Metabolic Modeling of Common Escherichia coli Strains in Human Gut Microbiome

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Yue-Dong Gao

2014-01-01

Full Text Available The recent high-throughput sequencing has enabled the composition of Escherichia coli strains in the human microbial community to be profiled en masse. However, there are two challenges to address: (1 exploring the genetic differences between E. coli strains in human gut and (2 dynamic responses of E. coli to diverse stress conditions. As a result, we investigated the E. coli strains in human gut microbiome using deep sequencing data and reconstructed genome-wide metabolic networks for the three most common E. coli strains, including E. coli HS, UTI89, and CFT073. The metabolic models show obvious strain-specific characteristics, both in network contents and in behaviors. We predicted optimal biomass production for three models on four different carbon sources (acetate, ethanol, glucose, and succinate and found that these stress-associated genes were involved in host-microbial interactions and increased in human obesity. Besides, it shows that the growth rates are similar among the models, but the flux distributions are different, even in E. coli core reactions. The correlations between human diabetes-associated metabolic reactions in the E. coli models were also predicted. The study provides a systems perspective on E. coli strains in human gut microbiome and will be helpful in integrating diverse data sources in the following study.

Systems biology of bacterial nitrogen fixation: High-throughput technology and its integrative description with constraint-based modeling

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Resendis-Antonio Osbaldo

2011-07-01

Full Text Available Abstract Background Bacterial nitrogen fixation is the biological process by which atmospheric nitrogen is uptaken by bacteroids located in plant root nodules and converted into ammonium through the enzymatic activity of nitrogenase. In practice, this biological process serves as a natural form of fertilization and its optimization has significant implications in sustainable agricultural programs. Currently, the advent of high-throughput technology supplies with valuable data that contribute to understanding the metabolic activity during bacterial nitrogen fixation. This undertaking is not trivial, and the development of computational methods useful in accomplishing an integrative, descriptive and predictive framework is a crucial issue to decoding the principles that regulated the metabolic activity of this biological process. Results In this work we present a systems biology description of the metabolic activity in bacterial nitrogen fixation. This was accomplished by an integrative analysis involving high-throughput data and constraint-based modeling to characterize the metabolic activity in Rhizobium etli bacteroids located at the root nodules of Phaseolus vulgaris (bean plant. Proteome and transcriptome technologies led us to identify 415 proteins and 689 up-regulated genes that orchestrate this biological process. Taking into account these data, we: 1 extended the metabolic reconstruction reported for R. etli; 2 simulated the metabolic activity during symbiotic nitrogen fixation; and 3 evaluated the in silico results in terms of bacteria phenotype. Notably, constraint-based modeling simulated nitrogen fixation activity in such a way that 76.83% of the enzymes and 69.48% of the genes were experimentally justified. Finally, to further assess the predictive scope of the computational model, gene deletion analysis was carried out on nine metabolic enzymes. Our model concluded that an altered metabolic activity on these enzymes induced

A Mathematical Model of Metabolism and Regulation Provides a Systems-Level View of How Escherichia coli Responds to Oxygen

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Michael eEderer

2014-03-01

Full Text Available The efficient redesign of bacteria for biotechnological purposes, such as biofuel production, waste disposal or specific biocatalytic functions, requires a quantitative systems-level understanding of energy supply, carbon and redox metabolism. The measurement of transcript levels, metabolite concentrations and metabolic fluxes per se gives an incomplete picture. An appreciation of the interdependencies between the different measurement values is essential for systems-level understanding. Mathematical modeling has the potential to provide a coherent and quantitative description of the interplay between gene expression, metabolite concentrations and metabolic fluxes. Escherichia coli undergoes major adaptations in central metabolism when the availability of oxygen changes. Thus, an integrated description of the oxygen response provides a benchmark of our understanding of carbon, energy and redox metabolism. We present the first comprehensive model of the central metabolism of E. coli that describes steady-state metabolism at different levels of oxygen availability. Variables of the model are metabolite concentrations, gene expression levels, transcription factor activities, metabolic fluxes and biomass concentration. We analyze the model with respect to the production capabilities of central metabolism of E. coli. In particular, we predict how precursor and biomass concentration are affected by product formation.

Integrative structure modeling with the Integrative Modeling Platform.

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Webb, Benjamin; Viswanath, Shruthi; Bonomi, Massimiliano; Pellarin, Riccardo; Greenberg, Charles H; Saltzberg, Daniel; Sali, Andrej

2018-01-01

Building models of a biological system that are consistent with the myriad data available is one of the key challenges in biology. Modeling the structure and dynamics of macromolecular assemblies, for example, can give insights into how biological systems work, evolved, might be controlled, and even designed. Integrative structure modeling casts the building of structural models as a computational optimization problem, for which information about the assembly is encoded into a scoring function that evaluates candidate models. Here, we describe our open source software suite for integrative structure modeling, Integrative Modeling Platform (https://integrativemodeling.org), and demonstrate its use. © 2017 The Protein Society.

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......, databases and platforms used to construct them. For generic human metabolic models, we pay attention to Recon 2 and HMR 2.0 with emphasis on data sources used to construct them. Draft and high-quality tissue/cell type-specific human metabolic models have been generated using these generic human metabolic...... 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....

Integrating the protein and metabolic engineering toolkits for next-generation chemical biosynthesis.

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Pirie, Christopher M; De Mey, Marjan; Jones Prather, Kristala L; Ajikumar, Parayil Kumaran

2013-04-19

Through microbial engineering, biosynthesis has the potential to produce thousands of chemicals used in everyday life. Metabolic engineering and synthetic biology are fields driven by the manipulation of genes, genetic regulatory systems, and enzymatic pathways for developing highly productive microbial strains. Fundamentally, it is the biochemical characteristics of the enzymes themselves that dictate flux through a biosynthetic pathway toward the product of interest. As metabolic engineers target sophisticated secondary metabolites, there has been little recognition of the reduced catalytic activity and increased substrate/product promiscuity of the corresponding enzymes compared to those of central metabolism. Thus, fine-tuning these enzymatic characteristics through protein engineering is paramount for developing high-productivity microbial strains for secondary metabolites. Here, we describe the importance of protein engineering for advancing metabolic engineering of secondary metabolism pathways. This pathway integrated enzyme optimization can enhance the collective toolkit of microbial engineering to shape the future of chemical manufacturing.

Urban water metabolism efficiency assessment: integrated analysis of available and virtual water.

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Huang, Chu-Long; Vause, Jonathan; Ma, Hwong-Wen; Yu, Chang-Ping

2013-05-01

Resolving the complex environmental problems of water pollution and shortage which occur during urbanization requires the systematic assessment of urban water metabolism efficiency (WME). While previous research has tended to focus on either available or virtual water metabolism, here we argue that the systematic problems arising during urbanization require an integrated assessment of available and virtual WME, using an indicator system based on material flow analysis (MFA) results. Future research should focus on the following areas: 1) analysis of available and virtual water flow patterns and processes through urban districts in different urbanization phases in years with varying amounts of rainfall, and their environmental effects; 2) based on the optimization of social, economic and environmental benefits, establishment of an indicator system for urban WME assessment using MFA results; 3) integrated assessment of available and virtual WME in districts with different urbanization levels, to facilitate study of the interactions between the natural and social water cycles; 4) analysis of mechanisms driving differences in WME between districts with different urbanization levels, and the selection of dominant social and economic driving indicators, especially those impacting water resource consumption. Combinations of these driving indicators could then be used to design efficient water resource metabolism solutions, and integrated management policies for reduced water consumption. Copyright © 2013 Elsevier B.V. All rights reserved.

Ensemble Kinetic Modeling of Metabolic Networks from Dynamic Metabolic Profiles

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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.

A Computational Model of Torque Generation: Neural, Contractile, Metabolic and Musculoskeletal Components

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Callahan, Damien M.; Umberger, Brian R.; Kent-Braun, Jane A.

2013-01-01

The pathway of voluntary joint torque production includes motor neuron recruitment and rate-coding, sarcolemmal depolarization and calcium release by the sarcoplasmic reticulum, force generation by motor proteins within skeletal muscle, and force transmission by tendon across the joint. The direct source of energetic support for this process is ATP hydrolysis. It is possible to examine portions of this physiologic pathway using various in vivo and in vitro techniques, but an integrated view of the multiple processes that ultimately impact joint torque remains elusive. To address this gap, we present a comprehensive computational model of the combined neuromuscular and musculoskeletal systems that includes novel components related to intracellular bioenergetics function. Components representing excitatory drive, muscle activation, force generation, metabolic perturbations, and torque production during voluntary human ankle dorsiflexion were constructed, using a combination of experimentally-derived data and literature values. Simulation results were validated by comparison with torque and metabolic data obtained in vivo. The model successfully predicted peak and submaximal voluntary and electrically-elicited torque output, and accurately simulated the metabolic perturbations associated with voluntary contractions. This novel, comprehensive model could be used to better understand impact of global effectors such as age and disease on various components of the neuromuscular system, and ultimately, voluntary torque output. PMID:23405245

Rodent Models for Metabolic Syndrome Research

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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.

LakeMetabolizer: An R package for estimating lake metabolism from free-water oxygen using diverse statistical models

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Winslow, Luke; Zwart, Jacob A.; Batt, Ryan D.; Dugan, Hilary; Woolway, R. Iestyn; Corman, Jessica; Hanson, Paul C.; Read, Jordan S.

2016-01-01

Metabolism is a fundamental process in ecosystems that crosses multiple scales of organization from individual organisms to whole ecosystems. To improve sharing and reuse of published metabolism models, we developed LakeMetabolizer, an R package for estimating lake metabolism from in situ time series of dissolved oxygen, water temperature, and, optionally, additional environmental variables. LakeMetabolizer implements 5 different metabolism models with diverse statistical underpinnings: bookkeeping, ordinary least squares, maximum likelihood, Kalman filter, and Bayesian. Each of these 5 metabolism models can be combined with 1 of 7 models for computing the coefficient of gas exchange across the air–water interface (k). LakeMetabolizer also features a variety of supporting functions that compute conversions and implement calculations commonly applied to raw data prior to estimating metabolism (e.g., oxygen saturation and optical conversion models). These tools have been organized into an R package that contains example data, example use-cases, and function documentation. The release package version is available on the Comprehensive R Archive Network (CRAN), and the full open-source GPL-licensed code is freely available for examination and extension online. With this unified, open-source, and freely available package, we hope to improve access and facilitate the application of metabolism in studies and management of lentic ecosystems.

A hybrid mathematical modeling approach of the metabolic fate of a fluorescent sphingolipid analogue to predict cancer chemosensitivity.

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Molina-Mora, J A; Kop-Montero, M; Quirós-Fernández, I; Quiros, S; Crespo-Mariño, J L; Mora-Rodríguez, R A

2018-04-13

Sphingolipid (SL) metabolism is a complex biological system that produces and transforms ceramides and other molecules able to modulate other cellular processes, including survival or death pathways key to cell fate decisions. This signaling pathway integrates several types of stress signals, including chemotherapy, into changes in the activity of its metabolic enzymes, altering thereby the cellular composition of bioactive SLs. Therefore, the SL pathway is a promising sensor of chemosensitivity in cancer and a target hub to overcome resistance. However, there is still a gap in our understanding of how chemotherapeutic drugs can disturb the SL pathway in order to control cellular fate. We propose to bridge this gap by a systems biology approach to integrate i) a dynamic model of SL analogue (BODIPY-FL fluorescent-sphingomyelin analogue, SM-BOD) metabolism, ii) a Gaussian mixture model (GMM) of the fluorescence features to identify how the SL pathway senses the effect of chemotherapy and iii) a fuzzy logic model (FLM) to associate SL composition with cell viability by semi-quantitative rules. Altogether, this hybrid model approach was able to predict the cell viability of double experimental perturbations with chemotherapy, indicating that the SL pathway is a promising sensor to design strategies to overcome drug resistance in cancer. Copyright © 2018 Elsevier Ltd. All rights reserved.

Integrative Metabolism: An Interactive Learning Tool for Nutrition, Biochemistry, and Physiology

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Carey, Gale

2010-01-01

Metabolism is a dynamic, simultaneous, and integrative science that cuts across nutrition, biochemistry, and physiology. Teaching this science can be a challenge. The use of a scenario-based, visually appealing, interactive, computer-animated CD may overcome the limitations of learning "one pathway at a time" and engage two- and…

Prediction of lithium-ion battery capacity with metabolic grey model

International Nuclear Information System (INIS)

Chen, Lin; Lin, Weilong; Li, Junzi; Tian, Binbin; Pan, Haihong

2016-01-01

Given the popularity of Lithium-ion batteries in EVs (electric vehicles), predicting the capacity quickly and accurately throughout a battery's full life-time is still a challenging issue for ensuring the reliability of EVs. This paper proposes an approach in predicting the varied capacity with discharge cycles based on metabolic grey theory and consider issues from two perspectives: 1) three metabolic grey models will be presented, including MGM (metabolic grey model), MREGM (metabolic Residual-error grey model), and MMREGM (metabolic Markov-residual-error grey model); 2) the universality of these models will be explored under different conditions (such as various discharge rates and temperatures). Furthermore, the research findings in this paper demonstrate the excellent performance of the prediction depending on the three models; however, the precision of the MREGM model is inferior compared to the others. Therefore, we have obtained the conclusion in which the MGM model and the MMREGM model have excellent performances in predicting the capacity under a variety of load conditions, even using few data points for modeling. Also, the universality of the metabolic grey prediction theory is verified by predicting the capacity of batteries under different discharge rates and different temperatures. - Highlights: • The metabolic mechanism is introduced in a grey system for capacity prediction. • Three metabolic grey models are presented and studied. • The universality of these models under different conditions is assessed. • A few data points are required for predicting the capacity with these models.

Modeling the autonomic and metabolic effects of obstructive sleep apnea: A simulation study.

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Limei eCheng

2012-01-01

Full Text Available Long term exposure to intermittent hypoxia and sleep fragmentation introduced by recurring obstructive sleep apnea has been linked to subsequent cardiovascular disease and Type 2 diabetes. The underlying mechanisms remain unclear, but impairment of the normal interactions among the systems that regulate autonomic and metabolic function is likely involved. We have extended an existing integrative model of respiratory, cardiovascular and sleep-wake state control, to incorporate a sub-model of glucose-insulin-fatty acid regulation. This computational model is capable of simulating the complex dynamics of cardiorespiratory control, chemoreflex and state-related control of breath-to-breath ventilation, state-related and chemoreflex control of upper airway potency, respiratory and circulatory mechanics, as well as the metabolic control of glucose insulin dynamics and its interactions with the autonomic control. The interactions between autonomic and metabolic control include the circadian regulation of epinephrine secretion, epinephrine regulation on dynamic fluctuations in glucose and free-fatty acid in plasma, metabolic coupling among tissues and organs provided by insulin and epinephrine, as well as the effect of insulin on peripheral vascular sympathetic activity. These model simulations provide insight into the relative importance of the various mechanisms that determine the acute and chronic physiological effects of sleep-disordered breathing. The model can also be used to investigate the effects of a variety of interventions, such as different glucose clamps, the intravenous glucose tolerance test and the application of continuous positive airway pressure on obstructive sleep apnea subjects. As such, this model provides the foundation on which future efforts to simulate disease progression and the long-term effects of pharmacological intervention can be based.

Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis

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Broddrick, Jared T.; Rubin, Benjamin E.; Welkie, David G.; Du, Niu; Mih, Nathan; Diamond, Spencer; Lee, Jenny J.; Golden, Susan S.; Palsson, Bernhard O.

2016-01-01

The model cyanobacterium, Synechococcus elongatus PCC 7942, is a genetically tractable obligate phototroph that is being developed for the bioproduction of high-value chemicals. Genome-scale models (GEMs) have been successfully used to assess and engineer cellular metabolism; however, GEMs of phototrophic metabolism have been limited by the lack of experimental datasets for model validation and the challenges of incorporating photon uptake. Here, we develop a GEM of metabolism in S. elongatus using random barcode transposon site sequencing (RB-TnSeq) essential gene and physiological data specific to photoautotrophic metabolism. The model explicitly describes photon absorption and accounts for shading, resulting in the characteristic linear growth curve of photoautotrophs. GEM predictions of gene essentiality were compared with data obtained from recent dense-transposon mutagenesis experiments. This dataset allowed major improvements to the accuracy of the model. Furthermore, discrepancies between GEM predictions and the in vivo dataset revealed biological characteristics, such as the importance of a truncated, linear TCA pathway, low flux toward amino acid synthesis from photorespiration, and knowledge gaps within nucleotide metabolism. Coupling of strong experimental support and photoautotrophic modeling methods thus resulted in a highly accurate model of S. elongatus metabolism that highlights previously unknown areas of S. elongatus biology. PMID:27911809

A Simplified Model of Human Alcohol Metabolism That Integrates Biotechnology and Human Health into a Mass Balance Team Project

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Yang, Allen H. J.; Dimiduk, Kathryn; Daniel, Susan

2011-01-01

We present a simplified human alcohol metabolism model for a mass balance team project. Students explore aspects of engineering in biotechnology: designing/modeling biological systems, testing the design/model, evaluating new conditions, and exploring cutting-edge "lab-on-a-chip" research. This project highlights chemical engineering's impact on…

Applications of computational modeling in metabolic engineering of yeast

DEFF Research Database (Denmark)

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

2015-01-01

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......, 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....

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.

Advantages of integrated and sustainability based assessment for metabolism based strategic planning of urban water systems.

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Behzadian, Kourosh; Kapelan, Zoran

2015-09-15

Despite providing water-related services as the primary purpose of urban water system (UWS), all relevant activities require capital investments and operational expenditures, consume resources (e.g. materials and chemicals), and may increase negative environmental impacts (e.g. contaminant discharge, emissions to water and air). Performance assessment of such a metabolic system may require developing a holistic approach which encompasses various system elements and criteria. This paper analyses the impact of integration of UWS components on the metabolism based performance assessment for future planning using a number of intervention strategies. It also explores the importance of sustainability based criteria in the assessment of long-term planning. Two assessment approaches analysed here are: (1) planning for only water supply system (WSS) as a part of the UWS and (2) planning for an integrated UWS including potable water, stormwater, wastewater and water recycling. WaterMet(2) model is used to simulate metabolic type processes in the UWS and calculate quantitative performance indicators. The analysis is demonstrated on the problem of strategic level planning of a real-world UWS to where optional intervention strategies are applied. The resulting performance is assessed using the multiple criteria of both conventional and sustainability type; and optional intervention strategies are then ranked using the Compromise Programming method. The results obtained show that the high ranked intervention strategies in the integrated UWS are those supporting both water supply and stormwater/wastewater subsystems (e.g. rainwater harvesting and greywater recycling schemes) whilst these strategies are ranked low in the WSS and those targeting improvement of water supply components only (e.g. rehabilitation of clean water pipes and addition of new water resources) are preferred instead. Results also demonstrate that both conventional and sustainability type performance indicators

Model integration and a theory of models

OpenAIRE

Dolk, Daniel R.; Kottemann, Jeffrey E.

1993-01-01

Model integration extends the scope of model management to include the dimension of manipulation as well. This invariably leads to comparisons with database theory. Model integration is viewed from four perspectives: Organizational, definitional, procedural, and implementational. Strategic modeling is discussed as the organizational motivation for model integration. Schema and process integration are examined as the logical and manipulation counterparts of model integr...

The Need for Integrated Approaches in Metabolic Engineering

Energy Technology Data Exchange (ETDEWEB)

Lechner, Anna; Brunk, Elizabeth; Keasling, Jay D.

2016-08-15

This review highlights state-of-the-art procedures for heterologous small-molecule biosynthesis, the associated bottlenecks, and new strategies that have the potential to accelerate future accomplishments in metabolic engineering. We emphasize that a combination of different approaches over multiple time and size scales must b e considered for successful pathway engineering in a heterologous host. We have classified these optimization procedures based on the "system" that is being manipulated: transcriptome, translatome, proteome, or reactome. By bridging multiple disciplines, including molecular biology, biochemistry, biophysics, and computational sciences, we can create an integral framework for the discovery and implementation of novel biosynthetic production routes.

A system dynamics model integrating physiology and biochemical regulation predicts extent of crassulacean acid metabolism (CAM) phases.

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Owen, Nick A; Griffiths, Howard

2013-12-01

A system dynamics (SD) approach was taken to model crassulacean acid metabolism (CAM) expression from measured biochemical and physiological constants. SD emphasizes state-dependent feedback interaction to describe the emergent properties of a complex system. These mechanisms maintain biological systems with homeostatic limits on a temporal basis. Previous empirical studies on CAM have correlated biological constants (e.g. enzyme kinetic parameters) with expression over the CAM diel cycle. The SD model integrates these constants within the architecture of the CAM 'system'. This allowed quantitative causal connections to be established between biological inputs and the four distinct phases of CAM delineated by gas exchange and malic acid accumulation traits. Regulation at flow junctions (e.g. stomatal and mesophyll conductance, and malic acid transport across the tonoplast) that are subject to feedback control (e.g. stomatal aperture, malic acid inhibition of phosphoenolpyruvate carboxylase, and enzyme kinetics) was simulated. Simulated expression for the leaf-succulent Kalanchoë daigremontiana and more succulent tissues of Agave tequilana showed strong correlation with measured gas exchange and malic acid accumulation (R(2)  = 0.912 and 0.937, respectively, for K. daigremontiana and R(2)  = 0.928 and 0.942, respectively, for A. tequilana). Sensitivity analyses were conducted to quantitatively identify determinants of diel CO2 uptake. The transition in CAM expression from low to high volume/area tissues (elimination of phase II-IV carbon-uptake signatures) was achieved largely by the manipulation three input parameters. © 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.

Metabolic engineering tools in model cyanobacteria.

Science.gov (United States)

Carroll, Austin L; Case, Anna E; Zhang, Angela; Atsumi, Shota

2018-03-26

Developing sustainable routes for producing chemicals and fuels is one of the most important challenges in metabolic engineering. Photoautotrophic hosts are particularly attractive because of their potential to utilize light as an energy source and CO 2 as a carbon substrate through photosynthesis. Cyanobacteria are unicellular organisms capable of photosynthesis and CO 2 fixation. While engineering in heterotrophs, such as Escherichia coli, has result in a plethora of tools for strain development and hosts capable of producing valuable chemicals efficiently, these techniques are not always directly transferable to cyanobacteria. However, recent efforts have led to an increase in the scope and scale of chemicals that cyanobacteria can produce. Adaptations of important metabolic engineering tools have also been optimized to function in photoautotrophic hosts, which include Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas9, 13 C Metabolic Flux Analysis (MFA), and Genome-Scale Modeling (GSM). This review explores innovations in cyanobacterial metabolic engineering, and highlights how photoautotrophic metabolism has shaped their development. Copyright © 2018 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Using a genome-scale metabolic network model to elucidate the mechanism of chloroquine action in Plasmodium falciparum

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Shivendra G. Tewari

2017-08-01

Full Text Available Chloroquine, long the default first-line treatment against malaria, is now abandoned in large parts of the world because of widespread drug-resistance in Plasmodium falciparum. In spite of its importance as a cost-effective and efficient drug, a coherent understanding of the cellular mechanisms affected by chloroquine and how they influence the fitness and survival of the parasite remains elusive. Here, we used a systems biology approach to integrate genome-scale transcriptomics to map out the effects of chloroquine, identify targeted metabolic pathways, and translate these findings into mechanistic insights. Specifically, we first developed a method that integrates transcriptomic and metabolomic data, which we independently validated against a recently published set of such data for Krebs-cycle mutants of P. falciparum. We then used the method to calculate the effect of chloroquine treatment on the metabolic flux profiles of P. falciparum during the intraerythrocytic developmental cycle. The model predicted dose-dependent inhibition of DNA replication, in agreement with earlier experimental results for both drug-sensitive and drug-resistant P. falciparum strains. Our simulations also corroborated experimental findings that suggest differences in chloroquine sensitivity between ring- and schizont-stage P. falciparum. Our analysis also suggests that metabolic fluxes that govern reduced thioredoxin and phosphoenolpyruvate synthesis are significantly decreased and are pivotal to chloroquine-based inhibition of P. falciparum DNA replication. The consequences of impaired phosphoenolpyruvate synthesis and redox metabolism are reduced carbon fixation and increased oxidative stress, respectively, both of which eventually facilitate killing of the parasite. Our analysis suggests that a combination of chloroquine (or an analogue and another drug, which inhibits carbon fixation and/or increases oxidative stress, should increase the clearance of P

Tools and Models for Integrating Multiple Cellular Networks

Energy Technology Data Exchange (ETDEWEB)

Gerstein, Mark [Yale Univ., New Haven, CT (United States). Gerstein Lab.

2015-11-06

In this grant, we have systematically investigated the integrated networks, which are responsible for the coordination of activity between metabolic pathways in prokaryotes. We have developed several computational tools to analyze the topology of the integrated networks consisting of metabolic, regulatory, and physical interaction networks. The tools are all open-source, and they are available to download from Github, and can be incorporated in the Knowledgebase. Here, we summarize our work as follow. Understanding the topology of the integrated networks is the first step toward understanding its dynamics and evolution. For Aim 1 of this grant, we have developed a novel algorithm to determine and measure the hierarchical structure of transcriptional regulatory networks [1]. The hierarchy captures the direction of information flow in the network. The algorithm is generally applicable to regulatory networks in prokaryotes, yeast and higher organisms. Integrated datasets are extremely beneficial in understanding the biology of a system in a compact manner due to the conflation of multiple layers of information. Therefore for Aim 2 of this grant, we have developed several tools and carried out analysis for integrating system-wide genomic information. To make use of the structural data, we have developed DynaSIN for protein-protein interactions networks with various dynamical interfaces [2]. We then examined the association between network topology with phenotypic effects such as gene essentiality. In particular, we have organized E. coli and S. cerevisiae transcriptional regulatory networks into hierarchies. We then correlated gene phenotypic effects by tinkering with different layers to elucidate which layers were more tolerant to perturbations [3]. In the context of evolution, we also developed a workflow to guide the comparison between different types of biological networks across various species using the concept of rewiring [4], and Furthermore, we have developed

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

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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.

Concepts, challenges, and successes in modeling thermodynamics of metabolism.

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Cannon, William R

2014-01-01

The modeling of the chemical reactions involved in metabolism is a daunting task. Ideally, the modeling of metabolism would use kinetic simulations, but these simulations require knowledge of the thousands of rate constants involved in the reactions. The measurement of rate constants is very labor intensive, and hence rate constants for most enzymatic reactions are not available. Consequently, constraint-based flux modeling has been the method of choice because it does not require the use of the rate constants of the law of mass action. However, this convenience also limits the predictive power of constraint-based approaches in that the law of mass action is used only as a constraint, making it difficult to predict metabolite levels or energy requirements of pathways. An alternative to both of these approaches is to model metabolism using simulations of states rather than simulations of reactions, in which the state is defined as the set of all metabolite counts or concentrations. While kinetic simulations model reactions based on the likelihood of the reaction derived from the law of mass action, states are modeled based on likelihood ratios of mass action. Both approaches provide information on the energy requirements of metabolic reactions and pathways. However, modeling states rather than reactions has the advantage that the parameters needed to model states (chemical potentials) are much easier to determine than the parameters needed to model reactions (rate constants). Herein, we discuss recent results, assumptions, and issues in using simulations of state to model metabolism.

