermented red ginseng (FRG on a high-fructose (HF diet induced metabolic disorders, and those effects were compared to RG and losartan. Animals were divided into four groups: a control group fed a regular diet and tap water, and fructose groups that were fed a 60% high-fructose (HF diet with/without RG 250 mg/kg/day or FRG 250 mg/kg/day for eight weeks, respectively. Treatment with FRG significantly suppressed the increments of body weight, liver weight, epididymal fat weight and adipocyte size. Moreover, FRG significantly prevented the development of metabolic disturbances such as hyperlipidemia and hypertension. Staining with Oil-red-o demonstrated a marked increase of hepatic accumulation of triglycerides, and this increase was prevented by FRG. FRG ameliorated endothelial dysfunction by downregulation of endothelin-1 (ET-1 and adhesion molecules in the aorta. In addition, FRG induced markedly upregulation of Insulin receptor substrate 1 (IRS-1 and glucose transporter type 4 (Glut4 in the muscle. These results indicate that FRG ameliorates obesity, dyslipidemia, hypertension and fatty liver in HF diet rats. More favorable pharmacological effects on HF diet induced metabolic disorders were observed with FRG, compared to an equal dose of RG. These results showed that the pharmacological activity of RG was enhanced by fermentation. Taken together, fermentated red ginseng might be a beneficial therapeutic approach for metabolic syndrome.

  8. Septins and the lateral compartmentalization of eukaryotic membranes.

    Science.gov (United States)

    Caudron, Fabrice; Barral, Yves

    2009-04-01

    Eukaryotic cells from neurons and epithelial cells to unicellular fungi frequently rely on cellular appendages such as axons, dendritic spines, cilia, and buds for their biology. The emergence and differentiation of these appendages depend on the formation of lateral diffusion barriers at their bases to insulate their membranes from the rest of the cell. Here, we review recent progress regarding the molecular mechanisms and functions of such barriers. This overview underlines the importance and conservation of septin-dependent diffusion barriers, which coordinately compartmentalize both plasmatic and internal membranes. We discuss their role in memory establishment and the control of cellular aging.

  9. Human memory T cells: generation, compartmentalization and homeostasis.

    Science.gov (United States)

    Farber, Donna L; Yudanin, Naomi A; Restifo, Nicholas P

    2014-01-01

    Memory T cells constitute the most abundant lymphocyte population in the body for the majority of a person's lifetime; however, our understanding of memory T cell generation, function and maintenance mainly derives from mouse studies, which cannot recapitulate the exposure to multiple pathogens that occurs over many decades in humans. In this Review, we discuss studies focused on human memory T cells that reveal key properties of these cells, including subset heterogeneity and diverse tissue residence in multiple mucosal and lymphoid tissue sites. We also review how the function and the adaptability of human memory T cells depend on spatial and temporal compartmentalization.

  10. GENOME-BASED MODELING AND DESIGN OF METABOLIC INTERACTIONS IN MICROBIAL COMMUNITIES

    Directory of Open Access Journals (Sweden)

    Radhakrishnan Mahadevan

    2012-10-01

    With the advent of genome sequencing, omics technologies, bioinformatics and genome-scale modeling, researchers now have unprecedented capabilities to analyze and engineer the metabolism of microbial communities. The goal of this review is to summarize recent applications of genome-scale metabolic modeling to microbial communities. A brief introduction to lumped community models is used to motivate the need for genome-level descriptions of individual species and their metabolic interactions. The review of genome-scale models begins with static modeling approaches, which are appropriate for communities where the extracellular environment can be assumed to be time invariant or slowly varying. Dynamic extensions of the static modeling approach are described, and then applications of genome-scale models for design of synthetic microbial communities are reviewed. The review concludes with a summary of metagenomic tools for analyzing community metabolism and an outlook for future research.

  11. In silico analysis of human metabolism: Reconstruction, contextualization and application of genome-scale models

    DEFF Research Database (Denmark)

    Geng, Jun; Nielsen, Jens

    2017-01-01

    framework for uncovering the mechanistic relationship between genotype and phenotype. GEMs hereby enable uncovering associations between cellular physiology and pathology in a systematic manner. Here we review the current progress on reconstruction of human GEMs and how they have been employed as scaffold......The arising prevalence of metabolic diseases calls for a holistic approach for analysis of the underlying nature of abnormalities in cellular functions. Through mathematic representation and topological analysis of cellular metabolism, GEnome scale metabolic Models (GEMs) provide a promising...

  12. Characterizing the metabolism of Dehalococcoides with a constraint-based model.

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    M Ahsanul Islam

    Full Text Available Dehalococcoides strains respire a wide variety of chloro-organic compounds and are important for the bioremediation of toxic, persistent, carcinogenic, and ubiquitous ground water pollutants. In order to better understand metabolism and optimize their application, we have developed a pan-genome-scale metabolic network and constraint-based metabolic model of Dehalococcoides. The pan-genome was constructed from publicly available complete genome sequences of Dehalococcoides sp. strain CBDB1, strain 195, strain BAV1, and strain VS. We found that Dehalococcoides pan-genome consisted of 1118 core genes (shared by all, 457 dispensable genes (shared by some, and 486 unique genes (found in only one genome. The model included 549 metabolic genes that encoded 356 proteins catalyzing 497 gene-associated model reactions. Of these 497 reactions, 477 were associated with core metabolic genes, 18 with dispensable genes, and 2 with unique genes. This study, in addition to analyzing the metabolism of an environmentally important phylogenetic group on a pan-genome scale, provides valuable insights into Dehalococcoides metabolic limitations, low growth yields, and energy conservation. The model also provides a framework to anchor and compare disparate experimental data, as well as to give insights on the physiological impact of "incomplete" pathways, such as the TCA-cycle, CO(2 fixation, and cobalamin biosynthesis pathways. The model, referred to as iAI549, highlights the specialized and highly conserved nature of Dehalococcoides metabolism, and suggests that evolution of Dehalococcoides species is driven by the electron acceptor availability.

  13. A novel ex vivo method for measuring whole brain metabolism in model systems.

    Science.gov (United States)

    Neville, Kathryn E; Bosse, Timothy L; Klekos, Mia; Mills, John F; Weicksel, Steven E; Waters, James S; Tipping, Marla

    2018-02-15

    Many neuronal and glial diseases have been associated with changes in metabolism. Therefore, metabolic reprogramming has become an important area of research to better understand disease at the cellular level, as well as to identify targets for treatment. Model systems are ideal for interrogating metabolic questions in a tissue dependent context. However, while new tools have been developed to study metabolism in cultured cells there has been less progress towards studies in vivo and ex vivo. We have developed a method using newly designed tissue restraints to adapt the Agilent XFe96 metabolic analyzer for whole brain analysis. These restraints create a chamber for Drosophila brains and other small model system tissues to reside undisrupted, while still remaining in the zone for measurements by sensor probes. This method generates reproducible oxygen consumption and extracellular acidification rate data for Drosophila larval and adult brains. Single brains are effectively treated with inhibitors and expected metabolic readings are observed. Measuring metabolic changes, such as glycolytic rate, in transgenic larval brains demonstrates the potential for studying how genotype affects metabolism. Current methodology either utilizes whole animal chambers to measure respiration, not allowing for targeted tissue analysis, or uses technically challenging MRI technology for in vivo analysis that is not suitable for smaller model systems. This new method allows for novel metabolic investigation of intact brains and other tissues ex vivo in a quick, and simplistic way with the potential for large-scale studies. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  14. Visualization of local phosphatidylcholine synthesis within hippocampal neurons using a compartmentalized culture system and imaging mass spectrometry.

    Science.gov (United States)

    Sugiyama, Eiji; Yao, Ikuko; Setou, Mitsutoshi

    2018-01-01

    Neurons extend neurites with an increased synthesis of phosphatidylcholine (PC) that is not only a membrane component but also a functional regulator with specific fatty acid composition. To analyze the local synthesis of the PC molecular species within neurons, we combined a compartmentalized culture system with matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS). We observed that a newly synthesized PC, which contains exogenously administered palmitic acid-d3, is accumulated at the cell bodies and the tips of the distal neurites. The local accumulation within distal neurites is formed by distinct metabolic activity from cell bodies, suggesting that the local extracellular composition of free fatty acid can be a key to regulate specific functions of each PC molecular species. We expect our simple method to be a starting point for more sophisticated in vitro analytical methods for unveiling detailed lipid metabolisms within neurons. Copyright © 2017 Elsevier Inc. All rights reserved.

  15. Mapping condition-dependent regulation of metabolism in yeast through genome-scale modeling

    DEFF Research Database (Denmark)

    Österlund, Tobias; Nookaew, Intawat; Bordel, Sergio

    2013-01-01

    ABSTRACT: BACKGROUND: The genome-scale metabolic model of Saccharomyces cerevisiae, first presented in 2003, was the first genome-scale network reconstruction for a eukaryotic organism. Since then continuous efforts have been made in order to improve and expand the yeast metabolic network. RESULTS......-filling methods and by introducing new reactions and pathways based on studies of the literature and databases. The model was shown to perform well both for growth simulations in different media and gene essentiality analysis for single and double knock-outs. Further, the model was used as a scaffold......-to-date collection of knowledge on yeast metabolism. The model was used for simulating the yeast metabolism under four different growth conditions and experimental data from these four conditions was integrated to the model. The model together with experimental data is a useful tool to identify condition...

  16. MDCT of hand and wrist infections: emphasis on compartmental anatomy.

    Science.gov (United States)

    Ahlawat, S; Corl, F M; LaPorte, D M; Fishman, E K; Fayad, L M

    2017-04-01

    Hand and wrist infections can present with a spectrum of manifestations ranging from cellulitis to deep-space collections. The various infectious processes can be categorised as superficial or deep infections based on their respective locations relative to the tendons. Superficial hand infections are located superficial to the tendons and are comprised of cellulitis, lymphangitis, paronychia, pulp-space infections, herpetic whitlow, and include volar as well as dorsal subcutaneous abscesses. Deep hand infections are located deep to the tendon sheaths and include synovial space infections, such as infectious tenosynovitis, deep fascial space infections, septic arthritis, necrotising fasciitis, and osteomyelitis. Knowledge of hand and wrist compartmental anatomy is essential for the accurate diagnosis and management of hand infections. Although early and superficial infections of the hand may respond to non-surgical management, most hand infections are surgical emergencies. Multidetector computed tomography (MDCT), with its muliplanar reformation (MPR) and three-dimensional (3D) capabilities, is a powerful tool in the emergency setting for the evaluation of acute hand and wrist pathology. The clinical and imaging features of hand and wrist infections as evident on MDCT will be reviewed with emphasis on contiguous and closed synovial and deep fascial spaces. Knowledge of hand compartmental anatomy enables accurate characterisation of the infectious process and localise the extent of disease in the acute setting. Copyright © 2016 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

  17. Lower extremity compartmental anatomy: clinical relevance to radiologists

    Energy Technology Data Exchange (ETDEWEB)

    Toomayan, Glen A.; Robertson, Fabienne; Major, Nancy M. [Duke University Medical Center, Department of Radiology, Durham (United States)

    2005-06-01

    A thorough understanding of compartmental anatomy is necessary for the radiologist participating in the care of a patient with a lower extremity musculoskeletal malignancy. Localization of tumor to compartment of origin and identification of extracompartmental spread preoperatively are needed to correctly stage a tumor and determine the appropriate surgical management. An understanding of the locations of fascial boundaries, extracompartmental tissues, and neurovascular structures of the thigh and lower leg facilitates this diagnostic process. For the radiologist planning to biopsy a suspicious musculoskeletal lesion, consultation with the referring orthopaedic surgeon is recommended in order to jointly select an appropriate percutaneous biopsy approach. Adequate preprocedural planning ensures selection of an approach which prevents iatrogenic tumor spread beyond the compartment of origin, protects neurovascular structures, and allows complete resection of the biopsy tract and scar at the time of surgical resection without jeopardizing a potential limb-sparing procedure. Cross-sectional anatomic review and case examples demonstrate the importance of a detailed understanding of compartmental anatomy when approaching the patient with a lower extremity musculoskeletal tumor. (orig.)

  18. Insights into the metabolic response to traumatic brain injury as revealed by 13C NMR spectroscopy.

    Directory of Open Access Journals (Sweden)

    Brenda eBartnik-Olson

    2013-10-01

    Full Text Available The present review highlights critical issues related to cerebral metabolism following traumatic brain injury (TBI and the use of 13C labeled substrates and nuclear magnetic resonance (NMR spectroscopy to study these changes. First we address some pathophysiologic factors contributing to metabolic dysfunction following TBI. We then examine how 13C NMR spectroscopy strategies have been used to investigate energy metabolism, neurotransmission, the intracellular redox state, and neuroglial compartmentation following injury. 13C NMR spectroscopy studies of brain extracts from animal models of TBI have revealed enhanced glycolytic production of lactate, evidence of pentose phosphate pathway (PPP activation, and alterations in neuronal and astrocyte oxidative metabolism that are dependent on injury severity. Differential incorporation of label into glutamate and glutamine from 13C labeled glucose or acetate also suggest TBI-induced adaptations to the glutamate-glutamine cycle.

  19. AlgaGEM--a genome-scale metabolic reconstruction of algae based on the Chlamydomonas reinhardtii genome.

    Science.gov (United States)

    Dal'Molin, Cristiana Gomes de Oliveira; Quek, Lake-Ee; Palfreyman, Robin W; Nielsen, Lars K

    2011-12-22

    Microalgae have the potential to deliver biofuels without the associated competition for land resources. In order to realise the rates and titres necessary for commercial production, however, system-level metabolic engineering will be required. Genome scale metabolic reconstructions have revolutionized microbial metabolic engineering and are used routinely for in silico analysis and design. While genome scale metabolic reconstructions have been developed for many prokaryotes and model eukaryotes, the application to less well characterized eukaryotes such as algae is challenging not at least due to a lack of compartmentalization data. We have developed a genome-scale metabolic network model (named AlgaGEM) covering the metabolism for a compartmentalized algae cell based on the Chlamydomonas reinhardtii genome. AlgaGEM is a comprehensive literature-based genome scale metabolic reconstruction that accounts for the functions of 866 unique ORFs, 1862 metabolites, 2249 gene-enzyme-reaction-association entries, and 1725 unique reactions. The reconstruction was compartmentalized into the cytoplasm, mitochondrion, plastid and microbody using available data for algae complemented with compartmentalisation data for Arabidopsis thaliana. AlgaGEM describes a functional primary metabolism of Chlamydomonas and significantly predicts distinct algal behaviours such as the catabolism or secretion rather than recycling of phosphoglycolate in photorespiration. AlgaGEM was validated through the simulation of growth and algae metabolic functions inferred from literature. Using efficient resource utilisation as the optimality criterion, AlgaGEM predicted observed metabolic effects under autotrophic, heterotrophic and mixotrophic conditions. AlgaGEM predicts increased hydrogen production when cyclic electron flow is disrupted as seen in a high producing mutant derived from mutational studies. The model also predicted the physiological pathway for H2 production and identified new targets

  20. A genome-scale metabolic model of the lipid-accumulating yeast Yarrowia lipolytica

    Directory of Open Access Journals (Sweden)

    Loira Nicolas

    2012-05-01

    Full Text Available Abstract Background Yarrowia lipolytica is an oleaginous yeast which has emerged as an important microorganism for several biotechnological processes, such as the production of organic acids, lipases and proteases. It is also considered a good candidate for single-cell oil production. Although some of its metabolic pathways are well studied, its metabolic engineering is hindered by the lack of a genome-scale model that integrates the current knowledge about its metabolism. Results Combining in silico tools and expert manual curation, we have produced an accurate genome-scale metabolic model for Y. lipolytica. Using a scaffold derived from a functional metabolic model of the well-studied but phylogenetically distant yeast S. cerevisiae, we mapped conserved reactions, rewrote gene associations, added species-specific reactions and inserted specialized copies of scaffold reactions to account for species-specific expansion of protein families. We used physiological measures obtained under lab conditions to validate our predictions. Conclusions Y. lipolytica iNL895 represents the first well-annotated metabolic model of an oleaginous yeast, providing a base for future metabolic improvement, and a starting point for the metabolic reconstruction of other species in the Yarrowia clade and other oleaginous yeasts.

  1. Mathematical Modeling and Dynamic Simulation of Metabolic Reaction Systems Using Metabolome Time Series Data

    Directory of Open Access Journals (Sweden)

    Kansuporn eSriyudthsak

    2016-05-01

    Full Text Available The high-throughput acquisition of metabolome data is greatly anticipated for the complete understanding of cellular metabolism in living organisms. A variety of analytical technologies have been developed to acquire large-scale metabolic profiles under different biological or environmental conditions. Time series data are useful for predicting the most likely metabolic pathways because they provide important information regarding the accumulation of metabolites, which implies causal relationships in the metabolic reaction network. Considerable effort has been undertaken to utilize these data for constructing a mathematical model merging system properties and quantitatively characterizing a whole metabolic system in toto. However, there are technical difficulties between benchmarking the provision and utilization of data. Although hundreds of metabolites can be measured, which provide information on the metabolic reaction system, simultaneous measurement of thousands of metabolites is still challenging. In addition, it is nontrivial to logically predict the dynamic behaviors of unmeasurable metabolite concentrations without sufficient information on the metabolic reaction network. Yet, consolidating the advantages of advancements in both metabolomics and mathematical modeling remain to be accomplished. This review outlines the conceptual basis of and recent advances in technologies in both the research fields. It also highlights the potential for constructing a large-scale mathematical model by estimating model parameters from time series metabolome data in order to comprehensively understand metabolism at the systems level.

  2. Mathematical Modeling and Dynamic Simulation of Metabolic Reaction Systems Using Metabolome Time Series Data.

    Science.gov (United States)

    Sriyudthsak, Kansuporn; Shiraishi, Fumihide; Hirai, Masami Yokota

    2016-01-01

    The high-throughput acquisition of metabolome data is greatly anticipated for the complete understanding of cellular metabolism in living organisms. A variety of analytical technologies have been developed to acquire large-scale metabolic profiles under different biological or environmental conditions. Time series data are useful for predicting the most likely metabolic pathways because they provide important information regarding the accumulation of metabolites, which implies causal relationships in the metabolic reaction network. Considerable effort has been undertaken to utilize these data for constructing a mathematical model merging system properties and quantitatively characterizing a whole metabolic system in toto. However, there are technical difficulties between benchmarking the provision and utilization of data. Although, hundreds of metabolites can be measured, which provide information on the metabolic reaction system, simultaneous measurement of thousands of metabolites is still challenging. In addition, it is nontrivial to logically predict the dynamic behaviors of unmeasurable metabolite concentrations without sufficient information on the metabolic reaction network. Yet, consolidating the advantages of advancements in both metabolomics and mathematical modeling remain to be accomplished. This review outlines the conceptual basis of and recent advances in technologies in both the research fields. It also highlights the potential for constructing a large-scale mathematical model by estimating model parameters from time series metabolome data in order to comprehensively understand metabolism at the systems level.

  3. Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints

    DEFF Research Database (Denmark)

    Sanchez, Benjamin J.; Zhang, Xi-Cheng; Nilsson, Avlant

    2017-01-01

    Genome-scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics......, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance...

  4. Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation

    Energy Technology Data Exchange (ETDEWEB)

    Bordbar, Aarash; Mo, Monica L.; Nakayasu, Ernesto S.; Rutledge, Alexandra C.; Kim, Young-Mo; Metz, Thomas O.; Jones, Marcus B.; Frank, Bryan C.; Smith, Richard D.; Peterson, Scott N.; Hyduke, Daniel R.; Adkins, Joshua N.; Palsson, Bernhard O.

    2012-06-26

    Macrophages are central players in the immune response, manifesting divergent phenotypes to control inflammation and innate immunity through the release of cytokines and other regulatory factor-dependent signaling pathways. In recent years, the focus on metabolism has been reemphasized as critical signaling and regulatory pathways of human pathophysiology, ranging from cancer to aging, often converge on metabolic responses. Here, we used genome-scale modeling and multi-omics (transcriptomics, proteomics, and metabolomics) analysis to assess metabolic features critical for macrophage functions. We constructed a genome-scale metabolic network for the RAW 264.7 cell line to determine metabolic modulators of macrophage activation. Metabolites well-known to be associated with immunoactivation (e.g., glucose and arginine) and immunosuppression (e.g., tryptophan and vitamin D3) were amongst the most critical effectors. Intracellular metabolic mechanisms linked to critical suppressive effectors were then assessed, identifying a suppressive role for de novo nucleotide synthesis. Finally, the underlying metabolic mechanisms of macrophage activation are identified by analyzing multi-omic data obtained from LPS-stimulated RAW cells in the context of our flux-based predictions. Our study demonstrates metabolism's role in regulating activation may be greater than previously anticipated and elucidates underlying metabolic connections between activation and metabolic effectors.

  5. Computational Modelling of the Metabolic States Regulated by the Kinase Akt

    Directory of Open Access Journals (Sweden)

    Ettore eMosca

    2012-11-01

    Full Text Available Signal transduction pathways and gene regulation determine a major reorganization of metabolic activities in order to support cell proliferation. Protein Kinase B (PKB, also known as Akt, participates in the PI3K/Akt/mTOR pathway, a master regulator of aerobic glycolysis and cellular biosynthesis, two activities shown by both normal and cancer proliferating cells. Not surprisingly considering its relevance for cellular metabolism, Akt/PKB is often found hyperactive in cancer cells. In the last decade, many efforts have been made to improve the understanding of the control of glucose metabolism and the identification of a therapeutic window between proliferating cancer cells and proliferating normal cells. In this context, we have modelled the link between the PI3K/Akt/mTOR pathway, glycolysis, lactic acid production and nucleotide biosynthesis. We used a computational model in order to compare two metabolic states generated by the specific variation of the metabolic fluxes regulated by the activity of the PI3K/Akt/mTOR pathway. One of the two states represented the metabolism of a growing cancer cell characterised by aerobic glycolysis and cellular biosynthesis, while the other state represented the same metabolic network with a reduced glycolytic rate and a higher mitochondrial pyruvate metabolism, as reported in literature in relation to the activity of the PI3K/Akt/mTOR. Some steps that link glycolysis and pentose phosphate pathway revealed their importance for controlling the dynamics of cancer glucose metabolism.

  6. Combining Targeted Metabolomic Data with a Model of Glucose Metabolism: Toward Progress in Chondrocyte Mechanotransduction.

    Science.gov (United States)

    Salinas, Daniel; Minor, Cody A; Carlson, Ross P; McCutchen, Carley N; Mumey, Brendan M; June, Ronald K

    2017-01-01

    Osteoarthritis is a debilitating disease likely involving altered metabolism of the chondrocytes in articular cartilage. Chondrocytes can respond metabolically to mechanical loads via cellular mechanotransduction, and metabolic changes are significant because they produce the precursors to the tissue matrix necessary for cartilage health. However, a comprehensive understanding of how energy metabolism changes with loading remains elusive. To improve our understanding of chondrocyte mechanotransduction, we developed a computational model to calculate the rate of reactions (i.e. flux) across multiple components of central energy metabolism based on experimental data. We calculated average reaction flux profiles of central metabolism for SW1353 human chondrocytes subjected to dynamic compression for 30 minutes. The profiles were obtained solving a bounded variable linear least squares problem, representing the stoichiometry of human central energy metabolism. Compression synchronized chondrocyte energy metabolism. These data are consistent with dynamic compression inducing early time changes in central energy metabolism geared towards more active protein synthesis. Furthermore, this analysis demonstrates the utility of combining targeted metabolomic data with a computational model to enable rapid analysis of cellular energy utilization.