Modeling of oxygen transport and cellular energetics explains observations on in vivo cardiac energy metabolism.

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Daniel A Beard

2006-09-01

Full Text Available Observations on the relationship between cardiac work rate and the levels of energy metabolites adenosine triphosphate (ATP, adenosine diphosphate (ADP, and phosphocreatine (CrP have not been satisfactorily explained by theoretical models of cardiac energy metabolism. Specifically, the in vivo stability of ATP, ADP, and CrP levels in response to changes in work and respiratory rate has eluded explanation. Here a previously developed model of mitochondrial oxidative phosphorylation, which was developed based on data obtained from isolated cardiac mitochondria, is integrated with a spatially distributed model of oxygen transport in the myocardium to analyze data obtained from several laboratories over the past two decades. The model includes the components of the respiratory chain, the F0F1-ATPase, adenine nucleotide translocase, and the mitochondrial phosphate transporter at the mitochondrial level; adenylate kinase, creatine kinase, and ATP consumption in the cytoplasm; and oxygen transport between capillaries, interstitial fluid, and cardiomyocytes. The integrated model is able to reproduce experimental observations on ATP, ADP, CrP, and inorganic phosphate levels in canine hearts over a range of workload and during coronary hypoperfusion and predicts that cytoplasmic inorganic phosphate level is a key regulator of the rate of mitochondrial respiration at workloads for which the rate of cardiac oxygen consumption is less than or equal to approximately 12 mumol per minute per gram of tissue. At work rates corresponding to oxygen consumption higher than 12 mumol min(-1 g(-1, model predictions deviate from the experimental data, indicating that at high work rates, additional regulatory mechanisms that are not currently incorporated into the model may be important. Nevertheless, the integrated model explains metabolite levels observed at low to moderate workloads and the changes in metabolite levels and tissue oxygenation observed during graded

Bioenergetics-based modeling of Plasmodium falciparum metabolism reveals its essential genes, nutritional requirements, and thermodynamic bottlenecks

Science.gov (United States)

Chiappino-Pepe, Anush; Ataman, Meriç

2017-01-01

Novel antimalarial therapies are urgently needed for the fight against drug-resistant parasites. The metabolism of malaria parasites in infected cells is an attractive source of drug targets but is rather complex. Computational methods can handle this complexity and allow integrative analyses of cell metabolism. In this study, we present a genome-scale metabolic model (iPfa) of the deadliest malaria parasite, Plasmodium falciparum, and its thermodynamics-based flux analysis (TFA). Using previous absolute concentration data of the intraerythrocytic parasite, we applied TFA to iPfa and predicted up to 63 essential genes and 26 essential pairs of genes. Of the 63 genes, 35 have been experimentally validated and reported in the literature, and 28 have not been experimentally tested and include previously hypothesized or novel predictions of essential metabolic capabilities. Without metabolomics data, four of the genes would have been incorrectly predicted to be non-essential. TFA also indicated that substrate channeling should exist in two metabolic pathways to ensure the thermodynamic feasibility of the flux. Finally, analysis of the metabolic capabilities of P. falciparum led to the identification of both the minimal nutritional requirements and the genes that can become indispensable upon substrate inaccessibility. This model provides novel insight into the metabolic needs and capabilities of the malaria parasite and highlights metabolites and pathways that should be measured and characterized to identify potential thermodynamic bottlenecks and substrate channeling. The hypotheses presented seek to guide experimental studies to facilitate a better understanding of the parasite metabolism and the identification of targets for more efficient intervention. PMID:28333921

Bioenergetics-based modeling of Plasmodium falciparum metabolism reveals its essential genes, nutritional requirements, and thermodynamic bottlenecks.

Directory of Open Access Journals (Sweden)

Anush Chiappino-Pepe

2017-03-01

Full Text Available Novel antimalarial therapies are urgently needed for the fight against drug-resistant parasites. The metabolism of malaria parasites in infected cells is an attractive source of drug targets but is rather complex. Computational methods can handle this complexity and allow integrative analyses of cell metabolism. In this study, we present a genome-scale metabolic model (iPfa of the deadliest malaria parasite, Plasmodium falciparum, and its thermodynamics-based flux analysis (TFA. Using previous absolute concentration data of the intraerythrocytic parasite, we applied TFA to iPfa and predicted up to 63 essential genes and 26 essential pairs of genes. Of the 63 genes, 35 have been experimentally validated and reported in the literature, and 28 have not been experimentally tested and include previously hypothesized or novel predictions of essential metabolic capabilities. Without metabolomics data, four of the genes would have been incorrectly predicted to be non-essential. TFA also indicated that substrate channeling should exist in two metabolic pathways to ensure the thermodynamic feasibility of the flux. Finally, analysis of the metabolic capabilities of P. falciparum led to the identification of both the minimal nutritional requirements and the genes that can become indispensable upon substrate inaccessibility. This model provides novel insight into the metabolic needs and capabilities of the malaria parasite and highlights metabolites and pathways that should be measured and characterized to identify potential thermodynamic bottlenecks and substrate channeling. The hypotheses presented seek to guide experimental studies to facilitate a better understanding of the parasite metabolism and the identification of targets for more efficient intervention.

Functional integration changes in regional brain glucose metabolism from childhood to adulthood.

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Trotta, Nicola; Archambaud, Frédérique; Goldman, Serge; Baete, Kristof; Van Laere, Koen; Wens, Vincent; Van Bogaert, Patrick; Chiron, Catherine; De Tiège, Xavier

2016-08-01

The aim of this study was to investigate the age-related changes in resting-state neurometabolic connectivity from childhood to adulthood (6-50 years old). Fifty-four healthy adult subjects and twenty-three pseudo-healthy children underwent [(18) F]-fluorodeoxyglucose positron emission tomography at rest. Using statistical parametric mapping (SPM8), age and age squared were first used as covariate of interest to identify linear and non-linear age effects on the regional distribution of glucose metabolism throughout the brain. Then, by selecting voxels of interest (VOI) within the regions showing significant age-related metabolic changes, a psychophysiological interaction (PPI) analysis was used to search for age-induced changes in the contribution of VOIs to the metabolic activity in other brain areas. Significant linear or non-linear age-related changes in regional glucose metabolism were found in prefrontal cortices (DMPFC/ACC), cerebellar lobules, and thalamo-hippocampal areas bilaterally. Decreases were found in the contribution of thalamic, hippocampal, and cerebellar regions to DMPFC/ACC metabolic activity as well as in the contribution of hippocampi to preSMA and right IFG metabolic activities. Increases were found in the contribution of the right hippocampus to insular cortex and of the cerebellar lobule IX to superior parietal cortex metabolic activities. This study evidences significant linear or non-linear age-related changes in regional glucose metabolism of mesial prefrontal, thalamic, mesiotemporal, and cerebellar areas, associated with significant modifications in neurometabolic connectivity involving fronto-thalamic, fronto-hippocampal, and fronto-cerebellar networks. These changes in functional brain integration likely represent a metabolic correlate of age-dependent effects on sensory, motor, and high-level cognitive functional networks. Hum Brain Mapp 37:3017-3030, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

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

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McAnulty, Michael J; Yen, Jiun Y; Freedman, Benjamin G; Senger, Ryan S

2012-05-14

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. 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. 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.

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 construc......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...

Integration of C1and C2metabolism in trees

OpenAIRE

Jardine, KJ; de Souza, VF; Oikawa, P; Higuchi, N; Bill, M; Porras, R; Niinemets, Ü; Chambers, JQ

2017-01-01

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. C 1 metabolism in plants is known to be involved in photorespiration, nitrogen and amino acid metabolism, as well as methylation and biosynthesis of metabolites and biopolymers. Although the flux of carbon through the C 1 pathway is thought to be large, its intermediates are difficult to measure and relatively little is known about this potentially ubiquitous pathway. In this study, we evaluated the C 1 pathway and its integration with...

Transcriptome data modeling for targeted plant metabolic engineering.

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Yonekura-Sakakibara, Keiko; Fukushima, Atsushi; Saito, Kazuki

2013-04-01

The massive data generated by omics technologies require the power of bioinformatics, especially network analysis, for data mining and doing data-driven biology. Gene coexpression analysis, a network approach based on comprehensive gene expression data using microarrays, is becoming a standard tool for predicting gene function and elucidating the relationship between metabolic pathways. Differential and comparative gene coexpression analyses suggest a change in coexpression relationships and regulators controlling common and/or specific biological processes. In conjunction with the newly emerging genome editing technology, network analysis integrated with other omics data should pave the way for robust and practical plant metabolic engineering. Copyright © 2012 Elsevier Ltd. All rights reserved.

Multi -omics and metabolic modelling pipelines: challenges and tools for systems microbiology.

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Fondi, Marco; Liò, Pietro

2015-02-01

Integrated -omics approaches are quickly spreading across microbiology research labs, leading to (i) the possibility of detecting previously hidden features of microbial cells like multi-scale spatial organization and (ii) tracing molecular components across multiple cellular functional states. This promises to reduce the knowledge gap between genotype and phenotype and poses new challenges for computational microbiologists. We underline how the capability to unravel the complexity of microbial life will strongly depend on the integration of the huge and diverse amount of information that can be derived today from -omics experiments. In this work, we present opportunities and challenges of multi -omics data integration in current systems biology pipelines. We here discuss which layers of biological information are important for biotechnological and clinical purposes, with a special focus on bacterial metabolism and modelling procedures. A general review of the most recent computational tools for performing large-scale datasets integration is also presented, together with a possible framework to guide the design of systems biology experiments by microbiologists. Copyright © 2015. Published by Elsevier GmbH.

Integrated in silico Analyses of Regulatory and Metabolic Networks of Synechococcus sp. PCC 7002 Reveal Relationships between Gene Centrality and Essentiality

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Hyun-Seob Song

2015-03-01

Full Text Available Cyanobacteria dynamically relay environmental inputs to intracellular adaptations through a coordinated adjustment of photosynthetic efficiency and carbon processing rates. The output of such adaptations is reflected through changes in transcriptional patterns and metabolic flux distributions that ultimately define growth strategy. To address interrelationships between metabolism and regulation, we performed integrative analyses of metabolic and gene co-expression networks in a model cyanobacterium, Synechococcus sp. PCC 7002. Centrality analyses using the gene co-expression network identified a set of key genes, which were defined here as “topologically important.” Parallel in silico gene knock-out simulations, using the genome-scale metabolic network, classified what we termed as “functionally important” genes, deletion of which affected growth or metabolism. A strong positive correlation was observed between topologically and functionally important genes. Functionally important genes exhibited variable levels of topological centrality; however, the majority of topologically central genes were found to be functionally essential for growth. Subsequent functional enrichment analysis revealed that both functionally and topologically important genes in Synechococcus sp. PCC 7002 are predominantly associated with translation and energy metabolism, two cellular processes critical for growth. This research demonstrates how synergistic network-level analyses can be used for reconciliation of metabolic and gene expression data to uncover fundamental biological principles.

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......, it introduces the capability to use C-13 labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale C-13 Metabolic Flux Analysis (2S-C-13 MFA). In addition, the library includes a demonstration of a method that uses proteomics data to produce actionable...... insights to increase biofuel production. Finally, the use of the jQMM library is illustrated through the addition of several Jupyter notebook demonstration files that enhance reproducibility and provide the capability to be adapted to the user's specific needs.Conclusions: jQMM will facilitate the design...

Modeling energy-economy interactions using integrated models

International Nuclear Information System (INIS)

Uyterlinde, M.A.

1994-06-01

Integrated models are defined as economic energy models that consist of several submodels, either coupled by an interface module, or embedded in one large model. These models can be used for energy policy analysis. Using integrated models yields the following benefits. They provide a framework in which energy-economy interactions can be better analyzed than in stand-alone models. Integrated models can represent both energy sector technological details, as well as the behaviour of the market and the role of prices. Furthermore, the combination of modeling methodologies in one model can compensate weaknesses of one approach with strengths of another. These advantages motivated this survey of the class of integrated models. The purpose of this literature survey therefore was to collect and to present information on integrated models. To carry out this task, several goals were identified. The first goal was to give an overview of what is reported on these models in general. The second one was to find and describe examples of such models. Other goals were to find out what kinds of models were used as component models, and to examine the linkage methodology. Solution methods and their convergence properties were also a subject of interest. The report has the following structure. In chapter 2, a 'conceptual framework' is given. In chapter 3 a number of integrated models is described. In a table, a complete overview is presented of all described models. Finally, in chapter 4, the report is summarized, and conclusions are drawn regarding the advantages and drawbacks of integrated models. 8 figs., 29 refs

A computer model simulating human glucose absorption and metabolism in health and metabolic disease states [version 1; referees: 2 approved, 1 approved with reservations

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Richard J. Naftalin

2016-04-01

Full Text Available A computer model designed to simulate integrated glucose-dependent changes in splanchnic blood flow with small intestinal glucose absorption, hormonal and incretin circulation and hepatic and systemic metabolism in health and metabolic diseases e.g. non-alcoholic fatty liver disease, (NAFLD, non-alcoholic steatohepatitis, (NASH and type 2 diabetes mellitus, (T2DM demonstrates how when glucagon-like peptide-1, (GLP-1 is synchronously released into the splanchnic blood during intestinal glucose absorption, it stimulates superior mesenteric arterial (SMA blood flow and by increasing passive intestinal glucose absorption, harmonizes absorption with its distribution and metabolism. GLP-1 also synergises insulin-dependent net hepatic glucose uptake (NHGU. When GLP-1 secretion is deficient post-prandial SMA blood flow is not increased and as NHGU is also reduced, hyperglycaemia follows. Portal venous glucose concentration is also raised, thereby retarding the passive component of intestinal glucose absorption.   Increased pre-hepatic sinusoidal resistance combined with portal hypertension leading to opening of intrahepatic portosystemic collateral vessels are NASH-related mechanical defects that alter the balance between splanchnic and systemic distributions of glucose, hormones and incretins.The model reveals the latent contribution of portosystemic shunting in development of metabolic disease. This diverts splanchnic blood content away from the hepatic sinuses to the systemic circulation, particularly during the glucose absorptive phase of digestion, resulting in inappropriate increases in insulin-dependent systemic glucose metabolism.  This hastens onset of hypoglycaemia and thence hyperglucagonaemia. The model reveals that low rates of GLP-1 secretion, frequently associated with T2DM and NASH, may be also be caused by splanchnic hypoglycaemia, rather than to intrinsic loss of incretin secretory capacity. These findings may have therapeutic

An integrative approach to energy, carbon, and redox metabolism in the cyanobacterium Synechocystis sp. PCC 6803

Energy Technology Data Exchange (ETDEWEB)

Overbeek, Ross; Fonstein, Veronika; Osterman, Andrei; Gerdes, Svetlana; Vassieva, Olga; Zagnitko, Olga; Rodionov, Dmitry

2005-02-15

The team of the Fellowship for Interpretation of Genomes (FIG) under the leadership of Ross Overbeek, began working on this Project in November 2003. During the previous year, the Project was performed at Integrated Genomics Inc. A transition from the industrial environment to the public domain prompted us to adjust some aspects of the Project. Notwithstanding the challenges, we believe that these adjustments had a strong positive impact on our deliverables. Most importantly, the work of the research team led by R. Overbeek resulted in the deployment of a new open source genomic platform, the SEED (Specific Aim 1). This platform provided a foundation for the development of CyanoSEED a specialized portal to comparative analysis and metabolic reconstruction of all available cyanobacterial genomes (Specific Aim 3). The SEED represents a new generation of software for genome analysis. Briefly, it is a portable and extendable system, containing one of the largest and permanently growing collections of complete and partial genomes. The complete system with annotations and tools is freely available via browsing or via installation on a user's Mac or Linux computer. One of the important unique features of the SEED is the support of metabolic reconstruction and comparative genome analysis via encoding and projection of functional subsystems. During the project period, the FIG research team has validated the new software by developing a significant number of core subsystems, covering many aspects of central metabolism (Specific Aim 2), as well as metabolic areas specific for cyanobacteria and other photoautotrophic organisms (Specific Aim 3). In addition to providing a proof of technology and a starting point for further community-based efforts, these subsystems represent a valuable asset. An extensive coverage of central metabolism provides the bulk of information required for metabolic modeling in Synechocystis sp.PCC 6803. Detailed analysis of several subsystems

INTEGRATED QUANTITATIVE ASSESSMENT OF CHANGES IN NEURO-ENDOCRINE-IMMUNE COMPLEX AND METABOLISM IN RATS EXPOSED TO ACUTE COLD-IMMOBILIZATION STRESS

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Sydoruk O Sydoruk

2016-09-01

    Abstracts Background. It is known that the reaction of the neuroendocrine-immune complex to acute and chronic stress are different. It is also known about sex differences in stress reactions. Previously we have been carry out integrated quantitative estimation of neuroendocrine and immune responses to chronic restraint stress at male rats. The purpose of this study - to carry out integrated quantitative estimation of neuroendocrine, immune and metabolic responses to acute stress at male and female rats. Material and research methods. The experiment is at 58 (28 male and 30 female white rats Wistar line weighing 170-280 g (Mean=220 g; SD=28 g. The day after acute (water immersion restraint stress determined HRV, endocrine, immune and metabolic parameters as well as gastric mucosa injuries and comparing them with parameters of intact animals. Results. Acute cold-immobilization stress caused moderate injuries the stomach mucosa as erosions and ulcers. Among the metabolic parameters revealed increased activity Acid Phosphatase, Asparagine and Alanine Aminotranspherase as well as Creatinephosphokinase. It was also found to reduce plasma Testosterone as well as serum Potassium and Phosphate probably due to increased Parathyrine and Mineralocorticoid activity and Sympathotonic shift of sympatho-vagal balance. Integrated quantitative measure manifestations of Acute Stress as mean of modules of Z-Scores makes for 10 metabolic parameters 0,75±0,10 σ and for 8 neuro-endocrine parameters 0,40±0,07 σ. Among immune parameters some proved resistant to acute stress factors, while 10 significant suppressed and 12 activated. Integrated quantitative measure poststressory changes makes 0,73±0,08 σ. Found significant differences integrated status intact males and females, whereas after stress differences are insignificant. Conclusion. The approach to integrated quantitative assessment of neuroendocrine-immune complex and metabolism may be useful for testing the

Cardiovascular Changes in Animal Models of Metabolic Syndrome

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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.

Acorn: A grid computing system for constraint based modeling and visualization of the genome scale metabolic reaction networks via a web interface

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Bushell Michael E

2011-05-01

Full Text Available Abstract Background Constraint-based approaches facilitate the prediction of cellular metabolic capabilities, based, in turn on predictions of the repertoire of enzymes encoded in the genome. Recently, genome annotations have been used to reconstruct genome scale metabolic reaction networks for numerous species, including Homo sapiens, which allow simulations that provide valuable insights into topics, including predictions of gene essentiality of pathogens, interpretation of genetic polymorphism in metabolic disease syndromes and suggestions for novel approaches to microbial metabolic engineering. These constraint-based simulations are being integrated with the functional genomics portals, an activity that requires efficient implementation of the constraint-based simulations in the web-based environment. Results Here, we present Acorn, an open source (GNU GPL grid computing system for constraint-based simulations of genome scale metabolic reaction networks within an interactive web environment. The grid-based architecture allows efficient execution of computationally intensive, iterative protocols such as Flux Variability Analysis, which can be readily scaled up as the numbers of models (and users increase. The web interface uses AJAX, which facilitates efficient model browsing and other search functions, and intuitive implementation of appropriate simulation conditions. Research groups can install Acorn locally and create user accounts. Users can also import models in the familiar SBML format and link reaction formulas to major functional genomics portals of choice. Selected models and simulation results can be shared between different users and made publically available. Users can construct pathway map layouts and import them into the server using a desktop editor integrated within the system. Pathway maps are then used to visualise numerical results within the web environment. To illustrate these features we have deployed Acorn and created a

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

orders of magnitude. Data values also have greatly varying magnitudes. Standard double-precision solvers may return inaccurate solutions or report that no solution exists. Exact simplex solvers based on rational arithmetic require a near-optimal warm start to be practical on large problems (current ME......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...... models have 70,000 constraints and variables and will grow larger). We have developed a quadrupleprecision version of our linear and nonlinear optimizer MINOS, and a solution procedure (DQQ) involving Double and Quad MINOS that achieves reliability and efficiency for ME models and other challenging...

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

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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.

A liver stress-endocrine nexus promotes metabolic integrity during dietary protein dilution

DEFF Research Database (Denmark)

Maida, Adriano; Zota, Annika; Sjøberg, Kim Anker

2016-01-01

of impaired glucose homeostasis independently of obesity and food intake. DPD-mediated metabolic inefficiency and improvement of glucose homeostasis were independent of uncoupling protein 1 (UCP1), but required expression of liver-derived fibroblast growth factor 21 (FGF21) in both lean and obese mice. FGF21...... expression and secretion as well as the associated metabolic remodeling induced by DPD also required induction of liver-integrated stress response-driven nuclear protein 1 (NUPR1). Insufficiency of select nonessential amino acids (NEAAs) was necessary and adequate for NUPR1 and subsequent FGF21 induction...... and secretion in hepatocytes in vitro and in vivo. Taken together, these data indicate that DPD promotes improved glucose homeostasis through an NEAA insufficiency-induced liver NUPR1/FGF21 axis....

LmSmdB: an integrated database for metabolic and gene regulatory network in Leishmania major and Schistosoma mansoni

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Priyanka Patel

2016-03-01

Full Text Available A database that integrates all the information required for biological processing is essential to be stored in one platform. We have attempted to create one such integrated database that can be a one stop shop for the essential features required to fetch valuable result. LmSmdB (L. major and S. mansoni database is an integrated database that accounts for the biological networks and regulatory pathways computationally determined by integrating the knowledge of the genome sequences of the mentioned organisms. It is the first database of its kind that has together with the network designing showed the simulation pattern of the product. This database intends to create a comprehensive canopy for the regulation of lipid metabolism reaction in the parasite by integrating the transcription factors, regulatory genes and the protein products controlled by the transcription factors and hence operating the metabolism at genetic level. Keywords: L.major, S.mansoni, Regulatory networks, Transcription factors, Database

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.

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.

Improving transcriptome construction in non-model organisms: integrating manual and automated gene definition in Emiliania huxleyi.

OpenAIRE

Feldmesser, Ester; Rosenwasser, Shilo; Vardi, Assaf; Ben-Dor, Shifra

2014-01-01

Background The advent of Next Generation Sequencing technologies and corresponding bioinformatics tools allows the definition of transcriptomes in non-model organisms. Non-model organisms are of great ecological and biotechnological significance, and consequently the understanding of their unique metabolic pathways is essential. Several methods that integrate de novo assembly with genome-based assembly have been proposed. Yet, there are many open challenges in defining genes, particularly whe...

Temporal expression-based analysis of metabolism.

Directory of Open Access Journals (Sweden)

Sara B Collins

Full Text Available Metabolic flux is frequently rerouted through cellular metabolism in response to dynamic changes in the intra- and extra-cellular environment. Capturing the mechanisms underlying these metabolic transitions in quantitative and predictive models is a prominent challenge in systems biology. Progress in this regard has been made by integrating high-throughput gene expression data into genome-scale stoichiometric models of metabolism. Here, we extend previous approaches to perform a Temporal Expression-based Analysis of Metabolism (TEAM. We apply TEAM to understanding the complex metabolic dynamics of the respiratorily versatile bacterium Shewanella oneidensis grown under aerobic, lactate-limited conditions. TEAM predicts temporal metabolic flux distributions using time-series gene expression data. Increased predictive power is achieved by supplementing these data with a large reference compendium of gene expression, which allows us to take into account the unique character of the distribution of expression of each individual gene. We further propose a straightforward method for studying the sensitivity of TEAM to changes in its fundamental free threshold parameter θ, and reveal that discrete zones of distinct metabolic behavior arise as this parameter is changed. By comparing the qualitative characteristics of these zones to additional experimental data, we are able to constrain the range of θ to a small, well-defined interval. In parallel, the sensitivity analysis reveals the inherently difficult nature of dynamic metabolic flux modeling: small errors early in the simulation propagate to relatively large changes later in the simulation. We expect that handling such "history-dependent" sensitivities will be a major challenge in the future development of dynamic metabolic-modeling techniques.

Modelling central metabolic fluxes by constraint-based optimization reveals metabolic reprogramming of developing Solanum lycopersicum (tomato) fruit.

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Colombié, Sophie; Nazaret, Christine; Bénard, Camille; Biais, Benoît; Mengin, Virginie; Solé, Marion; Fouillen, Laëtitia; Dieuaide-Noubhani, Martine; Mazat, Jean-Pierre; Beauvoit, Bertrand; Gibon, Yves

2015-01-01

Modelling of metabolic networks is a powerful tool to analyse the behaviour of developing plant organs, including fruits. Guided by our current understanding of heterotrophic metabolism of plant cells, a medium-scale stoichiometric model, including the balance of co-factors and energy, was constructed in order to describe metabolic shifts that occur through the nine sequential stages of Solanum lycopersicum (tomato) fruit development. The measured concentrations of the main biomass components and the accumulated metabolites in the pericarp, determined at each stage, were fitted in order to calculate, by derivation, the corresponding external fluxes. They were used as constraints to solve the model by minimizing the internal fluxes. The distribution of the calculated fluxes of central metabolism were then analysed and compared with known metabolic behaviours. For instance, the partition of the main metabolic pathways (glycolysis, pentose phosphate pathway, etc.) was relevant throughout fruit development. We also predicted a valid import of carbon and nitrogen by the fruit, as well as a consistent CO2 release. Interestingly, the energetic balance indicates that excess ATP is dissipated just before the onset of ripening, supporting the concept of the climacteric crisis. Finally, the apparent contradiction between calculated fluxes with low values compared with measured enzyme capacities suggest a complex reprogramming of the metabolic machinery during fruit development. With a powerful set of experimental data and an accurate definition of the metabolic system, this work provides important insight into the metabolic and physiological requirements of the developing tomato fruits. © 2014 The Authors The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.

Model-based design of bistable cell factories for metabolic engineering.

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Srinivasan, Shyam; Cluett, William R; Mahadevan, Radhakrishnan

2018-04-15

Metabolism can exhibit dynamic phenomena like bistability due to the presence of regulatory motifs like the positive feedback loop. As cell factories, microorganisms with bistable metabolism can have a high and a low product flux at the two stable steady states, respectively. The exclusion of metabolic regulation and network dynamics limits the ability of pseudo-steady state stoichiometric models to detect the presence of bistability, and reliably assess the outcomes of design perturbations to metabolic networks. Using kinetic models of metabolism, we assess the change in the bistable characteristics of the network, and suggest designs based on perturbations to the positive feedback loop to enable the network to produce at its theoretical maximum rate. We show that the most optimal production design in parameter space, for a small bistable metabolic network, may exist at the boundary of the bistable region separating it from the monostable region of low product fluxes. The results of our analysis can be broadly applied to other bistable metabolic networks with similar positive feedback network topologies. This can complement existing model-based design strategies by providing a smaller number of feasible designs that need to be tested in vivo. http://lmse.biozone.utoronto.ca/downloads/. krishna.mahadevan@utoronto.ca. Supplementary data are available at Bioinformatics online.

Metabolic reconstruction of Setaria italica: a systems biology approach for integrating tissue-specific omics and pathway analysis of bioenergy grasses

Directory of Open Access Journals (Sweden)

Cristiana Gomes De Oliveira Dal'molin

2016-08-01

Full Text Available The urgent need for major gains in industrial crops productivity and in biofuel production from bioenergy grasses have reinforced attention on understanding C4 photosynthesis. Systems biology studies of C4 model plants may reveal important features of C4 metabolism. Here we chose foxtail millet (Setaria italica, as a C4 model plant and developed protocols to perform systems biology studies. As part of the systems approach, we have developed and used a genome-scale metabolic reconstruction in combination with the use of multi-omics technologies to gain more insights into the metabolism of S.italica. mRNA, protein and metabolite abundances, were measured in mature and immature stem/leaf phytomers and the multi-omics data were integrated into the metabolic reconstruction framework to capture key metabolic features in different developmental stages of the plant. RNA-Seq reads were mapped to the S. italica resulting for 83% coverage of the protein coding genes of S. italica. Besides revealing similarities and differences in central metabolism of mature and immature tissues, transcriptome analysis indicates significant gene expression of two malic enzyme isoforms (NADP- ME and NAD-ME. Although much greater expression levels of NADP-ME genes are observed and confirmed by the correspondent protein abundances in the samples, the expression of multiple genes combined to the significant abundance of metabolites that participates in C4 metabolism of NAD-ME and NADP-ME subtypes suggest that S. italica may use mixed decarboxylation modes of C4 photosynthetic pathways under different plant developmental stages. The overall analysis also indicates different levels of regulation in mature and immature tissues in carbon fixation, glycolysis, TCA cycle, amino acids, fatty acids, lignin and cellulose syntheses. Altogether, the multi-omics analysis reveals different biological entities and their interrelation and regulation over plant development. With this study

Metabolic Reconstruction of Setaria italica: A Systems Biology Approach for Integrating Tissue-Specific Omics and Pathway Analysis of Bioenergy Grasses.

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de Oliveira Dal'Molin, Cristiana G; Orellana, Camila; Gebbie, Leigh; Steen, Jennifer; Hodson, Mark P; Chrysanthopoulos, Panagiotis; Plan, Manuel R; McQualter, Richard; Palfreyman, Robin W; Nielsen, Lars K

2016-01-01

The urgent need for major gains in industrial crops productivity and in biofuel production from bioenergy grasses have reinforced attention on understanding C4 photosynthesis. Systems biology studies of C4 model plants may reveal important features of C4 metabolism. Here we chose foxtail millet (Setaria italica), as a C4 model plant and developed protocols to perform systems biology studies. As part of the systems approach, we have developed and used a genome-scale metabolic reconstruction in combination with the use of multi-omics technologies to gain more insights into the metabolism of S. italica. mRNA, protein, and metabolite abundances, were measured in mature and immature stem/leaf phytomers, and the multi-omics data were integrated into the metabolic reconstruction framework to capture key metabolic features in different developmental stages of the plant. RNA-Seq reads were mapped to the S. italica resulting for 83% coverage of the protein coding genes of S. italica. Besides revealing similarities and differences in central metabolism of mature and immature tissues, transcriptome analysis indicates significant gene expression of two malic enzyme isoforms (NADP- ME and NAD-ME). Although much greater expression levels of NADP-ME genes are observed and confirmed by the correspondent protein abundances in the samples, the expression of multiple genes combined to the significant abundance of metabolites that participates in C4 metabolism of NAD-ME and NADP-ME subtypes suggest that S. italica may use mixed decarboxylation modes of C4 photosynthetic pathways under different plant developmental stages. The overall analysis also indicates different levels of regulation in mature and immature tissues in carbon fixation, glycolysis, TCA cycle, amino acids, fatty acids, lignin, and cellulose syntheses. Altogether, the multi-omics analysis reveals different biological entities and their interrelation and regulation over plant development. With this study, we demonstrated

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....

Microbial physiology-based model of ethanol metabolism in subsurface sediments

Science.gov (United States)

Jin, Qusheng; Roden, Eric E.

2011-07-01

A biogeochemical reaction model was developed based on microbial physiology to simulate ethanol metabolism and its influence on the chemistry of anoxic subsurface environments. The model accounts for potential microbial metabolisms that degrade ethanol, including those that oxidize ethanol directly or syntrophically by reducing different electron acceptors. Out of the potential metabolisms, those that are active in the environment can be inferred by fitting the model to experimental observations. This approach was applied to a batch sediment slurry experiment that examined ethanol metabolism in uranium-contaminated aquifer sediments from Area 2 at the U.S. Department of Energy Field Research Center in Oak Ridge, TN. According to the simulation results, complete ethanol oxidation by denitrification, incomplete ethanol oxidation by ferric iron reduction, ethanol fermentation to acetate and H 2, hydrogenotrophic sulfate reduction, and acetoclastic methanogenesis: all contributed significantly to the degradation of ethanol in the aquifer sediments. The assemblage of the active metabolisms provides a frame work to explore how ethanol amendment impacts the chemistry of the environment, including the occurrence and levels of uranium. The results can also be applied to explore how diverse microbial metabolisms impact the progress and efficacy of bioremediation strategies.

Integrated Network Analysis and Effective Tools in Plant Systems Biology

Directory of Open Access Journals (Sweden)

Atsushi eFukushima

2014-11-01

Full Text Available One of the ultimate goals in plant systems biology is to elucidate the genotype-phenotype relationship in plant cellular systems. Integrated network analysis that combines omics data with mathematical models has received particular attention. Here we focus on the latest cutting-edge computational advances that facilitate their combination. We highlight (1 network visualization tools, (2 pathway analyses, (3 genome-scale metabolic reconstruction, and (4 the integration of high-throughput experimental data and mathematical models. Multi-omics data that contain the genome, transcriptome, proteome, and metabolome and mathematical models are expected to integrate and expand our knowledge of complex plant metabolisms.

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.

Cerebral Metabolic Changes Related to Oxidative Metabolism in a Model of Bacterial Meningitis Induced by Lipopolysaccharide

DEFF Research Database (Denmark)

Munk, Michael; Rom Poulsen, Frantz; Larsen, Lykke

2018-01-01

BACKGROUND: Cerebral mitochondrial dysfunction is prominent in the pathophysiology of severe bacterial meningitis. In the present study, we hypothesize that the metabolic changes seen after intracisternal lipopolysaccharide (LPS) injection in a piglet model of meningitis is compatible...... with mitochondrial dysfunction and resembles the metabolic patterns seen in patients with bacterial meningitis. METHODS: Eight pigs received LPS injection in cisterna magna, and four pigs received NaCl in cisterna magna as a control. Biochemical variables related to energy metabolism were monitored by intracerebral...... dysfunction with increasing cerebral LPR due to increased lactate and normal pyruvate, PbtO2, and ICP. The metabolic pattern resembles the one observed in patients with bacterial meningitis. Metabolic monitoring in these patients is feasible to monitor for cerebral metabolic derangements otherwise missed...

An efficient tool for metabolic pathway construction and gene integration for Aspergillus niger.

Science.gov (United States)

Sarkari, Parveen; Marx, Hans; Blumhoff, Marzena L; Mattanovich, Diethard; Sauer, Michael; Steiger, Matthias G

2017-12-01

Metabolic engineering requires functional genetic tools for easy and quick generation of multiple pathway variants. A genetic engineering toolbox for A. niger is presented, which facilitates the generation of strains carrying heterologous expression cassettes at a defined genetic locus. The system is compatible with Golden Gate cloning, which facilitates the DNA construction process and provides high design flexibility. The integration process is mediated by a CRISPR/Cas9 strategy involving the cutting of both the genetic integration locus (pyrG) as well as the integrating plasmid. Only a transient expression of Cas9 is necessary and the carrying plasmid is readily lost using a size-reduced AMA1 variant. A high integration efficiency into the fungal genome of up to 100% can be achieved, thus reducing the screening process significantly. The feasibility of the approach was demonstrated by the integration of an expression cassette enabling the production of aconitic acid in A. niger. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

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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.

Dynamic optimal metabolic control theory: a cybernetic approach for modelling of the central nitrogen metabolism of S. cerevisiae

NARCIS (Netherlands)

Riel, van N.A.W.; Giuseppin, M.L.F.; Verrips, C.T.

2000-01-01

The theory of dynamic optimal metabolic control (DOMC), as developed by Giuseppin and Van Riel (Metab. Eng., 2000), is applied to model the central nitrogen metabolism (CNM) in Saccharomyces cerevisiae. The CNM represents a typical system encountered in advanced metabolic engineering. The CNM is the

In silico analysis of phytohormone metabolism and communication pathways in citrus transcriptome

Directory of Open Access Journals (Sweden)

Vera Quecini

2007-01-01

Full Text Available Plant hormones play a crucial role in integrating endogenous and exogenous signals and in determining developmental responses to form the plant body throughout its life cycle. In citrus species, several economically important processes are controlled by phytohormones, including seed germination, secondary growth, fruit abscission and ripening. Integrative genomics is a powerful tool for linking newly researched organisms, such as tropical woody species, to functional studies already carried out on established model organisms. Based on gene orthology analyses and expression patterns, we searched the Citrus Genome Sequencing Consortium (CitEST database for Expressed Sequence Tags (EST consensus sequences sharing similarity to known components of hormone metabolism and signaling pathways in model species. More than 600 homologs of functionally characterized hormone metabolism and signal transduction members from model species were identified in citrus, allowing us to propose a framework for phytohormone signaling mechanisms in citrus. A number of components from hormone-related metabolic pathways were absent in citrus, suggesting the presence of distinct metabolic pathways. Our results demonstrated the power of comparative genomics between model systems and economically important crop species to elucidate several aspects of plant physiology and metabolism.

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

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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

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

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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.

Integration of transcriptomic and metabolic data reveals hub transcription factors involved in drought stress response in sunflower (Helianthus annuus L.).

Science.gov (United States)

Moschen, Sebastián; Di Rienzo, Julio A; Higgins, Janet; Tohge, Takayuki; Watanabe, Mutsumi; González, Sergio; Rivarola, Máximo; García-García, Francisco; Dopazo, Joaquin; Hopp, H Esteban; Hoefgen, Rainer; Fernie, Alisdair R; Paniego, Norma; Fernández, Paula; Heinz, Ruth A

2017-07-01

By integration of transcriptional and metabolic profiles we identified pathways and hubs transcription factors regulated during drought conditions in sunflower, useful for applications in molecular and/or biotechnological breeding. Drought is one of the most important environmental stresses that effects crop productivity in many agricultural regions. Sunflower is tolerant to drought conditions but the mechanisms involved in this tolerance remain unclear at the molecular level. The aim of this study was to characterize and integrate transcriptional and metabolic pathways related to drought stress in sunflower plants, by using a system biology approach. Our results showed a delay in plant senescence with an increase in the expression level of photosynthesis related genes as well as higher levels of sugars, osmoprotectant amino acids and ionic nutrients under drought conditions. In addition, we identified transcription factors that were upregulated during drought conditions and that may act as hubs in the transcriptional network. Many of these transcription factors belong to families implicated in the drought response in model species. The integration of transcriptomic and metabolomic data in this study, together with physiological measurements, has improved our understanding of the biological responses during droughts and contributes to elucidate the molecular mechanisms involved under this environmental condition. These findings will provide useful biotechnological tools to improve stress tolerance while maintaining crop yield under restricted water availability.

Molecular processes in cellular arsenic metabolism

International Nuclear Information System (INIS)

Thomas, David J.

2007-01-01

Elucidating molecular processes that underlie accumulation, metabolism and binding of iAs and its methylated metabolites provides a basis for understanding the modes of action by which iAs acts as a toxin and a carcinogen. One approach to this problem is to construct a conceptual model that incorporates available information on molecular processes involved in the influx, metabolism, binding and efflux of arsenicals in cells. This conceptual model is initially conceived as a non-quantitative representation of critical molecular processes that can be used as a framework for experimental design and prediction. However, with refinement and incorporation of additional data, the conceptual model can be expressed in mathematical terms and should be useful for quantitative estimates of the kinetic and dynamic behavior of iAs and its methylated metabolites in cells. Development of a quantitative model will be facilitated by the availability of tools and techniques to manipulate molecular processes underlying transport of arsenicals across cell membranes or expression and activity of enzymes involved in methylation of arsenicals. This model of cellular metabolism might be integrated into more complex pharmacokinetic models for systemic metabolism of iAs and its methylated metabolites. It may also be useful in development of biologically based dose-response models describing the toxic and carcinogenic actions of arsenicals

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.

Science.gov (United States)

Dash, Ranjan K; Li, Yanjun; Kim, Jaeyeon; Beard, Daniel A; Saidel, Gerald M; Cabrera, Marco E

2008-09-09

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 metabolism and

Concurrence of High Fat Diet and APOE Gene Induces Allele Specific Metabolic and Mental Stress Changes in an AD Model

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Yifat Segev

2016-09-01

Full Text Available Aging is the main risk factor for neurodegenerative diseases, including Alzheimer’s disease (AD. However, evidence indicates that the pathological process begins long before actual cognitive or pathological symptoms are apparent. The long asymptomatic phase and complex integration between genetic, environmental, and metabolic factors make it one of the most challenging diseases to understand and cure. In the present study, we asked whether an environmental factor such as high-fat diet would synergize with a genetic factor to affect the metabolic and cognitive state in the ApoE4 mouse model of AD. Our data suggest that a high-fat diet induces diabetes mellitus-like metabolism in ApoE4 mice, as well as changes in BACE1 protein levels between the two ApoE strains. Furthermore, high-fat diet induces anxiety in this AD mouse model. Our results suggest that young ApoE4 carriers are prone to psychological stress and metabolic abnormalities related to AD, which can easily be triggered via high-fat nutrition.

Diverse methods for integrable models

NARCIS (Netherlands)

Fehér, G.

2017-01-01

This thesis is centered around three topics, sharing integrability as a common theme. This thesis explores different methods in the field of integrable models. The first two chapters are about integrable lattice models in statistical physics. The last chapter describes an integrable quantum chain.

Analysis of Aspergillus nidulans metabolism at the genome-scale

DEFF Research Database (Denmark)

David, Helga; Ozcelik, İlknur Ş; Hofmann, Gerald

2008-01-01

of relevant secondary metabolites, was reconstructed based on detailed metabolic reconstructions available for A. niger and Saccharomyces cerevisiae, and information on the genetics, biochemistry and physiology of A. nidulans. Thereby, it was possible to identify metabolic functions without a gene associated...... a function. Results: In this work, we have manually assigned functions to 472 orphan genes in the metabolism of A. nidulans, by using a pathway-driven approach and by employing comparative genomics tools based on sequence similarity. The central metabolism of A. nidulans, as well as biosynthetic pathways......, in an objective and systematic manner. The functional assignments served as a basis to develop a mathematical model, linking 666 genes (both previously and newly annotated) to metabolic roles. The model was used to simulate metabolic behavior and additionally to integrate, analyze and interpret large-scale gene...

Integrated approach in the treatment of metabolic syndrome

Directory of Open Access Journals (Sweden)

2014-03-01

Full Text Available The Goal of this study was to investigate the efficacy of the integrated approach for the treatment of metabolic syndrome (MS aiming to correct all of its components versus standard therapy using clinical outcomes (BMI, waist circumference, blood pressure, lipid levels, assessment of psychological status (Beck Depression Inventory, and quality of life (SF-36. Methods: A total of 60 patients with MS were included in the study. The study group (30 subjects mean age 41.0±11 years, women - 23 (76.7%, men - 7 (23.3% received the complex therapy of MS - pharmacotherapy of obesity (orlistat and insulin resistance (metformin, lipid-lowering therapy (statins or fibrates, antihypertensive therapy. Control group (30 patients mean age 43.4±9.5 years, women - 26 (86.7%, men - 4 (13.3% was treated with statins or fibrates and received antihypertensive therapy when needed. At the inclusion in the study and after 6 months of therapy all patients underwent clinical and laboratory investigation, assessment of depression and quality of life. Results: We found a more significant reduction of all clinical outcomes (body weight, blood pressure, improvement in glucose and lipid metabolism, a significant decrease in the prevalence and severity of the depression, and an improvement in the quality of life in patients of study group compared with standard therapy. Conclusion: Complex treatment of the MS, including pharmacotherapy of obesity (orlistat, Xenical and insulin resistance (metformin, Glucophage is characterized by a greater clinical efficacy compared with standard therapy.

Genome-based Modeling and Design of Metabolic Interactions in Microbial Communities.

Science.gov (United States)

Mahadevan, Radhakrishnan; Henson, Michael A

2012-01-01

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.

Interdisciplinary Pathways for Urban Metabolism Research

Science.gov (United States)

Newell, J. P.

2011-12-01

With its rapid rise as a metaphor to express coupled natural-human systems in cities, the concept of urban metabolism is evolving into a series of relatively distinct research frameworks amongst various disciplines, with varying definitions, theories, models, and emphases. In industrial ecology, housed primarily within the disciplinary domain of engineering, urban metabolism research has focused on quantifying material and energy flows into, within, and out of cities, using methodologies such as material flow analysis and life cycle assessment. In the field of urban ecology, which is strongly influenced by ecology and urban planning, research focus has been placed on understanding and modeling the complex patterns and processes of human-ecological systems within urban areas. Finally, in political ecology, closely aligned with human geography and anthropology, scholars theorize about the interwoven knots of social and natural processes, material flows, and spatial structures that form the urban metabolism. This paper offers three potential interdisciplinary urban metabolism research tracks that might integrate elements of these three "ecologies," thereby bridging engineering and the social and physical sciences. First, it presents the idea of infrastructure ecology, which explores the complex, emergent interdependencies between gray (water and wastewater, transportation, etc) and green (e.g. parks, greenways) infrastructure systems, as nested within a broader socio-economic context. For cities to be sustainable and resilient over time-space, the theory follows, these is a need to understand and redesign these infrastructure linkages. Second, there is the concept of an urban-scale carbon metabolism model which integrates consumption-based material flow analysis (including goods, water, and materials), with the carbon sink and source dynamics of the built environment (e.g. buildings, etc) and urban ecosystems. Finally, there is the political ecology of the material

Developing Integrated Care: Towards a development model for integrated care

NARCIS (Netherlands)

M.M.N. Minkman (Mirella)

2012-01-01

textabstractThe thesis adresses the phenomenon of integrated care. The implementation of integrated care for patients with a stroke or dementia is studied. Because a generic quality management model for integrated care is lacking, the study works towards building a development model for integrated

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 ...

Constraint based modeling of metabolism allows finding metabolic cancer hallmarks and identifying personalized therapeutic windows.

Science.gov (United States)

Bordel, Sergio

2018-04-13

In order to choose optimal personalized anticancer treatments, transcriptomic data should be analyzed within the frame of biological networks. The best known human biological network (in terms of the interactions between its different components) is metabolism. Cancer cells have been known to have specific metabolic features for a long time and currently there is a growing interest in characterizing new cancer specific metabolic hallmarks. In this article it is presented a method to find personalized therapeutic windows using RNA-seq data and Genome Scale Metabolic Models. This method is implemented in the python library, pyTARG. Our predictions showed that the most anticancer selective (affecting 27 out of 34 considered cancer cell lines and only 1 out of 6 healthy mesenchymal stem cell lines) single metabolic reactions are those involved in cholesterol biosynthesis. Excluding cholesterol biosynthesis, all the considered cell lines can be selectively affected by targeting different combinations (from 1 to 5 reactions) of only 18 metabolic reactions, which suggests that a small subset of drugs or siRNAs combined in patient specific manners could be at the core of metabolism based personalized treatments.

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.

DigitalHuman (DH): An Integrative Mathematical Model ofHuman Physiology

Science.gov (United States)

Hester, Robert L.; Summers, Richard L.; lIescu, Radu; Esters, Joyee; Coleman, Thomas G.

2010-01-01

Mathematical models and simulation are important tools in discovering the key causal relationships governing physiological processes and improving medical intervention when physiological complexity is a central issue. We have developed a model of integrative human physiology called DigitalHuman (DH) consisting of -5000 variables modeling human physiology describing cardiovascular, renal, respiratory, endocrine, neural and metabolic physiology. Users can view time-dependent solutions and interactively introduce perturbations by altering numerical parameters to investigate new hypotheses. The variables, parameters and quantitative relationships as well as all other model details are described in XML text files. All aspects of the model, including the mathematical equations describing the physiological processes are written in XML open source, text-readable files. Model structure is based upon empirical data of physiological responses documented within the peer-reviewed literature. The model can be used to understand proposed physiological mechanisms and physiological interactions that may not be otherwise intUitively evident. Some of the current uses of this model include the analyses of renal control of blood pressure, the central role of the liver in creating and maintaining insulin resistance, and the mechanisms causing orthostatic hypotension in astronauts. Additionally the open source aspect of the modeling environment allows any investigator to add detailed descriptions of human physiology to test new concepts. The model accurately predicts both qualitative and more importantly quantitative changes in clinically and experimentally observed responses. DigitalHuman provides scientists a modeling environment to understand the complex interactions of integrative physiology. This research was supported by.NIH HL 51971, NSF EPSCoR, and NASA

Systems Biology as an Integrated Platform for Bioinformatics, Systems Synthetic Biology, and Systems Metabolic Engineering

Science.gov (United States)

Chen, Bor-Sen; Wu, Chia-Chou

2013-01-01

Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i) system structure and the mechanism of biological networks such as gene regulation, protein interactions, signaling, and metabolic pathways; (ii) system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii) system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv) systematic design methods for the modification and construction of biological networks with desired behaviors, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This review describes current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers. We also discuss challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering. PMID:24709875

Systems Biology as an Integrated Platform for Bioinformatics, Systems Synthetic Biology, and Systems Metabolic Engineering

Directory of Open Access Journals (Sweden)

Bor-Sen Chen

2013-10-01

Full Text Available Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i system structure and the mechanism of biological networks such as gene regulation, protein interactions, signaling, and metabolic pathways; (ii system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv systematic design methods for the modification and construction of biological networks with desired behaviors, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This review describes current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers. We also discuss challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.

Integrative Metabolic Signatures for Hepatic Radiation Injury.

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Irwin Jack Kurland

Full Text Available Radiation-induced liver disease (RILD is a dose-limiting factor in curative radiation therapy (RT for liver cancers, making early detection of radiation-associated liver injury absolutely essential for medical intervention. A metabolomic approach was used to determine metabolic signatures that could serve as biomarkers for early detection of RILD in mice.Anesthetized C57BL/6 mice received 0, 10 or 50 Gy Whole Liver Irradiation (WLI and were contrasted to mice, which received 10 Gy whole body irradiation (WBI. Liver and plasma samples were collected at 24 hours after irradiation. The samples were processed using Gas Chromatography/Mass Spectrometry and Liquid Chromatography/Mass Spectrometry.Twenty four hours after WLI, 407 metabolites were detected in liver samples while 347 metabolites were detected in plasma. Plasma metabolites associated with 50 Gy WLI included several amino acids, purine and pyrimidine metabolites, microbial metabolites, and most prominently bradykinin and 3-indoxyl-sulfate. Liver metabolites associated with 50 Gy WLI included pentose phosphate, purine, and pyrimidine metabolites in liver. Plasma biomarkers in common between WLI and WBI were enriched in microbial metabolites such as 3 indoxyl sulfate, indole-3-lactic acid, phenyllactic acid, pipecolic acid, hippuric acid, and markers of DNA damage such as 2-deoxyuridine. Metabolites associated with tryptophan and indoles may reflect radiation-induced gut microbiome effects. Predominant liver biomarkers in common between WBI and WLI were amino acids, sugars, TCA metabolites (fumarate, fatty acids (lineolate, n-hexadecanoic acid and DNA damage markers (uridine.We identified a set of metabolomic markers that may prove useful as plasma biomarkers of RILD and WBI. Pathway analysis also suggested that the unique metabolic changes observed after liver irradiation was an integrative response of the intestine, liver and kidney.

Caenorhabditis elegans: A Useful Model for Studying Metabolic Disorders in Which Oxidative Stress Is a Contributing Factor

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Elizabeth Moreno-Arriola

2014-01-01

Full Text Available Caenorhabditis elegans is a powerful model organism that is invaluable for experimental research because it can be used to recapitulate most human diseases at either the metabolic or genomic level in vivo. This organism contains many key components related to metabolic and oxidative stress networks that could conceivably allow us to increase and integrate information to understand the causes and mechanisms of complex diseases. Oxidative stress is an etiological factor that influences numerous human diseases, including diabetes. C. elegans displays remarkably similar molecular bases and cellular pathways to those of mammals. Defects in the insulin/insulin-like growth factor-1 signaling pathway or increased ROS levels induce the conserved phase II detoxification response via the SKN-1 pathway to fight against oxidative stress. However, it is noteworthy that, aside from the detrimental effects of ROS, they have been proposed as second messengers that trigger the mitohormetic response to attenuate the adverse effects of oxidative stress. Herein, we briefly describe the importance of C. elegans as an experimental model system for studying metabolic disorders related to oxidative stress and the molecular mechanisms that underlie their pathophysiology.

Context-Specific Metabolic Model Extraction Based on Regularized Least Squares Optimization.

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Semidán Robaina Estévez

Full Text Available Genome-scale metabolic models have proven highly valuable in investigating cell physiology. Recent advances include the development of methods to extract context-specific models capable of describing metabolism under more specific scenarios (e.g., cell types. Yet, none of the existing computational approaches allows for a fully automated model extraction and determination of a flux distribution independent of user-defined parameters. Here we present RegrEx, a fully automated approach that relies solely on context-specific data and ℓ1-norm regularization to extract a context-specific model and to provide a flux distribution that maximizes its correlation to data. Moreover, the publically available implementation of RegrEx was used to extract 11 context-specific human models using publicly available RNAseq expression profiles, Recon1 and also Recon2, the most recent human metabolic model. The comparison of the performance of RegrEx and its contending alternatives demonstrates that the proposed method extracts models for which both the structure, i.e., reactions included, and the flux distributions are in concordance with the employed data. These findings are supported by validation and comparison of method performance on additional data not used in context-specific model extraction. Therefore, our study sets the ground for applications of other regularization techniques in large-scale metabolic modeling.