  7. Case Based Metabolic Unit (CBMU: A Model for Better Understanding of Metabolic Pathways in the Second Year Extended Modular Program Medical Students

    Directory of Open Access Journals (Sweden)

    Sanaa Eissa

    2016-12-01

    Full Text Available We designed a case based metabolic unit (CBMU which is a  hybrid learning model using case based learning and interactive lectures for 2nd year EMP medical students (n:113 who firstly attended blood module (Classical model in learning metabolism and then locomotor module (CBMU. At the end of both modules, they were evaluated through MCQ exams and the results of both exams were compared. The Majority of students (97-100% opined that case based metabolic unit learning model was interesting, motivating, and better than the classical model in understanding metabolic pathways and making learning stick. 99% of them demanded the application of this learning model in future biochemistry courses. Improvement in student’s exam scores ensures that they grasped the knowledge by this model. In conclusion, CBMU is a useful learning tool for metabolic pathways using human cases to aid in connecting theory to practice which is positively reflected on student’s academic performance.

  8. Generation and Evaluation of a Genome-Scale Metabolic Network Model of Synechococcus elongatus PCC7942

    Directory of Open Access Journals (Sweden)

    Julián Triana

    2014-08-01

    Full Text Available The reconstruction of genome-scale metabolic models and their applications represent a great advantage of systems biology. Through their use as metabolic flux simulation models, production of industrially-interesting metabolites can be predicted. Due to the growing number of studies of metabolic models driven by the increasing genomic sequencing projects, it is important to conceptualize steps of reconstruction and analysis. We have focused our work in the cyanobacterium Synechococcus elongatus PCC7942, for which several analyses and insights are unveiled. A comprehensive approach has been used, which can be of interest to lead the process of manual curation and genome-scale metabolic analysis. The final model, iSyf715 includes 851 reactions and 838 metabolites. A biomass equation, which encompasses elementary building blocks to allow cell growth, is also included. The applicability of the model is finally demonstrated by simulating autotrophic growth conditions of Synechococcus elongatus PCC7942.

  9. Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production.

    Directory of Open Access Journals (Sweden)

    Caroline Colijn

    2009-08-01

    Full Text Available Metabolism is central to cell physiology, and metabolic disturbances play a role in numerous disease states. Despite its importance, the ability to study metabolism at a global scale using genomic technologies is limited. In principle, complete genome sequences describe the range of metabolic reactions that are possible for an organism, but cannot quantitatively describe the behaviour of these reactions. We present a novel method for modeling metabolic states using whole cell measurements of gene expression. Our method, which we call E-Flux (as a combination of flux and expression, extends the technique of Flux Balance Analysis by modeling maximum flux constraints as a function of measured gene expression. In contrast to previous methods for metabolically interpreting gene expression data, E-Flux utilizes a model of the underlying metabolic network to directly predict changes in metabolic flux capacity. We applied E-Flux to Mycobacterium tuberculosis, the bacterium that causes tuberculosis (TB. Key components of mycobacterial cell walls are mycolic acids which are targets for several first-line TB drugs. We used E-Flux to predict the impact of 75 different drugs, drug combinations, and nutrient conditions on mycolic acid biosynthesis capacity in M. tuberculosis, using a public compendium of over 400 expression arrays. We tested our method using a model of mycolic acid biosynthesis as well as on a genome-scale model of M. tuberculosis metabolism. Our method correctly predicts seven of the eight known fatty acid inhibitors in this compendium and makes accurate predictions regarding the specificity of these compounds for fatty acid biosynthesis. Our method also predicts a number of additional potential modulators of TB mycolic acid biosynthesis. E-Flux thus provides a promising new approach for algorithmically predicting metabolic state from gene expression data.

  10. Mathematical modeling of the human energy metabolism based on the Selfish Brain Theory.

    Science.gov (United States)

    Chung, Matthias; Göbel, Britta

    2012-01-01

    Deregulations in the human energy metabolism may cause diseases such as obesity and type 2 diabetes mellitus. The origins of these pathologies are fairly unknown. The key role of the brain is the regulation of the complex whole body energy metabolism. The Selfish Brain Theory identifies the priority of brain energy supply in the competition for available energy resources within the organism. Here, we review mathematical models of the human energy metabolism supporting central aspects of the Selfish Brain Theory. First, we present a dynamical system modeling the whole body energy metabolism. This model takes into account the two central control mechanisms of the brain, i.e., allocation and appetite. Moreover, we present mathematical models of regulatory subsystems. We examine a neuronal model which specifies potential elements of the brain to sense and regulate cerebral energy content. We investigate a model of the HPA system regulating the allocation of energy within the organism. Finally, we present a robust modeling approach of appetite regulation. All models account for a systemic understanding of the human energy metabolism and thus do shed light onto defects causing metabolic diseases.

  11. Dynamic metabolic modeling of heterotrophic and mixotrophic microalgal growth on fermentative wastes.

    Directory of Open Access Journals (Sweden)

    Caroline Baroukh

    2017-06-01

    Full Text Available Microalgae are promising microorganisms for the production of numerous molecules of interest, such as pigments, proteins or triglycerides that can be turned into biofuels. Heterotrophic or mixotrophic growth on fermentative wastes represents an interesting approach to achieving higher biomass concentrations, while reducing cost and improving the environmental footprint. Fermentative wastes generally consist of a blend of diverse molecules and it is thus crucial to understand microalgal metabolism in such conditions, where switching between substrates might occur. Metabolic modeling has proven to be an efficient tool for understanding metabolism and guiding the optimization of biomass or target molecule production. Here, we focused on the metabolism of Chlorella sorokiniana growing heterotrophically and mixotrophically on acetate and butyrate. The metabolism was represented by 172 metabolic reactions. The DRUM modeling framework with a mildly relaxed quasi-steady-state assumption was used to account for the switching between substrates and the presence of light. Nine experiments were used to calibrate the model and nine experiments for the validation. The model efficiently predicted the experimental data, including the transient behavior during heterotrophic, autotrophic, mixotrophic and diauxic growth. It shows that an accurate model of metabolism can now be constructed, even in dynamic conditions, with the presence of several carbon substrates. It also opens new perspectives for the heterotrophic and mixotrophic use of microalgae, especially for biofuel production from wastes.

  12. C4GEM, a Genome-Scale Metabolic Model to Study C4 Plant Metabolism1[W][OA

    Science.gov (United States)

    de Oliveira Dal’Molin, Cristiana Gomes; Quek, Lake-Ee; Palfreyman, Robin William; Brumbley, Stevens Michael; Nielsen, Lars Keld

    2010-01-01

    Leaves of C4 grasses (such as maize [Zea mays], sugarcane [Saccharum officinarum], and sorghum [Sorghum bicolor]) form a classical Kranz leaf anatomy. Unlike C3 plants, where photosynthetic CO2 fixation proceeds in the mesophyll (M), the fixation process in C4 plants is distributed between two cell types, the M cell and the bundle sheath (BS) cell. Here, we develop a C4 genome-scale model (C4GEM) for the investigation of flux distribution in M and BS cells during C4 photosynthesis. C4GEM, to our knowledge, is the first large-scale metabolic model that encapsulates metabolic interactions between two different cell types. C4GEM is based on the Arabidopsis (Arabidopsis thaliana) model (AraGEM) but has been extended by adding reactions and transporters responsible to represent three different C4 subtypes (NADP-ME [for malic enzyme], NAD-ME, and phosphoenolpyruvate carboxykinase). C4GEM has been validated for its ability to synthesize 47 biomass components and consists of 1,588 unique reactions, 1,755 metabolites, 83 interorganelle transporters, and 29 external transporters (including transport through plasmodesmata). Reactions in the common C4 model have been associated with well-annotated C4 species (NADP-ME subtypes): 3,557 genes in sorghum, 11,623 genes in maize, and 3,881 genes in sugarcane. The number of essential reactions not assigned to genes is 131, 135, and 156 in sorghum, maize, and sugarcane, respectively. Flux balance analysis was used to assess the metabolic activity in M and BS cells during C4 photosynthesis. Our simulations were consistent with chloroplast proteomic studies, and C4GEM predicted the classical C4 photosynthesis pathway and its major effect in organelle function in M and BS. The model also highlights differences in metabolic activities around photosystem I and photosystem II for three different C4 subtypes. Effects of CO2 leakage were also explored. C4GEM is a viable framework for in silico analysis of cell cooperation between M and BS

  13. Integrating Kinetic Model of E. coli with Genome Scale Metabolic Fluxes Overcomes Its Open System Problem and Reveals Bistability in Central Metabolism.

    Science.gov (United States)

    Mannan, Ahmad A; Toya, Yoshihiro; Shimizu, Kazuyuki; McFadden, Johnjoe; Kierzek, Andrzej M; Rocco, Andrea

    2015-01-01

    An understanding of the dynamics of the metabolic profile of a bacterial cell is sought from a dynamical systems analysis of kinetic models. This modelling formalism relies on a deterministic mathematical description of enzyme kinetics and their metabolite regulation. However, it is severely impeded by the lack of available kinetic information, limiting the size of the system that can be modelled. Furthermore, the subsystem of the metabolic network whose dynamics can be modelled is faced with three problems: how to parameterize the model with mostly incomplete steady state data, how to close what is now an inherently open system, and how to account for the impact on growth. In this study we address these challenges of kinetic modelling by capitalizing on multi-'omics' steady state data and a genome-scale metabolic network model. We use these to generate parameters that integrate knowledge embedded in the genome-scale metabolic network model, into the most comprehensive kinetic model of the central carbon metabolism of E. coli realized to date. As an application, we performed a dynamical systems analysis of the resulting enriched model. This revealed bistability of the central carbon metabolism and thus its potential to express two distinct metabolic states. Furthermore, since our model-informing technique ensures both stable states are constrained by the same thermodynamically feasible steady state growth rate, the ensuing bistability represents a temporal coexistence of the two states, and by extension, reveals the emergence of a phenotypically heterogeneous population.

  14. Integrating Kinetic Model of E. coli with Genome Scale Metabolic Fluxes Overcomes Its Open System Problem and Reveals Bistability in Central Metabolism.

    Directory of Open Access Journals (Sweden)

    Ahmad A Mannan

    Full Text Available An understanding of the dynamics of the metabolic profile of a bacterial cell is sought from a dynamical systems analysis of kinetic models. This modelling formalism relies on a deterministic mathematical description of enzyme kinetics and their metabolite regulation. However, it is severely impeded by the lack of available kinetic information, limiting the size of the system that can be modelled. Furthermore, the subsystem of the metabolic network whose dynamics can be modelled is faced with three problems: how to parameterize the model with mostly incomplete steady state data, how to close what is now an inherently open system, and how to account for the impact on growth. In this study we address these challenges of kinetic modelling by capitalizing on multi-'omics' steady state data and a genome-scale metabolic network model. We use these to generate parameters that integrate knowledge embedded in the genome-scale metabolic network model, into the most comprehensive kinetic model of the central carbon metabolism of E. coli realized to date. As an application, we performed a dynamical systems analysis of the resulting enriched model. This revealed bistability of the central carbon metabolism and thus its potential to express two distinct metabolic states. Furthermore, since our model-informing technique ensures both stable states are constrained by the same thermodynamically feasible steady state growth rate, the ensuing bistability represents a temporal coexistence of the two states, and by extension, reveals the emergence of a phenotypically heterogeneous population.

  15. Reproductive and metabolic phenotype of a mouse model of PCOS

    NARCIS (Netherlands)

    E. Leonie (E.); A.F. van Houten (A.); P. Kramer (Piet); A. McLuskey; B. Karels (Bas); A.P.N. Themmen (Axel); J.A. Visser (Jenny)

    2012-01-01

    textabstractPolycystic ovary syndrome (PCOS), the most common endocrine disorder in women in their reproductive age, is characterized by both reproductive and metabolic features. Recent studies in human, nonhuman primates, and sheep suggest that hyperandrogenism plays an important role in the

  16. Genome-scale metabolic models: reconstruction and analysis

    NARCIS (Netherlands)

    Baart, G.J.; Martens, D.E.

    2012-01-01

    Metabolism can be defined as the complete set of chemical reactions that occur in living organisms in order to maintain life. Enzymes are the main players in this process as they are responsible for catalyzing the chemical reactions. The enzyme-reaction relationships can be used for the

  17. Metabolic effects of Tulbaghia violacea Harv. in a diabetic model ...

    African Journals Online (AJOL)

    Background: Diabetes mellitus (DM) is a cluster of metabolic diseases with chronic hyperglycemia as a defining feature, associated with long-term organ damage and dysfunction. In this study we examined the effect of Tulbaghia violacea rhizome methanolic extract on blood glucose and lipids in normal and ...

  18. A functional compartmental model of the Synechocystis PCC 6803 phycobilisome

    NARCIS (Netherlands)

    Stokkum, van Ivo H.M.; Gwizdala, Michal; Tian, Tian; Snellenburg, Joris J.; Grondelle, van Rienk; Amerongen, van Herbert; Berera, Rudi

    2017-01-01

    In the light-harvesting antenna of the Synechocystis PCC 6803 phycobilisome (PB), the core consists of three cylinders, each composed of four disks, whereas each of the six rods consists of up to three hexamers (Arteni et al., Biochim Biophys Acta 1787(4):272–279, 2009). The rods and core contain

  19. Building America Case Study: Field Testing of Compartmentalization Methods for Multifamily Construction (Fact Sheet)

    Energy Technology Data Exchange (ETDEWEB)

    2015-01-01

    The 2012 IECC has an airtightness requirement of 3 air changes per hour at 50 Pascals test pressure for both single family and multifamily construction in Climate Zones 3-8. Other programs (LEED, ASHRAE 189, ASHRAE 62.2) have similar or tighter compartmentalization requirements, thus driving the need for easier and more effective methods of compartmentalization in multifamily buildings.

  20. The Metabolic Inhibition Model Which Predicts the Intestinal Absorbability and Metabolizability of Drug: Theory and Experiment

    Directory of Open Access Journals (Sweden)

    Mizuma Takashi

    1998-01-01

    Full Text Available The intestinal absorption of analgesic peptides (leucine enkephalin and kyotorphin and modified peptides in rat were studied. Although these peptides were not absorbed, the absorbability (absorption clearance of these peptides were increased in the presence of peptidase inhibitors. In order to kinetically analyze these phenomena, we proposed the metabolic inhibition model, which incorporated the metabolic clearance (metabolizability with the absorption clearance. Metabolic activity was determined with intestinal homogenates. The higher the metabolic clearance was, the lower was the absorption clearance. The relationships between the absorption clearance and the metabolic clearance of the experimental data as well as of the theoretical values were hyperbolic. This model predicted the maximum absorption clearances of cellobiose-coupled leucine enkephalin (0.654 &mgr;l/min/cm and kyotorphin (0.247 &mgr;l/min/cm. Details of the experimental methods are described.

  1. Investigating genotype-phenotype relationships in Saccharomyces cerevisiae metabolic network through stoichiometric modeling

    DEFF Research Database (Denmark)

    Brochado, Ana Rita

    A holistic view of the cell is fundamental for gaining insights into genotype to phenotype relationships. Systems Biology is a discipline within Biology, which uses such holistic approach by focusing on the development and application of tools for studying the structure and dynamics of cellular...... processes. Metabolism is an extensively studied and characterised subcellular system, for which several modeling approaches have been proposed over the last 20 years. Nowadays, stoichiometric modeling of metabolism is done at the genome scale and it has diverse applications, many of them for helping....... This chapter aims at providing the reader with relevant state-of-the-art information concerning Systems Biology, Genome-Scale Metabolic Modeling and Metabolic Engineering. Particular attention is given to the yeast Saccharomyces cerevisiae, the eukaryotic model organism used thought the thesis....

  2. An in vitro model for screening estrogen activity of environmental samples after metabolism

    Energy Technology Data Exchange (ETDEWEB)

    Chahbane, N.; Schramm, K.W. [GSF - Forschungszentrum fuer Umwelt und Gesundheit Neuherberg GmbH, Oberschleissheim (Germany). Inst. fuer Oekologische Chemie; Kettrup, A. [Technische Univ. Muenchen, Freising (Germany). Lehrstuhl fuer Oekologische Chemie

    2004-09-15

    For a few years, yeast estrogen assay (YES) was accepted as a reliable and economic model for screening of environmental estrogens. Though the chemicals directly act with estrogen receptor (ER) can be filtered out by this model, there are still chemicals act with ER only after metabolism and some chemicals eliminate their estrogen activities after metabolism. That is to say, their metabolites exert or have stronger estrogen activities than themselves, which can be called bio-activation. In this case, for the lack of the metabolism enzyme system as human and other animals, only the assay with recombinant yeast cells is insufficient. So, it is necessary to combine the YES with metabolism procedure to evaluate the estrogen activities of these chemicals. The most common method used currently for in vitro metabolic activation in mutagenicity testing and also be applied to the estrogen screening field is S-9 mixture. Also, there is an attempt to develop a chemical model for cytochrome P450 as a bio-mimetic metabolic activation system. All these methods can be used as in vitro models for metabolism. Compare with these models, using whole H4II E cells for metabolism is an alternative and with superiorities. It has the excellence of short experiment period as all other in vitro models, but is much more close to the real surroundings as in vivo. Furthermore, the activity of 7-ethoxyresorufin-O-deethylase (EROD) can be easily measured during the whole incubation period for us to discuss the metabolic activities in a quantitative foundation, not only in qualitative. Methoxychlor is one of the chemicals with bio-activation ability. When directly used in the YES, it shows weak estrogen activity. But a main metabolite of methoxychlor, 2,2-bis (p-hydroxyphenyl) - 1,1,1-trichloroethane (HPTE) is a known estrogen mimic. For the long time using methoxychlor as a pesticide and its clear background, it is an ideal chemical to establish this in vitro system.

  3. NCAM2/OCAM/RNCAM: cell adhesion molecule with a role in neuronal compartmentalization.

    Science.gov (United States)

    Winther, Malene; Berezin, Vladimir; Walmod, Peter Schledermann

    2012-03-01

    Neural cell adhesion molecules 2 (NCAM2/OCAM/RNCAM), is a paralog of NCAM1. The protein exists in a transmembrane and a lipid-anchored isoform, and has an ectodomain consisting of five immunoglobulin modules and two fibronectin type 3 homology modules. Structural models of the NCAM2 ectodomain reveal that it facilitates cell adhesion through reciprocal interactions between the membrane-distal immunoglobulin modules. There are no known heterophilic NCAM2 binding partners, and NCAM2 is not glycosylated with polysialic acid, a posttranslational modification known to be a major modulator of NCAM1-mediated processes. This suggests that NCAM2 has a function or mode of action distinctly different from that of NCAM1. NCAM2 is primarily expressed in the brain, where it is believed to stimulate neurite outgrowth and to facilitate dendritic and axonal compartmentalization. Copyright © 2011 Elsevier Ltd. All rights reserved.

  4. PASMet: a web-based platform for prediction, modelling and analyses of metabolic systems

    OpenAIRE

    Sriyudthsak, Kansuporn; Mejia, Ramon Francisco; Arita, Masanori; Hirai, Masami Yokota

    2016-01-01

    PASMet (Prediction, Analysis and Simulation of Metabolic networks) is a web-based platform for proposing and verifying mathematical models to understand the dynamics of metabolism. The advantages of PASMet include user-friendliness and accessibility, which enable biologists and biochemists to easily perform mathematical modelling. PASMet offers a series of user-functions to handle the time-series data of metabolite concentrations. The functions are organised into four steps: (i) Prediction of...

  5. Lipid metabolism in adipose tissue during lactation: a model of a metabolic control system.

    Science.gov (United States)

    McNamara, J P

    1994-08-01

    The flux of energy-yielding compounds through the pathways of lipogenesis, esterification into triglycerides and lipolysis in adipose tissue plays a pivotal role in supplying the demands of lactation and maternal health. The critical importance of these pathways is demonstrated by the number of highly coordinated and redundant metabolic control elements that regulate the enzyme activity in these pathways, including protein and several steroid hormones, catecholamines, and blood concentrations of several nutrients. Control on these pathways is exerted by all of these elements during lactation. Insights have been gained recently into the adaptations of these pathway reactions due to genetic propensity for milk production, stage of lactation, and intake of energy-yielding components such as starch, cellulose and triglycerides. The rates of these pathways vary exponentially with the intakes of key substrates and demands for milk precursors. The parameters of equations describing these pathways are not constant, but vary with genotype and with prolonged changes in nutritional and environmental conditions. Two major regulatory systems are critical to alterations of carbon flux during the entire lactational period. One is the interaction of growth hormone and insulin to control lipogenesis; the other is the counter-regulation by norepinephrine and insulin on cyclic AMP-initiated enzyme phosphorylation to regulate lipolysis. Examples of specific control points having a critical impact on lactational success and that are associated with genetic selection for milk production are the activities of acetyl-CoA carboxylase and hormone sensitive lipase. Further insights into the mechanisms of these adaptations will help us to improve the efficiency of metabolic flux during lactation.(ABSTRACT TRUNCATED AT 250 WORDS)

  6. Compartmentalization of GPCR signalling controls unique cellular responses.

    Science.gov (United States)

    Ellisdon, Andrew M; Halls, Michelle L

    2016-04-15

    With >800 members, G protein-coupled receptors (GPCRs) are the largest class of cell-surface signalling proteins, and their activation mediates diverse physiological processes. GPCRs are ubiquitously distributed across all cell types, involved in many diseases and are major drug targets. However, GPCR drug discovery is still characterized by very high attrition rates. New avenues for GPCR drug discovery may be provided by a recent shift away from the traditional view of signal transduction as a simple chain of events initiated from the plasma membrane. It is now apparent that GPCR signalling is restricted to highly organized compartments within the cell, and that GPCRs activate distinct signalling pathways once internalized. A high-resolution understanding of how compartmentalized signalling is controlled will probably provide unique opportunities to selectively and therapeutically target GPCRs. © 2016 Authors; published by Portland Press Limited.

  7. Apartment Compartmentalization With an Aerosol-Based Sealing Process

    Energy Technology Data Exchange (ETDEWEB)

    Maxwell, S. [Consortium for Advanced Residential Buildings, Norwalk, CT (United States); Berger, D. [Consortium for Advanced Residential Buildings, Norwalk, CT (United States); Harrington, C. [Consortium for Advanced Residential Buildings, Norwalk, CT (United States)

    2015-03-01

    Air sealing of building enclosures is a difficult and time-consuming process. Current methods in new construction require laborers to physically locate small and sometimes large holes in multiple assemblies and then manually seal each of them. The innovation demonstrated under this research study was the automated air sealing and compartmentalization of buildings through the use of an aerosolized sealant, developed by the Western Cooling Efficiency Center at University of California Davis. CARB sought to demonstrate this new technology application in a multifamily building in Queens, NY. The effectiveness of the sealing process was evaluated by three methods: air leakage testing of overall apartment before and after sealing, point-source testing of individual leaks, and pressure measurements in the walls of the target apartment during sealing.

  8. An integrated in vitro model for simultaneous assessment of drug uptake, metabolism, and efflux.

    Science.gov (United States)

    Neve, Etienne P A; Artursson, Per; Ingelman-Sundberg, Magnus; Karlgren, Maria

    2013-08-05

    The absorption, distribution, metabolism, and excretion (ADME) of drugs in vivo are to a large extent dependent on different transport and metabolism routes. Elucidation of this complex transport-metabolism interplay is a major challenge in drug development and at present no in vitro models suitable for this purpose are at hand. The aim of this study was to develop flexible, well-controlled, easy-to-use, integrated cell models, where drug transport and drug metabolism processes could be studied simultaneously. HEK293 cells stably transfected with the organic anion transporting polypeptide 1B1 (OATP1B1) were subjected to either transient transfection or adenoviral infection to introduce the genes expressing cytochrome P450 3A4 (CYP3A4), NADPH cytochrome P450 oxidoreductase (POR), cytochrome b5 (CYB5A), and multidrug resistance protein 1 (MDR1), in different combinations. Thereafter, the time and concentration-dependent transport and metabolism of two well-characterized statins, atorvastatin (acid and lactone forms) and simvastatin (acid form), were determined in the different models. The results show that CYP3A4-dependent metabolism of the more hydrophilic atorvastatin acid was dependent on OATP1B1 uptake and influenced by MDR1 efflux. In contrast, the metabolism of the more lipophilic atorvastatin lactone was not affected by active transport, whereas the metabolism of simvastatin acid was less influenced by active transport than atorvastatin acid. Our results, together with the models being applicative for any combination of drug transporters and CYP metabolizing enzymes of choice, provide proof-of-concept for the potential of the new integrated cell models presented as valuable screening tools in drug discovery and development.

  9. GIM3E: Condition-specific Models of Cellular Metabolism Developed from Metabolomics and Expression Data

    Energy Technology Data Exchange (ETDEWEB)

    Schmidt, Brian; Ebrahim, Ali; Metz, Thomas O.; Adkins, Joshua N.; Palsson, Bernard O.; Hyduke, Daniel R.

    2013-11-15

    Motivation: Genome-scale metabolic models have been used extensively to investigate alterations in cellular metabolism. The accuracy of these models to represent cellular metabolism in specific conditions has been improved by constraining the model with omics data sources. However, few practical methods for integrating metabolomics data with other omics data sources into genome-scale models of metabolism have been reported. Results: GIMMME (Gene Inactivation Moderated by Metabolism, Metabolomics, and Expression) is an algorithm that enables the development of condition-specific models based on an objective function, transcriptomics, and intracellular metabolomics data. GIMMME establishes metabolite utilization requirements with metabolomics data, uses model-paired transcriptomics data to find experimentally supported solutions, and also provides calculations of the turnover (production / consumption) flux of metabolites. GIMMME was employed to investigate the effects of integrating additional omics datasets to create increasingly constrained solution spaces of Salmonella Typhimurium metabolism during growth in both rich and virulence media. This integration proved to be informative and resulted in a requirement of additional active reactions (12 in each case) or metabolites (26 or 29, respectively). The addition of constraints from transcriptomics also impacted the allowed solution space, and the cellular metabolites with turnover fluxes that were necessarily altered by the change in conditions increased from 118 to 271 of 1397. Availability: GIMMME has been implemented in Python and requires a COBRApy 0.2.x. The algorithm and sample data described here are freely available at: http://opencobra.sourceforge.net/

  10. GIM3E: condition-specific models of cellular metabolism developed from metabolomics and expression data.

    Science.gov (United States)

    Schmidt, Brian J; Ebrahim, Ali; Metz, Thomas O; Adkins, Joshua N; Palsson, Bernhard Ø; Hyduke, Daniel R

    2013-11-15

    Genome-scale metabolic models have been used extensively to investigate alterations in cellular metabolism. The accuracy of these models to represent cellular metabolism in specific conditions has been improved by constraining the model with omics data sources. However, few practical methods for integrating metabolomics data with other omics data sources into genome-scale models of metabolism have been developed. GIM(3)E (Gene Inactivation Moderated by Metabolism, Metabolomics and Expression) is an algorithm that enables the development of condition-specific models based on an objective function, transcriptomics and cellular metabolomics data. GIM(3)E establishes metabolite use requirements with metabolomics data, uses model-paired transcriptomics data to find experimentally supported solutions and provides calculations of the turnover (production/consumption) flux of metabolites. GIM(3)E was used to investigate the effects of integrating additional omics datasets to create increasingly constrained solution spaces of Salmonella Typhimurium metabolism during growth in both rich and virulence media. This integration proved to be informative and resulted in a requirement of additional active reactions (12 in each case) or metabolites (26 or 29, respectively). The addition of constraints from transcriptomics also impacted the allowed solution space, and the cellular metabolites with turnover fluxes that were necessarily altered by the change in conditions increased from 118 to 271 of 1397. GIM(3)E has been implemented in Python and requires a COBRApy 0.2.x. The algorithm and sample data described here are freely available at: http://opencobra.sourceforge.net/ brianjamesschmidt@gmail.com

  11. Animal models of NASH: getting both pathology and metabolic context right.

    Science.gov (United States)

    Larter, Claire Z; Yeh, Matthew M

    2008-11-01

    Non-alcoholic fatty liver disease (NAFLD) is the most common cause of referral to liver clinics, and its progressive form, non-alcoholic steatohepatitis (NASH), can lead to cirrhosis and end-stage liver disease. The main risk factors for NAFLD/NASH are the metabolic abnormalities commonly observed in metabolic syndrome: insulin resistance, visceral obesity, dyslipidemia and altered adipokine profile. At present, the causes of progression from NAFLD to NASH remain poorly defined, and research in this area has been limited by the availability of suitable animal models of this disease. In the past, the main models used to investigate the pathogenesis of steatohepatitis have either failed to reproduce the full spectrum of liver pathology that characterizes human NASH, or the liver pathology has developed in a metabolic context that is not representative of the human condition. In the last few years, a number of models have been described in which the full spectrum of liver pathology develops in an appropriate metabolic context. In general, the underlying cause of metabolic defects in these models is chronic caloric overconsumption, also known as overnutrition. Overnutrition has been achieved in a number of different ways, including forced feeding, administration of high-fat diets, the use of genetically hyperphagic animals, or a combination of these approaches. The purpose of the present review is to critique the liver pathology and metabolic abnormalities present in currently available animal models of NASH, with particular focus on models described in approximately the last 5 years.

  12. Mackinac: a bridge between ModelSEED and COBRApy to generate and analyze genome-scale metabolic models.

    Science.gov (United States)

    Mundy, Michael; Mendes-Soares, Helena; Chia, Nicholas

    2017-08-01

    Reconstructing and analyzing a large number of genome-scale metabolic models is a fundamental part of the integrated study of microbial communities; however, two of the most widely used frameworks for building and analyzing models use different metabolic network representations. Here we describe Mackinac, a Python package that combines ModelSEED's ability to automatically reconstruct metabolic models with COBRApy's advanced analysis capabilities to bridge the differences between the two frameworks and facilitate the study of the metabolic potential of microorganisms. This package works with Python 2.7, 3.4, and 3.5 on MacOS, Linux and Windows. The source code is available from https://github.com/mmundy42/mackinac . mundy.michael@mayo.edu or soares.maria@mayo.edu.

  13. Compartmental endoscopic surgical anatomy of the medial intraconal orbital space.

    Science.gov (United States)

    Bleier, Benjamin S; Healy, David Y; Chhabra, Nipun; Freitag, Suzanne

    2014-07-01

    Surgical management of intraconal pathology represents the next frontier in endoscopic endonasal surgery. Despite this, the medial intraconal space remains a relatively unexplored region, secondary to its variable and technically demanding anatomy. The purpose of this study is to define the neurovascular structures in this region and introduce a compartmentalized approach to enhance surgical planning. This study was an institutional review board (IRB)-exempt endoscopic anatomic study in 10 cadaveric orbits. After dissection of the medial intraconal space, the pattern and trajectory of the oculomotor nerve and ophthalmic arterial arborizations were analyzed. The position of all vessels as well as the length of the oculomotor trunk and branches relative to the sphenoid face were calculated. A mean of 1.5 arterial branches were identified (n = 15; range, 1-4) at a mean of 8.8 mm from the sphenoid face (range, 4-15 mm). The majority of the arteries (n = 7) inserted adjacent to the midline of medial rectus. The oculomotor nerve inserted at the level of the sphenoid face and arborized with a large proximal trunk 5.5 ± 1.1 mm in length and multiple branches extending 13.2 ± 2.7 mm from the sphenoid face. The most anterior nerve and vascular pedicle were identified at 17.0 and 15.0 mm from the sphenoid face, respectively. The neurovascular supply to the medial rectus muscle describes a varied but predictable pattern. This data allows the compartmentalization of the medial intraconal space into 3 zones relative to the neurovascular supply. These zones inform the complexity of the dissection and provide a guideline for safe medial rectus retraction relative to the fixed landmark of the sphenoid face. © 2014 ARS-AAOA, LLC.

  14. Improved Evidence-Based Genome-scale Metabolic Models for Maize Leaf, Embryo, and Endosperm.

    Directory of Open Access Journals (Sweden)

    Samuel eSeaver

    2015-03-01

    Full Text Available There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes.

  15. Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm

    Science.gov (United States)

    Seaver, Samuel M. D.; Bradbury, Louis M. T.; Frelin, Océane; Zarecki, Raphy; Ruppin, Eytan; Hanson, Andrew D.; Henry, Christopher S.

    2015-01-01

    There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes. PMID:25806041

  16. Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm

    Energy Technology Data Exchange (ETDEWEB)

    Seaver, Samuel M. D.; Bradbury, Louis M. T.; Frelin, Océane; Zarecki, Raphy; Ruppin, Eytan; Hanson, Andrew D.; Henry, Christopher S.

    2015-03-10

    There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes.

  17. Metabolic network modeling of redox balancing and biohydrogen production in purple nonsulfur bacteria

    Directory of Open Access Journals (Sweden)

    Grammel Hartmut

    2011-09-01

    Full Text Available Abstract Background Purple nonsulfur bacteria (PNSB are facultative photosynthetic bacteria and exhibit an extremely versatile metabolism. A central focus of research on PNSB dealt with the elucidation of mechanisms by which they manage to balance cellular redox under diverse conditions, in particular under photoheterotrophic growth. Results Given the complexity of the central metabolism of PNSB, metabolic modeling becomes crucial for an integrated analysis of the accumulated biological knowledge. We reconstructed a stoichiometric model capturing the central metabolism of three important representatives of PNSB (Rhodospirillum rubrum, Rhodobacter sphaeroides and Rhodopseudomonas palustris. Using flux variability analysis, the model reveals key metabolic constraints related to redox homeostasis in these bacteria. With the help of the model we can (i give quantitative explanations for non-intuitive, partially species-specific phenomena of photoheterotrophic growth of PNSB, (ii reproduce various quantitative experimental data, and (iii formulate several new hypotheses. For example, model analysis of photoheterotrophic growth reveals that - despite a large number of utilizable catabolic pathways - substrate-specific biomass and CO2 yields are fixed constraints, irrespective of the assumption of optimal growth. Furthermore, our model explains quantitatively why a CO2 fixing pathway such as the Calvin cycle is required by PNSB for many substrates (even if CO2 is released. We also analyze the role of other pathways potentially involved in redox metabolism and how they affect quantitatively the required capacity of the Calvin cycle. Our model also enables us to discriminate between different acetate assimilation pathways that were proposed recently for R. sphaeroides and R. rubrum, both lacking the isocitrate lyase. Finally, we demonstrate the value of the metabolic model also for potential biotechnological applications: we examine the theoretical

  18. Absorption and initial metabolism of 75Se-l-selenomethionine

    DEFF Research Database (Denmark)

    Grosse Ruse, Mareile; Søndergaard, Lasse R.; Ditlevsen, Susanne

    2015-01-01

    Selenomethionine (SeMet) is an important organic nutritional source of Se, but the uptake and metabolism of SeMet are poorly characterised in humans. Dynamic gamma camera images of the abdominal region were acquired from eight healthy young men after the ingestion of radioactive 75Se-l-SeMet (75Se...... to the plasma and 0·267 h–1 from the stomach to the plasma. The delay in the liver was 0·714 h. Gamma camera imaging provides data for use in compartmental modelling of 75Se-SeMet absorption and metabolism in humans. In clinical settings, the obtained rate constants and the delay in the liver may be useful...

  19. Human Induced Pluripotent Stem Cell Approaches to Model Inborn and Acquired Metabolic Heart Diseases

    Science.gov (United States)

    Chanana, Anita M.; Rhee, June-Wha; Wu, Joseph C.

    2016-01-01

    Purpose of review This article provides an overview of advances in the induced pluripotent stem cell field to model cardiomyopathies of inherited inborn errors of metabolism and acquired metabolic syndromes in vitro. Recent findings Several inborn errors of metabolism have been studied using “disease in a dish” model, including Pompe disease, Danon disease, Fabry disease, and Barth syndrome. Disease phenotypes of complex metabolic syndromes, such as diabetes mellitus and aldehyde dehydrogenase 2 deficiency have also been observed. Summary Differentiation of patient- and disease-specific induced pluripotent stem cell-derived cardiomyocytes has provided the capacity to model deleterious cardiometabolic diseases to understand molecular mechanisms, perform drug screens, and identify novel drug targets. PMID:27022891

  20. Plasma Proteome Profiles Associated with Diet-Induced Metabolic Syndrome and the Early Onset of Metabolic Syndrome in a Pig Model

    OpenAIRE

    te Pas, Marinus F. W.; Sietse-Jan Koopmans; Leo Kruijt; Mario P. L. Calus; Smits, Mari A.

    2013-01-01

    Obesity and related diabetes are important health threatening multifactorial metabolic diseases and it has been suggested that 25% of all diabetic patients are unaware of their patho-physiological condition. Biomarkers for monitoring and control are available, but early stage predictive biomarkers enabling prevention of these diseases are still lacking. We used the pig as a model to study metabolic disease because humans and pigs share a multitude of metabolic similarities. Diabetes was chemi...

  1. 21 CFR 888.3535 - Knee joint femorotibial (uni-compartmental) metal/polymer porous-coated uncemented prosthesis.

    Science.gov (United States)

    2010-04-01

    ... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Knee joint femorotibial (uni-compartmental) metal... Devices § 888.3535 Knee joint femorotibial (uni-compartmental) metal/polymer porous-coated uncemented prosthesis. (a) Identification. A knee joint femorotibial (uni-compartmental) metal/polymer porous-coated...

  2. The human hepatocyte cell lines IHH and HepaRG : models to study glucose, lipid and lipoprotein metabolism

    NARCIS (Netherlands)

    Samanez, Carolina Huaman; Caron, Sandrine; Briand, Olivier; Dehondt, Helene; Duplan, Isabelle; Kuipers, Folkert; Hennuyer, Nathalie; Clavey, Veronique; Staels, Bart

    Metabolic diseases reach epidemic proportions. A better knowledge of the associated alterations in the metabolic pathways in the liver is necessary. These studies need in vitro human cell models. Several human hepatoma models are used, but the response of many metabolic pathways to physiological

  3. A Genome-Scale Model of Shewanella piezotolerans Simulates Mechanisms of Metabolic Diversity and Energy Conservation.

    Science.gov (United States)

    Dufault-Thompson, Keith; Jian, Huahua; Cheng, Ruixue; Li, Jiefu; Wang, Fengping; Zhang, Ying

    2017-01-01

    Shewanella piezotolerans strain WP3 belongs to the group 1 branch of the Shewanella genus and is a piezotolerant and psychrotolerant species isolated from the deep sea. In this study, a genome-scale model was constructed for WP3 using a combination of genome annotation, ortholog mapping, and physiological verification. The metabolic reconstruction contained 806 genes, 653 metabolites, and 922 reactions, including central metabolic functions that represented nonhomologous replacements between the group 1 and group 2 Shewanella species. Metabolic simulations with the WP3 model demonstrated consistency with existing knowledge about the physiology of the organism. A comparison of model simulations with experimental measurements verified the predicted growth profiles under increasing concentrations of carbon sources. The WP3 model was applied to study mechanisms of anaerobic respiration through investigating energy conservation, redox balancing, and the generation of proton motive force. Despite being an obligate respiratory organism, WP3 was predicted to use substrate-level phosphorylation as the primary source of energy conservation under anaerobic conditions, a trait previously identified in other Shewanella species. Further investigation of the ATP synthase activity revealed a positive correlation between the availability of reducing equivalents in the cell and the directionality of the ATP synthase reaction flux. Comparison of the WP3 model with an existing model of a group 2 species, Shewanella oneidensis MR-1, revealed that the WP3 model demonstrated greater flexibility in ATP production under the anaerobic conditions. Such flexibility could be advantageous to WP3 for its adaptation to fluctuating availability of organic carbon sources in the deep sea. IMPORTANCE The well-studied nature of the metabolic diversity of Shewanella bacteria makes species from this genus a promising platform for investigating the evolution of carbon metabolism and energy conservation

  4. A systems biology framework for modeling metabolic enzyme inhibition of Mycobacterium tuberculosis

    Directory of Open Access Journals (Sweden)

    Reifman Jaques

    2009-09-01

    Full Text Available Abstract Background Because metabolism is fundamental in sustaining microbial life, drugs that target pathogen-specific metabolic enzymes and pathways can be very effective. In particular, the metabolic challenges faced by intracellular pathogens, such as Mycobacterium tuberculosis, residing in the infected host provide novel opportunities for therapeutic intervention. Results We developed a mathematical framework to simulate the effects on the growth of a pathogen when enzymes in its metabolic pathways are inhibited. Combining detailed models of enzyme kinetics, a complete metabolic network description as modeled by flux balance analysis, and a dynamic cell population growth model, we quantitatively modeled and predicted the dose-response of the 3-nitropropionate inhibitor on the growth of M. tuberculosis in a medium whose carbon source was restricted to fatty acids, and that of the 5'-O-(N-salicylsulfamoyl adenosine inhibitor in a medium with low-iron concentration. Conclusion The predicted results quantitatively reproduced the experimentally measured dose-response curves, ranging over three orders of magnitude in inhibitor concentration. Thus, by allowing for detailed specifications of the underlying enzymatic kinetics, metabolic reactions/constraints, and growth media, our model captured the essential chemical and biological factors that determine the effects of drug inhibition on in vitro growth of M. tuberculosis cells.

  5. Model of Tryptophan Metabolism, Readily Scalable Using Tissue-specific Gene Expression Data*

    Science.gov (United States)

    Stavrum, Anne-Kristin; Heiland, Ines; Schuster, Stefan; Puntervoll, Pål; Ziegler, Mathias

    2013-01-01

    Tryptophan is utilized in various metabolic routes including protein synthesis, serotonin, and melatonin synthesis and the kynurenine pathway. Perturbations in these pathways have been associated with neurodegenerative diseases and cancer. Here we present a comprehensive kinetic model of the complex network of human tryptophan metabolism based upon existing kinetic data for all enzymatic conversions and transporters. By integrating tissue-specific expression data, modeling tryptophan metabolism in liver and brain returned intermediate metabolite concentrations in the physiological range. Sensitivity and metabolic control analyses identified expected key enzymes to govern fluxes in the branches of the network. Combining tissue-specific models revealed a considerable impact of the kynurenine pathway in liver on the concentrations of neuroactive derivatives in the brain. Moreover, using expression data from a cancer study predicted metabolite changes that resembled the experimental observations. We conclude that the combination of the kinetic model with expression data represents a powerful diagnostic tool to predict alterations in tryptophan metabolism. The model is readily scalable to include more tissues, thereby enabling assessment of organismal tryptophan metabolism in health and disease. PMID:24129579

  6. Reconstruction and analysis of the genome-scale metabolic model of Lactobacillus casei LC2W.

    Science.gov (United States)

    Xu, Nan; Liu, Jie; Ai, Lianzhong; Liu, Liming

    2015-01-10

    Lactobacillus casei LC2W is a recently isolated probiotic lactic acid bacterial strain, which is widely used in the dairy and pharmaceutical industries and in clinical medicine. The first genome-scale metabolic model for L. casei, composed of 846 genes, 969 metabolic reactions, and 785 metabolites, was reconstructed using both manual genome annotation and an automatic SEED model. Then, the iJL846 model was validated by simulating cell growth on 15 reported carbon sources. The iJL846 model explored the metabolism of L. casei on a genome scale: (1) explanation of the genetic codes-metabolic functions of 342 genes were reannotated in this model; (2) characterization of the physiology-10 amino acids and 7 vitamins were identified to be essential nutrients for L. casei LC2W growth; (3) analyses of metabolic pathways-the transport and metabolism of the 17 essential nutrients and exopolysaccharide (EPS) biosynthesis-were performed; (4) exploration of metabolic capacity was conducted-for lactate, the importance of genes in its biosynthetic pathways was evaluated, and the requirements of amino acids were predicted for mixed acid fermentation; for flavor compounds, the effects of oxygen were analyzed, and three new knockout targets were selected for acetoin production; for EPS, 11 types of nutrients in the rich medium and important reactions in the biosynthetic pathway were identified that enhanced EPS production. In conclusion, the iJL846 model serves as a useful tool for understanding and engineering the metabolism of this probiotic strain. Copyright © 2014 Elsevier B.V. All rights reserved.

  7. OpenFLUX: efficient modelling software for 13C-based metabolic flux analysis

    Directory of Open Access Journals (Sweden)

    Nielsen Lars K

    2009-05-01

    Full Text Available Abstract Background The quantitative analysis of metabolic fluxes, i.e., in vivo activities of intracellular enzymes and pathways, provides key information on biological systems in systems biology and metabolic engineering. It is based on a comprehensive approach combining (i tracer cultivation on 13C substrates, (ii 13C labelling analysis by mass spectrometry and (iii mathematical modelling for experimental design, data processing, flux calculation and statistics. Whereas the cultivation and the analytical part is fairly advanced, a lack of appropriate modelling software solutions for all modelling aspects in flux studies is limiting the application of metabolic flux analysis. Results We have developed OpenFLUX as a user friendly, yet flexible software application for small and large scale 13C metabolic flux analysis. The application is based on the new Elementary Metabolite Unit (EMU framework, significantly enhancing computation speed for flux calculation. From simple notation of metabolic reaction networks defined in a spreadsheet, the OpenFLUX parser automatically generates MATLAB-readable metabolite and isotopomer balances, thus strongly facilitating model creation. The model can be used to perform experimental design, parameter estimation and sensitivity analysis either using the built-in gradient-based search or Monte Carlo algorithms or in user-defined algorithms. Exemplified for a microbial flux study with 71 reactions, 8 free flux parameters and mass isotopomer distribution of 10 metabolites, OpenFLUX allowed to automatically compile the EMU-based model from an Excel file containing metabolic reactions and carbon transfer mechanisms, showing it's user-friendliness. It reliably reproduced the published data and optimum flux distributions for the network under study were found quickly ( Conclusion We have developed a fast, accurate application to perform steady-state 13C metabolic flux analysis. OpenFLUX will strongly facilitate and

  8. A dynamic mechanistic model of lactic acid metabolism in the rumen

    NARCIS (Netherlands)

    Mills, J.A.N.; Crompton, L.A.; Ellis, J.L.; Dijkstra, J.; Bannink, A.; Hook, S.E.; Benchaar, C.; France, J.