Metabolic dysfunction and unabated respiration precede the loss of membrane integrity during dehydration of germinating radicles

NARCIS (Netherlands)

Leprince, O.; Harren, F.J.M.; Alberda, M.; Hoekstra, F.A.

2000-01-01

This study shows that dehydration induces imbalanced metabolism before loss of membrane integrity in desiccation-sensitive germinated radicles. Using a photoacoustic detection system, responses of CO2 emission and fermentation to drying were analyzed non-invasively in desiccation-tolerant and

The biology of lysine acetylation integrates transcriptional programming and metabolism

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Mujtaba Shiraz

2011-03-01

Full Text Available Abstract The biochemical landscape of lysine acetylation has expanded from a small number of proteins in the nucleus to a multitude of proteins in the cytoplasm. Since the first report confirming acetylation of the tumor suppressor protein p53 by a lysine acetyltransferase (KAT, there has been a surge in the identification of new, non-histone targets of KATs. Added to the known substrates of KATs are metabolic enzymes, cytoskeletal proteins, molecular chaperones, ribosomal proteins and nuclear import factors. Emerging studies demonstrate that no fewer than 2000 proteins in any particular cell type may undergo lysine acetylation. As described in this review, our analyses of cellular acetylated proteins using DAVID 6.7 bioinformatics resources have facilitated organization of acetylated proteins into functional clusters integral to cell signaling, the stress response, proteolysis, apoptosis, metabolism, and neuronal development. In addition, these clusters also depict association of acetylated proteins with human diseases. These findings not only support lysine acetylation as a widespread cellular phenomenon, but also impel questions to clarify the underlying molecular and cellular mechanisms governing target selectivity by KATs. Present challenges are to understand the molecular basis for the overlapping roles of KAT-containing co-activators, to differentiate between global versus dynamic acetylation marks, and to elucidate the physiological roles of acetylated proteins in biochemical pathways. In addition to discussing the cellular 'acetylome', a focus of this work is to present the widespread and dynamic nature of lysine acetylation and highlight the nexus that exists between epigenetic-directed transcriptional regulation and metabolism.

Analysis of Piscirickettsia salmonis Metabolism Using Genome-Scale Reconstruction, Modeling, and Testing

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María P. Cortés

2017-12-01

Full Text Available Piscirickettsia salmonis is an intracellular bacterial fish pathogen that causes piscirickettsiosis, a disease with highly adverse impact in the Chilean salmon farming industry. The development of effective treatment and control methods for piscireckttsiosis is still a challenge. To meet it the number of studies on P. salmonis has grown in the last couple of years but many aspects of the pathogen’s biology are still poorly understood. Studies on its metabolism are scarce and only recently a metabolic model for reference strain LF-89 was developed. We present a new genome-scale model for P. salmonis LF-89 with more than twice as many genes as in the previous model and incorporating specific elements of the fish pathogen metabolism. Comparative analysis with models of different bacterial pathogens revealed a lower flexibility in P. salmonis metabolic network. Through constraint-based analysis, we determined essential metabolites required for its growth and showed that it can benefit from different carbon sources tested experimentally in new defined media. We also built an additional model for strain A1-15972, and together with an analysis of P. salmonis pangenome, we identified metabolic features that differentiate two main species clades. Both models constitute a knowledge-base for P. salmonis metabolism and can be used to guide the efficient culture of the pathogen and the identification of specific drug targets.

A systems biology framework for modeling metabolic enzyme inhibition of Mycobacterium tuberculosis

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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.

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

Gut microbiota and metabolic syndrome.

Science.gov (United States)

Festi, Davide; Schiumerini, Ramona; Eusebi, Leonardo Henry; Marasco, Giovanni; Taddia, Martina; Colecchia, Antonio

2014-11-21

Gut microbiota exerts a significant role in the pathogenesis of the metabolic syndrome, as confirmed by studies conducted both on humans and animal models. Gut microbial composition and functions are strongly influenced by diet. This complex intestinal "superorganism" seems to affect host metabolic balance modulating energy absorption, gut motility, appetite, glucose and lipid metabolism, as well as hepatic fatty storage. An impairment of the fine balance between gut microbes and host's immune system could culminate in the intestinal translocation of bacterial fragments and the development of "metabolic endotoxemia", leading to systemic inflammation and insulin resistance. Diet induced weight-loss and bariatric surgery promote significant changes of gut microbial composition, that seem to affect the success, or the inefficacy, of treatment strategies. Manipulation of gut microbiota through the administration of prebiotics or probiotics could reduce intestinal low grade inflammation and improve gut barrier integrity, thus, ameliorating metabolic balance and promoting weight loss. However, further evidence is needed to better understand their clinical impact and therapeutic use.

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

DEFF Research Database (Denmark)

Brochado, Ana Rita

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.......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...

Metingear: a development environment for annotating genome-scale metabolic models.

Science.gov (United States)

May, John W; James, A Gordon; Steinbeck, Christoph

2013-09-01

Genome-scale metabolic models often lack annotations that would allow them to be used for further analysis. Previous efforts have focused on associating metabolites in the model with a cross reference, but this can be problematic if the reference is not freely available, multiple resources are used or the metabolite is added from a literature review. Associating each metabolite with chemical structure provides unambiguous identification of the components and a more detailed view of the metabolism. We have developed an open-source desktop application that simplifies the process of adding database cross references and chemical structures to genome-scale metabolic models. Annotated models can be exported to the Systems Biology Markup Language open interchange format. Source code, binaries, documentation and tutorials are freely available at http://johnmay.github.com/metingear. The application is implemented in Java with bundles available for MS Windows and Macintosh OS X.

Sustainability and Chinese Urban Settlements: Extending the Metabolism Model of Emergy Evaluation

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Lijie Gao

2016-05-01

Full Text Available Anthropogenic activity interacts with urban form and inner metabolic processes, ultimately impacting urban sustainability. China’s cities have experienced many environmental issues and metabolic disturbances since the nation-wide market-oriented “reform and opening-up” policy was adopted in the 1980s. To analyze urban reform policy impacts and metabolism sustainability at a settlement scale, this study provides an integrated analysis to evaluate settlement metabolism and sustainability using a combination of emergy analysis and sustainability indicators based on scrutiny of two typical settlements (one pre- and one post-reform. The results reveal that housing reform policy stimulated better planning and construction, thereby improving built environmental quality, mixed functional land use, and residential livability. The pre-reform work-unit settlements are comparatively denser in per capita area but have less mixed land use. Housing reform has spatially changed the work–housing balance and increased commuting travel demand. However, short commuting distances in pre-reform settlements will not always decrease overall motor vehicle usage. Integrating non-commuting transport with local mixed land-use functional planning is a necessary foundation for sustainable urban design. Functional planning should provide convenient facilities and infrastructure, green space, and a suitable household density, and allow for short travel distances; these characteristics are all present in the post-reform settlement.

SBMLmod: a Python-based web application and web service for efficient data integration and model simulation.

Science.gov (United States)

Schäuble, Sascha; Stavrum, Anne-Kristin; Bockwoldt, Mathias; Puntervoll, Pål; Heiland, Ines

2017-06-24

Systems Biology Markup Language (SBML) is the standard model representation and description language in systems biology. Enriching and analysing systems biology models by integrating the multitude of available data, increases the predictive power of these models. This may be a daunting task, which commonly requires bioinformatic competence and scripting. We present SBMLmod, a Python-based web application and service, that automates integration of high throughput data into SBML models. Subsequent steady state analysis is readily accessible via the web service COPASIWS. We illustrate the utility of SBMLmod by integrating gene expression data from different healthy tissues as well as from a cancer dataset into a previously published model of mammalian tryptophan metabolism. SBMLmod is a user-friendly platform for model modification and simulation. The web application is available at http://sbmlmod.uit.no , whereas the WSDL definition file for the web service is accessible via http://sbmlmod.uit.no/SBMLmod.wsdl . Furthermore, the entire package can be downloaded from https://github.com/MolecularBioinformatics/sbml-mod-ws . We envision that SBMLmod will make automated model modification and simulation available to a broader research community.

An Integrative Approach to Energy Carbon and Redox Metabolism In Cyanobacterium Synechocystis

Energy Technology Data Exchange (ETDEWEB)

Dr. Ross Overbeek

2003-06-30

The main objectives for the first year were to produce a detailed metabolic reconstruction of synechocystis sp.pcc6803 especially in interrelated arrears of photosynthesis respiration and central carbon metabolism to support a more complete understanding and modeling of this organism. Additionally, IG, Inc. provided detailed bioinformatic analysis of selected functional systems related to carbon and energy generation and utilization, and of the corresponding pathways functional roles and individual genes to support wet lab experiments by collaborators.

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

Constraint-based model of Shewanella oneidensis MR-1 metabolism: a tool for data analysis and hypothesis generation.

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Grigoriy E Pinchuk

2010-06-01

Full Text Available Shewanellae are gram-negative facultatively anaerobic metal-reducing bacteria commonly found in chemically (i.e., redox stratified environments. Occupying such niches requires the ability to rapidly acclimate to changes in electron donor/acceptor type and availability; hence, the ability to compete and thrive in such environments must ultimately be reflected in the organization and utilization of electron transfer networks, as well as central and peripheral carbon metabolism. To understand how Shewanella oneidensis MR-1 utilizes its resources, the metabolic network was reconstructed. The resulting network consists of 774 reactions, 783 genes, and 634 unique metabolites and contains biosynthesis pathways for all cell constituents. Using constraint-based modeling, we investigated aerobic growth of S. oneidensis MR-1 on numerous carbon sources. To achieve this, we (i used experimental data to formulate a biomass equation and estimate cellular ATP requirements, (ii developed an approach to identify cycles (such as futile cycles and circulations, (iii classified how reaction usage affects cellular growth, (iv predicted cellular biomass yields on different carbon sources and compared model predictions to experimental measurements, and (v used experimental results to refine metabolic fluxes for growth on lactate. The results revealed that aerobic lactate-grown cells of S. oneidensis MR-1 used less efficient enzymes to couple electron transport to proton motive force generation, and possibly operated at least one futile cycle involving malic enzymes. Several examples are provided whereby model predictions were validated by experimental data, in particular the role of serine hydroxymethyltransferase and glycine cleavage system in the metabolism of one-carbon units, and growth on different sources of carbon and energy. This work illustrates how integration of computational and experimental efforts facilitates the understanding of microbial metabolism at a

Qualitative Analysis of Integration Adapter Modeling

OpenAIRE

Ritter, Daniel; Holzleitner, Manuel

2015-01-01

Integration Adapters are a fundamental part of an integration system, since they provide (business) applications access to its messaging channel. However, their modeling and configuration remain under-represented. In previous work, the integration control and data flow syntax and semantics have been expressed in the Business Process Model and Notation (BPMN) as a semantic model for message-based integration, while adapter and the related quality of service modeling were left for further studi...

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.

Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model

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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.

Systematic Applications of Metabolomics in Metabolic Engineering

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Robert A. Dromms

2012-12-01

Full Text Available The goals of metabolic engineering are well-served by the biological information provided by metabolomics: information on how the cell is currently using its biochemical resources is perhaps one of the best ways to inform strategies to engineer a cell to produce a target compound. Using the analysis of extracellular or intracellular levels of the target compound (or a few closely related molecules to drive metabolic engineering is quite common. However, there is surprisingly little systematic use of metabolomics datasets, which simultaneously measure hundreds of metabolites rather than just a few, for that same purpose. Here, we review the most common systematic approaches to integrating metabolite data with metabolic engineering, with emphasis on existing efforts to use whole-metabolome datasets. We then review some of the most common approaches for computational modeling of cell-wide metabolism, including constraint-based models, and discuss current computational approaches that explicitly use metabolomics data. We conclude with discussion of the broader potential of computational approaches that systematically use metabolomics data to drive metabolic engineering.

Integrability of the Rabi Model

International Nuclear Information System (INIS)

Braak, D.

2011-01-01

The Rabi model is a paradigm for interacting quantum systems. It couples a bosonic mode to the smallest possible quantum model, a two-level system. I present the analytical solution which allows us to consider the question of integrability for quantum systems that do not possess a classical limit. A criterion for quantum integrability is proposed which shows that the Rabi model is integrable due to the presence of a discrete symmetry. Moreover, I introduce a generalization with no symmetries; the generalized Rabi model is the first example of a nonintegrable but exactly solvable system.

Modeling of Pharmaceutical Biotransformation by Enriched Nitrifying Culture under Different Metabolic Conditions

DEFF Research Database (Denmark)

Xu, Yifeng; Chen, Xueming; Yuan, Zhiguo

2018-01-01

Pharmaceutical removal could be significantly enhanced through cometabolism during nitrification processes. To date, pharmaceutical biotransformation models have not considered the formation of transformation products associated with the metabolic type of microorganisms. Here we report a comprehe......Pharmaceutical removal could be significantly enhanced through cometabolism during nitrification processes. To date, pharmaceutical biotransformation models have not considered the formation of transformation products associated with the metabolic type of microorganisms. Here we report...... a comprehensive model to describe and evaluate the biodegradation of pharmaceuticals and the formation of their biotransformation products by enriched nitrifying cultures. The biotransformation of parent compounds was linked to the microbial processes via cometabolism induced by ammonium-oxidizing bacteria (AOB......) growth, metabolism by AOB, cometabolism by heterotrophs (HET) growth, and metabolism by HET in the model framework. The model was calibrated and validated using experimental data from pharmaceutical biodegradation experiments at realistic levels, taking two pharmaceuticals as examples, i.e., atenolol...

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

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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.

EFFECTS OF SHORT TERM PRACTICE OF BHASTRIKA PRANAYAMA ON METABOLIC FITNESS (METF AND BONE INTEGRITY (BI

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Singh Bal Baljinder

2015-07-01

Full Text Available Purpose: The present study was conducted with the objective to determine the short term practice of bhastrika pranayama on Metabolic Fitness and Bone Integrity. Material: 30 university level females between the age group of 21-26 years were selected. The subjects were randomly matched and assigned into two groups: Group-A: Experimental (n 1=15; Group-B: Control (n 2=15. The subjects from Group-A: Experimental were provided to a 4-weeks bhastrika pranayama. Statistical Analysis: Student t test for paired samples was utilized to compare the means of the pre-test and the post-test. Results & Conclusions: Based on the analysis of the results obtained, we conclude that the significant differences were found in Metabolic Fitness (i.e., Maximal Oxygen Consumption (V O2max and blood pressure of University Level Girls. Insignificant between-group differences were noted in Blood Lipid, Blood Sugar and Bone Integrity of University Level Girls.

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....

Novel insights into obesity and diabetes through genome-scale metabolic modeling

Directory of Open Access Journals (Sweden)

Leif eVäremo

2013-04-01

Full Text Available The growing prevalence of metabolic diseases, such as obesity and diabetes, are putting a high strain on global healthcare systems as well as increasing the demand for efficient treatment strategies. More than 360 million people worldwide are suffering from type 2 diabetes and, with the current trends, the projection is that 10% of the global adult population will be affected by 2030. In light of the systemic properties of metabolic diseases as well as the interconnected nature of metabolism, it is necessary to begin taking a holistic approach to study these diseases. Human genome-scale metabolic models (GEMs are topological and mathematical representations of cell metabolism and have proven to be valuable tools in the area of systems biology. Successful applications of GEMs include the process of gaining further biological and mechanistic understanding of diseases, finding potential biomarkers and identifying new drug targets. This review will focus on the modeling of human metabolism in the field of obesity and diabetes, showing its vast range of applications of clinical importance as well as point out future challenges.

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

Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism

DEFF Research Database (Denmark)

Machado, Daniel; Herrgard, Markus

2014-01-01

of these methods has not been critically evaluated and compared. This work presents a survey of recently published methods that use transcript levels to try to improve metabolic flux predictions either by generating flux distributions or by creating context-specific models. A subset of these methods...... is then systematically evaluated using published data from three different case studies in E. coli and S. cerevisiae. The flux predictions made by different methods using transcriptomic data are compared against experimentally determined extracellular and intracellular fluxes (from 13C-labeling data). The sensitivity...... of the results to method-specific parameters is also evaluated, as well as their robustness to noise in the data. The results show that none of the methods outperforms the others for all cases. Also, it is observed that for many conditions, the predictions obtained by simple flux balance analysis using growth...

Computational Modelling of the Metabolic States Regulated by the Kinase Akt

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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.

Metabolic interrelationships software application: Interactive learning tool for intermediary metabolism

NARCIS (Netherlands)

A.J.M. Verhoeven (Adrie); M. Doets (Mathijs); J.M.J. Lamers (Jos); J.F. Koster (Johan)

2005-01-01

textabstractWe developed and implemented the software application titled Metabolic Interrelationships as a self-learning and -teaching tool for intermediary metabolism. It is used by undergraduate medical students in an integrated organ systems-based and disease-oriented core curriculum, which

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).

Homeostasis of exercise hyperpnea and optimal sensorimotor integration: the internal model paradigm.

Science.gov (United States)

Poon, Chi-Sang; Tin, Chung; Yu, Yunguo

2007-10-15

Homeostasis is a basic tenet of biomedicine and an open problem for many physiological control systems. Among them, none has been more extensively studied and intensely debated than the dilemma of exercise hyperpnea - a paradoxical homeostatic increase of respiratory ventilation that is geared to metabolic demands instead of the normal chemoreflex mechanism. Classical control theory has led to a plethora of "feedback/feedforward control" or "set point" hypotheses for homeostatic regulation, yet so far none of them has proved satisfactory in explaining exercise hyperpnea and its interactions with other respiratory inputs. Instead, the available evidence points to a far more sophisticated respiratory controller capable of integrating multiple afferent and efferent signals in adapting the ventilatory pattern toward optimality relative to conflicting homeostatic, energetic and other objectives. This optimality principle parsimoniously mimics exercise hyperpnea, chemoreflex and a host of characteristic respiratory responses to abnormal gas exchange or mechanical loading/unloading in health and in cardiopulmonary diseases - all without resorting to a feedforward "exercise stimulus". Rather, an emergent controller signal encoding the projected metabolic level is predicted by the principle as an exercise-induced 'mental percept' or 'internal model', presumably engendered by associative learning (operant conditioning or classical conditioning) which achieves optimality through continuous identification of, and adaptation to, the causal relationship between respiratory motor output and resultant chemical-mechanical afferent feedbacks. This internal model self-tuning adaptive control paradigm opens a new challenge and exciting opportunity for experimental and theoretical elucidations of the mechanisms of respiratory control - and of homeostatic regulation and sensorimotor integration in general.

Topological quantum theories and integrable models

International Nuclear Information System (INIS)

Keski-Vakkuri, E.; Niemi, A.J.; Semenoff, G.; Tirkkonen, O.

1991-01-01

The path-integral generalization of the Duistermaat-Heckman integration formula is investigated for integrable models. It is shown that for models with periodic classical trajectories the path integral reduces to a form similar to the finite-dimensional Duistermaat-Heckman integration formula. This provides a relation between exactness of the stationary-phase approximation and Morse theory. It is also argued that certain integrable models can be related to topological quantum theories. Finally, it is found that in general the stationary-phase approximation presumes that the initial and final configurations are in different polarizations. This is exemplified by the quantization of the SU(2) coadjoint orbit

Drosophila as a Model to Study the Link between Metabolism and Cancer

DEFF Research Database (Denmark)

Herranz, Hector; Cohen, Stephen M.

2017-01-01

new approaches to therapy. Drosophila melanogaster is emerging as a valuable model to study multiple aspects of tumor formation and malignant transformation. In this review, we discuss the use of Drosophila as model to study how changes in cellular metabolism, as well as metabolic disease, contribute...

Implications of market integration for cardiovascular and metabolic health among an indigenous Amazonian Ecuadorian population.

Science.gov (United States)

Liebert, Melissa A; Snodgrass, J Josh; Madimenos, Felicia C; Cepon, Tara J; Blackwell, Aaron D; Sugiyama, Lawrence S

2013-05-01

Market integration (MI), the suite of social and cultural changes that occur with economic development, has been associated with negative health outcomes such as cardiovascular disease; however, key questions remain about how this transition manifests at the local level. The present paper investigates the effects of MI on health among Shuar, an indigenous lowland Ecuadorian population, with the goal of better understanding the mechanisms responsible for this health transition. This study examines associations between measures of MI and several dimensions of cardiovascular and metabolic health (fasting glucose, lipids [LDL, HDL and total cholesterol; triglycerides] and blood pressure) among 348 adults. Overall, Shuar males and females have relatively favourable cardiovascular and metabolic health. Shuar who live closer to town have higher total (p market foods (r = 0.140; p = 0.045) and ownership of consumer products (r = 0.184; p = 0.029). This study provides evidence that MI among Shuar is not a uniformly negative process but instead produces complex cardiovascular and metabolic health outcomes.

Definition of metabolism-dependent xenobiotic toxicity with co-cultures of human hepatocytes and mouse 3T3 fibroblasts in the novel integrated discrete multiple organ co-culture (IdMOC) experimental system: results with model toxicants aflatoxin B1, cyclophosphamide and tamoxifen.

Science.gov (United States)

Li, Albert P; Uzgare, Aarti; LaForge, Yumiko S

2012-07-30

The integrated discrete multiple organ co-culture system (IdMOC) allows the co-culturing of multiple cell types as physically separated cells interconnected by a common overlying medium. We report here the application of IdMOC with two cell types: the metabolically competent primary human hepatocytes, and a metabolically incompetent cell line, mouse 3T3 fibroblasts, in the definition of the role of hepatic metabolism on the cytotoxicity of three model toxicants: cyclophosphamide (CPA), aflatoxin B1 (AFB) and tamoxifen (TMX). The presence of hepatic metabolism in IdMOC with human hepatocytes was demonstrated by the metabolism of the P450 isoform 3A4 substrate, luciferin-IPA. The three model toxicants showed three distinct patterns of cytotoxic profile: TMX was cytotoxic to 3T3 cells in the absence of hepatocytes, with slightly lower cytotoxicity towards both 3T3 cells and hepatocytes in the IdMOC. AFB was selective toxic towards the human hepatocytes and relatively noncytotoxic towards 3T3 cells both in the presence and absence of the hepatocytes. CPA cytotoxicity to the 3T3 cells was found to be significantly enhanced by the presence of the hepatocytes, with the cytotoxicity dependent of the number of hepatocytes, and with the cytotoxicity attenuated by the presence of a non-specific P450 inhibitor, 1-aminobenzotriazole. We propose here the following classification of toxicants based on the role of hepatic metabolism as defined by the human hepatocyte-3T3 cell IdMOC assay: type I: direct-acting cytotoxicants represented by TMX as indicated by cytotoxicity in 3T3 cells in the absence of hepatocytes; type II: metabolism-dependent cytotoxicity represented by AFB1 with effects localized within the site of metabolic activation (i. e. hepatocytes); and type III: metabolism-dependent cytotoxicity with metabolites that can diffuse out of the hepatocytes to cause toxicity in cells distal from the site of metabolism, as exemplified by CPA. Copyright © 2012 Elsevier Ireland

Business and technology integrated model

OpenAIRE

Noce, Irapuan; Carvalho, João Álvaro

2011-01-01

There is a growing interest in business modeling and architecture in the areas of management and information systems. One of the issues in the area is the lack of integration between the modeling techniques that are employed to support business development and those used for technology modeling. This paper proposes a modeling approach that is capable of integrating the modeling of the business and of the technology. By depicting the business model, the organization structure and the technolog...

Concurrence of High Fat Diet and APOE Gene Induces Allele Specific Metabolic and Mental Stress Changes in a Mouse Model of Alzheimer's Disease.

Science.gov (United States)

Segev, Yifat; Livne, Adva; Mints, Meshi; Rosenblum, Kobi

2016-01-01

Aging is the main risk factor for neurodegenerative diseases, including Alzheimer's disease (AD). However, evidence indicates that the pathological process begins long before actual cognitive or pathological symptoms are apparent. The long asymptomatic phase and complex integration between genetic, environmental and metabolic factors make it one of the most challenging diseases to understand and cure. In the present study, we asked whether an environmental factor such as high-fat (HF) diet would synergize with a genetic factor to affect the metabolic and cognitive state in the Apolipoprotein E (ApoE4) mouse model of AD. Our data suggest that a HF diet induces diabetes mellitus (DM)-like metabolism in ApoE4 mice, as well as changes in β-site amyloid precursor protein-cleaving enzyme 1 (BACE1) protein levels between the two ApoE strains. Furthermore, HF diet induces anxiety in this AD mouse model. Our results suggest that young ApoE4 carriers are prone to psychological stress and metabolic abnormalities related to AD, which can easily be triggered via HF nutrition.

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.

INTEGRATED CORPORATE STRATEGY MODEL

Directory of Open Access Journals (Sweden)

CATALINA SORIANA SITNIKOV

2014-02-01

Full Text Available Corporations are at present operating in demanding and highly unsure periods, facing a mixture of increased macroeconomic need, competitive and capital market dangers, and in many cases, the prospect for significant technical and regulative gap. Throughout these demanding and highly unsure times, the corporations must pay particular attention to corporate strategy. In present times, corporate strategy must be perceived and used as a function of various fields, covers, and characters as well as a highly interactive system. For the corporation's strategy to become a competitive advantage is necessary to understand and also to integrate it in a holistic model to ensure sustainable progress of corporation activities under the optimum conditions of profitability. The model proposed in this paper is aimed at integrating the two strategic models, Hoshin Kanri and Integrated Strategy Model, as well as their consolidation with the principles of sound corporate governance set out by the OECD.

Systems assessment of transcriptional regulation on central carbon metabolism by Cra and CRP.

Science.gov (United States)

Kim, Donghyuk; Seo, Sang Woo; Gao, Ye; Nam, Hojung; Guzman, Gabriela I; Cho, Byung-Kwan; Palsson, Bernhard O

2018-04-06

Two major transcriptional regulators of carbon metabolism in bacteria are Cra and CRP. CRP is considered to be the main mediator of catabolite repression. Unlike for CRP, in vivo DNA binding information of Cra is scarce. Here we generate and integrate ChIP-exo and RNA-seq data to identify 39 binding sites for Cra and 97 regulon genes that are regulated by Cra in Escherichia coli. An integrated metabolic-regulatory network was formed by including experimentally-derived regulatory information and a genome-scale metabolic network reconstruction. Applying analysis methods of systems biology to this integrated network showed that Cra enables optimal bacterial growth on poor carbon sources by redirecting and repressing glycolysis flux, by activating the glyoxylate shunt pathway, and by activating the respiratory pathway. In these regulatory mechanisms, the overriding regulatory activity of Cra over CRP is fundamental. Thus, elucidation of interacting transcriptional regulation of core carbon metabolism in bacteria by two key transcription factors was possible by combining genome-wide experimental measurement and simulation with a genome-scale metabolic model.