    2014-01-01

    Current feed evaluation systems for ruminants are too imprecise to describe diets in terms of their acidosis risk. The dynamic mechanistic model described herein arises from the integration of a lactic acid (La) metabolism module into an extant model of whole-rumen function. The model was evaluated

  9. Comparative Metabolism of Carbon Tetrachloride in Rats, Mice and Hamsters Using Gas Uptake and PBPK Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Thrall, Karla D.(BATTELLE (PACIFIC NW LAB)); Vucelick, Mark E.(FLUOR HANFORD, INC); Gies, Richard A.(BATTELLE (PACIFIC NW LAB)); Zangar, Richard C.(BATTELLE (PACIFIC NW LAB)); Weitz, Karl K.(BATTELLE (PACIFIC NW LAB)); Poet, Torka S.(BATTELLE (PACIFIC NW LAB)); Springer, David L.(BATTELLE (PACIFIC NW LAB)); Grant, Donna M.(BATTELLE (PACIFIC NW LAB)); Benson, Janet M.(Inhalation Toxicology Research Institute)

    2000-08-25

    No study has comprehensively compared the rate of metabolism of carbon tetrachloride (CCl4) across species. Therefore, the in vivo metabolism of CCl4 was evaluated using groups of male animals (F344 rats, B6C3F1 mice, and Syrian hamsters) exposed to 40-1800 ppm CCl4 in a closed, recirculating gas-uptake system. For each species, an optimal fit of the family of uptake curves was obtained by adjusting Michaelis-Menten metabolic constants Km (affinity) and Vmax (capacity) using a physiologically based pharmacokinetic (PBPK) model. The results show that the mouse has a slightly higher capacity and lower affinity for metabolizing CCl4 compared to the rat, while the hamster has a higher capacity and lower affinity than either rat or mouse. A comparison of the Vmax to Km ratio, normalized for mg of liver protein (L/hr/mg) across species indicates that hamsters metabolize more CCl4 than either rats or mice, and should be more susceptible to CCl4-induced hepatotoxicity. These species comparisons were evaluated against toxicokinetic studies conducted in animals exposed by nose-only inhalation to 20 ppm 14C-labeled CCl4 for 4 hours. The toxicokinetic study results are consistent with the in vivo rates of metabolism, with rats eliminating less radioactivity associated with metabolism (14CO2 and urine/feces) and more radioactivity associated with the parent compound (radioactivity trapped on charcoal) compared to either hamsters or mice. The in vivo metabolic constants determined here, together with in vitro constants determined using rat, mouse, hamster and human liver microsomes, were used to estimate human in vivo metabolic rates of 1.49 mg/hr/kg body weight and 0.25 mg/L for Vmax and Km, respectively. Normalizing the rate of metabolism (Vmax/Km) by mg liver protein, the rate of metabolism of CCl4 differs across species, with hamster > mouse& > rat > human.

  10. Metabolic Network Modeling of Microbial Interactions in Natural and Engineered Environmental Systems

    Science.gov (United States)

    Perez-Garcia, Octavio; Lear, Gavin; Singhal, Naresh

    2016-01-01

    We review approaches to characterize metabolic interactions within microbial communities using Stoichiometric Metabolic Network (SMN) models for applications in environmental and industrial biotechnology. SMN models are computational tools used to evaluate the metabolic engineering potential of various organisms. They have successfully been applied to design and optimize the microbial production of antibiotics, alcohols and amino acids by single strains. To date however, such models have been rarely applied to analyze and control the metabolism of more complex microbial communities. This is largely attributed to the diversity of microbial community functions, metabolisms, and interactions. Here, we firstly review different types of microbial interaction and describe their relevance for natural and engineered environmental processes. Next, we provide a general description of the essential methods of the SMN modeling workflow including the steps of network reconstruction, simulation through Flux Balance Analysis (FBA), experimental data gathering, and model calibration. Then we broadly describe and compare four approaches to model microbial interactions using metabolic networks, i.e., (i) lumped networks, (ii) compartment per guild networks, (iii) bi-level optimization simulations, and (iv) dynamic-SMN methods. These approaches can be used to integrate and analyze diverse microbial physiology, ecology and molecular community data. All of them (except the lumped approach) are suitable for incorporating species abundance data but so far they have been used only to model simple communities of two to eight different species. Interactions based on substrate exchange and competition can be directly modeled using the above approaches. However, interactions based on metabolic feedbacks, such as product inhibition and synthropy require extensions to current models, incorporating gene regulation and compounding accumulation mechanisms. SMN models of microbial interactions can

  11. Metabolic network modeling of microbial interactions in natural and engineered environmental systems

    Directory of Open Access Journals (Sweden)

    Octavio ePerez-Garcia

    2016-05-01

    Full Text Available We review approaches to characterize metabolic interactions within microbial communities using Stoichiometric Metabolic Network (SMN models for applications in environmental and industrial biotechnology. SMN models are computational tools used to evaluate the metabolic engineering potential of various organisms. They have successfully been applied to design and optimize the microbial production of antibiotics, alcohols and amino acids by single strains. To date however, such models have been rarely applied to analyze and control the metabolism of more complex microbial communities. This is largely attributed to the diversity of microbial community functions, metabolisms and interactions. Here, we firstly review different types of microbial interaction and describe their relevance for natural and engineered environmental processes. Next, we provide a general description of the essential methods of the SMN modeling workflow including the steps of network reconstruction, simulation through Flux Balance Analysis (FBA, experimental data gathering, and model calibration. Then we broadly describe and compare four approaches to model microbial interactions using metabolic networks, i.e. i lumped networks, ii compartment per guild networks, iii bi-level optimization simulations and iv dynamic-SMN methods. These approaches can be used to integrate and analyze diverse microbial physiology, ecology and molecular community data. All of them (except the lumped approach are suitable for incorporating species abundance data but so far they have been used only to model simple communities of two to eight different species. Interactions based on substrate exchange and competition can be directly modeled using the above approaches. However, interactions based on metabolic feedbacks, such as product inhibition and synthropy require extensions to current models, incorporating gene regulation and compounding accumulation mechanisms. SMN models of microbial

  12. Improving the precision of lake ecosystem metabolism estimates by identifying predictors of model uncertainty

    Science.gov (United States)

    Rose, Kevin C.; Winslow, Luke A.; Read, Jordan S.; Read, Emily K.; Solomon, Christopher T.; Adrian, Rita; Hanson, Paul C.

    2014-01-01

    Diel changes in dissolved oxygen are often used to estimate gross primary production (GPP) and ecosystem respiration (ER) in aquatic ecosystems. Despite the widespread use of this approach to understand ecosystem metabolism, we are only beginning to understand the degree and underlying causes of uncertainty for metabolism model parameter estimates. Here, we present a novel approach to improve the precision and accuracy of ecosystem metabolism estimates by identifying physical metrics that indicate when metabolism estimates are highly uncertain. Using datasets from seventeen instrumented GLEON (Global Lake Ecological Observatory Network) lakes, we discovered that many physical characteristics correlated with uncertainty, including PAR (photosynthetically active radiation, 400-700 nm), daily variance in Schmidt stability, and wind speed. Low PAR was a consistent predictor of high variance in GPP model parameters, but also corresponded with low ER model parameter variance. We identified a threshold (30% of clear sky PAR) below which GPP parameter variance increased rapidly and was significantly greater in nearly all lakes compared with variance on days with PAR levels above this threshold. The relationship between daily variance in Schmidt stability and GPP model parameter variance depended on trophic status, whereas daily variance in Schmidt stability was consistently positively related to ER model parameter variance. Wind speeds in the range of ~0.8-3 m s–1 were consistent predictors of high variance for both GPP and ER model parameters, with greater uncertainty in eutrophic lakes. Our findings can be used to reduce ecosystem metabolism model parameter uncertainty and identify potential sources of that uncertainty.

  13. Metabolic cleavage of cell-penetrating peptides in contact with epithelial models

    DEFF Research Database (Denmark)

    Tréhin, Rachel; Nielsen, Hanne Mørck; Jahnke, Heinz-Georg

    2004-01-01

    We assessed the metabolic degradation kinetics and cleavage patterns of some selected CPP (cell-penetrating peptides) after incubation with confluent epithelial models. Synthesis of N-terminal CF [5(6)-carboxyfluorescein]-labelled CPP, namely hCT (human calcitonin)-derived sequences, Tat(47...... models and the CPP. The Calu-3 model exhibited the highest proteolytic activity. The patterns of metabolic cleavage of hCT(9-32) were similar in all three models. Initial cleavage of this peptide occurred at the N-terminal domain, possibly by endopeptidase activity yielding both the N- and the C......-57) and penetratin(43-58), was through Fmoc (fluoren-9-ylmethoxycarbonyl) chemistry. Metabolic degradation kinetics of the tested CPP in contact with three cell-cultured epithelial models, MDCK (Madin-Darby canine kidney), Calu-3 and TR146, was evaluated by reversed-phase HPLC. Identification of the resulting...

  14. Thermodynamic analysis of regulation in metabolic networks using constraint-based modeling

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    Mahadevan Radhakrishnan

    2010-05-01

    Full Text Available Abstract Background Geobacter sulfurreducens is a member of the Geobacter species, which are capable of oxidation of organic waste coupled to the reduction of heavy metals and electrode with applications in bioremediation and bioenergy generation. While the metabolism of this organism has been studied through the development of a stoichiometry based genome-scale metabolic model, the associated regulatory network has not yet been well studied. In this manuscript, we report on the implementation of a thermodynamics based metabolic flux model for Geobacter sulfurreducens. We use this updated model to identify reactions that are subject to regulatory control in the metabolic network of G. sulfurreducens using thermodynamic variability analysis. Findings As a first step, we have validated the regulatory sites and bottleneck reactions predicted by the thermodynamic flux analysis in E. coli by evaluating the expression ranges of the corresponding genes. We then identified ten reactions in the metabolic network of G. sulfurreducens that are predicted to be candidates for regulation. We then compared the free energy ranges for these reactions with the corresponding gene expression fold changes under conditions of different environmental and genetic perturbations and show that the model predictions of regulation are consistent with data. In addition, we also identify reactions that operate close to equilibrium and show that the experimentally determined exchange coefficient (a measure of reversibility is significant for these reactions. Conclusions Application of the thermodynamic constraints resulted in identification of potential bottleneck reactions not only from the central metabolism but also from the nucleotide and amino acid subsystems, thereby showing the highly coupled nature of the thermodynamic constraints. In addition, thermodynamic variability analysis serves as a valuable tool in estimating the ranges of ΔrG' of every reaction in the model

  15. Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis

    DEFF Research Database (Denmark)

    Huang, Sijia; Chong, Nicole; Lewis, Nathan

    2016-01-01

    metabolomics data for disease diagnosis. Applying this method to blood-based breast cancer metabolomics data, we have discovered crucial metabolic pathway signatures for breast cancer diagnosis, especially early diagnosis. Further, this modeling approach may be generalized to other omics data types for disease.......993. Moreover, important metabolic pathways, such as taurine and hypotaurine metabolism and the alanine, aspartate, and glutamate pathway, are revealed as critical biological pathways for early diagnosis of breast cancer. Conclusions: We have successfully developed a new type of pathway-based model to study......Background: More accurate diagnostic methods are pressingly needed to diagnose breast cancer, the most common malignant cancer in women worldwide. Blood-based metabolomics is a promising diagnostic method for breast cancer. However, many metabolic biomarkers are difficult to replicate among studies...

  16. Genetic analyses of HIV-1 env sequences demonstrate limited compartmentalization in breast milk and suggest viral replication within the breast that increases with mastitis.

    Science.gov (United States)

    Gantt, Soren; Carlsson, Jacquelyn; Heath, Laura; Bull, Marta E; Shetty, Avinash K; Mutsvangwa, Junior; Musingwini, Georgina; Woelk, Godfrey; Zijenah, Lynn S; Katzenstein, David A; Mullins, James I; Frenkel, Lisa M

    2010-10-01

    The concentration of human immunodeficiency virus type 1 (HIV-1) is generally lower in breast milk than in blood. Mastitis, or inflammation of the breast, is associated with increased levels of milk HIV-1 and risk of mother-to-child transmission through breastfeeding. We hypothesized that mastitis facilitates the passage of HIV-1 from blood into milk or stimulates virus production within the breast. HIV-1 env sequences were generated from single amplicons obtained from breast milk and blood samples in a cross-sectional study. Viral compartmentalization was evaluated using several statistical methods, including the Slatkin and Maddison (SM) test. Mastitis was defined as an elevated milk sodium (Na(+)) concentration. The association between milk Na(+) and the pairwise genetic distance between milk and blood viral sequences was modeled using linear regression. HIV-1 was compartmentalized within milk by SM testing in 6/17 (35%) specimens obtained from 9 women, but all phylogenetic clades included viral sequences from milk and blood samples. Monotypic sequences were more prevalent in milk samples than in blood samples (22% versus 13%; P = 0.012), which accounted for half of the compartmentalization observed. Mastitis was not associated with compartmentalization by SM testing (P = 0.621), but Na(+) was correlated with greater genetic distance between milk and blood HIV-1 populations (P = 0.041). In conclusion, local production of HIV-1 within the breast is suggested by compartmentalization of virus and a higher prevalence of monotypic viruses in milk specimens. However, phylogenetic trees demonstrate extensive mixing of viruses between milk and blood specimens. HIV-1 replication in breast milk appears to increase with inflammation, contributing to higher milk viral loads during mastitis.

  17. Generation and Validation of the iKp1289 Metabolic Model for Klebsiella pneumoniae KPPR1

    Energy Technology Data Exchange (ETDEWEB)

    Henry, Christopher S.; Rotman, Ella; Lathem, Wyndham W.; Tyo, Keith E. J.; Hauser, Alan R.; Mandel, Mark J.

    2017-02-15

    Klebsiella pneumoniae has a reputation for causing a wide range of infectious conditions, with numerous highly virulent and antibiotic-resistant strains. Metabolic models have the potential to provide insights into the growth behavior, nutrient requirements, essential genes, and candidate drug targets in these strains. Here we develop a metabolic model for KPPR1, a highly virulent strain of K. pneumoniae. We apply a combination of Biolog phenotype data and fitness data to validate and refine our KPPR1 model. The final model displays a predictive accuracy of 75% in identifying potential carbon and nitrogen sources for K. pneumoniae and of 99% in predicting nonessential genes in rich media. We demonstrate how this model is useful in studying the differences in the metabolic capabilities of the low-virulence MGH 78578 strain and the highly virulent KPPR1 strain. For example, we demonstrate that these strains differ in carbohydrate metabolism, including the ability to metabolize dulcitol as a primary carbon source. Our model makes numerous other predictions for follow-up verification and analysis.

  18. A Prochlorococcus proving ground for constraint-based metabolic modeling and multi-`omics data integration

    Science.gov (United States)

    Casey, J.; Ji, B.; Shaoie, S.; Mardinoglu, A.; Sarathi Sen, P.; Jahn, O.; Reda, K.; Leigh, J.; Follows, M. J.; Nielsen, J.; Karl, D. M.

    2016-02-01

    Representatives of the oligotrophic marine cyanobacterium Prochlorococcus marinus are the smallest free-living photosynthetic organisms, both in terms of physical size and genome size, yet are the most abundant photoautotrophic microbes in the oceans and profoundly influence global biogeochemical cycles. Physiological and regulatory control of nutrient and light stress has been observed in MED4 in culture and in its closely related `ecotype' eMED4 in the field, however its metabolism has not been investigated in detail. We present a genome-scale metabolic network reconstruction of the high-light adapted axenic strain MED4ax ("iJCMED4") for the quantitative analysis of a range of its metabolic phenotypes. The resulting structure is a proving ground for the incorporation of enzyme kinetics, biochemical and elemental compositional data, transcriptomic, proteomic, metabolomic, and fluxomic datasets which can be implemented within a constraint-based metabolic modeling environment. The iJCMED4 stoichiometric model consists of 523 metabolic genes encoding 787 reactions with 673 unique metabolites distributed in 5 sub-cellular compartments and is mass, charge, and thermodynamically balanced. Several variants of flux balance analysis were used to simulate growth and metabolic fluxes over the diel cycle, under various stress conditions (e.g., nitrogen, phosphorus, light), and within the framework of a global biogeochemical model (DARWIN). Model simulations accurately predicted growth rates in culture under a variety of defined medium compositions and there was close agreement of photosynthetic performance, biomass and energy yields and efficiencies, and transporter fluxes for iJCMED4 and culture experiments. In addition to a nearly optimal photosynthetic quotient and central carbon metabolism efficiency, MED4 has made dramatic alterations to redox and phosphorus metabolism across biosynthetic and intermediate pathways. We propose that reductions in phosphate reaction

  19. Multiscale Metabolic Modeling of C4 Plants: Connecting Nonlinear Genome-Scale Models to Leaf-Scale Metabolism in Developing Maize Leaves.

    Directory of Open Access Journals (Sweden)

    Eli Bogart

    Full Text Available C4 plants, such as maize, concentrate carbon dioxide in a specialized compartment surrounding the veins of their leaves to improve the efficiency of carbon dioxide assimilation. Nonlinear relationships between carbon dioxide and oxygen levels and reaction rates are key to their physiology but cannot be handled with standard techniques of constraint-based metabolic modeling. We demonstrate that incorporating these relationships as constraints on reaction rates and solving the resulting nonlinear optimization problem yields realistic predictions of the response of C4 systems to environmental and biochemical perturbations. Using a new genome-scale reconstruction of maize metabolism, we build an 18000-reaction, nonlinearly constrained model describing mesophyll and bundle sheath cells in 15 segments of the developing maize leaf, interacting via metabolite exchange, and use RNA-seq and enzyme activity measurements to predict spatial variation in metabolic state by a novel method that optimizes correlation between fluxes and expression data. Though such correlations are known to be weak in general, we suggest that developmental gradients may be particularly suited to the inference of metabolic fluxes from expression data, and we demonstrate that our method predicts fluxes that achieve high correlation with the data, successfully capture the experimentally observed base-to-tip transition between carbon-importing tissue and carbon-exporting tissue, and include a nonzero growth rate, in contrast to prior results from similar methods in other systems.

  20. Insights into metabolic osmoadaptation of the ectoines-producer bacterium Chromohalobacter salexigens through a high-quality genome scale metabolic model.

    Science.gov (United States)

    Piubeli, Francine; Salvador, Manuel; Argandoña, Montserrat; Nieto, Joaquín J; Bernal, Vicente; Pastor, Jose M; Cánovas, Manuel; Vargas, Carmen

    2018-01-09

    The halophilic bacterium Chromohalobacter salexigens is a natural producer of ectoines, compatible solutes with current and potential biotechnological applications. As production of ectoines is an osmoregulated process that draws away TCA intermediates, bacterial metabolism needs to be adapted to cope with salinity changes. To explore and use C. salexigens as cell factory for ectoine(s) production, a comprehensive knowledge at the systems level of its metabolism is essential. For this purpose, the construction of a robust and high-quality genome-based metabolic model of C. salexigens was approached. We generated and validated a high quality genome-based C. salexigens metabolic model (iFP764). This comprised an exhaustive reconstruction process based on experimental information, analysis of genome sequence, manual re-annotation of metabolic genes, and in-depth refinement. The model included three compartments (periplasmic, cytoplasmic and external medium), and two salinity-specific biomass compositions, partially based on experimental results from C. salexigens. Using previous metabolic data as constraints, the metabolic model allowed us to simulate and analyse the metabolic osmoadaptation of C. salexigens under conditions for low and high production of ectoines. The iFP764 model was able to reproduce the major metabolic features of C. salexigens. Flux Balance Analysis (FBA) and Monte Carlo Random sampling analysis showed salinity-specific essential metabolic genes and different distribution of fluxes and variation in the patterns of correlation of reaction sets belonging to central C and N metabolism, in response to salinity. Some of them were related to bioenergetics or production of reducing equivalents, and probably related to demand for ectoines. Ectoines metabolic reactions were distributed according to its correlation in four modules. Interestingly, the four modules were independent both at low and high salinity conditions, as they did not correlate to each

  1. Consistency Analysis of Genome-Scale Models of Bacterial Metabolism: A Metamodel Approach

    Science.gov (United States)

    Ponce-de-Leon, Miguel; Calle-Espinosa, Jorge; Peretó, Juli; Montero, Francisco

    2015-01-01

    Genome-scale metabolic models usually contain inconsistencies that manifest as blocked reactions and gap metabolites. With the purpose to detect recurrent inconsistencies in metabolic models, a large-scale analysis was performed using a previously published dataset of 130 genome-scale models. The results showed that a large number of reactions (~22%) are blocked in all the models where they are present. To unravel the nature of such inconsistencies a metamodel was construed by joining the 130 models in a single network. This metamodel was manually curated using the unconnected modules approach, and then, it was used as a reference network to perform a gap-filling on each individual genome-scale model. Finally, a set of 36 models that had not been considered during the construction of the metamodel was used, as a proof of concept, to extend the metamodel with new biochemical information, and to assess its impact on gap-filling results. The analysis performed on the metamodel allowed to conclude: 1) the recurrent inconsistencies found in the models were already present in the metabolic database used during the reconstructions process; 2) the presence of inconsistencies in a metabolic database can be propagated to the reconstructed models; 3) there are reactions not manifested as blocked which are active as a consequence of some classes of artifacts, and; 4) the results of an automatic gap-filling are highly dependent on the consistency and completeness of the metamodel or metabolic database used as the reference network. In conclusion the consistency analysis should be applied to metabolic databases in order to detect and fill gaps as well as to detect and remove artifacts and redundant information. PMID:26629901

  2. Probing soil C metabolism in response to temperature: results from experiments and modeling

    Science.gov (United States)

    Dijkstra, P.; Dalder, J.; Blankinship, J.; Selmants, P. C.; Schwartz, E.; Koch, G. W.; Hart, S.; Hungate, B. A.

    2010-12-01

    C use efficiency (CUE) is one of the least understood aspects of soil C cycling, has a very large effect on soil respiration and C sequestration, and decreases with elevated temperature. CUE is directly related to substrate partitioning over energy production and biosynthesis. The production of energy and metabolic precursors occurs in well-known processes such as glycolysis and Krebs cycle. We have developed a new stable isotope approach using position-specific 13C-labeled metabolic tracers to measure these fundamental metabolic processes in intact soil communities (1). We use this new approach, combined with models of soil metabolic flux patterns, to analyze the response of microbial energy production, biosynthesis, and CUE to temperature. The method consists of adding small but precise amounts of position-specific 13C -labeled metabolic tracers to parallel soil incubations, in this case 1-13C and 2,3-13C pyruvate and 1-13C and U-13C glucose. The measurement of CO2 released from the labeled tracers is used to calculate the C flux rates through various metabolic pathways. A simplified metabolic model consisting of 23 reactions is iteratively solved using results of the metabolic tracer experiments and information on microbial precursor demand under different temperatures. This new method enables direct study of fundamental aspects of microbial energy production, C use efficiency, and soil organic matter formation in response to temperature. (1) Dijkstra P, Blankinship JC, Selmants PC, Hart SC, Koch GW, Schwarz E and Hungate BA. Probing metabolic flux patterns of soil microbial communities using parallel position-specific tracer labeling. Soil Biology and Biochemistry (accepted)

  3. Development of an Experimental Model of Diabetes Co-Existing with Metabolic Syndrome in Rats

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    Rajesh Kumar Suman

    2016-01-01

    Full Text Available Background. The incidence of metabolic syndrome co-existing with diabetes mellitus is on the rise globally. Objective. The present study was designed to develop a unique animal model that will mimic the pathological features seen in individuals with diabetes and metabolic syndrome, suitable for pharmacological screening of drugs. Materials and Methods. A combination of High-Fat Diet (HFD and low dose of streptozotocin (STZ at 30, 35, and 40 mg/kg was used to induce metabolic syndrome in the setting of diabetes mellitus in Wistar rats. Results. The 40 mg/kg STZ produced sustained hyperglycemia and the dose was thus selected for the study to induce diabetes mellitus. Various components of metabolic syndrome such as dyslipidemia (increased triglyceride, total cholesterol, LDL cholesterol, and decreased HDL cholesterol, diabetes mellitus (blood glucose, HbA1c, serum insulin, and C-peptide, and hypertension {systolic blood pressure} were mimicked in the developed model of metabolic syndrome co-existing with diabetes mellitus. In addition to significant cardiac injury, atherogenic index, inflammation (hs-CRP, decline in hepatic and renal function were observed in the HF-DC group when compared to NC group rats. The histopathological assessment confirmed presence of edema, necrosis, and inflammation in heart, pancreas, liver, and kidney of HF-DC group as compared to NC. Conclusion. The present study has developed a unique rodent model of metabolic syndrome, with diabetes as an essential component.