Norepinephrine metabolism in humans. Kinetic analysis and model

International Nuclear Information System (INIS)

Linares, O.A.; Jacquez, J.A.; Zech, L.A.; Smith, M.J.; Sanfield, J.A.; Morrow, L.A.; Rosen, S.G.; Halter, J.B.

1987-01-01

The present study was undertaken to quantify more precisely and to begin to address the problem of heterogeneity of the kinetics of distribution and metabolism of norepinephrine (NE) in humans, by using compartmental analysis. Steady-state NE specific activity in arterialized plasma during [ 3 H]NE infusion and postinfusion plasma disappearance of [ 3 H]NE were measured in eight healthy subjects in the supine and upright positions. Two exponentials were clearly identified in the plasma [ 3 H]NE disappearance curves of each subject studied in the supine (r = 0.94-1.00, all P less than 0.01) and upright (r = 0.90-0.98, all P less than 0.01) positions. A two-compartment model was the minimal model necessary to simultaneously describe the kinetics of NE in the supine and upright positions. The NE input rate into the extravascular compartment 2, estimated with the minimal model, increased with upright posture (1.87 +/- 0.08 vs. 3.25 +/- 0.2 micrograms/min per m2, P less than 0.001). Upright posture was associated with a fall in the volume of distribution of NE in compartment 1 (7.5 +/- 0.6 vs. 4.7 +/- 0.3 liters, P less than 0.001), and as a result of that, there was a fall in the metabolic clearance rate of NE from compartment 1 (1.80 +/- 0.11 vs. 1.21 +/- 0.08 liters/min per m2, P less than 0.001). We conclude that a two-compartment model is the minimal model that can accurately describe the kinetics of distribution and metabolism of NE in humans

Separations and safeguards model integration.

Energy Technology Data Exchange (ETDEWEB)

Cipiti, Benjamin B.; Zinaman, Owen

2010-09-01

Research and development of advanced reprocessing plant designs can greatly benefit from the development of a reprocessing plant model capable of transient solvent extraction chemistry. This type of model can be used to optimize the operations of a plant as well as the designs for safeguards, security, and safety. Previous work has integrated a transient solvent extraction simulation module, based on the Solvent Extraction Process Having Interaction Solutes (SEPHIS) code developed at Oak Ridge National Laboratory, with the Separations and Safeguards Performance Model (SSPM) developed at Sandia National Laboratory, as a first step toward creating a more versatile design and evaluation tool. The goal of this work was to strengthen the integration by linking more variables between the two codes. The results from this integrated model show expected operational performance through plant transients. Additionally, ORIGEN source term files were integrated into the SSPM to provide concentrations, radioactivity, neutron emission rate, and thermal power data for various spent fuels. This data was used to generate measurement blocks that can determine the radioactivity, neutron emission rate, or thermal power of any stream or vessel in the plant model. This work examined how the code could be expanded to integrate other separation steps and benchmark the results to other data. Recommendations for future work will be presented.

Lipoprotein metabolism indicators improve cardiovascular risk prediction.

Directory of Open Access Journals (Sweden)

Daniël B van Schalkwijk

Full Text Available BACKGROUND: Cardiovascular disease risk increases when lipoprotein metabolism is dysfunctional. We have developed a computational model able to derive indicators of lipoprotein production, lipolysis, and uptake processes from a single lipoprotein profile measurement. This is the first study to investigate whether lipoprotein metabolism indicators can improve cardiovascular risk prediction and therapy management. METHODS AND RESULTS: We calculated lipoprotein metabolism indicators for 1981 subjects (145 cases, 1836 controls from the Framingham Heart Study offspring cohort in which NMR lipoprotein profiles were measured. We applied a statistical learning algorithm using a support vector machine to select conventional risk factors and lipoprotein metabolism indicators that contributed to predicting risk for general cardiovascular disease. Risk prediction was quantified by the change in the Area-Under-the-ROC-Curve (ΔAUC and by risk reclassification (Net Reclassification Improvement (NRI and Integrated Discrimination Improvement (IDI. Two VLDL lipoprotein metabolism indicators (VLDLE and VLDLH improved cardiovascular risk prediction. We added these indicators to a multivariate model with the best performing conventional risk markers. Our method significantly improved both CVD prediction and risk reclassification. CONCLUSIONS: Two calculated VLDL metabolism indicators significantly improved cardiovascular risk prediction. These indicators may help to reduce prescription of unnecessary cholesterol-lowering medication, reducing costs and possible side-effects. For clinical application, further validation is required.

Arachidonic Acid Metabolism Pathway Is Not Only Dominant in Metabolic Modulation but Associated With Phenotypic Variation After Acute Hypoxia Exposure

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Chang Liu

2018-03-01

Full Text Available Background: The modulation of arachidonic acid (AA metabolism pathway is identified in metabolic alterations after hypoxia exposure, but its biological function is controversial. We aimed at integrating plasma metabolomic and transcriptomic approaches to systematically explore the roles of the AA metabolism pathway in response to acute hypoxia using an acute mountain sickness (AMS model.Methods: Blood samples were obtained from 53 enrolled subjects before and after exposure to high altitude. Ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry and RNA sequencing were separately performed for metabolomic and transcriptomic profiling, respectively. Influential modules comprising essential metabolites and genes were identified by weighted gene co-expression network analysis (WGCNA after integrating metabolic information with phenotypic and transcriptomic datasets, respectively.Results: Enrolled subjects exhibited diverse response manners to hypoxia. Combined with obviously altered heart rate, oxygen saturation, hemoglobin, and Lake Louise Score (LLS, metabolomic profiling detected that 36 metabolites were highly related to clinical features in hypoxia responses, out of which 27 were upregulated and nine were downregulated, and could be mapped to AA metabolism pathway significantly. Integrated analysis of metabolomic and transcriptomic data revealed that these dominant molecules showed remarkable association with genes in gas transport incapacitation and disorders of hemoglobin metabolism pathways, such as ALAS2, HEMGN. After detailed description of AA metabolism pathway, we found that the molecules of 15-d-PGJ2, PGA2, PGE2, 12-O-3-OH-LTB4, LTD4, LTE4 were significantly up-regulated after hypoxia stimuli, and increased in those with poor response manner to hypoxia particularly. Further analysis in another cohort showed that genes in AA metabolism pathway such as PTGES, PTGS1, GGT1, TBAS1 et al. were excessively

Genome-scale metabolic models applied to human health and disease.

Science.gov (United States)

Cook, Daniel J; Nielsen, Jens

2017-11-01

Advances in genome sequencing, high throughput measurement of gene and protein expression levels, data accessibility, and computational power have allowed genome-scale metabolic models (GEMs) to become a useful tool for understanding metabolic alterations associated with many different diseases. Despite the proven utility of GEMs, researchers confront multiple challenges in the use of GEMs, their application to human health and disease, and their construction and simulation in an organ-specific and disease-specific manner. Several approaches that researchers are taking to address these challenges include using proteomic and transcriptomic-informed methods to build GEMs for individual organs, diseases, and patients and using constraints on model behavior during simulation to match observed metabolic fluxes. We review the challenges facing researchers in the use of GEMs, review the approaches used to address these challenges, and describe advances that are on the horizon and could lead to a better understanding of human metabolism. WIREs Syst Biol Med 2017, 9:e1393. doi: 10.1002/wsbm.1393 For further resources related to this article, please visit the WIREs website. © 2017 Wiley Periodicals, Inc.

Systems Biology of Metabolism: A Driver for Developing Personalized and Precision Medicine

DEFF Research Database (Denmark)

Nielsen, Jens

2017-01-01

for advancing the development of personalized and precision medicine to treat metabolic diseases like insulin resistance, obesity, NAFLD, NASH, and cancer. It will be illustrated how the concept of genome-scale metabolic models can be used for integrative analysis of big data with the objective of identifying...... novel biomarkers that are foundational for personalized and precision medicine....

Formulation, construction and analysis of kinetic models of metabolism: A review of modelling frameworks

DEFF Research Database (Denmark)

Saa, Pedro A.; Nielsen, Lars K.

2017-01-01

Kinetic models are critical to predict the dynamic behaviour of metabolic networks. Mechanistic kinetic models for large networks remain uncommon due to the difficulty of fitting their parameters. Recent modelling frameworks promise new ways to overcome this obstacle while retaining predictive ca...

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.

Reconstruction of the central carbon metabolism of Aspergillus niger

DEFF Research Database (Denmark)

David, Helga; Åkesson, Mats Fredrik; Nielsen, Jens

2003-01-01

The topology of central carbon metabolism of Aspergillus niger was identified and the metabolic network reconstructed, by integrating genomic, biochemical and physiological information available for this microorganism and other related fungi. The reconstructed network may serve as a valuable...... of metabolic fluxes using metabolite balancing. This framework was employed to perform an in silico characterisation of the phenotypic behaviour of A. niger grown on different carbon sources. The effects on growth of single reaction deletions were assessed and essential biochemical reactions were identified...... for different carbon sources. Furthermore, application of the stoichiometric model for assessing the metabolic capabilities of A. niger to produce metabolites was evaluated by using succinate production as a case study....

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.

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.

Circadian physiology of metabolism.

Science.gov (United States)

Panda, Satchidananda

2016-11-25

A majority of mammalian genes exhibit daily fluctuations in expression levels, making circadian expression rhythms the largest known regulatory network in normal physiology. Cell-autonomous circadian clocks interact with daily light-dark and feeding-fasting cycles to generate approximately 24-hour oscillations in the function of thousands of genes. Circadian expression of secreted molecules and signaling components transmits timing information between cells and tissues. Such intra- and intercellular daily rhythms optimize physiology both by managing energy use and by temporally segregating incompatible processes. Experimental animal models and epidemiological data indicate that chronic circadian rhythm disruption increases the risk of metabolic diseases. Conversely, time-restricted feeding, which imposes daily cycles of feeding and fasting without caloric reduction, sustains robust diurnal rhythms and can alleviate metabolic diseases. These findings highlight an integrative role of circadian rhythms in physiology and offer a new perspective for treating chronic diseases in which metabolic disruption is a hallmark. Copyright © 2016, American Association for the Advancement of Science.

Plasma proteome profiles associated with diet-induced metabolic syndrome and the early onset of metabolic syndrome in a pig model.

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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.

Metabolic network reconstruction and genome-scale model of butanol-producing strain Clostridium beijerinckii NCIMB 8052

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Kim Pan-Jun

2011-08-01

Full Text Available Abstract Background Solventogenic clostridia offer a sustainable alternative to petroleum-based production of butanol--an important chemical feedstock and potential fuel additive or replacement. C. beijerinckii is an attractive microorganism for strain design to improve butanol production because it (i naturally produces the highest recorded butanol concentrations as a byproduct of fermentation; and (ii can co-ferment pentose and hexose sugars (the primary products from lignocellulosic hydrolysis. Interrogating C. beijerinckii metabolism from a systems viewpoint using constraint-based modeling allows for simulation of the global effect of genetic modifications. Results We present the first genome-scale metabolic model (iCM925 for C. beijerinckii, containing 925 genes, 938 reactions, and 881 metabolites. To build the model we employed a semi-automated procedure that integrated genome annotation information from KEGG, BioCyc, and The SEED, and utilized computational algorithms with manual curation to improve model completeness. Interestingly, we found only a 34% overlap in reactions collected from the three databases--highlighting the importance of evaluating the predictive accuracy of the resulting genome-scale model. To validate iCM925, we conducted fermentation experiments using the NCIMB 8052 strain, and evaluated the ability of the model to simulate measured substrate uptake and product production rates. Experimentally observed fermentation profiles were found to lie within the solution space of the model; however, under an optimal growth objective, additional constraints were needed to reproduce the observed profiles--suggesting the existence of selective pressures other than optimal growth. Notably, a significantly enriched fraction of actively utilized reactions in simulations--constrained to reflect experimental rates--originated from the set of reactions that overlapped between all three databases (P = 3.52 × 10-9, Fisher's exact test

Integrated Inflammatory Stress (ITIS) Model

DEFF Research Database (Denmark)

Bangsgaard, Elisabeth O.; Hjorth, Poul G.; Olufsen, Mette S.

2017-01-01

maintains a long-term level of the stress hormone cortisol which is also anti-inflammatory. A new integrated model of the interaction between these two subsystems of the inflammatory system is proposed and coined the integrated inflammatory stress (ITIS) model. The coupling mechanisms describing....... A constant activation results in elevated levels of the variables in the model while a prolonged change of the oscillations in ACTH and cortisol concentrations is the most pronounced result of different LPS doses predicted by the model....

Non-integrable quantum field theories as perturbations of certain integrable models

International Nuclear Information System (INIS)

Delfino, G.; Simonetti, P.

1996-03-01

We approach the study of non-integrable models of two-dimensional quantum field theory as perturbations of the integrable ones. By exploiting the knowledge of the exact S-matrix and Form Factors of the integrable field theories we obtain the first order corrections to the mass ratios, the vacuum energy density and the S-matrix of the non-integrable theories. As interesting applications of the formalism, we study the scaling region of the Ising model in an external magnetic field at T ∼ T c and the scaling region around the minimal model M 2 , τ . For these models, a remarkable agreement is observed between the theoretical predictions and the data extracted by a numerical diagonalization of their Hamiltonian. (author). 41 refs, 9 figs, 1 tab

An architecture for integration of multidisciplinary models

DEFF Research Database (Denmark)

Belete, Getachew F.; Voinov, Alexey; Holst, Niels

2014-01-01

Integrating multidisciplinary models requires linking models: that may operate at different temporal and spatial scales; developed using different methodologies, tools and techniques; different levels of complexity; calibrated for different ranges of inputs and outputs, etc. On the other hand......, Enterprise Application Integration, and Integration Design Patterns. We developed an architecture of a multidisciplinary model integration framework that brings these three aspects of integration together. Service-oriented-based platform independent architecture that enables to establish loosely coupled...

Systematic construction of kinetic models from genome-scale metabolic networks.

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Natalie J Stanford

Full Text Available The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments.

Systematic Construction of Kinetic Models from Genome-Scale Metabolic Networks

Science.gov (United States)

Smallbone, Kieran; Klipp, Edda; Mendes, Pedro; Liebermeister, Wolfram

2013-01-01

The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments. PMID:24324546

Data assimilation in integrated hydrological modelling

DEFF Research Database (Denmark)

Rasmussen, Jørn

Integrated hydrological models are useful tools for water resource management and research, and advances in computational power and the advent of new observation types has resulted in the models generally becoming more complex and distributed. However, the models are often characterized by a high...... degree of parameterization which results in significant model uncertainty which cannot be reduced much due to observations often being scarce and often taking the form of point measurements. Data assimilation shows great promise for use in integrated hydrological models , as it allows for observations...... to be efficiently combined with models to improve model predictions, reduce uncertainty and estimate model parameters. In this thesis, a framework for assimilating multiple observation types and updating multiple components and parameters of a catchment scale integrated hydrological model is developed and tested...

Metabolic dysfunction and altered mitochondrial dynamics in the utrophin-dystrophin deficient mouse model of duchenne muscular dystrophy.

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Meghna Pant

Full Text Available The utrophin-dystrophin deficient (DKO mouse model has been widely used to understand the progression of Duchenne muscular dystrophy (DMD. However, it is unclear as to what extent muscle pathology affects metabolism. Therefore, the present study was focused on understanding energy expenditure in the whole animal and in isolated extensor digitorum longus (EDL muscle and to determine changes in metabolic enzymes. Our results show that the 8 week-old DKO mice consume higher oxygen relative to activity levels. Interestingly the EDL muscle from DKO mouse consumes higher oxygen per unit integral force, generates less force and performs better in the presence of pyruvate thus mimicking a slow twitch muscle. We also found that the expression of hexokinase 1 and pyruvate kinase M2 was upregulated several fold suggesting increased glycolytic flux. Additionally, there is a dramatic increase in dynamin-related protein 1 (Drp 1 and mitofusin 2 protein levels suggesting increased mitochondrial fission and fusion, a feature associated with increased energy demand and altered mitochondrial dynamics. Collectively our studies point out that the dystrophic disease has caused significant changes in muscle metabolism. To meet the increased energetic demand, upregulation of metabolic enzymes and regulators of mitochondrial fusion and fission is observed in the dystrophic muscle. A better understanding of the metabolic demands and the accompanied alterations in the dystrophic muscle can help us design improved intervention therapies along with existing drug treatments for the DMD patients.

Dynamic Modeling of Cell-Free Biochemical Networks Using Effective Kinetic Models

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Joseph A. Wayman

2015-03-01

Full Text Available Cell-free systems offer many advantages for the study, manipulation and modeling of metabolism compared to in vivo processes. Many of the challenges confronting genome-scale kinetic modeling can potentially be overcome in a cell-free system. For example, there is no complex transcriptional regulation to consider, transient metabolic measurements are easier to obtain, and we no longer have to consider cell growth. Thus, cell-free operation holds several significant advantages for model development, identification and validation. Theoretically, genome-scale cell-free kinetic models may be possible for industrially important organisms, such as E. coli, if a simple, tractable framework for integrating allosteric regulation with enzyme kinetics can be formulated. Toward this unmet need, we present an effective biochemical network modeling framework for building dynamic cell-free metabolic models. The key innovation of our approach is the integration of simple effective rules encoding complex allosteric regulation with traditional kinetic pathway modeling. We tested our approach by modeling the time evolution of several hypothetical cell-free metabolic networks. We found that simple effective rules, when integrated with traditional enzyme kinetic expressions, captured complex allosteric patterns such as ultrasensitivity or non-competitive inhibition in the absence of mechanistic information. Second, when integrated into network models, these rules captured classic regulatory patterns such as product-induced feedback inhibition. Lastly, we showed, at least for the network architectures considered here, that we could simultaneously estimate kinetic parameters and allosteric connectivity from synthetic data starting from an unbiased collection of possible allosteric structures using particle swarm optimization. However, when starting with an initial population that was heavily enriched with incorrect structures, our particle swarm approach could converge

Multisite Kinetic Modeling of 13C Metabolic MR Using [1-13C]Pyruvate

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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.

Enabling model customization and integration

Science.gov (United States)

Park, Minho; Fishwick, Paul A.

2003-09-01

Until fairly recently, the idea of dynamic model content and presentation were treated synonymously. For example, if one was to take a data flow network, which captures the dynamics of a target system in terms of the flow of data through nodal operators, then one would often standardize on rectangles and arrows for the model display. The increasing web emphasis on XML, however, suggests that the network model can have its content specified in an XML language, and then the model can be represented in a number of ways depending on the chosen style. We have developed a formal method, based on styles, that permits a model to be specified in XML and presented in 1D (text), 2D, and 3D. This method allows for customization and personalization to exert their benefits beyond e-commerce, to the area of model structures used in computer simulation. This customization leads naturally to solving the bigger problem of model integration - the act of taking models of a scene and integrating them with that scene so that there is only one unified modeling interface. This work focuses mostly on customization, but we address the integration issue in the future work section.

Integrated modelling in materials and process technology

DEFF Research Database (Denmark)

Hattel, Jesper Henri

2008-01-01

Integrated modelling of entire process sequences and the subsequent in-service conditions, and multiphysics modelling of the single process steps are areas that increasingly support optimisation of manufactured parts. In the present paper, three different examples of modelling manufacturing...... processes from the viewpoint of combined materials and process modelling are presented: solidification of thin walled ductile cast iron, integrated modelling of spray forming and multiphysics modelling of friction stir welding. The fourth example describes integrated modelling applied to a failure analysis...

Effect of commercial long-term extenders on metabolic activity and membrane integrity of boar spermatozoa stored at 17 degrees C.

Science.gov (United States)

Dziekońska, A; Fraser, L; Majewska, A; Lecewicz, M; Zasiadczyk, Ł; Kordan, W

2013-01-01

This study was aimed to analyze the metabolic activity and membrane integrity of boar spermatozoa following storage in long-term semen extenders. Boar semen was diluted with Androhep EnduraGuard (AeG), DILU-Cell (DC), SafeCell Plus (SCP) and Vitasem LD (VLD) extenders and stored for 10 days at 17 degrees C. Parameters of the analyzed sperm metabolic activity included total motility (TMOT), progressive motility (PMOT), high mitochondrial membrane potential (MMP) and ATP content, whereas those of the membrane integrity included plasma membrane integrity (PMI) and normal apical ridge (NAR) acrosome. Extender type was a significant (P semen storage. In all extenders the metabolic activity and membrane integrity of the stored spermatozoa decreased continuously over time. Among the four analyzed extenders, AeG and SCP showed the best performance in terms of TMOT and PMI on Days 5, 7 and 10 of storage. Marked differences in the proportions of spermatozoa with high MMP were observed between the extenders, particularly on Day 10 of storage. There were not any marked differences in sperm ATP content between the extenders, regardless of the storage time. Furthermore, the percentage of spermatozoa with NAR acrosomes decreased during prolonged storage, being markedly lower in DC-diluted semen compared with semen diluted with either AeG or SCP extender. The results of this study indicated that components of the long-term extenders have different effects on the sperm functionality and prolonged semen longevity by delaying the processes associated with sperm ageing during liquid storage.

DMPy: a Python package for automated mathematical model construction of large-scale metabolic systems.

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Smith, Robert W; van Rosmalen, Rik P; Martins Dos Santos, Vitor A P; Fleck, Christian

2018-06-19

Models of metabolism are often used in biotechnology and pharmaceutical research to identify drug targets or increase the direct production of valuable compounds. Due to the complexity of large metabolic systems, a number of conclusions have been drawn using mathematical methods with simplifying assumptions. For example, constraint-based models describe changes of internal concentrations that occur much quicker than alterations in cell physiology. Thus, metabolite concentrations and reaction fluxes are fixed to constant values. This greatly reduces the mathematical complexity, while providing a reasonably good description of the system in steady state. However, without a large number of constraints, many different flux sets can describe the optimal model and we obtain no information on how metabolite levels dynamically change. Thus, to accurately determine what is taking place within the cell, finer quality data and more detailed models need to be constructed. In this paper we present a computational framework, DMPy, that uses a network scheme as input to automatically search for kinetic rates and produce a mathematical model that describes temporal changes of metabolite fluxes. The parameter search utilises several online databases to find measured reaction parameters. From this, we take advantage of previous modelling efforts, such as Parameter Balancing, to produce an initial mathematical model of a metabolic pathway. We analyse the effect of parameter uncertainty on model dynamics and test how recent flux-based model reduction techniques alter system properties. To our knowledge this is the first time such analysis has been performed on large models of metabolism. Our results highlight that good estimates of at least 80% of the reaction rates are required to accurately model metabolic systems. Furthermore, reducing the size of the model by grouping reactions together based on fluxes alters the resulting system dynamics. The presented pipeline automates the

Inferring metabolic states in uncharacterized environments using gene-expression measurements.

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Sergio Rossell

Full Text Available The large size of metabolic networks entails an overwhelming multiplicity in the possible steady-state flux distributions that are compatible with stoichiometric constraints. This space of possibilities is largest in the frequent situation where the nutrients available to the cells are unknown. These two factors: network size and lack of knowledge of nutrient availability, challenge the identification of the actual metabolic state of living cells among the myriad possibilities. Here we address this challenge by developing a method that integrates gene-expression measurements with genome-scale models of metabolism as a means of inferring metabolic states. Our method explores the space of alternative flux distributions that maximize the agreement between gene expression and metabolic fluxes, and thereby identifies reactions that are likely to be active in the culture from which the gene-expression measurements were taken. These active reactions are used to build environment-specific metabolic models and to predict actual metabolic states. We applied our method to model the metabolic states of Saccharomyces cerevisiae growing in rich media supplemented with either glucose or ethanol as the main energy source. The resulting models comprise about 50% of the reactions in the original model, and predict environment-specific essential genes with high sensitivity. By minimizing the sum of fluxes while forcing our predicted active reactions to carry flux, we predicted the metabolic states of these yeast cultures that are in large agreement with what is known about yeast physiology. Most notably, our method predicts the Crabtree effect in yeast cells growing in excess glucose, a long-known phenomenon that could not have been predicted by traditional constraint-based modeling approaches. Our method is of immediate practical relevance for medical and industrial applications, such as the identification of novel drug targets, and the development of

An integrated development environment for PMESII model authoring, integration, validation, and debugging

Science.gov (United States)

Pioch, Nicholas J.; Lofdahl, Corey; Sao Pedro, Michael; Krikeles, Basil; Morley, Liam

2007-04-01

To foster shared battlespace awareness in Air Operations Centers supporting the Joint Forces Commander and Joint Force Air Component Commander, BAE Systems is developing a Commander's Model Integration and Simulation Toolkit (CMIST), an Integrated Development Environment (IDE) for model authoring, integration, validation, and debugging. CMIST is built on the versatile Eclipse framework, a widely used open development platform comprised of extensible frameworks that enable development of tools for building, deploying, and managing software. CMIST provides two distinct layers: 1) a Commander's IDE for supporting staff to author models spanning the Political, Military, Economic, Social, Infrastructure, Information (PMESII) taxonomy; integrate multiple native (third-party) models; validate model interfaces and outputs; and debug the integrated models via intuitive controls and time series visualization, and 2) a PMESII IDE for modeling and simulation developers to rapidly incorporate new native simulation tools and models to make them available for use in the Commander's IDE. The PMESII IDE provides shared ontologies and repositories for world state, modeling concepts, and native tool characterization. CMIST includes extensible libraries for 1) reusable data transforms for semantic alignment of native data with the shared ontology, and 2) interaction patterns to synchronize multiple native simulations with disparate modeling paradigms, such as continuous-time system dynamics, agent-based discrete event simulation, and aggregate solution methods such as Monte Carlo sampling over dynamic Bayesian networks. This paper describes the CMIST system architecture, our technical approach to addressing these semantic alignment and synchronization problems, and initial results from integrating Political-Military-Economic models of post-war Iraq spanning multiple modeling paradigms.

Open source integrated modeling environment Delta Shell

Science.gov (United States)

Donchyts, G.; Baart, F.; Jagers, B.; van Putten, H.

2012-04-01

In the last decade, integrated modelling has become a very popular topic in environmental modelling since it helps solving problems, which is difficult to model using a single model. However, managing complexity of integrated models and minimizing time required for their setup remains a challenging task. The integrated modelling environment Delta Shell simplifies this task. The software components of Delta Shell are easy to reuse separately from each other as well as a part of integrated environment that can run in a command-line or a graphical user interface mode. The most components of the Delta Shell are developed using C# programming language and include libraries used to define, save and visualize various scientific data structures as well as coupled model configurations. Here we present two examples showing how Delta Shell simplifies process of setting up integrated models from the end user and developer perspectives. The first example shows coupling of a rainfall-runoff, a river flow and a run-time control models. The second example shows how coastal morphological database integrates with the coastal morphological model (XBeach) and a custom nourishment designer. Delta Shell is also available as open-source software released under LGPL license and accessible via http://oss.deltares.nl.