  4. Pantograph: A template-based method for genome-scale metabolic model reconstruction.

    Science.gov (United States)

    Loira, Nicolas; Zhukova, Anna; Sherman, David James

    2015-04-01

    Genome-scale metabolic models are a powerful tool to study the inner workings of biological systems and to guide applications. The advent of cheap sequencing has brought the opportunity to create metabolic maps of biotechnologically interesting organisms. While this drives the development of new methods and automatic tools, network reconstruction remains a time-consuming process where extensive manual curation is required. This curation introduces specific knowledge about the modeled organism, either explicitly in the form of molecular processes, or indirectly in the form of annotations of the model elements. Paradoxically, this knowledge is usually lost when reconstruction of a different organism is started. We introduce the Pantograph method for metabolic model reconstruction. This method combines a template reaction knowledge base, orthology mappings between two organisms, and experimental phenotypic evidence, to build a genome-scale metabolic model for a target organism. Our method infers implicit knowledge from annotations in the template, and rewrites these inferences to include them in the resulting model of the target organism. The generated model is well suited for manual curation. Scripts for evaluating the model with respect to experimental data are automatically generated, to aid curators in iterative improvement. We present an implementation of the Pantograph method, as a toolbox for genome-scale model reconstruction, curation and validation. This open source package can be obtained from: http://pathtastic.gforge.inria.fr.

  5. Forecasting societies' adaptive capacities through a demographic metabolism model

    Science.gov (United States)

    Lutz, Wolfgang; Muttarak, Raya

    2017-03-01

    In seeking to understand how future societies will be affected by climate change we cannot simply assume they will be identical to those of today, because climate and societies are both dynamic. Here we propose that the concept of demographic metabolism and the associated methods of multi-dimensional population projections provide an effective analytical toolbox to forecast important aspects of societal change that affect adaptive capacity. We present an example of how the changing educational composition of future populations can influence societies' adaptive capacity. Multi-dimensional population projections form the human core of the Shared Socioeconomic Pathways scenarios, and knowledge and analytical tools from demography have great value in assessing the likely implications of climate change on future human well-being.

  6. METABOLIC MODELLING IN THE DEVELOPMENT OF CELL FACTORIES BY SYNTHETIC BIOLOGY

    Directory of Open Access Journals (Sweden)

    Paula Jouhten

    2012-10-01

    Full Text Available Cell factories are commonly microbial organisms utilized for bioconversion of renewable resources to bulk or high value chemicals. Introduction of novel production pathways in chassis strains is the core of the development of cell factories by synthetic biology. Synthetic biology aims to create novel biological functions and systems not found in nature by combining biology with engineering. The workflow of the development of novel cell factories with synthetic biology is ideally linear which will be attainable with the quantitative engineering approach, high-quality predictive models, and libraries of well-characterized parts. Different types of metabolic models, mathematical representations of metabolism and its components, enzymes and metabolites, are useful in particular phases of the synthetic biology workflow. In this minireview, the role of metabolic modelling in synthetic biology will be discussed with a review of current status of compatible methods and models for the in silico design and quantitative evaluation of a cell factory.

  7. Metabolic modelling in the development of cell factories by synthetic biology

    Directory of Open Access Journals (Sweden)

    Paula Tuulia Jouhten

    2012-10-01

    Full Text Available Cell factories are commonly microbial organisms utilized for bioconversion of renewable resources to bulk or high value chemicals. Introduction of novel production pathways in chassis strains is the core of the development of cell factories by synthetic biology. Synthetic biology aims to create novel biological functions and systems not found in nature by combining biology with engineering. The workflow of the development of novel cell factories with synthetic biology is ideally linear which will be attainable with the quantitative engineering approach, high-quality predictive models, and libraries of well-characterized parts. Different types of metabolic models, mathematical representations of metabolism and its components, enzymes and metabolites, are useful in particular phases of the synthetic biology workflow. In this minireview, the role of metabolic modelling in synthetic biology will be discussed with a review of current status of compatible methods and models for the in silico design and quantitative evaluation of a cell factory.

  8. Genome-scale metabolic model of Pichia pastoris with native and humanized glycosylation of recombinant proteins

    DEFF Research Database (Denmark)

    Irani, Zahra Azimzadeh; Kerkhoven, Eduard J.; Shojaosadati, Seyed Abbas

    2016-01-01

    native nor humanized N-glycosylation, and we therefore developed ihGlycopastoris, an extension to the iLC915 model with both native and humanized N-glycosylation for recombinant protein production, but also an estimation of N-glycosylation of P. pastoris native proteins. This new model gives a better......Pichia pastoris is used for commercial production of human therapeutic proteins, and genome-scale models of P. pastoris metabolism have been generated in the past to study the metabolism and associated protein production by this yeast. A major challenge with clinical usage of recombinant proteins...... produced by P. pastoris is the difference in N-glycosylation of proteins produced by humans and this yeast. However, through metabolic engineering, a P. pastoris strain capable of producing humanized N-glycosylated proteins was constructed. The current genome-scale models of P. pastoris do not address...

  9. Incorporating Protein Biosynthesis into the Saccharomyces cerevisiae Genome-scale Metabolic Model

    DEFF Research Database (Denmark)

    Olivares Hernandez, Roberto

    Based on stoichiometric biochemical equations that occur into the cell, the genome-scale metabolic models can quantify the metabolic fluxes, which are regarded as the final representation of the physiological state of the cell. For Saccharomyces Cerevisiae the genome scale model has been......, translation initiation, translation elongation, translation termination, translation elongation, and mRNA decay. Considering these information from the mechanisms of transcription and translation, we will include this stoichiometric reactions into the genome scale model for S. Cerevisiae to obtain the first...

  10. Enhancement of rapamycin production by metabolic engineering in Streptomyces hygroscopicus based on genome-scale metabolic model.

    Science.gov (United States)

    Dang, Lanqing; Liu, Jiao; Wang, Cheng; Liu, Huanhuan; Wen, Jianping

    2017-02-01

    Rapamycin, as a macrocyclic polyketide with immunosuppressive, antifungal, and anti-tumor activity produced by Streptomyces hygroscopicus, is receiving considerable attention for its significant contribution in medical field. However, the production capacity of the wild strain is very low. Hereby, a computational guided engineering approach was proposed to improve the capability of rapamycin production. First, a genome-scale metabolic model of Streptomyces hygroscopicus ATCC 29253 was constructed based on its annotated genome and biochemical information. The model consists of 1003 reactions, 711 metabolites after manual refinement. Subsequently, several potential genetic targets that likely guaranteed an improved yield of rapamycin were identified by flux balance analysis and minimization of metabolic adjustment algorithm. Furthermore, according to the results of model prediction, target gene pfk (encoding 6-phosphofructokinase) was knocked out, and target genes dahP (encoding 3-deoxy-D-arabino-heptulosonate-7-phosphate synthase) and rapK (encoding chorismatase) were overexpressed in the parent strain ATCC 29253. The yield of rapamycin increased by 30.8% by knocking out gene pfk and increased by 36.2 and 44.8% by overexpression of rapK and dahP, respectively, compared with parent strain. Finally, the combined effect of the genetic modifications was evaluated. The titer of rapamycin reached 250.8 mg/l by knockout of pfk and co-expression of genes dahP and rapK, corresponding to a 142.3% increase relative to that of the parent strain. The relationship between model prediction and experimental results demonstrates the validity and rationality of this approach for target identification and rapamycin production improvement.

  11. GEM System: automatic prototyping of cell-wide metabolic pathway models from genomes

    Directory of Open Access Journals (Sweden)

    Nakayama Yoichi

    2006-03-01

    Full Text Available Abstract Background Successful realization of a "systems biology" approach to analyzing cells is a grand challenge for our understanding of life. However, current modeling approaches to cell simulation are labor-intensive, manual affairs, and therefore constitute a major bottleneck in the evolution of computational cell biology. Results We developed the Genome-based Modeling (GEM System for the purpose of automatically prototyping simulation models of cell-wide metabolic pathways from genome sequences and other public biological information. Models generated by the GEM System include an entire Escherichia coli metabolism model comprising 968 reactions of 1195 metabolites, achieving 100% coverage when compared with the KEGG database, 92.38% with the EcoCyc database, and 95.06% with iJR904 genome-scale model. Conclusion The GEM System prototypes qualitative models to reduce the labor-intensive tasks required for systems biology research. Models of over 90 bacterial genomes are available at our web site.

  12. Acute metabolic decompensation due to influenza in a mouse model of ornithine transcarbamylase deficiency

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    Peter J. McGuire

    2014-02-01

    Full Text Available The urea cycle functions to incorporate ammonia, generated by normal metabolism, into urea. Urea cycle disorders (UCDs are caused by loss of function in any of the enzymes responsible for ureagenesis, and are characterized by life-threatening episodes of acute metabolic decompensation with hyperammonemia (HA. A prospective analysis of interim HA events in a cohort of individuals with ornithine transcarbamylase (OTC deficiency, the most common UCD, revealed that intercurrent infection was the most common precipitant of acute HA and was associated with markers of increased morbidity when compared with other precipitants. To further understand these clinical observations, we developed a model system of metabolic decompensation with HA triggered by viral infection (PR8 influenza using spf-ash mice, a model of OTC deficiency. Both wild-type (WT and spf-ash mice displayed similar cytokine profiles and lung viral titers in response to PR8 influenza infection. During infection, spf-ash mice displayed an increase in liver transaminases, suggesting a hepatic sensitivity to the inflammatory response and an altered hepatic immune response. Despite having no visible pathological changes by histology, WT and spf-ash mice had reduced CPS1 and OTC enzyme activities, and, unlike WT, spf-ash mice failed to increase ureagenesis. Depression of urea cycle function was seen in liver amino acid analysis, with reductions seen in aspartate, ornithine and arginine during infection. In conclusion, we developed a model system of acute metabolic decompensation due to infection in a mouse model of a UCD. In addition, we have identified metabolic perturbations during infection in the spf-ash mice, including a reduction of urea cycle intermediates. This model of acute metabolic decompensation with HA due to infection in UCD serves as a platform for exploring biochemical perturbations and the efficacy of treatments, and could be adapted to explore acute decompensation in other

  13. Compositions and methods for modeling Saccharomyces cerevisiae metabolism

    DEFF Research Database (Denmark)

    2012-01-01

    The invention provides an in silica model for determining a S. cerevisiae physiological function. The model includes a data structure relating a plurality of S. cerevisiae reactants to a plurality of S. cerevisiae reactions, a constraint set for the plurality of S. cerevisiae reactions......, and commands for determining a distribution of flux through the reactions that is predictive of a S. cerevisiae physiological function. A model of the invention can further include a gene database containing information characterizing the associated gene or genes. The invention further provides methods...... for making an in silica S. cerevisiae model and methods for determining a S. cerevisiae physiological function using a model of the invention. The invention provides an in silica model for determining a S. cerevisiae physiological function. The model includes a data structure relating a plurality of S...

  14. Recent Advances of Computational Modeling for Predicting Drug Metabolism: A perspective.

    Science.gov (United States)

    Kar, Supratik; Leszczynski, Jerzy

    2017-06-06

    Absorption, Distribution, Metabolism, Excretion (ADME) properties along with drug induced adverse effects are the major reasons for the late stage failure of drug candidates as well as cause for the expensive withdrawal of many approved drugs from the market. Considering adverse effects of drugs, metabolism factor has great importance in medicinal chemistry and clinical pharmacology because it influences the deactivation, activation, detoxification and toxification of drugs. Computational methods are effective approaches to reduce the number of safety issues by analyzing possible links between chemical structures and metabolism followed by adverse effects, as they serve the integration of information on several levels to enhance the reliability of outcomes. In silico profiling of drug metabolism can help progress only those molecules along the discovery chain those are less likely to fail later in the drug discovery process. This positively impacts the very high costs of drug discovery and development. Understanding the science behind computational tools, their opportunities, and limitations is essential to make a true influence on drug discovery at different levels. If applied in a scientifically consequential way, computational tools may improve the capability to identify and evaluate potential drug molecules considering pharmacokinetic properties of drugs. Herein, current trends in computational modeling for predicting drug metabolism are reviewed highlighting new computational tools for drug metabolism prediction followed by reporting large and integrated databases of approved drug associated with diverse metabolism issues. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  15. A new insulin-glucose metabolic model of type 1 diabetes mellitus: An in silico study.

    Science.gov (United States)

    Fang, Qiang; Yu, Lei; Li, Peng

    2015-06-01

    Diabetes mellitus is a serious metabolic disease that threatens people's health. The artificial pancreas system (APS) has been generally considered as the ultimate cure of type 1 diabetes mellitus (T1DM). The simulation model of insulin-glucose metabolism is an essential part of an APS as it processes the measured glucose level and generates control signal to the insulin infusion system. This paper presents a new insulin-glucose metabolic model using model reduction methods applied to the popular but complex Cobelli's model. The performances of three different model reduction methods, namely Padé approximation, Routh approximation and system identification, are compared. The results of in silico simulation based on 30 virtual patients of three groups for adults, adolescents, and children show that the approximation error between this new model and the original Cobelli's model is so small that can be neglected. It can be concluded that the proposed simplified model can describe the insulin-glucose metabolism process rather accurately as well as can be easily implemented and integrated into an APS to make the APS technology more mature and closer to clinical use. The FPGA implementation, testing and further simplification possibility will be explored in the next stage of research. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  16. Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression

    DEFF Research Database (Denmark)

    Ma, Ding; Yang, Laurence; Fleming, Ronan M. T.

    2017-01-01

    Constraint-Based Reconstruction and Analysis (COBRA) is currently the only methodology that permits integrated modeling of Metabolism and macromolecular Expression (ME) at genome-scale. Linear optimization computes steady-state flux solutions to ME models, but flux values are spread over many...

  17. Multivariate modelling of endophenotypes associated with the metabolic syndrome in Chinese twins

    DEFF Research Database (Denmark)

    Pang, Z; Zhang, D; Li, S

    2010-01-01

    AIMS/HYPOTHESIS: The common genetic and environmental effects on endophenotypes related to the metabolic syndrome have been investigated using bivariate and multivariate twin models. This paper extends the pairwise analysis approach by introducing independent and common pathway models to Chinese...

  18. Multisite Kinetic Modeling of 13C Metabolic MR Using [1-13C]Pyruvate

    Directory of Open Access Journals (Sweden)

    Pedro A. Gómez Damián

    2014-01-01

    Full Text Available Hyperpolarized 13C imaging allows real-time in vivo measurements of metabolite levels. Quantification of metabolite conversion between [1-13C]pyruvate and downstream metabolites [1-13C]alanine, [1-13C]lactate, and [13C]bicarbonate can be achieved through kinetic modeling. Since pyruvate interacts dynamically and simultaneously with its downstream metabolites, the purpose of this work is the determination of parameter values through a multisite, dynamic model involving possible biochemical pathways present in MR spectroscopy. Kinetic modeling parameters were determined by fitting the multisite model to time-domain dynamic metabolite data. The results for different pyruvate doses were compared with those of different two-site models to evaluate the hypothesis that for identical data the uncertainty of a model and the signal-to-noise ratio determine the sensitivity in detecting small physiological differences in the target metabolism. In comparison to the two-site exchange models, the multisite model yielded metabolic conversion rates with smaller bias and smaller standard deviation, as demonstrated in simulations with different signal-to-noise ratio. Pyruvate dose effects observed previously were confirmed and quantified through metabolic conversion rate values. Parameter interdependency allowed an accurate quantification and can therefore be useful for monitoring metabolic activity in different tissues.

  19. Identifying anti-growth factors for human cancer cell lines through genome-scale metabolic modeling

    DEFF Research Database (Denmark)

    Ghaffari, Pouyan; Mardinoglu, Adil; Asplund, Anna

    2015-01-01

    Human cancer cell lines are used as important model systems to study molecular mechanisms associated with tumor growth, hereunder how genomic and biological heterogeneity found in primary tumors affect cellular phenotypes. We reconstructed Genome scale metabolic models (GEMs) for eleven cell line...

  20. The human body metabolism process mathematical simulation based on Lotka-Volterra model

    Science.gov (United States)

    Oliynyk, Andriy; Oliynyk, Eugene; Pyptiuk, Olexandr; DzierŻak, RóŻa; Szatkowska, Małgorzata; Uvaysova, Svetlana; Kozbekova, Ainur

    2017-08-01

    The mathematical model of metabolism process in human organism based on Lotka-Volterra model has beeng proposed, considering healing regime, nutrition system, features of insulin and sugar fragmentation process in the organism. The numerical algorithm of the model using IV-order Runge-Kutta method has been realized. After the result of calculations the conclusions have been made, recommendations about using the modeling results have been showed, the vectors of the following researches are defined.

  1. Genome-based Modeling and Design of Metabolic Interactions in Microbial Communities

    Directory of Open Access Journals (Sweden)

    Radhakrishnan Mahadevan

    2012-10-01

    Full Text Available Biotechnology research is traditionally focused on individual microbial strains that are perceived to have the necessary metabolic functions, or the capability to have these functions introduced, to achieve a particular task. For many important applications, the development of such omnipotent microbes is an extremely challenging if not impossible task. By contrast, nature employs a radically different strategy based on synergistic combinations of different microbial species that collectively achieve the desired task. These natural communities have evolved to exploit the native metabolic capabilities of each species and are highly adaptive to changes in their environments. However, microbial communities have proven difficult to study due to a lack of suitable experimental and computational tools.With the advent of genome sequencing, omics technologies, bioinformatics and genome-scale modeling, researchers now have unprecedented capabilities to analyze and engineer the metabolism of microbial communities. The goal of this review is to summarize recent applications of genome-scale metabolic modeling to microbial communities. A brief introduction to lumped community models is used to motivate the need for genome-level descriptions of individual species and their metabolic interactions. The review of genome-scale models begins with static modeling approaches, which are appropriate for communities where the extracellular environment can be assumed to be time invariant or slowly varying. Dynamic extensions of the static modeling approach are described, and then applications of genome-scale models for design of synthetic microbial communities are reviewed. The review concludes with a summary of metagenomic tools for analyzing community metabolism and an outlook for future research.

  2. Deriving metabolic engineering strategies from genome-scale modeling with flux ratio constraints.

    Science.gov (United States)

    Yen, Jiun Y; Nazem-Bokaee, Hadi; Freedman, Benjamin G; Athamneh, Ahmad I M; Senger, Ryan S

    2013-05-01

    Optimized production of bio-based fuels and chemicals from microbial cell factories is a central goal of systems metabolic engineering. To achieve this goal, a new computational method of using flux balance analysis with flux ratios (FBrAtio) was further developed in this research and applied to five case studies to evaluate and design metabolic engineering strategies. The approach was implemented using publicly available genome-scale metabolic flux models. Synthetic pathways were added to these models along with flux ratio constraints by FBrAtio to achieve increased (i) cellulose production from Arabidopsis thaliana; (ii) isobutanol production from Saccharomyces cerevisiae; (iii) acetone production from Synechocystis sp. PCC6803; (iv) H2 production from Escherichia coli MG1655; and (v) isopropanol, butanol, and ethanol (IBE) production from engineered Clostridium acetobutylicum. The FBrAtio approach was applied to each case to simulate a metabolic engineering strategy already implemented experimentally, and flux ratios were continually adjusted to find (i) the end-limit of increased production using the existing strategy, (ii) new potential strategies to increase production, and (iii) the impact of these metabolic engineering strategies on product yield and culture growth. The FBrAtio approach has the potential to design "fine-tuned" metabolic engineering strategies in silico that can be implemented directly with available genomic tools. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. Determining host metabolic limitations on viral replication via integrated modeling and experimental perturbation.

    Directory of Open Access Journals (Sweden)

    Elsa W Birch

    Full Text Available Viral replication relies on host metabolic machinery and precursors to produce large numbers of progeny - often very rapidly. A fundamental example is the infection of Escherichia coli by bacteriophage T7. The resource draw imposed by viral replication represents a significant and complex perturbation to the extensive and interconnected network of host metabolic pathways. To better understand this system, we have integrated a set of structured ordinary differential equations quantifying T7 replication and an E. coli flux balance analysis metabolic model. Further, we present here an integrated simulation algorithm enforcing mutual constraint by the models across the entire duration of phage replication. This method enables quantitative dynamic prediction of virion production given only specification of host nutritional environment, and predictions compare favorably to experimental measurements of phage replication in multiple environments. The level of detail of our computational predictions facilitates exploration of the dynamic changes in host metabolic fluxes that result from viral resource consumption, as well as analysis of the limiting processes dictating maximum viral progeny production. For example, although it is commonly assumed that viral infection dynamics are predominantly limited by the amount of protein synthesis machinery in the host, our results suggest that in many cases metabolic limitation is at least as strict. Taken together, these results emphasize the importance of considering viral infections in the context of host metabolism.

  4. Development of Computational Tools for Metabolic Model Curation, Flux Elucidation and Strain Design

    Energy Technology Data Exchange (ETDEWEB)

    Maranas, Costas D

    2012-05-21

    An overarching goal of the Department of Energy mission is the efficient deployment and engineering of microbial and plant systems to enable biomass conversion in pursuit of high energy density liquid biofuels. This has spurred the pace at which new organisms are sequenced and annotated. This torrent of genomic information has opened the door to understanding metabolism in not just skeletal pathways and a handful of microorganisms but for truly genome-scale reconstructions derived for hundreds of microbes and plants. Understanding and redirecting metabolism is crucial because metabolic fluxes are unique descriptors of cellular physiology that directly assess the current cellular state and quantify the effect of genetic engineering interventions. At the same time, however, trying to keep pace with the rate of genomic data generation has ushered in a number of modeling and computational challenges related to (i) the automated assembly, testing and correction of genome-scale metabolic models, (ii) metabolic flux elucidation using labeled isotopes, and (iii) comprehensive identification of engineering interventions leading to the desired metabolism redirection.

  5. High-fat diets: modeling the metabolic disorders of human obesity in rodents.

    Science.gov (United States)

    Buettner, Roland; Schölmerich, Jürgen; Bollheimer, L Cornelius

    2007-04-01

    High-fat (HF) diet feeding can induce obesity and metabolic disorders in rodents that resemble the human metabolic syndrome. However, this dietary intervention is not standardized, and the HF-induced phenotype varies distinctly among different studies. The question which HF diet type is best to model the metabolic deterioration seen in human obesity remains unclear. Therefore, in this review, metabolic data obtained with different HF diet approaches are compiled. Both whole-body and organ-specific diet effects are analyzed. On the basis of these results, we conclude that animal fats and omega-6/omega-9-containing plant oils can be used to generate an obese and insulin-resistant phenotype in rodents, whereas fish oil-fed animals do not develop these disorders. Looking at the present data, it does not seem possible to define an ideal HF diet, and an exact definition of diet composition and a thorough metabolic characterization of the HF diet effects in a researcher's specific laboratory setting remains essential for metabolic studies with this model.

  6. Compartmentalized nitric oxide signaling in the resistance vasculature.

    Science.gov (United States)

    Mutchler, Stephanie M; Straub, Adam C

    2015-09-15

    Nitric oxide (NO) was first described as a bioactive molecule through its ability to stimulate soluble guanylate cyclase, but the revelation that NO was the endothelium derived relaxation factor drove the field to its modern state. The wealth of research conducted over the past 30 years has provided us with a picture of how diverse NO signaling can be within the vascular wall, going beyond simple vasodilation to include such roles as signaling through protein S-nitrosation. This expanded view of NO's actions requires highly regulated and compartmentalized production. Importantly, resistance arteries house multiple proteins involved in the production and transduction of NO allowing for efficient movement of the molecule to regulate vascular tone and reactivity. In this review, we focus on the many mechanisms regulating NO production and signaling action in the vascular wall, with a focus on the control of endothelial nitric oxide synthase (eNOS), the enzyme responsible for synthesizing most of the NO within these confines. We also explore how cross talk between the endothelium and smooth muscle in the microcirculation can modulate NO signaling, illustrating that this one small molecule has the capability to produce a plethora of responses. Copyright © 2015 Elsevier Inc. All rights reserved.