Osteoarthritis and metabolic dysregulation: insights from a preclinical model

NARCIS (Netherlands)

Visser, H.M. de

2018-01-01

This thesis aims to identify the effect of metabolic factors, inflammatory processes and obesity in the pathophysiology of osteoarthritis (OA), using a high-fat diet and/or traumatic injury in a small animal model. The first part of this thesis describes, the rat Groove model of OA, using a one-time

Energetics of glucose metabolism: a phenomenological approach to metabolic network modeling.

Science.gov (United States)

Diederichs, Frank

2010-08-12

A new formalism to describe metabolic fluxes as well as membrane transport processes was developed. The new flux equations are comparable to other phenomenological laws. Michaelis-Menten like expressions, as well as flux equations of nonequilibrium thermodynamics, can be regarded as special cases of these new equations. For metabolic network modeling, variable conductances and driving forces are required to enable pathway control and to allow a rapid response to perturbations. When applied to oxidative phosphorylation, results of simulations show that whole oxidative phosphorylation cannot be described as a two-flux-system according to nonequilibrium thermodynamics, although all coupled reactions per se fulfill the equations of this theory. Simulations show that activation of ATP-coupled load reactions plus glucose oxidation is brought about by an increase of only two different conductances: a [Ca(2+)] dependent increase of cytosolic load conductances, and an increase of phosphofructokinase conductance by [AMP], which in turn becomes increased through [ADP] generation by those load reactions. In ventricular myocytes, this feedback mechanism is sufficient to increase cellular power output and O(2) consumption several fold, without any appreciable impairment of energetic parameters. Glucose oxidation proceeds near maximal power output, since transformed input and output conductances are nearly equal, yielding an efficiency of about 0.5. This conductance matching is fulfilled also by glucose oxidation of β-cells. But, as a price for the metabolic mechanism of glucose recognition, β-cells have only a limited capability to increase their power output.

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.

System-level modeling of acetone-butanol-ethanol fermentation.

Science.gov (United States)

Liao, Chen; Seo, Seung-Oh; Lu, Ting

2016-05-01

Acetone-butanol-ethanol (ABE) fermentation is a metabolic process of clostridia that produces bio-based solvents including butanol. It is enabled by an underlying metabolic reaction network and modulated by cellular gene regulation and environmental cues. Mathematical modeling has served as a valuable strategy to facilitate the understanding, characterization and optimization of this process. In this review, we highlight recent advances in system-level, quantitative modeling of ABE fermentation. We begin with an overview of integrative processes underlying the fermentation. Next we survey modeling efforts including early simple models, models with a systematic metabolic description, and those incorporating metabolism through simple gene regulation. Particular focus is given to a recent system-level model that integrates the metabolic reactions, gene regulation and environmental cues. We conclude by discussing the remaining challenges and future directions towards predictive understanding of ABE fermentation. © FEMS 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Improved annotation through genome-scale metabolic modeling of Aspergillus oryzae

DEFF Research Database (Denmark)

Vongsangnak, Wanwipa; Olsen, Peter; Hansen, Kim

2008-01-01

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...... 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...... model was validated and shown to correctly describe the phenotypic behavior of A. oryzae grown on different carbon sources. Conclusion: A much enhanced annotation of the A. oryzae genome was performed and a genomescale metabolic model of A. oryzae was reconstructed. The model accurately predicted...

A general model for metabolic scaling in self-similar asymmetric networks.

Directory of Open Access Journals (Sweden)

Alexander Byers Brummer

2017-03-01

Full Text Available How a particular attribute of an organism changes or scales with its body size is known as an allometry. Biological allometries, such as metabolic scaling, have been hypothesized to result from selection to maximize how vascular networks fill space yet minimize internal transport distances and resistances. The West, Brown, Enquist (WBE model argues that these two principles (space-filling and energy minimization are (i general principles underlying the evolution of the diversity of biological networks across plants and animals and (ii can be used to predict how the resulting geometry of biological networks then governs their allometric scaling. Perhaps the most central biological allometry is how metabolic rate scales with body size. A core assumption of the WBE model is that networks are symmetric with respect to their geometric properties. That is, any two given branches within the same generation in the network are assumed to have identical lengths and radii. However, biological networks are rarely if ever symmetric. An open question is: Does incorporating asymmetric branching change or influence the predictions of the WBE model? We derive a general network model that relaxes the symmetric assumption and define two classes of asymmetrically bifurcating networks. We show that asymmetric branching can be incorporated into the WBE model. This asymmetric version of the WBE model results in several theoretical predictions for the structure, physiology, and metabolism of organisms, specifically in the case for the cardiovascular system. We show how network asymmetry can now be incorporated in the many allometric scaling relationships via total network volume. Most importantly, we show that the 3/4 metabolic scaling exponent from Kleiber's Law can still be attained within many asymmetric networks.

A proposed model for the transfer of environmental tritium to man and tritium metabolism in model animals

International Nuclear Information System (INIS)

Saito, Masahiro; Ishida, M.R.

1987-01-01

To evaluate the accumulated dose in human bodies due to the environmental tritium, it is of required to establish an adequate model for the tritium transfer from the environment to man and to obtain enough information on the metabolic behaviour of tritium in animal bodies using model animal system. In this report, first we describe about a proposed model for the transfer of environmental tritium to man and secondly mention briefly about the recent works on the tritium metabolism in newborn animals which have been treated as a model system of tritium intake through food chain. (author)

Metabolic Trade-offs between Biomass Synthesis and Photosynthate Export at Different Light Intensities in a Genome–Scale Metabolic Model of Rice

Directory of Open Access Journals (Sweden)

Mark Graham Poolman

2014-11-01

Full Text Available Previously we have used a genome scale model of rice metabolism to describe how metabolism reconfigures at different light intensities in an expanding leaf of rice. Although this established that the metabolism of the leaf was adequatelyrepresented, in the model, the scenario was not that of the typical function of the leaf --- to provide material for the rest of the plant. Here we extend our analysis to explore the transition to a source leaf as export of photosynthate increases at the expense of making leaf biomass precursors, again as a function of light intensity. In particular we investigate whether, when the leaf is making a smaller range of compounds for export to the phloem, the same changes occur in the interactions between mitochondrial and chloroplast metabolism as seen in biomass synthesis for growth when light intensity increases. Our results show that the same changes occur qualitatively, though there are slight quantitative differences reflecting differences in the energy and redox requirements for the different metabolic outputs.

Integrated Debugging of Modelica Models

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Adrian Pop

2014-04-01

Full Text Available The high abstraction level of equation-based object-oriented (EOO languages such as Modelica has the drawback that programming and modeling errors are often hard to find. In this paper we present integrated static and dynamic debugging methods for Modelica models and a debugger prototype that addresses several of those problems. The goal is an integrated debugging framework that combines classical debugging techniques with special techniques for equation-based languages partly based on graph visualization and interaction. To our knowledge, this is the first Modelica debugger that supports both equation-based transformational and algorithmic code debugging in an integrated fashion.

Systems genetics of metabolism: the use of the BXD murine reference panel for multiscalar integration of traits

NARCIS (Netherlands)

Andreux, Pénélope A.; Williams, Evan G.; Koutnikova, Hana; Houtkooper, Riekelt H.; Champy, Marie-France; Henry, Hugues; Schoonjans, Kristina; Williams, Robert W.; Auwerx, Johan

2012-01-01

Metabolic homeostasis is achieved by complex molecular and cellular networks that differ significantly among individuals and are difficult to model with genetically engineered lines of mice optimized to study single gene function. Here, we systematically acquired metabolic phenotypes by using the

An integrated analysis of genes and pathways exhibiting metabolic differences between estrogen receptor positive breast cancer cells

International Nuclear Information System (INIS)

Mandal, Soma; Davie, James R

2007-01-01

The sex hormone estrogen (E2) is pivotal to normal mammary gland growth and differentiation and in breast carcinogenesis. In this in silico study, we examined metabolic differences between ER(+)ve breast cancer cells during E2 deprivation. Public repositories of SAGE and MA gene expression data generated from E2 deprived ER(+)ve breast cancer cell lines, MCF-7 and ZR75-1 were compared with normal breast tissue. We analyzed gene ontology (GO), enrichment, clustering, chromosome localization, and pathway profiles and performed multiple comparisons with cell lines and tumors with different ER status. In all GO terms, biological process (BP), molecular function (MF), and cellular component (CC), MCF-7 had higher gene utilization than ZR75-1. Various analyses showed a down-regulated immune function, an up-regulated protein (ZR75-1) and glucose metabolism (MCF-7). A greater percentage of 77 common genes localized to the q arm of all chromosomes, but in ZR75-1 chromosomes 11, 16, and 19 harbored more overexpressed genes. Despite differences in gene utilization (electron transport, proteasome, glycolysis/gluconeogenesis) and expression (ribosome) in both cells, there was an overall similarity of ZR75-1 with ER(-)ve cell lines and ER(+)ve/ER(-)ve breast tumors. This study demonstrates integral metabolic differences may exist within the same cell subtype (luminal A) in representative ER(+)ve cell line models. Selectivity of gene and pathway usage for strategies such as energy requirement minimization, sugar utilization by ZR75-1 contrasted with MCF-7 cells, expressing genes whose protein products require ATP utilization. Such characteristics may impart aggressiveness to ZR75-1 and may be prognostic determinants of ER(+)ve breast tumors

Towards an integrative computational model of the guinea pig cardiac myocyte

Directory of Open Access Journals (Sweden)

Laura Doyle Gauthier

2012-07-01

Full Text Available The local control theory of excitation-contraction (EC coupling asserts that regulation of calcium (Ca2+ release occurs at the nanodomain level, where openings of single L-type Ca2+ channels (LCCs trigger openings of small clusters of ryanodine receptors (RyRs co-localized within the dyad. A consequence of local control is that the whole-cell Ca2+ transient is a smooth continuous function of influx of Ca2+ through LCCs. While this so-called graded release property has been known for some time, it’s functional importance to the integrated behavior of the cardiac ventricular myocyte has not been fully appreciated. We previously formulated a biophysically-based model, in which LCCs and RyRs interact via a coarse-grained representation of the dyadic space. The model captures key features of local control using a low-dimensional system of ordinary differential equations. Voltage-dependent gain and graded Ca2+ release are emergent properties of this model by virtue of the fact that model formulation is closely based on the sub-cellular basis of local control. In this current work, we have incorporated this graded release model into a prior model of guinea pig ventricular myocyte electrophysiology, metabolism, and isometric force production. The resulting integrative model predicts the experimentally-observed causal relationship between action potential (AP shape and timing of Ca2+ and force transients, a relationship that is not explained by models lacking the graded release property. Model results suggest that even relatively subtle changes in AP morphology that may result, for example, from remodeling of membrane transporter expression in disease or spatial variation in cell properties, may have major impact on the temporal waveform of Ca2+ transients, thus influencing tissue-level electro-mechanical function.

Toward an integrative computational model of the Guinea pig cardiac myocyte.

Science.gov (United States)

Gauthier, Laura Doyle; Greenstein, Joseph L; Winslow, Raimond L

2012-01-01

The local control theory of excitation-contraction (EC) coupling asserts that regulation of calcium (Ca(2+)) release occurs at the nanodomain level, where openings of single L-type Ca(2+) channels (LCCs) trigger openings of small clusters of ryanodine receptors (RyRs) co-localized within the dyad. A consequence of local control is that the whole-cell Ca(2+) transient is a smooth continuous function of influx of Ca(2+) through LCCs. While this so-called graded release property has been known for some time, its functional importance to the integrated behavior of the cardiac ventricular myocyte has not been fully appreciated. We previously formulated a biophysically based model, in which LCCs and RyRs interact via a coarse-grained representation of the dyadic space. The model captures key features of local control using a low-dimensional system of ordinary differential equations. Voltage-dependent gain and graded Ca(2+) release are emergent properties of this model by virtue of the fact that model formulation is closely based on the sub-cellular basis of local control. In this current work, we have incorporated this graded release model into a prior model of guinea pig ventricular myocyte electrophysiology, metabolism, and isometric force production. The resulting integrative model predicts the experimentally observed causal relationship between action potential (AP) shape and timing of Ca(2+) and force transients, a relationship that is not explained by models lacking the graded release property. Model results suggest that even relatively subtle changes in AP morphology that may result, for example, from remodeling of membrane transporter expression in disease or spatial variation in cell properties, may have major impact on the temporal waveform of Ca(2+) transients, thus influencing tissue level electromechanical function.

Structural Systems Biology Evaluation of Metabolic Thermotolerance in Escherichia coli

DEFF Research Database (Denmark)

Chang, Roger L.; Andrews, Kathleen; Kim, Donghyuk

2013-01-01

Improve the System A "systems biology" approach may clarify, for example, how particular proteins determine sensitivity of bacteria to extremes of temperature. Chang et al. (p. 1220) integrated information on protein structure with a model of metabolism, thus associating the protein structure of ...

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 ...

An Integrative Bioinformatics Framework for Genome-scale Multiple Level Network Reconstruction of Rice

Directory of Open Access Journals (Sweden)

Liu Lili

2013-06-01

Full Text Available Understanding how metabolic reactions translate the genome of an organism into its phenotype is a grand challenge in biology. Genome-wide association studies (GWAS statistically connect genotypes to phenotypes, without any recourse to known molecular interactions, whereas a molecular mechanistic description ties gene function to phenotype through gene regulatory networks (GRNs, protein-protein interactions (PPIs and molecular pathways. Integration of different regulatory information levels of an organism is expected to provide a good way for mapping genotypes to phenotypes. However, the lack of curated metabolic model of rice is blocking the exploration of genome-scale multi-level network reconstruction. Here, we have merged GRNs, PPIs and genome-scale metabolic networks (GSMNs approaches into a single framework for rice via omics’ regulatory information reconstruction and integration. Firstly, we reconstructed a genome-scale metabolic model, containing 4,462 function genes, 2,986 metabolites involved in 3,316 reactions, and compartmentalized into ten subcellular locations. Furthermore, 90,358 pairs of protein-protein interactions, 662,936 pairs of gene regulations and 1,763 microRNA-target interactions were integrated into the metabolic model. Eventually, a database was developped for systematically storing and retrieving the genome-scale multi-level network of rice. This provides a reference for understanding genotype-phenotype relationship of rice, and for analysis of its molecular regulatory network.

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

A model to study intestinal and hepatic metabolism of propranolol in the dog.

Science.gov (United States)

Mills, P C; Siebert, G A; Roberts, M S

2004-02-01

A model to investigate hepatic drug uptake and metabolism in the dog was developed for this study. Catheters were placed in the portal and hepatic veins during exploratory laparotomy to collect pre- and posthepatic blood samples at defined intervals. Drug concentrations in the portal vein were taken to reflect intestinal uptake and metabolism of an p.o. administered drug (propranolol), while differences in drug and metabolite concentrations between portal and hepatic veins reflected hepatic uptake and metabolism. A significant difference in propranolol concentration between hepatic and portal veins confirmed a high hepatic extraction of this therapeutic agent in the dog. This technically uncomplicated model may be used experimentally or clinically to determine hepatic function and metabolism of drugs that may be administered during anaesthesia and surgery.

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.

Metabolic flexibility of mitochondrial respiratory chain disorders predicted by computer modelling.

Science.gov (United States)

Zieliński, Łukasz P; Smith, Anthony C; Smith, Alexander G; Robinson, Alan J

2016-11-01

Mitochondrial respiratory chain dysfunction causes a variety of life-threatening diseases affecting about 1 in 4300 adults. These diseases are genetically heterogeneous, but have the same outcome; reduced activity of mitochondrial respiratory chain complexes causing decreased ATP production and potentially toxic accumulation of metabolites. Severity and tissue specificity of these effects varies between patients by unknown mechanisms and treatment options are limited. So far most research has focused on the complexes themselves, and the impact on overall cellular metabolism is largely unclear. To illustrate how computer modelling can be used to better understand the potential impact of these disorders and inspire new research directions and treatments, we simulated them using a computer model of human cardiomyocyte mitochondrial metabolism containing over 300 characterised reactions and transport steps with experimental parameters taken from the literature. Overall, simulations were consistent with patient symptoms, supporting their biological and medical significance. These simulations predicted: complex I deficiencies could be compensated using multiple pathways; complex II deficiencies had less metabolic flexibility due to impacting both the TCA cycle and the respiratory chain; and complex III and IV deficiencies caused greatest decreases in ATP production with metabolic consequences that parallel hypoxia. Our study demonstrates how results from computer models can be compared to a clinical phenotype and used as a tool for hypothesis generation for subsequent experimental testing. These simulations can enhance understanding of dysfunctional mitochondrial metabolism and suggest new avenues for research into treatment of mitochondrial disease and other areas of mitochondrial dysfunction. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Arcuate NPY neurons sense and integrate peripheral metabolic signals to control feeding.

Science.gov (United States)

Kohno, Daisuke; Yada, Toshihiko

2012-12-01

NPY neuron in the hypothalamic arcuate nucleus is a key feeding center. Studies have shown that NPY neuron in the arcuate nucleus has a role to induce food intake. The arcuate nucleus is structurally unique with lacking blood brain barrier. Peripheral energy signals including hormones and nutrition can reach the arcuate nucleus. In this review, we discuss sensing and integrating peripheral signals in NPY neurons. In the arcuate nucleus, ghrelin mainly activates NPY neurons. Leptin and insulin suppress the ghrelin-induced activation in 30-40% of the ghrelin-activated NPY neurons. Lowering glucose concentration activates 40% of NPY neurons. These results indicate that NPY neuron in the arcuate nucleus is a feeding center in which major peripheral energy signals are directly sensed and integrated. Furthermore, there are subpopulations of NPY neurons in regard to their responsiveness to peripheral signals. These findings suggest that NPY neuron in the arcuate nucleus is an essential feeding center to induce food intake in response to peripheral metabolic state. Copyright © 2012 Elsevier Ltd. All rights reserved.

Probiotic modulation of symbiotic gut microbial–host metabolic interactions in a humanized microbiome mouse model

Science.gov (United States)

Martin, Francois-Pierre J; Wang, Yulan; Sprenger, Norbert; Yap, Ivan K S; Lundstedt, Torbjörn; Lek, Per; Rezzi, Serge; Ramadan, Ziad; van Bladeren, Peter; Fay, Laurent B; Kochhar, Sunil; Lindon, John C; Holmes, Elaine; Nicholson, Jeremy K

2008-01-01

The transgenomic metabolic effects of exposure to either Lactobacillus paracasei or Lactobacillus rhamnosus probiotics have been measured and mapped in humanized extended genome mice (germ-free mice colonized with human baby flora). Statistical analysis of the compartmental fluctuations in diverse metabolic compartments, including biofluids, tissue and cecal short-chain fatty acids (SCFAs) in relation to microbial population modulation generated a novel top-down systems biology view of the host response to probiotic intervention. Probiotic exposure exerted microbiome modification and resulted in altered hepatic lipid metabolism coupled with lowered plasma lipoprotein levels and apparent stimulated glycolysis. Probiotic treatments also altered a diverse range of pathways outcomes, including amino-acid metabolism, methylamines and SCFAs. The novel application of hierarchical-principal component analysis allowed visualization of multicompartmental transgenomic metabolic interactions that could also be resolved at the compartment and pathway level. These integrated system investigations demonstrate the potential of metabolic profiling as a top-down systems biology driver for investigating the mechanistic basis of probiotic action and the therapeutic surveillance of the gut microbial activity related to dietary supplementation of probiotics. PMID:18197175

Genome-scale metabolic modeling of Mucor circinelloides and comparative analysis with other oleaginous species.

Science.gov (United States)

Vongsangnak, Wanwipa; Klanchui, Amornpan; Tawornsamretkit, Iyarest; Tatiyaborwornchai, Witthawin; Laoteng, Kobkul; Meechai, Asawin

2016-06-01

We present a novel genome-scale metabolic model iWV1213 of Mucor circinelloides, which is an oleaginous fungus for industrial applications. The model contains 1213 genes, 1413 metabolites and 1326 metabolic reactions across different compartments. We demonstrate that iWV1213 is able to accurately predict the growth rates of M. circinelloides on various nutrient sources and culture conditions using Flux Balance Analysis and Phenotypic Phase Plane analysis. Comparative analysis of three oleaginous genome-scale models, including M. circinelloides (iWV1213), Mortierella alpina (iCY1106) and Yarrowia lipolytica (iYL619_PCP) revealed that iWV1213 possesses a higher number of genes involved in carbohydrate, amino acid, and lipid metabolisms that might contribute to its versatility in nutrient utilization. Moreover, the identification of unique and common active reactions among the Zygomycetes oleaginous models using Flux Variability Analysis unveiled a set of gene/enzyme candidates as metabolic engineering targets for cellular improvement. Thus, iWV1213 offers a powerful metabolic engineering tool for multi-level omics analysis, enabling strain optimization as a cell factory platform of lipid-based production. Copyright © 2016 Elsevier B.V. All rights reserved.

Copper metabolism: a multicompartmental model of copper kinetics in the rat

International Nuclear Information System (INIS)

Dunn, M.A.

1985-01-01

A qualitative multicompartmental model was developed that describes the whole-body kinetics of copper metabolism in the adult rat. The model was developed from radiocopper percent dose vs. time data measured over a three day period in plasma, liver, skin, skeletal muscle, bile and feces after the intravenous injection of 10 μg copper labeled with 64 Cu. Plasma radiocopper was separated into ceruloplasmin (Cp) and nonceruloplasmin (NCp) fractions. Liver cytosolic radiocopper was fractionated into void volume superoxide dismutase (SOD) containing and metallothionein fractions by gel filtration. Liver particulate fractions were isolated by differential centrifugation. The SAAM and CONSAM modeling programs were used to develop the model. The sizes of compartments, fractional rate constants and mass transfer rates between compartments were evaluated. The intracellular metabolism of copper was similar in hepatic and extrahepatic tissues being comprised of a faster turning over compartment (FTC) exchanging copper with NCp and a slower turning over compartment (STC) with input from Cp. Output from the STC was into the FTC. In the liver the STC was postulated to represent SOD copper which unlike the extrahepatic tissues received much of its input from the FTC. A small amount of biliary copper (9%) was postulated to return to plasma NCp by enterohepatic recycling. The model developed was contrasted and compared with two previous models of copper metabolism

Genome-scale modelling of microbial metabolism with temporal and spatial resolution.

Science.gov (United States)

Henson, Michael A

2015-12-01

Most natural microbial systems have evolved to function in environments with temporal and spatial variations. A major limitation to understanding such complex systems is the lack of mathematical modelling frameworks that connect the genomes of individual species and temporal and spatial variations in the environment to system behaviour. The goal of this review is to introduce the emerging field of spatiotemporal metabolic modelling based on genome-scale reconstructions of microbial metabolism. The extension of flux balance analysis (FBA) to account for both temporal and spatial variations in the environment is termed spatiotemporal FBA (SFBA). Following a brief overview of FBA and its established dynamic extension, the SFBA problem is introduced and recent progress is described. Three case studies are reviewed to illustrate the current state-of-the-art and possible future research directions are outlined. The author posits that SFBA is the next frontier for microbial metabolic modelling and a rapid increase in methods development and system applications is anticipated. © 2015 Authors; published by Portland Press Limited.

Multi site Kinetic Modeling of 13C Metabolic MR Using [1-13C]Pyruvate

International Nuclear Information System (INIS)

Damian, P.A.G.; Sperl, J.I.; Janich, M.A.; Wiesinger, F.; Schulte, R.F.; Menzel, M.I.; Damian, P.A.G.; Damian, P.A.G.; Haase, A.; Janich, M.A.; Schwaiger, M.; Janich, M.A.; Khegai, O.; Glaser, S.J.

2014-01-01

Hyperpolarized 13 C imaging allows real-time in vivo measurements of metabolite levels. Quantification of metabolite conversion between [1- 13 C]pyruvate and downstream metabolites [1- 13 C]alanine, [1- 13 C]lactate, and [ 13 C] 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 multi site, dynamic model involving possible biochemical pathways present in MR spectroscopy. Kinetic modeling parameters were determined by fitting the multi site 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 multi site 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

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.

Acute metabolic decompensation due to influenza in a mouse model of ornithine transcarbamylase deficiency

Directory of Open Access Journals (Sweden)

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

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 lines...... based on RNA-Seq data and validated the functionality of these models with data from metabolite profiling. We used cell line-specific GEMs to analyze the differences in the metabolism of cancer cell lines, and to explore the heterogeneous expression of the metabolic subsystems. Furthermore, we predicted...... for inhibition of cell growth may provide leads for the development of efficient cancer treatment strategies....

Systems Nutrigenomics Reveals Brain Gene Networks Linking Metabolic and Brain Disorders.

Science.gov (United States)

Meng, Qingying; Ying, Zhe; Noble, Emily; Zhao, Yuqi; Agrawal, Rahul; Mikhail, Andrew; Zhuang, Yumei; Tyagi, Ethika; Zhang, Qing; Lee, Jae-Hyung; Morselli, Marco; Orozco, Luz; Guo, Weilong; Kilts, Tina M; Zhu, Jun; Zhang, Bin; Pellegrini, Matteo; Xiao, Xinshu; Young, Marian F; Gomez-Pinilla, Fernando; Yang, Xia

2016-05-01

Nutrition plays a significant role in the increasing prevalence of metabolic and brain disorders. Here we employ systems nutrigenomics to scrutinize the genomic bases of nutrient-host interaction underlying disease predisposition or therapeutic potential. We conducted transcriptome and epigenome sequencing of hypothalamus (metabolic control) and hippocampus (cognitive processing) from a rodent model of fructose consumption, and identified significant reprogramming of DNA methylation, transcript abundance, alternative splicing, and gene networks governing cell metabolism, cell communication, inflammation, and neuronal signaling. These signals converged with genetic causal risks of metabolic, neurological, and psychiatric disorders revealed in humans. Gene network modeling uncovered the extracellular matrix genes Bgn and Fmod as main orchestrators of the effects of fructose, as validated using two knockout mouse models. We further demonstrate that an omega-3 fatty acid, DHA, reverses the genomic and network perturbations elicited by fructose, providing molecular support for nutritional interventions to counteract diet-induced metabolic and brain disorders. Our integrative approach complementing rodent and human studies supports the applicability of nutrigenomics principles to predict disease susceptibility and to guide personalized medicine. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

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.

Toward an Integrative Model of Global Business Strategy

DEFF Research Database (Denmark)

Li, Xin

fragmentation-integration-fragmentation-integration upward spiral. In response to the call for integrative approach to strategic management research, we propose an integrative model of global business strategy that aims at integrating not only strategy and IB but also the different paradigms within the strategy...... field. We also discuss the merit and limitation of our model....

Model integration and the economics of nuclear power

International Nuclear Information System (INIS)

Lundgren, S.

1985-01-01

The author proposes and applies a specific approach to model integration, i.e. the merger of two or several independently developed models. The approach is intended for integrations of activity analysis sector models and applied general equilibrium models. Model integration makes it possible to extend the range of applicability of applied general equilibrium models by exploiting the information contained in sector models. It also makes it possible to evaluate the validity of the partial equilibrium analyses in which sector models often are employed. The proposed approach is used to integrate a sector model of electricity and heat production with a general equilibrium model of the Swedish economy. Both models have been constructed within the research programme. The author uses the integrated model to look at two issues concerning the role of nuclear power on the Swedish electricity market: What are the likely consequences of a nuclear power discontinuation and how does the nuclear power investment programme of the 1970's and the early 1980's compare with a socially efficient one. (Author)

Genetic dissection in a mouse model reveals interactions between carotenoids and lipid metabolism[S

Science.gov (United States)

Palczewski, Grzegorz; Widjaja-Adhi, M. Airanthi K.; Amengual, Jaume; Golczak, Marcin; von Lintig, Johannes

2016-01-01

Carotenoids affect a rich variety of physiological functions in nature and are beneficial for human health. However, knowledge about their biological action and the consequences of their dietary accumulation in mammals is limited. Progress in this research field is limited by the expeditious metabolism of carotenoids in rodents and the confounding production of apocarotenoid signaling molecules. Herein, we established a mouse model lacking the enzymes responsible for carotenoid catabolism and apocarotenoid production, fed on either a β-carotene- or a zeaxanthin-enriched diet. Applying a genome wide microarray analysis, we assessed the effects of the parent carotenoids on the liver transcriptome. Our analysis documented changes in pathways for liver lipid metabolism and mitochondrial respiration. We biochemically defined these effects, and observed that β-carotene accumulation resulted in an elevation of liver triglycerides and liver cholesterol, while zeaxanthin accumulation increased serum cholesterol levels. We further show that carotenoids were predominantly transported within HDL particles in the serum of mice. Finally, we provide evidence that carotenoid accumulation influenced whole-body respiration and energy expenditure. Thus, we observed that accumulation of parent carotenoids interacts with lipid metabolism and that structurally related carotenoids display distinct biological functions in mammals. PMID:27389691

Effect of low shear modeled microgravity on phenotypic and central chitin metabolism in the filamentous fungi Aspergillus niger and Penicillium chrysogenum.

Science.gov (United States)

Sathishkumar, Yesupatham; Velmurugan, Natarajan; Lee, Hyun Mi; Rajagopal, Kalyanaraman; Im, Chan Ki; Lee, Yang Soo

2014-08-01

Phenotypic and genotypic changes in Aspergillus niger and Penicillium chrysogenum, spore forming filamentous fungi, with respect to central chitin metabolism were studied under low shear modeled microgravity, normal gravity and static conditions. Low shear modeled microgravity (LSMMG) response showed a similar spore germination rate with normal gravity and static conditions. Interestingly, high ratio of multiple germ tube formation of A. niger in LSMMG condition was observed. Confocal laser scanning microscopy images of calcofluor flurophore stained A. niger and P. chrysogenum showed no significant variations between different conditions tested. Transmission electron microscopy images revealed number of mitochondria increased in P. chrysogenum in low shear modeled microgravity condition but no stress related-woronin bodies in fungal hyphae were observed. To gain additional insight into the cell wall integrity under different conditions, transcription level of a key gene involved in cell wall integrity gfaA, encoding the glutamine: fructose-6-phosphate amidotransferase enzyme, was evaluated using qRT-PCR. The transcription level showed no variation among different conditions. Overall, the results collectively indicate that the LSMMG has shown no significant stress on spore germination, mycelial growth, cell wall integrity of potentially pathogenic fungi, A. niger and P. chrysogenum.

Modeling with a view to target identification in metabolic engineering: a critical evaluation of the available tools.

Science.gov (United States)

Maertens, Jo; Vanrolleghem, Peter A

2010-01-01

The state of the art tools for modeling metabolism, typically used in the domain of metabolic engineering, were reviewed. The tools considered are stoichiometric network analysis (elementary modes and extreme pathways), stoichiometric modeling (metabolic flux analysis, flux balance analysis, and carbon modeling), mechanistic and approximative modeling, cybernetic modeling, and multivariate statistics. In the context of metabolic engineering, one should be aware that the usefulness of these tools to optimize microbial metabolism for overproducing a target compound depends predominantly on the characteristic properties of that compound. Because of their shortcomings not all tools are suitable for every kind of optimization; issues like the dependence of the target compound's synthesis on severe (redox) constraints, the characteristics of its formation pathway, and the achievable/desired flux towards the target compound should play a role when choosing the optimization strategy.

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.

Modern model of integrated corporate communication

Directory of Open Access Journals (Sweden)

Milica Slijepčević

2018-03-01

Full Text Available The main purpose of this paper is to present the modern model of integrated corporate communication. Beside this, the authors will describe the changes occurring in the corporate environment and importance of changing the model of corporate communication. This paper also discusses the importance of implementation of the suggested model, the use of new media and effects of these changes on corporations. The approach used in this paper is the literature review. The authors explore the importance of implementation of the suggested model and the new media in corporate communication, both internal and external, addressing all the stakeholders and communication contents. The paper recommends implementation of a modern model of integrated corporate communication as a response to constant development of the new media and generation changes taking place. Practical implications: the modern model of integrated corporate communication can be used as an upgrade of the conventional communication models. This modern model empowers companies to sustain and build up the existing relationships with stakeholders, and to find out and create new relationships with stakeholders who were previously inaccessible and invisible.

Pharmacokinetic models relevant to toxicity and metabolism for uranium in humans and animals

International Nuclear Information System (INIS)

Wrenn, M.E.; Lipsztein, J.; Bertelli, L.

1988-01-01

The aim of this paper is to summarize pharmacokinetic models of uranium metabolism. Fortunately, others have recently reviewed metabolic models of all types, not just pharmacokinetic models. Their papers should be consulted for greater biological detail than is possible here. Improvements in the models since these other papers are noted. Models for assessing the biological consequences of exposure should account for the kinetics of intake by ingestion, inhalation, and injection, and the chemical form of uranium; predict the time dependent concentration in red blood cells, plasma, urine, kidney, bone and other organs (or compartments); and be adaptable to calculating these concentrations for varying regimens of intake. The biological parameters in the models come from metabolic data in humans and animals. Some of these parameters are reasonably well defined. For example, the cumulative urinary excretion at 24 hours post injection of soluble uranium in man is about 70%, the absorbed fraction for soluble uranium ingested by man in drinking water during normal dietary conditions is about 1%, and the half time in the mammalian kidney is several days. 17 refs., 8 figs

Integration of design applications with building models

DEFF Research Database (Denmark)

Eastman, C. M.; Jeng, T. S.; Chowdbury, R.

1997-01-01

This paper reviews various issues in the integration of applications with a building model... (Truncated.)......This paper reviews various issues in the integration of applications with a building model... (Truncated.)...

Lipid metabolism in myelinating glial cells: lessons from human inherited disorders and mouse models

NARCIS (Netherlands)

Chrast, R.; Saher, G.; Nave, K.A.; Verheijen, M.H.G.

2011-01-01

The integrity of central and peripheral nervous system myelin is affected in numerous lipid metabolism disorders. This vulnerability was so far mostly attributed to the extraordinarily high level of lipid synthesis that is required for the formation of myelin, and to the relative autonomy in lipid

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

Simultaneous and Sequential Integration by Cre/loxP Site-Specific Recombination in Saccharomyces cerevisiae.

Science.gov (United States)

Choi, Ho-Jung; Kim, Yeon-Hee

2018-05-28

A Cre/ loxP -δ-integration system was developed to allow sequential and simultaneous integration of a multiple gene expression cassette in Saccharomyces cerevisiae . To allow repeated integrations, the reusable Candida glabrata MARKER ( CgMARKER ) carrying loxP sequences was used, and the integrated CgMARKER was efficiently removed by inducing Cre recombinase. The XYLP and XYLB genes encoding endoxylanase and β-xylosidase, respectively, were used as model genes for xylan metabolism in this system, and the copy number of these genes was increased to 15.8 and 16.9 copies/cell, respectively, by repeated integration. This integration system is a promising approach for the easy construction of yeast strains with enhanced metabolic pathways through multicopy gene expression.

Model-integrating software components engineering flexible software systems

CERN Document Server

Derakhshanmanesh, Mahdi

2015-01-01

In his study, Mahdi Derakhshanmanesh builds on the state of the art in modeling by proposing to integrate models into running software on the component-level without translating them to code. Such so-called model-integrating software exploits all advantages of models: models implicitly support a good separation of concerns, they are self-documenting and thus improve understandability and maintainability and in contrast to model-driven approaches there is no synchronization problem anymore between the models and the code generated from them. Using model-integrating components, software will be

An accurate description of Aspergillus niger organic acid batch fermentation through dynamic metabolic modelling.

Science.gov (United States)

Upton, Daniel J; McQueen-Mason, Simon J; Wood, A Jamie

2017-01-01

Aspergillus niger fermentation has provided the chief source of industrial citric acid for over 50 years. Traditional strain development of this organism was achieved through random mutagenesis, but advances in genomics have enabled the development of genome-scale metabolic modelling that can be used to make predictive improvements in fermentation performance. The parent citric acid-producing strain of A. niger , ATCC 1015, has been described previously by a genome-scale metabolic model that encapsulates its response to ambient pH. Here, we report the development of a novel double optimisation modelling approach that generates time-dependent citric acid fermentation using dynamic flux balance analysis. The output from this model shows a good match with empirical fermentation data. Our studies suggest that citric acid production commences upon a switch to phosphate-limited growth and this is validated by fitting to empirical data, which confirms the diauxic growth behaviour and the role of phosphate storage as polyphosphate. The calibrated time-course model reflects observed metabolic events and generates reliable in silico data for industrially relevant fermentative time series, and for the behaviour of engineered strains suggesting that our approach can be used as a powerful tool for predictive metabolic engineering.

In Vitro Disease Model of Microgravity Conditioning on Human Energy Metabolism

Science.gov (United States)

Snyder, Jessica; Culbertson, C.; Zhang, Ye; Emami, K.; Wu, H.; Sun, Wei

2010-01-01

NASA and its partners are committed to introducing appropriate new technology to enable learning and living safely beyond the Earth for extended periods of time in a sustainable and possibly indefinite manner. In the responsible acquisition of that goal, life sciences is tasked to tune and advance current medical technology to prepare for human health and wellness in the space environment. The space environment affects the condition and function of biological systems from organ level function to shape of individual organelles. The objective of this paper is to study the effect of microgravity on kinetics of drug metabolism. This fundamental characterization is meaningful to (1) scientific understanding of the response of biology to microgravity and (2) clinical dosing requirements and pharmacological thresholds during long term manned space exploration. Metabolism kinetics of the anti-nausea drug promethazine (PMZ) were determined by an in vitro ground model of 3-dimensional aggregates of human hepatocytes conditioned to weightlessness using a rotating wall bioreactor. The authors observed up-regulated PMZ conversion in model microgravity conditions and attribute this to effect to model microgravity conditioning acting on metabolic mechanisms of the cells. Further work is necessary to determine which particular cellular mechanisms are governing the experimental observations, but the authors conclude kinetics of drug metabolism are responsive to gravitational fields and further study of this sensitivity would improve dosing of pharmaceuticals to persons exposed to a microgravity environment.

Modeling metabolism and stage-specific growth of Plasmodium falciparum HB3 during the intraerythrocytic developmental cycle.

Science.gov (United States)

Fang, Xin; Reifman, Jaques; Wallqvist, Anders

2014-10-01

The human malaria parasite Plasmodium falciparum goes through a complex life cycle, including a roughly 48-hour-long intraerythrocytic developmental cycle (IDC) in human red blood cells. A better understanding of the metabolic processes required during the asexual blood-stage reproduction will enhance our basic knowledge of P. falciparum and help identify critical metabolic reactions and pathways associated with blood-stage malaria. We developed a metabolic network model that mechanistically links time-dependent gene expression, metabolism, and stage-specific growth, allowing us to predict the metabolic fluxes, the biomass production rates, and the timing of production of the different biomass components during the IDC. We predicted time- and stage-specific production of precursors and macromolecules for P. falciparum (strain HB3), allowing us to link specific metabolites to specific physiological functions. For example, we hypothesized that coenzyme A might be involved in late-IDC DNA replication and cell division. Moreover, the predicted ATP metabolism indicated that energy was mainly produced from glycolysis and utilized for non-metabolic processes. Finally, we used the model to classify the entire tricarboxylic acid cycle into segments, each with a distinct function, such as superoxide detoxification, glutamate/glutamine processing, and metabolism of fumarate as a byproduct of purine biosynthesis. By capturing the normal metabolic and growth progression in P. falciparum during the IDC, our model provides a starting point for further elucidation of strain-specific metabolic activity, host-parasite interactions, stress-induced metabolic responses, and metabolic responses to antimalarial drugs and drug candidates.

Modeling Inborn Errors of Hepatic Metabolism Using Induced Pluripotent Stem Cells.

Science.gov (United States)

Pournasr, Behshad; Duncan, Stephen A

2017-11-01

Inborn errors of hepatic metabolism are because of deficiencies commonly within a single enzyme as a consequence of heritable mutations in the genome. Individually such diseases are rare, but collectively they are common. Advances in genome-wide association studies and DNA sequencing have helped researchers identify the underlying genetic basis of such diseases. Unfortunately, cellular and animal models that accurately recapitulate these inborn errors of hepatic metabolism in the laboratory have been lacking. Recently, investigators have exploited molecular techniques to generate induced pluripotent stem cells from patients' somatic cells. Induced pluripotent stem cells can differentiate into a wide variety of cell types, including hepatocytes, thereby offering an innovative approach to unravel the mechanisms underlying inborn errors of hepatic metabolism. Moreover, such cell models could potentially provide a platform for the discovery of therapeutics. In this mini-review, we present a brief overview of the state-of-the-art in using pluripotent stem cells for such studies. © 2017 American Heart Association, Inc.

Systematic optimization of fed-batch simultaneous saccharification and fermentation at high-solid loading based on enzymatic hydrolysis and dynamic metabolic modeling of Saccharomyces cerevisiae.

Science.gov (United States)

Unrean, Pornkamol; Khajeeram, Sutamat; Laoteng, Kobkul

2016-03-01

An integrative simultaneous saccharification and fermentation (SSF) modeling is a useful guiding tool for rapid process optimization to meet the techno-economic requirement of industrial-scale lignocellulosic ethanol production. In this work, we have developed the SSF model composing of a metabolic network of a Saccharomyces cerevisiae cell associated with fermentation kinetics and enzyme hydrolysis model to quantitatively capture dynamic responses of yeast cell growth and fermentation during SSF. By using model-based design of feeding profiles for substrate and yeast cell in the fed-batch SSF process, an efficient ethanol production with high titer of up to 65 g/L and high yield of 85 % of theoretical yield was accomplished. The ethanol titer and productivity was increased by 47 and 41 %, correspondingly, in optimized fed-batch SSF as compared to batch process. The developed integrative SSF model is, therefore, considered as a promising approach for systematic design of economical and sustainable SSF bioprocessing of lignocellulose.

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

Topological and kinetic determinants of the modal matrices of dynamic models of metabolism.

Directory of Open Access Journals (Sweden)

Bin Du

Full Text Available Large-scale kinetic models of metabolism are becoming increasingly comprehensive and accurate. A key challenge is to understand the biochemical basis of the dynamic properties of these models. Linear analysis methods are well-established as useful tools for characterizing the dynamic response of metabolic networks. Central to linear analysis methods are two key matrices: the Jacobian matrix (J and the modal matrix (M-1 arising from its eigendecomposition. The modal matrix M-1 contains dynamically independent motions of the kinetic model near a reference state, and it is sparse in practice for metabolic networks. However, connecting the structure of M-1 to the kinetic properties of the underlying reactions is non-trivial. In this study, we analyze the relationship between J, M-1, and the kinetic properties of the underlying network for kinetic models of metabolism. Specifically, we describe the origin of mode sparsity structure based on features of the network stoichiometric matrix S and the reaction kinetic gradient matrix G. First, we show that due to the scaling of kinetic parameters in real networks, diagonal dominance occurs in a substantial fraction of the rows of J, resulting in simple modal structures with clear biological interpretations. Then, we show that more complicated modes originate from topologically-connected reactions that have similar reaction elasticities in G. These elasticities represent dynamic equilibrium balances within reactions and are key determinants of modal structure. The work presented should prove useful towards obtaining an understanding of the dynamics of kinetic models of metabolism, which are rooted in the network structure and the kinetic properties of reactions.

Selective upregulation of lipid metabolism in skeletal muscle of foraging juvenile king penguins: an integrative study.

Science.gov (United States)

Teulier, Loic; Dégletagne, Cyril; Rey, Benjamin; Tornos, Jérémy; Keime, Céline; de Dinechin, Marc; Raccurt, Mireille; Rouanet, Jean-Louis; Roussel, Damien; Duchamp, Claude

2012-06-22

The passage from shore to marine life of juvenile penguins represents a major energetic challenge to fuel intense and prolonged demands for thermoregulation and locomotion. Some functional changes developed at this crucial step were investigated by comparing pre-fledging king penguins with sea-acclimatized (SA) juveniles (Aptenodytes patagonicus). Transcriptomic analysis of pectoralis muscle biopsies revealed that most genes encoding proteins involved in lipid transport or catabolism were upregulated, while genes involved in carbohydrate metabolism were mostly downregulated in SA birds. Determination of muscle enzymatic activities showed no changes in enzymes involved in the glycolytic pathway, but increased 3-hydroxyacyl-CoA dehydrogenase, an enzyme of the β-oxidation pathway. The respiratory rates of isolated muscle mitochondria were much higher with a substrate arising from lipid metabolism (palmitoyl-L-carnitine) in SA juveniles than in terrestrial controls, while no difference emerged with a substrate arising from carbohydrate metabolism (pyruvate). In vivo, perfusion of a lipid emulsion induced a fourfold larger thermogenic effect in SA than in control juveniles. The present integrative study shows that fuel selection towards lipid oxidation characterizes penguin acclimatization to marine life. Such acclimatization may involve thyroid hormones through their nuclear beta receptor and nuclear coactivators.

Integrable models in classical and quantum mechanics

International Nuclear Information System (INIS)

Jurco, B.

1991-01-01

Integrable systems are investigated, especially the rational and trigonometric Gaudin models. The Gaudin models are diagonalized for the case of classical Lie algebras. Their relation to the other integrable models and to the quantum inverse scattering method is investigated. Applications in quantum optics and plasma physics are discussed. (author). 94 refs

Metabolic Models of Protein Allocation Call for the Kinetome

DEFF Research Database (Denmark)

Nilsson, Avlant; Nielsen, Jens; Palsson, Bernhard

2017-01-01

The flux of metabolites in the living cell depend on enzyme activities. Recently, many metabolic phenotypes have been explained by computer models that incorporate enzyme activity data. To move further, the scientific community needs to measure the kinetics of all enzymes in a systematic way....

Bioprinting of Micro-Organ Tissue Analog for Drug Metabolism Study

Science.gov (United States)

Sun, Wei

An evolving application of tissue engineering is to develop in vitro 3D cell/tissue models for drug screening and pharmacological study. In order to test in space, these in vitro models are mostly manufactured through micro-fabrication techniques and incorporate living cells with MEMS or microfluidic devices. These cell-integrated microfluidic devices, or referred as microorgans, are effective in furnishing reliable and inexpensive drug metabolism and toxicity studies [1-3]. This paper will present an on-going research collaborated between Drexel University and NASA JSC Radiation Physics Laboratory for applying a direct cell printing technique to freeform fabrication of 3D liver tissue analog in drug metabolism study. The paper will discuss modeling, design, and solid freeform fabrication of micro-fluidic flow patterns and bioprinting of 3D micro-liver chamber that biomimics liver physiological microenvironment for enhanced drug metabolization. Technical details to address bioprinting of 3D liver tissue analog, integration with a microfluidic device, and basic drug metabolism study for NASA's interests will presented. 1. Holtorf H. Leslie J. Chang R, Nam J, Culbertson C, Sun W, Gonda S, "Development of a Three-Dimensional Tissue-on-a-Chip Micro-Organ Device for Pharmacokinetic Analysis", the 47th Annual Meeting of the American Society for Cell Biology, Washington, DC, December 1-5, 2007. 2. Chang, R., Nam, J., Culbertson C., Holtorf, H., Jeevarajan, A., Gonda, S. and Sun, W., "Bio-printing and Modeling of Flow Patterns for Cell Encapsulated 3D Liver Chambers For Pharmacokinetic Study", TERMIS North America 2007 Conference and Exposition, Westin Harbour Castle, Toronto, Canada, June 13-16, 2007. 3.Starly, B., Chang, R., Sun, W., Culbertson, C., Holtorf, H. and Gonda, S., "Bioprinted Tissue-on-chip Application for Pharmacokinetic Studies", Proceedings of World Congress on Tissue Engineering and Regenerative Medicine, Pittsburgh, PA, USA, April 24-27, 2006.

MEASURING INFORMATION INTEGR-ATION MODEL FOR CAD/CMM

Institute of Scientific and Technical Information of China (English)

无

2003-01-01

A CAD/CMM workpiece modeling system based on IGES file is proposed. The modeling system is implemented by using a new method for labelling the tolerance items of 3D workpiece. The concept-"feature face" is used in the method. First the CAD data of workpiece are extracted and recognized automatically. Then a workpiece model is generated, which is the integration of pure 3D geometry form with its corresponding inspection items. The principle of workpiece modeling is also presented. At last, the experiment results are shown and correctness of the model is certified.

Integrated Site Model Process Model Report

International Nuclear Information System (INIS)

Booth, T.

2000-01-01

The Integrated Site Model (ISM) provides a framework for discussing the geologic features and properties of Yucca Mountain, which is being evaluated as a potential site for a geologic repository for the disposal of nuclear waste. The ISM is important to the evaluation of the site because it provides 3-D portrayals of site geologic, rock property, and mineralogic characteristics and their spatial variabilities. The ISM is not a single discrete model; rather, it is a set of static representations that provide three-dimensional (3-D), computer representations of site geology, selected hydrologic and rock properties, and mineralogic-characteristics data. These representations are manifested in three separate model components of the ISM: the Geologic Framework Model (GFM), the Rock Properties Model (RPM), and the Mineralogic Model (MM). The GFM provides a representation of the 3-D stratigraphy and geologic structure. Based on the framework provided by the GFM, the RPM and MM provide spatial simulations of the rock and hydrologic properties, and mineralogy, respectively. Functional summaries of the component models and their respective output are provided in Section 1.4. Each of the component models of the ISM considers different specific aspects of the site geologic setting. Each model was developed using unique methodologies and inputs, and the determination of the modeled units for each of the components is dependent on the requirements of that component. Therefore, while the ISM represents the integration of the rock properties and mineralogy into a geologic framework, the discussion of ISM construction and results is most appropriately presented in terms of the three separate components. This Process Model Report (PMR) summarizes the individual component models of the ISM (the GFM, RPM, and MM) and describes how the three components are constructed and combined to form the ISM

The CanOE strategy: integrating genomic and metabolic contexts across multiple prokaryote genomes to find candidate genes for orphan enzymes.

Directory of Open Access Journals (Sweden)

Adam Alexander Thil Smith

2012-05-01

Full Text Available Of all biochemically characterized metabolic reactions formalized by the IUBMB, over one out of four have yet to be associated with a nucleic or protein sequence, i.e. are sequence-orphan enzymatic activities. Few bioinformatics annotation tools are able to propose candidate genes for such activities by exploiting context-dependent rather than sequence-dependent data, and none are readily accessible and propose result integration across multiple genomes. Here, we present CanOE (Candidate genes for Orphan Enzymes, a four-step bioinformatics strategy that proposes ranked candidate genes for sequence-orphan enzymatic activities (or orphan enzymes for short. The first step locates "genomic metabolons", i.e. groups of co-localized genes coding proteins catalyzing reactions linked by shared metabolites, in one genome at a time. These metabolons can be particularly helpful for aiding bioanalysts to visualize relevant metabolic data. In the second step, they are used to generate candidate associations between un-annotated genes and gene-less reactions. The third step integrates these gene-reaction associations over several genomes using gene families, and summarizes the strength of family-reaction associations by several scores. In the final step, these scores are used to rank members of gene families which are proposed for metabolic reactions. These associations are of particular interest when the metabolic reaction is a sequence-orphan enzymatic activity. Our strategy found over 60,000 genomic metabolons in more than 1,000 prokaryote organisms from the MicroScope platform, generating candidate genes for many metabolic reactions, of which more than 70 distinct orphan reactions. A computational validation of the approach is discussed. Finally, we present a case study on the anaerobic allantoin degradation pathway in Escherichia coli K-12.

Development of an internet based system for modeling biotin metabolism using Bayesian networks.

Science.gov (United States)

Zhou, Jinglei; Wang, Dong; Schlegel, Vicki; Zempleni, Janos

2011-11-01

Biotin is an essential water-soluble vitamin crucial for maintaining normal body functions. The importance of biotin for human health has been under-appreciated but there is plenty of opportunity for future research with great importance for human health. Currently, carrying out predictions of biotin metabolism involves tedious manual manipulations. In this paper, we report the development of BiotinNet, an internet based program that uses Bayesian networks to integrate published data on various aspects of biotin metabolism. Users can provide a combination of values on the levels of biotin related metabolites to obtain the predictions on other metabolites that are not specified. As an inherent feature of Bayesian networks, the uncertainty of the prediction is also quantified and reported to the user. This program enables convenient in silico experiments regarding biotin metabolism, which can help researchers design future experiments while new data can be continuously incorporated. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Development of a generalized integral jet model

DEFF Research Database (Denmark)

Duijm, Nijs Jan; Kessler, A.; Markert, Frank

2017-01-01

Integral type models to describe stationary plumes and jets in cross-flows (wind) have been developed since about 1970. These models are widely used for risk analysis, to describe the consequences of many different scenarios. Alternatively, CFD codes are being applied, but computational requireme......Integral type models to describe stationary plumes and jets in cross-flows (wind) have been developed since about 1970. These models are widely used for risk analysis, to describe the consequences of many different scenarios. Alternatively, CFD codes are being applied, but computational...... requirements still limit the number of scenarios that can be dealt with using CFD only. The integral models, however, are not suited to handle transient releases, such as releases from pressurized equipment, where the initially high release rate decreases rapidly with time. Further, on gas ignition, a second...... model is needed to describe the rapid combustion of the flammable part of the plume (flash fire) and a third model has to be applied for the remaining jet fire. The objective of this paper is to describe the first steps of the development of an integral-type model describing the transient development...