  7. Plasma proteome profiles associated with diet-induced metabolic syndrome and the early onset of metabolic syndrome in a pig model.

    Directory of Open Access Journals (Sweden)

    Marinus F W te Pas

    Full Text Available Obesity and related diabetes are important health threatening multifactorial metabolic diseases and it has been suggested that 25% of all diabetic patients are unaware of their patho-physiological condition. Biomarkers for monitoring and control are available, but early stage predictive biomarkers enabling prevention of these diseases are still lacking. We used the pig as a model to study metabolic disease because humans and pigs share a multitude of metabolic similarities. Diabetes was chemically induced and control and diabetic pigs were either fed a high unsaturated fat (Mediterranean diet or a high saturated fat/cholesterol/sugar (cafeteria diet. Physiological parameters related to fat metabolism and diabetes were measured. Diabetic pigs' plasma proteome profiles differed more between the two diets than control pigs plasma proteome profiles. The expression levels of several proteins correlated well with (pathophysiological parameters related to the fat metabolism (cholesterol, VLDL, LDL, NEFA and diabetes (Glucose and to the diet fed to the animals. Studying only the control pigs as a model for metabolic syndrome when fed the two diets showed correlations to the same parameters but now more focused on insulin, glucose and abdominal fat depot parameters. We conclude that proteomic profiles can be used as a biomarker to identify pigs with developing metabolic syndrome (prediabetes and diabetes when fed a cafeteria diet. It could be developed into a potential biomarkers for the early recognition of metabolic diseases.

  8. Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model

    Directory of Open Access Journals (Sweden)

    Hui Chen

    2014-01-01

    Full Text Available Metabolic syndrome is worldwide public health problem and is a serious threat to people's health and lives. Understanding the relationship between metabolic syndrome and the physical symptoms is a difficult and challenging task, and few studies have been performed in this field. It is important to classify adults who are at high risk of metabolic syndrome without having to use a biochemical index and, likewise, it is important to develop technology that has a high economic rate of return to simplify the complexity of this detection. In this paper, an artificial intelligence model was developed to identify adults at risk of metabolic syndrome based on physical signs; this artificial intelligence model achieved more powerful capacity for classification compared to the PCLR (principal component logistic regression model. A case study was performed based on the physical signs data, without using a biochemical index, that was collected from the staff of Lanzhou Grid Company in Gansu province of China. The results show that the developed artificial intelligence model is an effective classification system for identifying individuals at high risk of metabolic syndrome.

  9. PASMet: a web-based platform for prediction, modelling and analyses of metabolic systems.

    Science.gov (United States)

    Sriyudthsak, Kansuporn; Mejia, Ramon Francisco; Arita, Masanori; Hirai, Masami Yokota

    2016-07-08

    PASMet (Prediction, Analysis and Simulation of Metabolic networks) is a web-based platform for proposing and verifying mathematical models to understand the dynamics of metabolism. The advantages of PASMet include user-friendliness and accessibility, which enable biologists and biochemists to easily perform mathematical modelling. PASMet offers a series of user-functions to handle the time-series data of metabolite concentrations. The functions are organised into four steps: (i) Prediction of a probable metabolic pathway and its regulation; (ii) Construction of mathematical models; (iii) Simulation of metabolic behaviours; and (iv) Analysis of metabolic system characteristics. Each function contains various statistical and mathematical methods that can be used independently. Users who may not have enough knowledge of computing or programming can easily and quickly analyse their local data without software downloads, updates or installations. Users only need to upload their files in comma-separated values (CSV) format or enter their model equations directly into the website. Once the time-series data or mathematical equations are uploaded, PASMet automatically performs computation on server-side. Then, users can interactively view their results and directly download them to their local computers. PASMet is freely available with no login requirement at http://pasmet.riken.jp/ from major web browsers on Windows, Mac and Linux operating systems. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  10. Computational Prediction of Synthetic Lethals in Genome-Scale Metabolic Models Using Fast-SL.

    Science.gov (United States)

    Raman, Karthik; Pratapa, Aditya; Mohite, Omkar; Balachandran, Shankar

    2018-01-01

    In this chapter, we describe Fast-SL, an in silico approach to predict synthetic lethals in genome-scale metabolic models. Synthetic lethals are sets of genes or reactions where only the simultaneous removal of all genes or reactions in the set abolishes growth of an organism. In silico approaches to predict synthetic lethals are based on Flux Balance Analysis (FBA), a popular constraint-based analysis method based on linear programming. FBA has been shown to accurately predict the viability of various genome-scale metabolic models. Fast-SL builds on the framework of FBA and enables the prediction of synthetic lethal reactions or genes in different organisms, under various environmental conditions. Predicting synthetic lethals in metabolic network models allows us to generate hypotheses on possible novel genetic interactions and potential candidates for combinatorial therapy, in case of pathogenic organisms. We here summarize the Fast-SL approach for analyzing metabolic networks and detail the procedure to predict synthetic lethals in any given metabolic model. We illustrate the approach by predicting synthetic lethals in Escherichia coli. The Fast-SL implementation for MATLAB is available from https://github.com/RamanLab/FastSL/ .

  11. Systems biology from micro-organisms to human metabolic diseases: the role of detailed kinetic models.

    Science.gov (United States)

    Bakker, Barbara M; van Eunen, Karen; Jeneson, Jeroen A L; van Riel, Natal A W; Bruggeman, Frank J; Teusink, Bas

    2010-10-01

    Human metabolic diseases are typically network diseases. This holds not only for multifactorial diseases, such as metabolic syndrome or Type 2 diabetes, but even when a single gene defect is the primary cause, where the adaptive response of the entire network determines the severity of disease. The latter may differ between individuals carrying the same mutation. Understanding the adaptive responses of human metabolism naturally requires a systems biology approach. Modelling of metabolic pathways in micro-organisms and some mammalian tissues has yielded many insights, qualitative as well as quantitative, into their control and regulation. Yet, even for a well-known pathway such as glycolysis, precise predictions of metabolite dynamics from experimentally determined enzyme kinetics have been only moderately successful. In the present review, we compare kinetic models of glycolysis in three cell types (African trypanosomes, yeast and skeletal muscle), evaluate their predictive power and identify limitations in our understanding. Although each of these models has its own merits and shortcomings, they also share common features. For example, in each case independently measured enzyme kinetic parameters were used as input. Based on these 'lessons from glycolysis', we will discuss how to make best use of kinetic computer models to advance our understanding of human metabolic diseases.

  12. A mathematical model of liver metabolism: from steady state to dynamic

    Energy Technology Data Exchange (ETDEWEB)

    Calvetti, D; Kuceyeski, A [Case Western Reserve University, Department of Mathematics, 10900 Euclid Avenue, Cleveland, OH 44106 (United States); Somersalo, E [Helsinki University of Technology, Institute of Mathematics, P. O. Box 1100, FIN-02015 HUT (Finland)], E-mail: daniela.calvetti@case.edu, E-mail: amy.kuceyeski@case.edu, E-mail: erkki.somersalo@hut.fi

    2008-07-15

    The increase in Type 2 diabetes and other metabolic disorders has led to an intense focus on the areas of research related to metabolism. Because the liver is essential in regulating metabolite concentrations that maintain life, it is especially important to have good knowledge of the functions within this organ. In silico mathematical models that can adequately describe metabolite concentrations, flux and transport rates in the liver in vivo can be a useful predictive tool. Fully dynamic models, which contain expressions for Michaelis-Menten reaction kinetics can be utilized to investigate different metabolic states, for example exercise, fed or starved state. In this paper we describe a two compartment (blood and tissue) spatially lumped liver metabolism model. First, we use Bayesian Flux Balance Analysis (BFBA) to estimate the values of flux and transport rates at steady state, which agree closely with values from the literature. These values are then used to find a set of Michaelis-Menten parameters and initial concentrations which identify a dynamic model that can be used for exploring different metabolic states. In particular, we investigate the effect of doubling the concentration of lactate entering the system via the hepatic artery and portal vein. This change in lactate concentration forces the system to a new steady state, where glucose production is increased.

  13. Computational modeling to predict nitrogen balance during acute metabolic decompensation in patients with urea cycle disorders.

    Science.gov (United States)

    MacLeod, Erin L; Hall, Kevin D; McGuire, Peter J

    2016-01-01

    Nutritional management of acute metabolic decompensation in amino acid inborn errors of metabolism (AA IEM) aims to restore nitrogen balance. While nutritional recommendations have been published, they have never been rigorously evaluated. Furthermore, despite these recommendations, there is a wide variation in the nutritional strategies employed amongst providers, particularly regarding the inclusion of parenteral lipids for protein-free caloric support. Since randomized clinical trials during acute metabolic decompensation are difficult and potentially dangerous, mathematical modeling of metabolism can serve as a surrogate for the preclinical evaluation of nutritional interventions aimed at restoring nitrogen balance during acute decompensation in AA IEM. A validated computational model of human macronutrient metabolism was adapted to predict nitrogen balance in response to various nutritional interventions in a simulated patient with a urea cycle disorder (UCD) during acute metabolic decompensation due to dietary non-adherence or infection. The nutritional interventions were constructed from published recommendations as well as clinical anecdotes. Overall, dextrose alone (DEX) was predicted to be better at restoring nitrogen balance and limiting nitrogen excretion during dietary non-adherence and infection scenarios, suggesting that the published recommended nutritional strategy involving dextrose and parenteral lipids (ISO) may be suboptimal. The implications for patients with AA IEM are that the medical course during acute metabolic decompensation may be influenced by the choice of protein-free caloric support. These results are also applicable to intensive care patients undergoing catabolism (postoperative phase or sepsis), where parenteral nutritional support aimed at restoring nitrogen balance may be more tailored regarding metabolic fuel selection.

  14. Predicting dynamic metabolic demands in the photosynthetic eukaryote Chlorella vulgaris

    Energy Technology Data Exchange (ETDEWEB)

    Zuniga, Cristal; Levering, Jennifer; Antoniewicz, Maciek R.; Guarnieri, Michael T.; Betenbaugh, Michael J.; Zengler, Karsten

    2017-09-26

    Phototrophic organisms exhibit a highly dynamic proteome, adapting their biomass composition in response to diurnal light/dark cycles and nutrient availability. Here, we used experimentally determined biomass compositions over the course of growth to determine and constrain the biomass objective function (BOF) in a genome-scale metabolic model of Chlorella vulgaris UTEX 395 over time. Changes in the BOF, which encompasses all metabolites necessary to produce biomass, influence the state of the metabolic network thus directly affecting predictions. Simulations using dynamic BOFs predicted distinct proteome demands during heterotrophic or photoautotrophic growth. Model-driven analysis of extracellular nitrogen concentrations and predicted nitrogen uptake rates revealed an intracellular nitrogen pool, which contains 38% of the total nitrogen provided in the medium for photoautotrophic and 13% for heterotrophic growth. Agreement between flux and gene expression trends was determined by statistical comparison. Accordance between predicted fluxes trends and gene expression trends was found for 65% of multi-subunit enzymes and 75% of allosteric reactions. Reactions with the highest agreement between simulations and experimental data were associated with energy metabolism, terpenoid biosynthesis, fatty acids, nucleotides, and amino acids metabolism. Furthermore, predicted flux distributions at each time point were compared with gene expression data to gain new insights into intracellular compartmentalization, specifically for transporters. A total of 103 genes related to internal transport reactions were identified and added to the updated model of C. vulgaris, iCZ946, thus increasing our knowledgebase by 10% for this model green alga.

  15. The cellular and compartmental profile of mouse retinal glycolysis, tricarboxylic acid cycle, oxidative phosphorylation, and ~P transferring kinases.

    Science.gov (United States)

    Rueda, Elda M; Johnson, Jerry E; Giddabasappa, Anand; Swaroop, Anand; Brooks, Matthew J; Sigel, Irena; Chaney, Shawnta Y; Fox, Donald A

    2016-01-01

    The homeostatic regulation of cellular ATP is achieved by the coordinated activity of ATP utilization, synthesis, and buffering. Glucose is the major substrate for ATP synthesis through glycolysis and oxidative phosphorylation (OXPHOS), whereas intermediary metabolism through the tricarboxylic acid (TCA) cycle utilizes non-glucose-derived monocarboxylates, amino acids, and alpha ketoacids to support mitochondrial ATP and GTP synthesis. Cellular ATP is buffered by specialized equilibrium-driven high-energy phosphate (~P) transferring kinases. Our goals were twofold: 1) to characterize the gene expression, protein expression, and activity of key synthesizing and regulating enzymes of energy metabolism in the whole mouse retina, retinal compartments, and/or cells and 2) to provide an integrative analysis of the results related to function. mRNA expression data of energy-related genes were extracted from our whole retinal Affymetrix microarray data. Fixed-frozen retinas from adult C57BL/6N mice were used for immunohistochemistry, laser scanning confocal microscopy, and enzymatic histochemistry. The immunoreactivity levels of well-characterized antibodies, for all major retinal cells and their compartments, were obtained using our established semiquantitative confocal and imaging techniques. Quantitative cytochrome oxidase (COX) and lactate dehydrogenase (LDH) activity was determined histochemically. The Affymetrix data revealed varied gene expression patterns of the ATP synthesizing and regulating enzymes found in the muscle, liver, and brain. Confocal studies showed differential cellular and compartmental distribution of isozymes involved in glucose, glutamate, glutamine, lactate, and creatine metabolism. The pattern and intensity of the antibodies and of the COX and LDH activity showed the high capacity of photoreceptors for aerobic glycolysis and OXPHOS. Competition assays with pyruvate revealed that LDH-5 was localized in the photoreceptor inner segments. The

  16. Na+ compartmentalization related to salinity stress tolerance in upland cotton (Gossypium hirsutum) seedlings

    OpenAIRE

    Zhen Peng; Shoupu He; Junling Sun; Zhaoe Pan; Wenfang Gong; Yanli Lu; Xiongming Du

    2016-01-01

    The capacity for ion compartmentalization among different tissues and cells is the key mechanism regulating salt tolerance in plants. In this study, we investigated the ion compartmentalization capacity of two upland cotton genotypes with different salt tolerances under salt shock at the tissue, cell and molecular levels. We found that the leaf glandular trichome could secrete more salt ions in the salt-tolerant genotype than in the sensitive genotype, demonstrating the excretion of ions from...

  17. OpenFLUX: efficient modelling software for 13C-based metabolic flux analysis.

    Science.gov (United States)

    Quek, Lake-Ee; Wittmann, Christoph; Nielsen, Lars K; Krömer, Jens O

    2009-05-01

    The quantitative analysis of metabolic fluxes, i.e., in vivo activities of intracellular enzymes and pathways, provides key information on biological systems in systems biology and metabolic engineering. It is based on a comprehensive approach combining (i) tracer cultivation on 13C substrates, (ii) 13C labelling analysis by mass spectrometry and (iii) mathematical modelling for experimental design, data processing, flux calculation and statistics. Whereas the cultivation and the analytical part is fairly advanced, a lack of appropriate modelling software solutions for all modelling aspects in flux studies is limiting the application of metabolic flux analysis. We have developed OpenFLUX as a user friendly, yet flexible software application for small and large scale 13C metabolic flux analysis. The application is based on the new Elementary Metabolite Unit (EMU) framework, significantly enhancing computation speed for flux calculation. From simple notation of metabolic reaction networks defined in a spreadsheet, the OpenFLUX parser automatically generates MATLAB-readable metabolite and isotopomer balances, thus strongly facilitating model creation. The model can be used to perform experimental design, parameter estimation and sensitivity analysis either using the built-in gradient-based search or Monte Carlo algorithms or in user-defined algorithms. Exemplified for a microbial flux study with 71 reactions, 8 free flux parameters and mass isotopomer distribution of 10 metabolites, OpenFLUX allowed to automatically compile the EMU-based model from an Excel file containing metabolic reactions and carbon transfer mechanisms, showing it's user-friendliness. It reliably reproduced the published data and optimum flux distributions for the network under study were found quickly (studies. By providing the software open source, we hope it will evolve with the rapidly growing field of fluxomics.

  18. iAK692: a genome-scale metabolic model of Spirulina platensis C1.

    Science.gov (United States)

    Klanchui, Amornpan; Khannapho, Chiraphan; Phodee, Atchara; Cheevadhanarak, Supapon; Meechai, Asawin

    2012-06-15

    Spirulina (Arthrospira) platensis is a well-known filamentous cyanobacterium used in the production of many industrial products, including high value compounds, healthy food supplements, animal feeds, pharmaceuticals and cosmetics, for example. It has been increasingly studied around the world for scientific purposes, especially for its genome, biology, physiology, and also for the analysis of its small-scale metabolic network. However, the overall description of the metabolic and biotechnological capabilities of S. platensis requires the development of a whole cellular metabolism model. Recently, the S. platensis C1 (Arthrospira sp. PCC9438) genome sequence has become available, allowing systems-level studies of this commercial cyanobacterium. In this work, we present the genome-scale metabolic network analysis of S. platensis C1, iAK692, its topological properties, and its metabolic capabilities and functions. The network was reconstructed from the S. platensis C1 annotated genomic sequence using Pathway Tools software to generate a preliminary network. Then, manual curation was performed based on a collective knowledge base and a combination of genomic, biochemical, and physiological information. The genome-scale metabolic model consists of 692 genes, 837 metabolites, and 875 reactions. We validated iAK692 by conducting fermentation experiments and simulating the model under autotrophic, heterotrophic, and mixotrophic growth conditions using COBRA toolbox. The model predictions under these growth conditions were consistent with the experimental results. The iAK692 model was further used to predict the unique active reactions and essential genes for each growth condition. Additionally, the metabolic states of iAK692 during autotrophic and mixotrophic growths were described by phenotypic phase plane (PhPP) analysis. This study proposes the first genome-scale model of S. platensis C1, iAK692, which is a predictive metabolic platform for a global understanding of

  19. iAK692: A genome-scale metabolic model of Spirulina platensis C1

    Directory of Open Access Journals (Sweden)

    Klanchui Amornpan

    2012-06-01

    Full Text Available Abstract Background Spirulina (Arthrospira platensis is a well-known filamentous cyanobacterium used in the production of many industrial products, including high value compounds, healthy food supplements, animal feeds, pharmaceuticals and cosmetics, for example. It has been increasingly studied around the world for scientific purposes, especially for its genome, biology, physiology, and also for the analysis of its small-scale metabolic network. However, the overall description of the metabolic and biotechnological capabilities of S. platensis requires the development of a whole cellular metabolism model. Recently, the S. platensis C1 (Arthrospira sp. PCC9438 genome sequence has become available, allowing systems-level studies of this commercial cyanobacterium. Results In this work, we present the genome-scale metabolic network analysis of S. platensis C1, iAK692, its topological properties, and its metabolic capabilities and functions. The network was reconstructed from the S. platensis C1 annotated genomic sequence using Pathway Tools software to generate a preliminary network. Then, manual curation was performed based on a collective knowledge base and a combination of genomic, biochemical, and physiological information. The genome-scale metabolic model consists of 692 genes, 837 metabolites, and 875 reactions. We validated iAK692 by conducting fermentation experiments and simulating the model under autotrophic, heterotrophic, and mixotrophic growth conditions using COBRA toolbox. The model predictions under these growth conditions were consistent with the experimental results. The iAK692 model was further used to predict the unique active reactions and essential genes for each growth condition. Additionally, the metabolic states of iAK692 during autotrophic and mixotrophic growths were described by phenotypic phase plane (PhPP analysis. Conclusions This study proposes the first genome-scale model of S. platensis C1, iAK692, which is a

  20. The contribution of atom accessibility to site of metabolism models for cytochromes P450

    DEFF Research Database (Denmark)

    Rydberg, Patrik; Rostkowski, M.; Gloriam, D.E.

    2013-01-01

    Three different types of atom accessibility descriptors are investigated in relation to site of metabolism predictions. To enable the integration of local accessibility we have constructed 2DSASA, a method for the calculation of the atomic solvent accessible surface area that is independent of 3D...... coordinates. The method was implemented in the SMARTCyp site of metabolism prediction models and improved the results by up to 4 percentage points for nine cytochrome P450 isoforms. The final models are made available at http://www.farma.ku.dk/smartcyp....

  1. Investigating host-pathogen behavior and their interaction using genome-scale metabolic network models.

    Science.gov (United States)

    Sadhukhan, Priyanka P; Raghunathan, Anu

    2014-01-01

    Genome Scale Metabolic Modeling methods represent one way to compute whole cell function starting from the genome sequence of an organism and contribute towards understanding and predicting the genotype-phenotype relationship. About 80 models spanning all the kingdoms of life from archaea to eukaryotes have been built till date and used to interrogate cell phenotype under varying conditions. These models have been used to not only understand the flux distribution in evolutionary conserved pathways like glycolysis and the Krebs cycle but also in applications ranging from value added product formation in Escherichia coli to predicting inborn errors of Homo sapiens metabolism. This chapter describes a protocol that delineates the process of genome scale metabolic modeling for analysing host-pathogen behavior and interaction using flux balance analysis (FBA). The steps discussed in the process include (1) reconstruction of a metabolic network from the genome sequence, (2) its representation in a precise mathematical framework, (3) its translation to a model, and (4) the analysis using linear algebra and optimization. The methods for biological interpretations of computed cell phenotypes in the context of individual host and pathogen models and their integration are also discussed.

  2. A stochastic model for the evolution of metabolic networks with neighbor dependence.

    Science.gov (United States)

    Mithani, Aziz; Preston, Gail M; Hein, Jotun

    2009-06-15

    Most current research in network evolution focuses on networks that follow a Duplication Attachment model where the network is only allowed to grow. The evolution of metabolic networks, however, is characterized by gain as well as loss of reactions. It would be desirable to have a biologically relevant model of network evolution that could be used to calculate the likelihood of homologous metabolic networks. We describe metabolic network evolution as a discrete space continuous time Markov process and introduce a neighbor-dependent model for the evolution of metabolic networks where the rates with which reactions are added or removed depend on the fraction of neighboring reactions present in the network. We also present a Gibbs sampler for estimating the parameters of evolution without exploring the whole search space by iteratively sampling from the conditional distributions of the paths and parameters. A Metropolis-Hastings algorithm for sampling paths between two networks and calculating the likelihood of evolution is also presented. The sampler is used to estimate the parameters of evolution of metabolic networks in the genus Pseudomonas. An implementation of the Gibbs sampler in Java is available at http://www.stats.ox.ac.uk/ approximately mithani/networkGibbs/. Supplementary data are available at the Bioinformatics online.

  3. A metabolic system-wide characterisation of the pig: a model for human physiology.

    Science.gov (United States)

    Merrifield, Claire A; Lewis, Marie; Claus, Sandrine P; Beckonert, Olaf P; Dumas, Marc-Emmanuel; Duncker, Swantje; Kochhar, Sunil; Rezzi, Serge; Lindon, John C; Bailey, Mick; Holmes, Elaine; Nicholson, Jeremy K

    2011-09-01

    The pig is a single-stomached omnivorous mammal and is an important model of human disease and nutrition. As such, it is necessary to establish a metabolic framework from which pathology-based variation can be compared. Here, a combination of one and two-dimensional (1)H and (13)C nuclear magnetic resonance spectroscopy (NMR) and high-resolution magic angle spinning (HR-MAS) NMR was used to provide a systems overview of porcine metabolism via characterisation of the urine, serum, liver and kidney metabolomes. The metabolites observed in each of these biological compartments were found to be qualitatively comparable to the metabolic signature of the same biological matrices in humans and rodents. The data were modelled using a combination of principal components analysis and Venn diagram mapping. Urine represented the most metabolically distinct biological compartment studied, with a relatively greater number of NMR detectable metabolites present, many of which are implicated in gut-microbial co-metabolic processes. The major inter-species differences observed were in the phase II conjugation of extra-genomic metabolites; the pig was observed to conjugate p-cresol, a gut microbial metabolite of tyrosine, with glucuronide rather than sulfate as seen in man. These observations are important to note when considering the translatability of experimental data derived from porcine models.

  4. Systems toxicology: modelling biomarkers of glutathione homeostasis and paracetamol metabolism.

    Science.gov (United States)

    Stahl, Simone H; Yates, James W; Nicholls, Andrew W; Kenna, J Gerry; Coen, Muireann; Ortega, Fernando; Nicholson, Jeremy K; Wilson, Ian D

    2015-08-01

    One aim of systems toxicology is to deliver mechanistic, mathematically rigorous, models integrating biochemical and pharmacological processes that result in toxicity to enhance the assessment of the risk posed to humans by drugs and other xenobiotics. The benefits of such 'in silico' models would be in enabling the rapid and robust prediction of the effects of compounds over a range of exposures, improving in vitro-in vivo correlations and the translation from preclinical species to humans. Systems toxicology models of organ toxicities that result in high attrition rates during drug discovery and development, or post-marketing withdrawals (e.g., drug-induced liver injury (DILI)) should facilitate the discovery of safe new drugs. Here, systems toxicology as applied to the effects of paracetamol (acetaminophen, N-acetyl-para-aminophenol (APAP)) is used to exemplify the potential of the approach. Copyright © 2015 Elsevier Ltd. All rights reserved.

  5. High-fat diet induces significant metabolic disorders in a mouse model of polycystic ovary syndrome.

    Science.gov (United States)

    Lai, Hao; Jia, Xiao; Yu, Qiuxiao; Zhang, Chenglu; Qiao, Jie; Guan, Youfei; Kang, Jihong

    2014-11-01

    Polycystic ovary syndrome (PCOS) is the most common female endocrinopathy associated with both reproductive and metabolic disorders. Dehydroepiandrosterone (DHEA) is currently used to induce a PCOS mouse model. High-fat diet (HFD) has been shown to cause obesity and infertility in female mice. The possible effect of an HFD on the phenotype of DHEA-induced PCOS mice is unknown. The aim of the present study was to investigate both reproductive and metabolic features of DHEA-induced PCOS mice fed a normal chow or a 60% HFD. Prepubertal C57BL/6 mice (age 25 days) on the normal chow or an HFD were injected (s.c.) daily with the vehicle sesame oil or DHEA for 20 consecutive days. At the end of the experiment, both reproductive and metabolic characteristics were assessed. Our data show that an HFD did not affect the reproductive phenotype of DHEA-treated mice. The treatment of HFD, however, caused significant metabolic alterations in DHEA-treated mice, including obesity, glucose intolerance, dyslipidemia, and pronounced liver steatosis. These findings suggest that HFD induces distinct metabolic features in DHEA-induced PCOS mice. The combined DHEA and HFD treatment may thus serve as a means of studying the mechanisms involved in metabolic derangements of this syndrome, particularly in the high prevalence of hepatic steatosis in women with PCOS. © 2014 by the Society for the Study of Reproduction, Inc.

  6. Circadian desynchrony promotes metabolic disruption in a mouse model of shiftwork.

    Directory of Open Access Journals (Sweden)

    Johanna L Barclay

    Full Text Available Shiftwork is associated with adverse metabolic pathophysiology, and the rising incidence of shiftwork in modern societies is thought to contribute to the worldwide increase in obesity and metabolic syndrome. The underlying mechanisms are largely unknown, but may involve direct physiological effects of nocturnal light exposure, or indirect consequences of perturbed endogenous circadian clocks. This study employs a two-week paradigm in mice to model the early molecular and physiological effects of shiftwork. Two weeks of timed sleep restriction has moderate effects on diurnal activity patterns, feeding behavior, and clock gene regulation in the circadian pacemaker of the suprachiasmatic nucleus. In contrast, microarray analyses reveal global disruption of diurnal liver transcriptome rhythms, enriched for pathways involved in glucose and lipid metabolism and correlating with first indications of altered metabolism. Although altered food timing itself is not sufficient to provoke these effects, stabilizing peripheral clocks by timed food access can restore molecular rhythms and metabolic function under sleep restriction conditions. This study suggests that peripheral circadian desynchrony marks an early event in the metabolic disruption associated with chronic shiftwork. Thus, strengthening the peripheral circadian system by minimizing food intake during night shifts may counteract the adverse physiological consequences frequently observed in human shift workers.

  7. Reconstruction of the microalga Nannochloropsis salina genome-scale metabolic model with applications to lipid production.

    Science.gov (United States)

    Loira, Nicolás; Mendoza, Sebastian; Paz Cortés, María; Rojas, Natalia; Travisany, Dante; Genova, Alex Di; Gajardo, Natalia; Ehrenfeld, Nicole; Maass, Alejandro

    2017-07-04

    Nannochloropsis salina (= Eustigmatophyceae) is a marine microalga which has become a biotechnological target because of its high capacity to produce polyunsaturated fatty acids and triacylglycerols. It has been used as a source of biofuel, pigments and food supplements, like Omega 3. Only some Nannochloropsis species have been sequenced, but none of them benefit from a genome-scale metabolic model (GSMM), able to predict its metabolic capabilities. We present iNS934, the first GSMM for N. salina, including 2345 reactions, 934 genes and an exhaustive description of lipid and nitrogen metabolism. iNS934 has a 90% of accuracy when making simple growth/no-growth predictions and has a 15% error rate in predicting growth rates in different experimental conditions. Moreover, iNS934 allowed us to propose 82 different knockout strategies for strain optimization of triacylglycerols. iNS934 provides a powerful tool for metabolic improvement, allowing predictions and simulations of N. salina metabolism under different media and genetic conditions. It also provides a systemic view of N. salina metabolism, potentially guiding research and providing context to -omics data.

  8. Metabolic network segmentation: A probabilistic graphical modeling approach to identify the sites and sequential order of metabolic regulation from non-targeted metabolomics data.

    Directory of Open Access Journals (Sweden)

    Andreas Kuehne

    2017-06-01

    Full Text Available In recent years, the number of large-scale metabolomics studies on various cellular processes in different organisms has increased drastically. However, it remains a major challenge to perform a systematic identification of mechanistic regulatory events that mediate the observed changes in metabolite levels, due to complex interdependencies within metabolic networks. We present the metabolic network segmentation (MNS algorithm, a probabilistic graphical modeling approach that enables genome-scale, automated prediction of regulated metabolic reactions from differential or serial metabolomics data. The algorithm sections the metabolic network into modules of metabolites with consistent changes. Metabolic reactions that connect different modules are the most likely sites of metabolic regulation. In contrast to most state-of-the-art methods, the MNS algorithm is independent of arbitrary pathway definitions, and its probabilistic nature facilitates assessments of noisy and incomplete measurements. With serial (i.e., time-resolved data, the MNS algorithm also indicates the sequential order of metabolic regulation. We demonstrated the power and flexibility of the MNS algorithm with three, realistic case studies with bacterial and human cells. Thus, this approach enables the identification of mechanistic regulatory events from large-scale metabolomics data, and contributes to the understanding of metabolic processes and their interplay with cellular signaling and regulation processes.

  9. Metabolic network segmentation: A probabilistic graphical modeling approach to identify the sites and sequential order of metabolic regulation from non-targeted metabolomics data.

    Science.gov (United States)

    Kuehne, Andreas; Mayr, Urs; Sévin, Daniel C; Claassen, Manfred; Zamboni, Nicola

    2017-06-01

    In recent years, the number of large-scale metabolomics studies on various cellular processes in different organisms has increased drastically. However, it remains a major challenge to perform a systematic identification of mechanistic regulatory events that mediate the observed changes in metabolite levels, due to complex interdependencies within metabolic networks. We present the metabolic network segmentation (MNS) algorithm, a probabilistic graphical modeling approach that enables genome-scale, automated prediction of regulated metabolic reactions from differential or serial metabolomics data. The algorithm sections the metabolic network into modules of metabolites with consistent changes. Metabolic reactions that connect different modules are the most likely sites of metabolic regulation. In contrast to most state-of-the-art methods, the MNS algorithm is independent of arbitrary pathway definitions, and its probabilistic nature facilitates assessments of noisy and incomplete measurements. With serial (i.e., time-resolved) data, the MNS algorithm also indicates the sequential order of metabolic regulation. We demonstrated the power and flexibility of the MNS algorithm with three, realistic case studies with bacterial and human cells. Thus, this approach enables the identification of mechanistic regulatory events from large-scale metabolomics data, and contributes to the understanding of metabolic processes and their interplay with cellular signaling and regulation processes.

  10. Predicting effects of structural stress in a genome-reduced model bacterial metabolism

    Science.gov (United States)

    Güell, Oriol; Sagués, Francesc; Serrano, M. Ángeles

    2012-08-01

    Mycoplasma pneumoniae is a human pathogen recently proposed as a genome-reduced model for bacterial systems biology. Here, we study the response of its metabolic network to different forms of structural stress, including removal of individual and pairs of reactions and knockout of genes and clusters of co-expressed genes. Our results reveal a network architecture as robust as that of other model bacteria regarding multiple failures, although less robust against individual reaction inactivation. Interestingly, metabolite motifs associated to reactions can predict the propagation of inactivation cascades and damage amplification effects arising in double knockouts. We also detect a significant correlation between gene essentiality and damages produced by single gene knockouts, and find that genes controlling high-damage reactions tend to be expressed independently of each other, a functional switch mechanism that, simultaneously, acts as a genetic firewall to protect metabolism. Prediction of failure propagation is crucial for metabolic engineering or disease treatment.

  11. Improving the iMM904 S. cerevisiae metabolic model using essentiality and synthetic lethality data

    Directory of Open Access Journals (Sweden)

    Maranas Costas D

    2010-12-01

    Full Text Available Abstract Background Saccharomyces cerevisiae is the first eukaryotic organism for which a multi-compartment genome-scale metabolic model was constructed. Since then a sequence of improved metabolic reconstructions for yeast has been introduced. These metabolic models have been extensively used to elucidate the organizational principles of yeast metabolism and drive yeast strain engineering strategies for targeted overproductions. They have also served as a starting point and a benchmark for the reconstruction of genome-scale metabolic models for other eukaryotic organisms. In spite of the successive improvements in the details of the described metabolic processes, even the recent yeast model (i.e., iMM904 remains significantly less predictive than the latest E. coli model (i.e., iAF1260. This is manifested by its significantly lower specificity in predicting the outcome of grow/no grow experiments in comparison to the E. coli model. Results In this paper we make use of the automated GrowMatch procedure for restoring consistency with single gene deletion experiments in yeast and extend the procedure to make use of synthetic lethality data using the genome-scale model iMM904 as a basis. We identified and vetted using literature sources 120 distinct model modifications including various regulatory constraints for minimal and YP media. The incorporation of the suggested modifications led to a substantial increase in the fraction of correctly predicted lethal knockouts (i.e., specificity from 38.84% (87 out of 224 to 53.57% (120 out of 224 for the minimal medium and from 24.73% (45 out of 182 to 40.11% (73 out of 182 for the YP medium. Synthetic lethality predictions improved from 12.03% (16 out of 133 to 23.31% (31 out of 133 for the minimal medium and from 6.96% (8 out of 115 to 13.04% (15 out of 115 for the YP medium. Conclusions Overall, this study provides a roadmap for the computationally driven correction of multi-compartment genome

  12. Metabolic flux distribution and mathematical models for dynamic ...

    African Journals Online (AJOL)

    In the second part of this study, computer simulation was made to study the dynamics of the intracellular metabolite concentrations in E. coli in particular for the glycolysis and pentosephosphate pathway based on the kinetic rate equations. The model successfully simulates the main features of the time course without ...

  13. Functional ingredient production: application of global metabolic models

    NARCIS (Netherlands)

    Smid, E.J.; Molenaar, D.; Hugenholtz, J.; Vos, de W.M.; Teusink, B.

    2005-01-01

    The biotechnology industry continuously explores new ways to improve the performance of microbial strains in fermentation processes. Recent focus has been on new genome-wide modelling approaches in functional genomics, which aim to take full advantage of genome sequence data, transcription

  14. Modeling metabolic response to changes of enzyme amount in ...

    African Journals Online (AJOL)

    Jane

    2010-10-11

    Oct 11, 2010 ... (2001), in an in silico model of glycolysis, Saccharomyces cerevisiae is established by introducing an enzyme amount multiple factor (ɑ) into the ... not only biochemical characteristic but also location of enzymes in the network should be considered. Key words: Glycolysis, yeast, metabolites level, enzyme ...

  15. An extended dynamic model of Lactococcus lactis metabolism for mannitol and 2,3-butanediol production.

    Science.gov (United States)

    Costa, Rafael S; Hartmann, Andras; Gaspar, Paula; Neves, Ana R; Vinga, Susana

    2014-03-04

    Biomedical research and biotechnological production are greatly benefiting from the results provided by the development of dynamic models of microbial metabolism. Although several kinetic models of Lactococcus lactis (a Lactic Acid Bacterium (LAB) commonly used in the dairy industry) have been developed so far, most of them are simplified and focus only on specific metabolic pathways. Therefore, the application of mathematical models in the design of an engineering strategy for the production of industrially important products by L. lactis has been very limited. In this work, we extend the existing kinetic model of L. lactis central metabolism to include industrially relevant production pathways such as mannitol and 2,3-butanediol. In this way, we expect to study the dynamics of metabolite production and make predictive simulations in L. lactis. We used a system of ordinary differential equations (ODEs) with approximate Michaelis-Menten-like kinetics for each reaction, where the parameters were estimated from multivariate time-series metabolite concentrations obtained by our team through in vivo Nuclear Magnetic Resonance (NMR). The results show that the model captures observed transient dynamics when validated under a wide range of experimental conditions. Furthermore, we analyzed the model using global perturbations, which corroborate experimental evidence about metabolic responses upon enzymatic changes. These include that mannitol production is very sensitive to lactate dehydrogenase (LDH) in the wild type (W.T.) strain, and to mannitol phosphoenolpyruvate: a phosphotransferase system (PTS(Mtl)) in a LDH mutant strain. LDH reduction has also a positive control on 2,3-butanediol levels. Furthermore, it was found that overproduction of mannitol-1-phosphate dehydrogenase (MPD) in a LDH/PTS(Mtl) deficient strain can increase the mannitol levels. The results show that this model has prediction capability over new experimental conditions and offers promising

  16. Compartmentation and complexation of metals in hyperaccumulator plants

    Directory of Open Access Journals (Sweden)

    Barbara eLeitenmaier

    2013-09-01

    Full Text Available Hyperaccumulators are being intensely investigated. They are not only interesting in scientific context due to their strange behaviour in terms of dealing with high concentrations of metals, but also because of their use in phytoremediation and phytomining, for which understanding the mechanisms of hyperaccumulation is crucial. Hyperaccumulators naturally use metal accumulation as a defence against herbivores and pathogens, and therefore deal with accumulated metals in very specific ways of complexation and compartmentation, different from non-hyperaccumulator plants and also non-hyperaccumulated metals. For example, in contrast to non-hyperaccumulators, in hyperaccumulators even the classical phytochelatin-inducing metal, cadmium, is predominantly not bound by such sulfur ligands, but only by weak oxygen ligands. This applies to all hyperaccumulated metals investigated so far, as well as hyperaccumulation of the metalloid arsenic. Stronger ligands, as they have been shown to complex metals in non-hyperaccumulators, are in hyperaccumulators used for transient binding during transport to the storage sites. This confirmed that enhanced active metal transport, and not metal complexation, is the key mechanism of hyperaccumulation. Hyperaccumulators tolerate the high amount of accumulated heavy metals by sequestering them into vacuoles, usually in large storage cells of the epidermis. This is mediated by strongly elevated expression of specific transport proteins in various tissues from metal uptake in the shoots up to the storage sites in the leaf epidermis. However, this mechanism seems to be very metal specific. Non-hyperaccumulated metals in hyperaccumulators seem to be dealt with like in non-hyperaccumulator plants, i.e. detoxified by binding to strong ligands such as metallothioneins.

  17. Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism.

    Science.gov (United States)

    Angione, Claudio

    2018-02-01

    Despite being often perceived as the main contributors to cell fate and physiology, genes alone cannot predict cellular phenotype. During the process of gene expression, 95% of human genes can code for multiple proteins due to alternative splicing. While most splice variants of a gene carry the same function, variants within some key genes can have remarkably different roles. To bridge the gap between genotype and phenotype, condition- and tissue-specific models of metabolism have been constructed. However, current metabolic models only include information at the gene level. Consequently, as recently acknowledged by the scientific community, common situations where changes in splice-isoform expression levels alter the metabolic outcome cannot be modeled. We here propose GEMsplice, the first method for the incorporation of splice-isoform expression data into genome-scale metabolic models. Using GEMsplice, we make full use of RNA-Seq quantitative expression profiles to predict, for the first time, the effects of splice isoform-level changes in the metabolism of 1455 patients with 31 different breast cancer types. We validate GEMsplice by generating cancer-versus-normal predictions on metabolic pathways, and by comparing with gene-level approaches and available literature on pathways affected by breast cancer. GEMsplice is freely available for academic use at https://github.com/GEMsplice/GEMsplice_code. Compared to state-of-the-art methods, we anticipate that GEMsplice will enable for the first time computational analyses at transcript level with splice-isoform resolution. https://github.com/GEMsplice/GEMsplice_code. c.angione@tees.ac.uk. Supplementary data are available at Bioinformatics online.

  18. Atmospheric reaction systems as null-models to identify structural traces of evolution in metabolism.

    Science.gov (United States)

    Holme, Petter; Huss, Mikael; Lee, Sang Hoon

    2011-05-06

    The metabolism is the motor behind the biological complexity of an organism. One problem of characterizing its large-scale structure is that it is hard to know what to compare it to. All chemical reaction systems are shaped by the same physics that gives molecules their stability and affinity to react. These fundamental factors cannot be captured by standard null-models based on randomization. The unique property of organismal metabolism is that it is controlled, to some extent, by an enzymatic machinery that is subject to evolution. In this paper, we explore the possibility that reaction systems of planetary atmospheres can serve as a null-model against which we can define metabolic structure and trace the influence of evolution. We find that the two types of data can be distinguished by their respective degree distributions. This is especially clear when looking at the degree distribution of the reaction network (of reaction connected to each other if they involve the same molecular species). For the Earth's atmospheric network and the human metabolic network, we look into more detail for an underlying explanation of this deviation. However, we cannot pinpoint a single cause of the difference, rather there are several concurrent factors. By examining quantities relating to the modular-functional organization of the metabolism, we confirm that metabolic networks have a more complex modular organization than the atmospheric networks, but not much more. We interpret the more variegated modular arrangement of metabolism as a trace of evolved functionality. On the other hand, it is quite remarkable how similar the structures of these two types of networks are, which emphasizes that the constraints from the chemical properties of the molecules has a larger influence in shaping the reaction system than does natural selection.

  19. Ocular pharmacokinetics of a novel tetrahydroquinoline analog in rabbit: compartmental analysis and PK-PD evaluation.

    Science.gov (United States)

    Pamulapati, Chandrasena R; Schoenwald, Ronald D

    2012-01-01

    The elimination kinetics of the pharmacologically active compound 1-ethyl-6-fluoro-1,2,3,4-tetrahydroquinoline (MC4) were characterized along with pharmacodynamic (PD) measurements. Four compartmental models based on ocular anatomy, physiology, and possible absorption and disposition pathways were proposed to model the pharmacokinetic (PK) data in WinNonlin and the best model was chosen based on statistical and goodness-of-fit criteria. A three-compartment physiologic-based PK model with a bidirectional transfer between cornea and aqueous humor and a unidirectional transfer between aqueous humor and iris-ciliary body best described the data. The ocular PD parameters, maximum effect attributed to drug (E(max)) and drug concentration which produces 50% of maximum effect (EC(50)), were estimated with change in intraocular pressure (ΔIOP) as the effect (PD response) in the effect compartment model (PK-PD link model) using aqueous humor concentration-time and ΔIOP-time profiles. The link model better described the effect compartment concentrations than a simple E(max) model that used iris-ciliary body concentration-time data, indicating that there is an apparent temporal displacement between aqueous humor concentration (plasma/central compartment equivalent) and pharmacological effect. A physiologically plausible value of 0.0159 min(-1) was obtained for the drug elimination rate constant (k(eo)) from the effect site to account for equilibration time in the biophase. Hysteresis was observed for the iris-ciliary body, aqueous humor drug concentrations, and effect data, further confirming the utility of the link model to describe the PD of MC4. Copyright © 2011 Wiley-Liss, Inc.

  20. Reconstruction and analysis of genome-scale metabolic model of a photosynthetic bacterium

    Directory of Open Access Journals (Sweden)

    Urchueguía Javier F

    2010-11-01

    Full Text Available Abstract Background Synechocystis sp. PCC6803 is a cyanobacterium considered as a candidate photo-biological production platform - an attractive cell factory capable of using CO2 and light as carbon and energy source, respectively. In order to enable efficient use of metabolic potential of Synechocystis sp. PCC6803, it is of importance to develop tools for uncovering stoichiometric and regulatory principles in the Synechocystis metabolic network. Results We report the most comprehensive metabolic model of Synechocystis sp. PCC6803 available, iSyn669, which includes 882 reactions, associated with 669 genes, and 790 metabolites. The model includes a detailed biomass equation which encompasses elementary building blocks that are needed for cell growth, as well as a detailed stoichiometric representation of photosynthesis. We demonstrate applicability of iSyn669 for stoichiometric analysis by simulating three physiologically relevant growth conditions of Synechocystis sp. PCC6803, and through in silico metabolic engineering simulations that allowed identification of a set of gene knock-out candidates towards enhanced succinate production. Gene essentiality and hydrogen production potential have also been assessed. Furthermore, iSyn669 was used as a transcriptomic data integration scaffold and thereby we found metabolic hot-spots around which gene regulation is dominant during light-shifting growth regimes. Conclusions iSyn669 provides a platform for facilitating the development of cyanobacteria as microbial cell factories.

  1. Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery.

    Science.gov (United States)

    Kell, Douglas B; Goodacre, Royston

    2014-02-01

    Metabolism represents the 'sharp end' of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of the human metabolic network that include the important transporters. Small molecule 'drug' transporters are in fact metabolite transporters, because drugs bear structural similarities to metabolites known from the network reconstructions and from measurements of the metabolome. Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia: (i) the effects of inborn errors of metabolism; (ii) which metabolites are exometabolites, and (iii) how metabolism varies between tissues and cellular compartments. However, even these qualitative network models are not yet complete. As our understanding improves so do we recognise more clearly the need for a systems (poly)pharmacology. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

  2. Integration and Validation of the Genome-Scale Metabolic Models of Pichia pastoris: A Comprehensive Update of Protein Glycosylation Pathways, Lipid and Energy Metabolism.

    Science.gov (United States)

    Tomàs-Gamisans, Màrius; Ferrer, Pau; Albiol, Joan

    2016-01-01

    Genome-scale metabolic models (GEMs) are tools that allow predicting a phenotype from a genotype under certain environmental conditions. GEMs have been developed in the last ten years for a broad range of organisms, and are used for multiple purposes such as discovering new properties of metabolic networks, predicting new targets for metabolic engineering, as well as optimizing the cultivation conditions for biochemicals or recombinant protein production. Pichia pastoris is one of the most widely used organisms for heterologous protein expression. There are different GEMs for this methylotrophic yeast of which the most relevant and complete in the published literature are iPP668, PpaMBEL1254 and iLC915. However, these three models differ regarding certain pathways, terminology for metabolites and reactions and annotations. Moreover, GEMs for some species are typically built based on the reconstructed models of related model organisms. In these cases, some organism-specific pathways could be missing or misrepresented. In order to provide an updated and more comprehensive GEM for P. pastoris, we have reconstructed and validated a consensus model integrating and merging all three existing models. In this step a comprehensive review and integration of the metabolic pathways included in each one of these three versions was performed. In addition, the resulting iMT1026 model includes a new description of some metabolic processes. Particularly new information described in recently published literature is included, mainly related to fatty acid and sphingolipid metabolism, glycosylation and cell energetics. Finally the reconstructed model was tested and validated, by comparing the results of the simulations with available empirical physiological datasets results obtained from a wide range of experimental conditions, such as different carbon sources, distinct oxygen availability conditions, as well as producing of two different recombinant proteins. In these simulations, the

  3. An EMG-driven model applied for predicting metabolic energy consumption during movement.

    Science.gov (United States)

    Bisi, Maria Cristina; Stagni, Rita; Houdijk, Han; Gnudi, Gianni

    2011-12-01

    The relationship between mechanical work and metabolic energy cost during movement is not yet clear. Many studies demonstrated the utility of forward-dynamic musculoskeletal models combined with experimental data to address such question. The aim of this study was to evaluate the applicability of a muscle energy expenditure model at whole body level, using an EMG-driven approach. Four participants performed a 5-min squat exercise on unilateral leg press at two different frequencies and two load levels. Data collected were kinematics, EMG, forces and moments under the foot and gas-exchange data. This same task was simulated using a musculoskeletal model, which took EMG and kinematics as inputs and gave muscle forces and muscle energetics as outputs. Model parameters were taken from literature, but maximal isometric muscle force was optimized in order to match predicted joint moments with measured ones. Energy rates predicted by the model were compared with energy consumption measured by the gas-exchange data. Model results on metabolic energy consumption were close to the values obtained through indirect calorimetry. At the higher frequency level, the model underestimated measured energy consumption. This underestimation can be explained with an increase in energy consumption of the non-muscular mass with movement velocity. In conclusion, results obtained in comparing model predictions with experimental data were promising. More research is needed to evaluate this way of computing mechanical and metabolic work. Copyright © 2011 Elsevier Ltd. All rights reserved.

  4. Metabolic modelling of full-scale enhanced biological phosphorus removal sludge.

    Science.gov (United States)

    Lanham, Ana B; Oehmen, Adrian; Saunders, Aaron M; Carvalho, Gilda; Nielsen, Per H; Reis, Maria A M

    2014-12-01

    This study investigates, for the first time, the application of metabolic models incorporating polyphosphate accumulating organisms (PAOs) and glycogen accumulating organisms (GAOs) towards describing the biochemical transformations of full-scale enhanced biological phosphorus removal (EBPR) activated sludge from wastewater treatment plants (WWTPs). For this purpose, it was required to modify previous metabolic models applied to lab-scale systems by incorporating the anaerobic utilisation of the TCA cycle and the aerobic maintenance processes based on sequential utilisation of polyhydroxyalkanoates, followed by glycogen and polyphosphate. The abundance of the PAO and GAO populations quantified by fluorescence in situ hybridisation served as the initial conditions of each biomass fraction, whereby the models were able to describe accurately the experimental data. The kinetic rates were found to change among the four different WWTPs studied or even in the same plant during different seasons, either suggesting the presence of additional PAO or GAO organisms, or varying microbial activities for the same organisms. Nevertheless, these variations in kinetic rates were largely found to be proportional to the difference in acetate uptake rate, suggesting a viable means of calibrating the metabolic model. The application of the metabolic model to full-scale sludge also revealed that different Accumulibacter clades likely possess different acetate uptake mechanisms, as a correlation was observed between the energetic requirement for acetate transport across the cell membrane with the diversity of Accumulibacter present. Using the model as a predictive tool, it was shown that lower acetate concentrations in the feed as well as longer aerobic retention times favour the dominance of the TCA metabolism over glycolysis, which could explain why the anaerobic TCA pathway seems to be more relevant in full-scale WWTPs than in lab-scale systems. Copyright © 2014 Elsevier Ltd. All

  5. Models and mechanisms of metabolic regulation: genes, stress, and the HPA and HPG axes.

    Science.gov (United States)

    Boersma, G J; Salton, S R; Spritzer, P M; Steele, C T; Carbone, D L

    2012-07-01

    A variety of models have been developed to better understand the mechanisms underlying individual variation in susceptibility to obesity. This review discusses several of these models and explores their role in understanding individual vulnerability to metabolic disease and the environmental factors around which metabolic perturbations occur. Recently, the focus of models has shifted towards heterogeneous populations, in which individuals characterized by a high vulnerability and individuals that are seemingly resistant can be identified. The use of these heterogeneous studies has lead to the identification of several novel biomarkers predicting obesity. This review therefore focuses on nontraditional factors, which are not directly implicated in metabolic regulation. First, the evidence from rodent knockout models for genetic factors involved in obesity is discussed. Second, the role of a stressful environment, particularly the early life environment is investigated along with a discussion of circadian disruption and metabolic disorders. Finally, the impact of sex-steroids, as exemplified by polycystic ovarian syndrome, is discussed. Overall, the data presented in our review demonstrate that in most cases interplay between genetic and environmental factors best predicts disease development. Our review shows that susceptibility to obesity may be explained by complex interactions between traditional homeostatic mechanisms, such as the hypothalamic peptide, and less studied mechanisms, like steroids and neurotrophic factors. © Georg Thieme Verlag KG Stuttgart · New York.

  6. Simulating the physiology of athletes during endurance sports events: Modelling human energy conversion and metabolism

    NARCIS (Netherlands)

    Beek, J.H.G.M. van; Supandi, F.; Gavai, A.K.; Graaf, A.A. de; Binsl, T.W.; Hettling, H.

    2011-01-01

    The human physiological system is stressed to its limits during endurance sports competition events.We describe a whole body computational model for energy conversion during bicycle racing. About 23 per cent of the metabolic energy is used for muscle work, the rest is converted to heat. We

  7. Modelling metabolic evolution on phenotypic fitness landscapes: a case study on C4 photosynthesis.

    Science.gov (United States)

    Heckmann, David

    2015-12-01

    How did the complex metabolic systems we observe today evolve through adaptive evolution? The fitness landscape is the theoretical framework to answer this question. Since experimental data on natural fitness landscapes is scarce, computational models are a valuable tool to predict landscape topologies and evolutionary trajectories. Careful assumptions about the genetic and phenotypic features of the system under study can simplify the design of such models significantly. The analysis of C4 photosynthesis evolution provides an example for accurate predictions based on the phenotypic fitness landscape of a complex metabolic trait. The C4 pathway evolved multiple times from the ancestral C3 pathway and models predict a smooth 'Mount Fuji' landscape accordingly. The modelled phenotypic landscape implies evolutionary trajectories that agree with data on modern intermediate species, indicating that evolution can be predicted based on the phenotypic fitness landscape. Future directions will have to include structural changes of metabolic fitness landscape structure with changing environments. This will not only answer important evolutionary questions about reversibility of metabolic traits, but also suggest strategies to increase crop yields by engineering the C4 pathway into C3 plants. © 2015 Authors; published by Portland Press Limited.

  8. A mathematical model for metabolic tradeoffs, minimal requirements, and evolutionary transitions. (Invited)

    Science.gov (United States)

    Kempes, C.; Hoehler, T. M.; Follows, M. J.; Dutkiewicz, S.

    2013-12-01

    Understanding the minimal energy requirements for life is a difficult challenge because of the great variety of processes required for life. Our approach is to discover general trends applicable to diverse species in order to understand the average constraints faced by life. We then leverage these trends to predict minimal requirements for life. We have focused on broad trends in metabolism, growth, basic bioenergetics, and overall genomic structure and composition. We have developed a simple mathematical model of metabolic partitioning which is able to capture the growth of both single cells and populations of cells for diverse organisms spanning the three domains of life. This model also anticipates the observed interspecific trends in population growth rate and predicts the observed minimum size of a bacterium. Our model connects evolutionary limitations and transitions, including minimal life, to energetic constraints imposed by body architecture and the metabolism of a given species. This model can also be connected to genomic variation across species in order to describe the tradeoffs associated with various genes and their functionality. This forms the basis for a theory of the possibility space for minimal physiological function given evolutionary tradeoffs, general metabolic and biological architecture, and the energetic limitations of the environment.

  9. Demystifying the West, Brown & Enquist model of the allometry of metabolism

    NARCIS (Netherlands)

    Etienne, RS; Apol, MEF; Olff, H; Chown, S.L.

    The allometry of metabolic rate has long been one of the key relationships in ecology. While its existence is generally agreed on, the exact value of the scaling exponent, and the key mechanisms that determine its value, are still hotly debated. The network model of West, Brown & Enquist (Science

  10. Demystifying the West, Brown & Enquist model of the allometry of metabolism

    NARCIS (Netherlands)

    Etienne, R.S.; Apol, M.E.F.; Olff, H.

    2006-01-01

    The allometry of metabolic rate has long been one of the key relationships in ecology. While its existence is generally agreed on, the exact value of the scaling exponent, and the key mechanisms that determine its value, are still hotly debated. The network model of West, Brown & Enquist

  11. Multi-Organ Contribution to the Metabolic Plasma Profile Using Hierarchical Modelling.

    Directory of Open Access Journals (Sweden)

    Frida Torell

    Full Text Available Hierarchical modelling was applied in order to identify the organs that contribute to the levels of metabolites in plasma. Plasma and organ samples from gut, kidney, liver, muscle and pancreas were obtained from mice. The samples were analysed using gas chromatography time-of-flight mass spectrometry (GC TOF-MS at the Swedish Metabolomics centre, Umeå University, Sweden. The multivariate analysis was performed by means of principal component analysis (PCA and orthogonal projections to latent structures (OPLS. The main goal of this study was to investigate how each organ contributes to the metabolic plasma profile. This was performed using hierarchical modelling. Each organ was found to have a unique metabolic profile. The hierarchical modelling showed that the gut, kidney and liver demonstrated the greatest contribution to the metabolic pattern of plasma. For example, we found that metabolites were absorbed in the gut and transported to the plasma. The kidneys excrete branched chain amino acids (BCAAs and fatty acids are transported in the plasma to the muscles and liver. Lactic acid was also found to be transported from the pancreas to plasma. The results indicated that hierarchical modelling can be utilized to identify the organ contribution of unknown metabolites to the metabolic profile of plasma.

  12. Maintenance of basal levels of autophagy in Huntington's disease mouse models displaying metabolic dysfunction.

    Science.gov (United States)

    Baldo, Barbara; Soylu, Rana; Petersén, Asa

    2013-01-01

    Huntington's disease (HD) is a fatal neurodegenerative disorder caused by an expanded polyglutamine repeat in the huntingtin protein. Neuropathology in the basal ganglia and in the cerebral cortex has been linked to the motor and cognitive symptoms whereas recent work has suggested that the hypothalamus might be involved in the metabolic dysfunction. Several mouse models of HD that display metabolic dysfunction have hypothalamic pathology, and expression of mutant huntingtin in the hypothalamus has been causally linked to the development of metabolic dysfunction in mice. Although the pathogenic mechanisms by which mutant huntingtin exerts its toxic functions in the HD brain are not fully known, several studies have implicated a role for the lysososomal degradation pathway of autophagy. Interestingly, changes in autophagy in the hypothalamus have been associated with the development of metabolic dysfunction in wild-type mice. We hypothesized that expression of mutant huntingtin might lead to changes in the autophagy pathway in the hypothalamus in mice with metabolic dysfunction. We therefore investigated whether there were changes in basal levels of autophagy in a mouse model expressing a fragment of 853 amino acids of mutant huntingtin selectively in the hypothalamus using a recombinant adeno-associate viral vector approach as well as in the transgenic BACHD mice. We performed qRT-PCR and Western blot to investigate the mRNA and protein expression levels of selected autophagy markers. Our results show that basal levels of autophagy are maintained in the hypothalamus despite the presence of metabolic dysfunction in both mouse models. Furthermore, although there were no major changes in autophagy in the striatum and cortex of BACHD mice, we detected modest, but significant differences in levels of some markers in mice at 12 months of age. Taken together, our results indicate that overexpression of mutant huntingtin in mice do not significantly perturb basal levels of

  13. Modeling phenotypic metabolic adaptations of Mycobacterium tuberculosis H37Rv under hypoxia.

    Directory of Open Access Journals (Sweden)

    Xin Fang

    Full Text Available The ability to adapt to different conditions is key for Mycobacterium tuberculosis, the causative agent of tuberculosis (TB, to successfully infect human hosts. Adaptations allow the organism to evade the host immune responses during acute infections and persist for an extended period of time during the latent infectious stage. In latently infected individuals, estimated to include one-third of the human population, the organism exists in a variety of metabolic states, which impedes the development of a simple strategy for controlling or eradicating this disease. Direct knowledge of the metabolic states of M. tuberculosis in patients would aid in the management of the disease as well as in forming the basis for developing new drugs and designing more efficacious drug cocktails. Here, we propose an in silico approach to create state-specific models based on readily available gene expression data. The coupling of differential gene expression data with a metabolic network model allowed us to characterize the metabolic adaptations of M. tuberculosis H37Rv to hypoxia. Given the microarray data for the alterations in gene expression, our model predicted reduced oxygen uptake, ATP production changes, and a global change from an oxidative to a reductive tricarboxylic acid (TCA program. Alterations in the biomass composition indicated an increase in the cell wall metabolites required for cell-wall growth, as well as heightened accumulation of triacylglycerol in preparation for a low-nutrient, low metabolic activity life style. In contrast, the gene expression program in the deletion mutant of dosR, which encodes the immediate hypoxic response regulator, failed to adapt to low-oxygen stress. Our predictions were compatible with recent experimental observations of M. tuberculosis activity under hypoxic and anaerobic conditions. Importantly, alterations in the flow and accumulation of a particular metabolite were not necessarily directly linked to

  14. An energetic model for oxygen-limited metabolism

    Energy Technology Data Exchange (ETDEWEB)

    Beronio, P.B. Jr. (Amoco Oil Co., Naperville, IL (United States). Amoco Research Center); Tsao, G.T. (Purdue Univ., West Lafayette, IN (United States). Lab. of Renewable Resources Engineering)

    1993-12-01

    Microbial production of 2,3-butanediol by Klebsiella oxytoca occurs under conditions of an oxygen limitation. The extent to which substrate is oxidized to 2,3-butanediol and its coproducts, (acetic acid, acetoin, and ethanol) and the relative flow rates of substrate to energetic and biosynthetic pathways are controlled by the degree of oxygen limitation. Two energetic relationships which describe the response to an oxygen limitation have been derived. The first relationship describes the coupling between growth and energy production observed under oxygen-limited conditions. This allows calculation of energetic parameters and modeling of the cell mass and substrate profiles in terms of the degree of oxygen limitation only. The second relationship describes the average degree of oxidation and the rate of the end-product flow. The model has been tested with both batch and continuous culture. During these kinetic studies, two phases of growth have been observed: energy-coupled growth, which was described above; and, energy-uncoupled growth, which arises when the degree of oxygen limitation reaches a critical value. Optimal culture performance with respect to 2,3-butanediol productivity occurs during energy-coupled growth.

  15. Genome-wide metabolic model to improve understanding of CD4(+) T cell metabolism, immunometabolism and application in drug design.

    Science.gov (United States)

    Han, Feifei; Li, Gonghua; Dai, Shaoxing; Huang, Jingfei

    2016-02-01

    CD4(+) T cells play a critical role in adaptive immunity and have been well studied in past decades. However, the systematic metabolism features are less clear. Here, we reconstructed the genome-wide metabolic network of naïve CD4(+) T cells, CD4T1670, by integrating transcriptome and metabolism data. We performed simulations for three critical metabolic subsystems (carbohydrate metabolism, fatty acid metabolism and glutaminolysis). The results were consistent with most experimental observations. Furthermore, we found that depletion of either glucose or glutamine did not significantly affect ATP production and biomass, but dramatically unbalanced the metabolic network and increased the release of some inflammation or anti-inflammation related factors, such as lysophosphatidylcholine, leukotriene and hyaluronan. Genome-wide single gene knockout analysis showed that acetyl-CoA carboxylase 1 (ACC1) was essential for T cell activation. We further investigated the role of immunometabolic genes in metabolic network stability, and found that over 25% of them were essential. The results also showed that although PTEN is a well-studied proliferation inhibitor, it was essential for maintaining the stability of CD4 metabolic networks. Finally, we applied CD4T1670 to evaluate the side-effects of certain drugs in preclinical experiments. These results suggested that CD4T1670 would be useful in understanding CD4(+) T cells and drug design systematically.

  16. Use of genome-scale metabolic models in evolutionary systems biology.

    Science.gov (United States)

    Papp, Balázs; Szappanos, Balázs; Notebaart, Richard A

    2011-01-01

    One of the major aims of the nascent field of evolutionary systems biology is to test evolutionary hypotheses that are not only realistic from a population genetic point of view but also detailed in terms of molecular biology mechanisms. By providing a mapping between genotype and phenotype for hundreds of genes, genome-scale systems biology models of metabolic networks have already provided valuable insights into the evolution of metabolic gene contents and phenotypes of yeast and other microbial species. Here we review the recent use of these computational models to predict the fitness effect of mutations, genetic interactions, evolutionary outcomes, and to decipher the mechanisms of mutational robustness. While these studies have demonstrated that even simplified models of biochemical reaction networks can be highly informative for evolutionary analyses, they have also revealed the weakness of this modeling framework to quantitatively predict mutational effects, a challenge that needs to be addressed for future progress in evolutionary systems biology.

  17. SEED servers: high-performance access to the SEED genomes, annotations, and metabolic models.

    Directory of Open Access Journals (Sweden)

    Ramy K Aziz

    Full Text Available The remarkable advance in sequencing technology and the rising interest in medical and environmental microbiology, biotechnology, and synthetic biology resulted in a deluge of published microbial genomes. Yet, genome annotation, comparison, and modeling remain a major bottleneck to the translation of sequence information into biological knowledge, hence computational analysis tools are continuously being developed for rapid genome annotation and interpretation. Among the earliest, most comprehensive resources for prokaryotic genome analysis, the SEED project, initiated in 2003 as an integration of genomic data and analysis tools, now contains >5,000 complete genomes, a constantly updated set of curated annotations embodied in a large and growing collection of encoded subsystems, a derived set of protein families, and hundreds of genome-scale metabolic models. Until recently, however, maintaining current copies of the SEED code and data at remote locations has been a pressing issue. To allow high-performance remote access to the SEED database, we developed the SEED Servers (http://www.theseed.org/servers: four network-based servers intended to expose the data in the underlying relational database, support basic annotation services, offer programmatic access to the capabilities of the RAST annotation server, and provide access to a growing collection of metabolic models that support flux balance analysis. The SEED servers offer open access to regularly updated data, the ability to annotate prokaryotic genomes, the ability to create metabolic reconstructions and detailed models of metabolism, and access to hundreds of existing metabolic models. This work offers and supports a framework upon which other groups can build independent research efforts. Large integrations of genomic data represent one of the major intellectual resources driving research in biology, and programmatic access to the SEED data will provide significant utility to a broad

  18. Insulin signaling, resistance, and the metabolic syndrome: insights from mouse models into disease mechanisms.

    Science.gov (United States)

    Guo, Shaodong

    2014-02-01

    Insulin resistance is a major underlying mechanism responsible for the 'metabolic syndrome', which is also known as insulin resistance syndrome. The incidence of the metabolic syndrome is increasing at an alarming rate, becoming a major public and clinical problem worldwide. The metabolic syndrome is represented by a group of interrelated disorders, including obesity, hyperglycemia, hyperlipidemia, and hypertension. It is also a significant risk factor for cardiovascular disease and increased morbidity and mortality. Animal studies have demonstrated that insulin and its signaling cascade normally control cell growth, metabolism, and survival through the activation of MAPKs and activation of phosphatidylinositide-3-kinase (PI3K), in which the activation of PI3K associated with insulin receptor substrate 1 (IRS1) and IRS2 and subsequent Akt→Foxo1 phosphorylation cascade has a central role in the control of nutrient homeostasis and organ survival. The inactivation of Akt and activation of Foxo1, through the suppression IRS1 and IRS2 in different organs following hyperinsulinemia, metabolic inflammation, and overnutrition, may act as the underlying mechanisms for the metabolic syndrome in humans. Targeting the IRS→Akt→Foxo1 signaling cascade will probably provide a strategy for therapeutic intervention in the treatment of type 2 diabetes and its complications. This review discusses the basis of insulin signaling, insulin resistance in different mouse models, and how a deficiency of insulin signaling components in different organs contributes to the features of the metabolic syndrome. Emphasis is placed on the role of IRS1, IRS2, and associated signaling pathways that are coupled to Akt and the forkhead/winged helix transcription factor Foxo1.

  19. Insulin Signaling, Resistance, and the Metabolic Syndrome: Insights from Mouse Models to Disease Mechanisms

    Science.gov (United States)

    Guo, Shaodong

    2014-01-01

    Insulin resistance is a major underlying mechanism for the “metabolic syndrome”, which is also known as insulin resistance syndrome. Metabolic syndrome is increasing at an alarming rate, becoming a major public and clinical problem worldwide. Metabolic syndrome is represented by a group of interrelated disorders, including obesity, hyperglycemia, hyperlipidemia, and hypertension. It is also a significant risk factor for cardiovascular disease and increased morbidity and mortality. Animal studies demonstrate that insulin and its signaling cascade normally control cell growth, metabolism and survival through activation of mitogen-activated protein kinases (MAPKs) and phosphotidylinositide-3-kinase (PI3K), of which activation of PI-3K-associated with insulin receptor substrate-1 and -2 (IRS1, 2) and subsequent Akt→Foxo1 phosphorylation cascade has a central role in control of nutrient homeostasis and organ survival. Inactivation of Akt and activation of Foxo1, through suppression IRS1 and IRS2 in different organs following hyperinsulinemia, metabolic inflammation, and over nutrition may provide the underlying mechanisms for metabolic syndrome in humans. Targeting the IRS→Akt→Foxo1 signaling cascade will likely provide a strategy for therapeutic intervention in the treatment of type 2 diabetes and its complications. This review discusses the basis of insulin signaling, insulin resistance in different mouse models, and how a deficiency of insulin signaling components in different organs contributes to the feature of the metabolic syndrome. Emphasis will be placed on the role of IRS1, IRS2, and associated signaling pathways that couple to Akt and the forkhead/winged helix transcription factor Foxo1. PMID:24281010

  20. The protease inhibitors ritonavir and saquinavir influence lipid metabolism: a pig model for the rapid evaluation of new drugs

    DEFF Research Database (Denmark)

    Petersen, E.; Mu, Huiling; Porsgaard, Trine

    2010-01-01

    Background: Studies of the effects of antiretroviral drugs on lipid metabolism are limited by the availability of suitable models. We have thus developed an animal model utilising Gottingen mini-pigs. The normal lipid metabolism of mini-pigs closely reflects that of humans and they are expected t...