Oral absorption and oxidative metabolism of atrazine in rats evaluated by physiological modeling approaches

International Nuclear Information System (INIS)

McMullin, Tami S.; Hanneman, William H.; Cranmer, Brian K.; Tessari, John D.; Andersen, Melvin E.

2007-01-01

Atrazine (ATRA) is metabolized by cytochrome P450s to the chlorinated metabolites, 2-chloro-4-ethylamino-6-amino-1,3,5-triazine (ETHYL), 2-chloro-4-amino-6-isopropylamino-1, 3, 5-triazine (ISO), and diaminochlorotriazine (DACT). Here, we develop a set of physiologically based pharmacokinetic (PBPK) models that describe the influence of oral absorption and oxidative metabolism on the blood time course curves of individual chlorotriazines (Cl-TRIs) in rat after oral dosing of ATRA. These models first incorporated in vitro metabolic parameters to describe time course plasma concentrations of DACT, ETHYL, and ISO after dosing with each compound. Parameters from each individual model were linked together into a final composite model in order to describe the time course of all 4 Cl-TRIs after ATRA dosing. Oral administration of ISO, ETHYL and ATRA produced double peaks of the compounds in plasma time courses that were described by multiple absorption phases from gut. An adequate description of the uptake and bioavailability of absorbed ATRA also required inclusion of additional oxidative metabolic clearance of ATRA to the mono-dealkylated metabolites occurring in GI a tract compartment. These complex processes regulating tissue dosimetry of atrazine and its chlorinated metabolites likely reflect limited compound solubility in the gut from dosing with an emulsion, and sequential absorption and metabolism along the GI tract at these high oral doses

Model-Based Integration and Interpretation of Data

DEFF Research Database (Denmark)

Petersen, Johannes

2004-01-01

Data integration and interpretation plays a crucial role in supervisory control. The paper defines a set of generic inference steps for the data integration and interpretation process based on a three-layer model of system representations. The three-layer model is used to clarify the combination...... of constraint and object-centered representations of the work domain throwing new light on the basic principles underlying the data integration and interpretation process of Rasmussen's abstraction hierarchy as well as other model-based approaches combining constraint and object-centered representations. Based...

Modular Architecture for Integrated Model-Based Decision Support.

Science.gov (United States)

Gaebel, Jan; Schreiber, Erik; Oeser, Alexander; Oeltze-Jafra, Steffen

2018-01-01

Model-based decision support systems promise to be a valuable addition to oncological treatments and the implementation of personalized therapies. For the integration and sharing of decision models, the involved systems must be able to communicate with each other. In this paper, we propose a modularized architecture of dedicated systems for the integration of probabilistic decision models into existing hospital environments. These systems interconnect via web services and provide model sharing and processing capabilities for clinical information systems. Along the lines of IHE integration profiles from other disciplines and the meaningful reuse of routinely recorded patient data, our approach aims for the seamless integration of decision models into hospital infrastructure and the physicians' daily work.

Population-based family case-control proband study on familial aggregation of metabolic syndrome: finding from Taiwanese people involved in Keelung community-based integrated screening (KCIS no. 5).

Science.gov (United States)

Chiu, Yueh-Hsia; Lin, Wen-Yuan; Wang, Po-En; Chen, Yao-Der; Wang, Ting-Ting; Warwick, Jane; Chen, Tony Hsiu-Hsi

2007-03-01

A population-based case-control proband study was undertaken to elucidate familial aggregation, independent environmental factors, and the interaction between them. A total of 7308 metabolic syndrome (MET-S) cases were identified from the Keelung community-based integrated screening programme between 1999 and 2002. The study has a case-control/family sampling design. A total of 1417 case probands were randomly selected from 3225 metabolic syndrome cases and the corresponding 2458 controls selected from 16,519 subjects without metabolic syndrome by matching on sex, age (+/-3 years) and place of residence. The generalized estimation equation model was used to estimate odds ratios and corresponding 95% confidence intervals. The risk for having metabolic syndrome among family members for cases versus control probands was 1.56-fold (1.29-1.89) after controlling for significant environmental factors. Higher risk of metabolic syndrome was found in parents than spouse. Low education against high education had 2.06-fold (1.36-3.13) risk for metabolic syndrome. Betel quid chewing was positively associated with the risk of MET-S, with 1.99-fold (1.13-3.53) risk for 1-9 pieces and 1.76-fold (0.96-3.23) risk for >or=10 pieces compared with non-chewer. Moderate and high intensity of non-occupational exercise led to 21.0% (OR=0.79 (0.63-0.98)) and 26.0% (OR=0.74 (0.59-0.94)) reduction in the risk for metabolic syndrome, respectively. The frequent consumption of vegetable reduced 24.0% (OR=0.76 (0.62-0.92)) risk for MET-S. The frequent consumption of coffee was associated the increased risk for metabolic syndrome (OR=1.32 (1.07-1.64)). The present study confirmed the risk of metabolic syndrome not only has the tendency towards familial aggregation but is affected by independent effect of environmental or individual correlates.

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.

Modeling integrated biomass gasification business concepts

Science.gov (United States)

Peter J. Ince; Ted Bilek; Mark A. Dietenberger

2011-01-01

Biomass gasification is an approach to producing energy and/or biofuels that could be integrated into existing forest product production facilities, particularly at pulp mills. Existing process heat and power loads tend to favor integration at existing pulp mills. This paper describes a generic modeling system for evaluating integrated biomass gasification business...

Diet-induced metabolic hamster model of nonalcoholic fatty liver disease

Directory of Open Access Journals (Sweden)

Bhathena J

2011-06-01

Full Text Available Jasmine Bhathena, Arun Kulamarva, Christopher Martoni, Aleksandra Malgorzata Urbanska, Meenakshi Malhotra, Arghya Paul, Satya PrakashBiomedical Technology and Cell Therapy Research Laboratory, Department of Biomedical Engineering, Artificial Cells and Organs Research Centre, Faculty of Medicine, McGill University, Montreal, Québec, CanadaBackground: Obesity, hypercholesterolemia, elevated triglycerides, and type 2 diabetes are major risk factors for metabolic syndrome. Hamsters, unlike rats or mice, respond well to diet-induced obesity, increase body mass and adiposity on group housing, and increase food intake due to social confrontation-induced stress. They have a cardiovascular and hepatic system similar to that of humans, and can thus be a useful model for human pathophysiology.Methods: Experiments were planned to develop a diet-induced Bio F1B Golden Syrian hamster model of dyslipidemia and associated nonalcoholic fatty liver disease in the metabolic syndrome. Hamsters were fed a normal control diet, a high-fat/high-cholesterol diet, a high-fat/high-cholesterol/methionine-deficient/choline-devoid diet, and a high-fat/high-cholesterol/choline-deficient diet. Serum total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, glucose, atherogenic index, and body weight were quantified biweekly. Fat deposition in the liver was observed and assessed following lipid staining with hematoxylin and eosin and with oil red O.Results: In this study, we established a diet-induced Bio F1B Golden Syrian hamster model for studying dyslipidemia and associated nonalcoholic fatty liver disease in the metabolic syndrome. Hyperlipidemia and elevated serum glucose concentrations were induced using this diet. Atherogenic index was elevated, increasing the risk for a cardiovascular event. Histological analysis of liver specimens at the end of four weeks showed increased fat deposition in the liver of animals fed

Uncoupling of Metabolic Health from Longevity through Genetic Alteration of Adipose Tissue Lipid-Binding Proteins

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Khanichi N. Charles

2017-10-01

Full Text Available Summary: Deterioration of metabolic health is a hallmark of aging and generally assumed to be detrimental to longevity. Exposure to a high-calorie diet impairs metabolism and accelerates aging; conversely, calorie restriction (CR prevents age-related metabolic diseases and extends lifespan. However, it is unclear whether preservation of metabolic health is sufficient to extend lifespan. We utilized a genetic mouse model lacking Fabp4/5 that confers protection against metabolic diseases and shares molecular and lipidomic features with CR to address this question. Fabp-deficient mice exhibit extended metabolic healthspan, with protection against insulin resistance and glucose intolerance, inflammation, deterioration of adipose tissue integrity, and fatty liver disease. Surprisingly, however, Fabp-deficient mice did not exhibit any extension of lifespan. These data indicate that extension of metabolic healthspan in the absence of CR can be uncoupled from lifespan, indicating the potential for independent drivers of these pathways, at least in laboratory mice. : Deterioration of metabolic health is a hallmark of aging and generally thought to be detrimental to longevity. Charles et al. utilize FABP-deficient mice as a model to demonstrate that the preservation of metabolic health in this model persists throughout life, even under metabolic stress, but does not increase longevity. Keywords: fatty acid binding protein, aging, calorie restriction, metabolic health, inflammation, metaflammation, diabetes, obesity, de novo lipogenesis

HEPATOKIN1 is a biochemistry-based model of liver metabolism for applications in medicine and pharmacology.

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Berndt, Nikolaus; Bulik, Sascha; Wallach, Iwona; Wünsch, Tilo; König, Matthias; Stockmann, Martin; Meierhofer, David; Holzhütter, Hermann-Georg

2018-06-19

The epidemic increase of non-alcoholic fatty liver diseases (NAFLD) requires a deeper understanding of the regulatory circuits controlling the response of liver metabolism to nutritional challenges, medical drugs, and genetic enzyme variants. As in vivo studies of human liver metabolism are encumbered with serious ethical and technical issues, we developed a comprehensive biochemistry-based kinetic model of the central liver metabolism including the regulation of enzyme activities by their reactants, allosteric effectors, and hormone-dependent phosphorylation. The utility of the model for basic research and applications in medicine and pharmacology is illustrated by simulating diurnal variations of the metabolic state of the liver at various perturbations caused by nutritional challenges (alcohol), drugs (valproate), and inherited enzyme disorders (galactosemia). Using proteomics data to scale maximal enzyme activities, the model is used to highlight differences in the metabolic functions of normal hepatocytes and malignant liver cells (adenoma and hepatocellular carcinoma).

1H NMR-based metabolic profiling reveals inherent biological variation in yeast and nematode model systems

International Nuclear Information System (INIS)

Szeto, Samuel S. W.; Reinke, Stacey N.; Lemire, Bernard D.

2011-01-01

The application of metabolomics to human and animal model systems is poised to provide great insight into our understanding of disease etiology and the metabolic changes that are associated with these conditions. However, metabolomic studies have also revealed that there is significant, inherent biological variation in human samples and even in samples from animal model systems where the animals are housed under carefully controlled conditions. This inherent biological variability is an important consideration for all metabolomics analyses. In this study, we examined the biological variation in 1 H NMR-based metabolic profiling of two model systems, the yeast Saccharomyces cerevisiae and the nematode Caenorhabditis elegans. Using relative standard deviations (RSD) as a measure of variability, our results reveal that both model systems have significant amounts of biological variation. The C. elegans metabolome possesses greater metabolic variance with average RSD values of 29 and 39%, depending on the food source that was used. The S. cerevisiae exometabolome RSD values ranged from 8% to 12% for the four strains examined. We also determined whether biological variation occurs between pairs of phenotypically identical yeast strains. Multivariate statistical analysis allowed us to discriminate between pair members based on their metabolic phenotypes. Our results highlight the variability of the metabolome that exists even for less complex model systems cultured under defined conditions. We also highlight the efficacy of metabolic profiling for defining these subtle metabolic alterations.

Scaffold Attachment Factor B1: A Novel Chromatin Regulator of Prostate Cancer Metabolism

Science.gov (United States)

2016-10-01

AWARD NUMBER: W81XWH-14-1-0152 TITLE: Scaffold Attachment Factor B1: A Novel Chromatin Regulator of Prostate Cancer Metabolism PRINCIPAL...TITLE AND SUBTITLE 5a. CONTRACT NUMBER W81XWH-14-1-0152 Scaffold Attachment Factor B1: A Novel Chromatin Regulator of Prostate Cancer Metabolism... chromatin immunoprecipitation-next generation DNA sequencing (ChIP-seq) and integrative network modeling to identify the SAFB1 cistrome and the extent of

Integrated Medical Model – Chest Injury Model

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National Aeronautics and Space Administration — The Exploration Medical Capability (ExMC) Element of NASA's Human Research Program (HRP) developed the Integrated Medical Model (IMM) to forecast the resources...

The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum

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Agren, Rasmus; Liu, Liming; Shoaie, Saeed; Vongsangnak, Wanwipa; Nookaew, Intawat; Nielsen, Jens

2013-01-01

We present the RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox: a software suite that allows for semi-automated reconstruction of genome-scale models. It makes use of published models and/or the KEGG database, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. The software is a useful tool for system-wide data analysis in a metabolic context and for streamlined reconstruction of metabolic networks based on protein homology. The RAVEN Toolbox workflow was applied in order to reconstruct a genome-scale metabolic model for the important microbial cell factory Penicillium chrysogenum Wisconsin54-1255. The model was validated in a bibliomic study of in total 440 references, and it comprises 1471 unique biochemical reactions and 1006 ORFs. It was then used to study the roles of ATP and NADPH in the biosynthesis of penicillin, and to identify potential metabolic engineering targets for maximization of penicillin production. PMID:23555215

Challenges in horizontal model integration.

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Kolczyk, Katrin; Conradi, Carsten

2016-03-11

Systems Biology has motivated dynamic models of important intracellular processes at the pathway level, for example, in signal transduction and cell cycle control. To answer important biomedical questions, however, one has to go beyond the study of isolated pathways towards the joint study of interacting signaling pathways or the joint study of signal transduction and cell cycle control. Thereby the reuse of established models is preferable, as it will generally reduce the modeling effort and increase the acceptance of the combined model in the field. Obtaining a combined model can be challenging, especially if the submodels are large and/or come from different working groups (as is generally the case, when models stored in established repositories are used). To support this task, we describe a semi-automatic workflow based on established software tools. In particular, two frequent challenges are described: identification of the overlap and subsequent (re)parameterization of the integrated model. The reparameterization step is crucial, if the goal is to obtain a model that can reproduce the data explained by the individual models. For demonstration purposes we apply our workflow to integrate two signaling pathways (EGF and NGF) from the BioModels Database.

A quantitative theory of solid tumor growth, metabolic rate and vascularization.

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Alexander B Herman

Full Text Available The relationships between cellular, structural and dynamical properties of tumors have traditionally been studied separately. Here, we construct a quantitative, predictive theory of solid tumor growth, metabolic rate, vascularization and necrosis that integrates the relationships between these properties. To accomplish this, we develop a comprehensive theory that describes the interface and integration of the tumor vascular network and resource supply with the cardiovascular system of the host. Our theory enables a quantitative understanding of how cells, tissues, and vascular networks act together across multiple scales by building on recent theoretical advances in modeling both healthy vasculature and the detailed processes of angiogenesis and tumor growth. The theory explicitly relates tumor vascularization and growth to metabolic rate, and yields extensive predictions for tumor properties, including growth rates, metabolic rates, degree of necrosis, blood flow rates and vessel sizes. Besides these quantitative predictions, we explain how growth rates depend on capillary density and metabolic rate, and why similar tumors grow slower and occur less frequently in larger animals, shedding light on Peto's paradox. Various implications for potential therapeutic strategies and further research are discussed.

Gravitational interactions of integrable models

International Nuclear Information System (INIS)

Abdalla, E.; Abdalla, M.C.B.

1995-10-01

We couple non-linear σ-models to Liouville gravity, showing that integrability properties of symmetric space models still hold for the matter sector. Using similar arguments for the fermionic counterpart, namely Gross-Neveu-type models, we verify that such conclusions must also hold for them, as recently suggested. (author). 18 refs

Development of a tree shrew metabolic syndrome model and use of umbilical cord mesenchymal stem cell transplantation for treatment.

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Pan, Xing-Hua; Zhu, Lu; Yao, Xiang; Liu, Ju-Fen; Li, Zi-An; Yang, Jian-Yong; Pang, Rong-Qing; Ruan, Guang-Ping

2016-12-01

The aim of this study was to establish a tree shrew metabolic syndrome model and demonstrate the utility of MSCs in treating metabolic syndrome. We used tree shrew umbilical cord mesenchymal stem cell (TS-UC-MSC) transplantation for the treatment of metabolic syndrome to demonstrate the clinical application of these stem cells and to provide a theoretical basis and reference methods for this treatment. Tree shrew metabolic syndrome model showed significant insulin resistance, high blood sugar, lipid metabolism disorders, and hypertension, consistent with the diagnostic criteria. TS-UC-MSC transplantation at 16 weeks significantly reduced blood sugar and lipid levels, improved insulin resistance and the regulation of insulin secretion, and reduced the expression levels of the pro-inflammatory cytokines IL-1 and IL-6 (P metabolic syndrome model and showed that MSC migrate in diseased organs and can attenuate metabolic syndrome severity in a tree shrew model.

Modifying factors for metabolic parameters

International Nuclear Information System (INIS)

Inaba, Jiro

1990-01-01

Studies on factors which influence the metabolic parameter for calculation of radiation doses from intakes of radionuclides are very important for estimation of the doses for the general public, because the present procedures recommended by the International Commission on Radiological Protection is for occupationally exposed workers and the underlying metabolic and dosimetric models have been developed from studies on adult man and experiments on adult animals and from observations on radionuclides in physico-chemically simple form. Many factors have been reported to influence the metabolic parameters. Among them, the food-chain involvement of radionuclides and the age-dependence in humans and animals are most significant as environmental and physiological factor, respectively. In connection with the age-dependence of dose calculation, the ICRP started a new programme. They organized a Task Group on Age-Dependent Dose-Factors where relevant information on metabolic and biokinetic parameters are presently being reviewed for development of a set of dose factors for the following age-groups: infant, 1-year-old, 5-year-old, 10-year-old, 15-year-old, and ICRP Reference Man. The first stage of the work is for age-dependent integrated organ and effective dose factors for radioisotopes of the following elements: hydrogen, carbon, iodine, cesium, strontium, plutonium and americium. (author)

Novel insights into iron metabolism by integrating deletome and transcriptome analysis in an iron deficiency model of the yeast Saccharomyces cerevisiae

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Arkin Adam P

2009-03-01

Full Text Available Abstract Background Iron-deficiency anemia is the most prevalent form of anemia world-wide. The yeast Saccharomyces cerevisiae has been used as a model of cellular iron deficiency, in part because many of its cellular pathways are conserved. To better understand how cells respond to changes in iron availability, we profiled the yeast genome with a parallel analysis of homozygous deletion mutants to identify essential components and cellular processes required for optimal growth under iron-limited conditions. To complement this analysis, we compared those genes identified as important for fitness to those that were differentially-expressed in the same conditions. The resulting analysis provides a global perspective on the cellular processes involved in iron metabolism. Results Using functional profiling, we identified several genes known to be involved in high affinity iron uptake, in addition to novel genes that may play a role in iron metabolism. Our results provide support for the primary involvement in iron homeostasis of vacuolar and endosomal compartments, as well as vesicular transport to and from these compartments. We also observed an unexpected importance of the peroxisome for growth in iron-limited media. Although these components were essential for growth in low-iron conditions, most of them were not differentially-expressed. Genes with altered expression in iron deficiency were mainly associated with iron uptake and transport mechanisms, with little overlap with those that were functionally required. To better understand this relationship, we used expression-profiling of selected mutants that exhibited slow growth in iron-deficient conditions, and as a result, obtained additional insight into the roles of CTI6, DAP1, MRS4 and YHR045W in iron metabolism. Conclusion Comparison between functional and gene expression data in iron deficiency highlighted the complementary utility of these two approaches to identify important functional

Neuroinflammatory basis of metabolic syndrome.

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Purkayastha, Sudarshana; Cai, Dongsheng

2013-10-05

Inflammatory reaction is a fundamental defense mechanism against threat towards normal integrity and physiology. On the other hand, chronic diseases such as obesity, type 2 diabetes, hypertension and atherosclerosis, have been causally linked to chronic, low-grade inflammation in various metabolic tissues. Recent cross-disciplinary research has led to identification of hypothalamic inflammatory changes that are triggered by overnutrition, orchestrated by hypothalamic immune system, and sustained through metabolic syndrome-associated pathophysiology. While continuing research is actively trying to underpin the identity and mechanisms of these inflammatory stimuli and actions involved in metabolic syndrome disorders and related diseases, proinflammatory IκB kinase-β (IKKβ), the downstream nuclear transcription factor NF-κB and some related molecules in the hypothalamus were discovered to be pathogenically significant. This article is to summarize recent progresses in the field of neuroendocrine research addressing the central integrative role of neuroinflammation in metabolic syndrome components ranging from obesity, glucose intolerance to cardiovascular dysfunctions.

Development of an Experimental Model of Diabetes Co-Existing with Metabolic Syndrome in Rats.

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Suman, Rajesh Kumar; Ray Mohanty, Ipseeta; Borde, Manjusha K; Maheshwari, Ujwala; Deshmukh, Y A

2016-01-01

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.

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.

Systems biology from micro-organisms to human metabolic diseases: the role of detailed kinetic models.

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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.

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

diagnosis. We applied this method to predict breast cancer occurrence, in combination with correlation feature selection (CFS) and classification methods. Results: The resulting all-stage and early-stage diagnosis models are highly accurate in two sets of testing blood samples, with average AUCs (Area Under.......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...... 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...

Metabolic fate of 14-C-fenitrothion in a rice field model ecosystem

International Nuclear Information System (INIS)

Nashriyah binti Mat; Nambu, K.; Miyashita, T.; Sakata, S.; Ohshima, M.

1991-01-01

Pesticide fenitrothion (Sumithion sup R)is widely used to control rice stem borer and other pests. Its metabolic fate and degradation was studied using the sup 14 C-ring labelled fenitrothion in a model ecosystem consisting of Takarazuka paddy field soil, rice plant (Oryza sativa var. nihonbare), carp fish (Cyprinus carpio L.) and dechlorinated water. Radioactive fenitrothion was applied at a normal rate as used by Japanese farmers and samples of rice plant, fish soil and water were analysed after ten days of application. Fenitrothion was readily metabolized in rice plant and fish and also readily degraded to a number of metabolites in water and flooded soil. Most of the radioactivity applied was found in the soil component of the ecosystem. A trace amount of fenitrooxon, the activated metabolite of fenitrothion was detected only in soil and water. A possible metabolic pathway of fenitrothion in the rice model ecosystem was proposed

Two-Scale 13C Metabolic Flux Analysis for Metabolic Engineering.

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Ando, David; Garcia Martin, Hector

2018-01-01

Accelerating the Design-Build-Test-Learn (DBTL) cycle in synthetic biology is critical to achieving rapid and facile bioengineering of organisms for the production of, e.g., biofuels and other chemicals. The Learn phase involves using data obtained from the Test phase to inform the next Design phase. As part of the Learn phase, mathematical models of metabolic fluxes give a mechanistic level of comprehension to cellular metabolism, isolating the principle drivers of metabolic behavior from the peripheral ones, and directing future experimental designs and engineering methodologies. Furthermore, the measurement of intracellular metabolic fluxes is specifically noteworthy as providing a rapid and easy-to-understand picture of how carbon and energy flow throughout the cell. Here, we present a detailed guide to performing metabolic flux analysis in the Learn phase of the DBTL cycle, where we show how one can take the isotope labeling data from a 13 C labeling experiment and immediately turn it into a determination of cellular fluxes that points in the direction of genetic engineering strategies that will advance the metabolic engineering process.For our modeling purposes we use the Joint BioEnergy Institute (JBEI) Quantitative Metabolic Modeling (jQMM) library, which provides an open-source, python-based framework for modeling internal metabolic fluxes and making actionable predictions on how to modify cellular metabolism for specific bioengineering goals. It presents a complete toolbox for performing different types of flux analysis such as Flux Balance Analysis, 13 C Metabolic Flux Analysis, and it introduces the capability to use 13 C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale 13 C Metabolic Flux Analysis (2S- 13 C MFA) [1]. In addition to several other capabilities, the jQMM is also able to predict the effects of knockouts using the MoMA and ROOM methodologies. The use of the jQMM library is

Renewed mer model of integral management

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Janko Belak

2015-12-01

Full Text Available Background: The research work on entrepreneurship, enterprise's policy and management, which started in 1992, successfully continued in the following years. Between 1992 and 2011, more than 400 academics and other researchers have participated in research work (MER research program whose main orientation has been the creation of their own model of integral management. Results: In past years, academics (researchers and authors of published papers from Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Byelorussia, Canada, the Czech Republic, Croatia, Estonia, France, Germany, Hungary, Italy, Poland, Romania, Russia, the Slovak Republic, Slovenia, Switzerland, Ukraine, and the US have cooperated in MER programs, coming from more than fifty institutions. Thus, scientific doctrines of different universities influenced the development of the MER model which is based on both horizontal and vertical integration of the enterprises' governance and management processes, instruments and institutions into a consistently operating unit. Conclusions: The presented MER model is based on the multi-layer integration of governance and management with an enterprise and its environment, considering the fundamental desires for the enterprises' existence and, thus, their quantitative as well as qualitative changes. The process, instrumental, and institutional integrity of the governance and management is also the initial condition for the implementation of all other integration factors.

Integrated Modelling - the next steps (Invited)

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Moore, R. V.

2010-12-01

Integrated modelling (IM) has made considerable advances over the past decade but it has not yet been taken up as an operational tool in the way that its proponents had hoped. The reasons why will be discussed in Session U17. This talk will propose topics for a research and development programme and suggest an institutional structure which, together, could overcome the present obstacles. Their combined aim would be first to make IM into an operational tool useable by competent public authorities and commercial companies and, in time, to see it evolve into the modelling equivalent of Google Maps, something accessible and useable by anyone with a PC or an iphone and an internet connection. In a recent study, a number of government agencies, water authorities and utilities applied integrated modelling to operational problems. While the project demonstrated that IM could be used in an operational setting and had benefit, it also highlighted the advances that would be required for its widespread uptake. These were: greatly improving the ease with which models could be a) made linkable, b) linked and c) run; developing a methodology for applying integrated modelling; developing practical options for calibrating and validating linked models; addressing the science issues that arise when models are linked; extending the range of modelling concepts that can be linked; enabling interface standards to pass uncertainty information; making the interface standards platform independent; extending the range of platforms to include those for high performance computing; developing the concept of modelling components as web services; separating simulation code from the model’s GUI, so that all the results from the linked models can be viewed through a single GUI; developing scenario management systems so that that there is an audit trail of the version of each model and dataset used in each linked model run. In addition to the above, there is a need to build a set of integrated

Influence of integral and decaffeinated coffee brews on metabolic parameters of rats fed with hiperlipidemic diets

Directory of Open Access Journals (Sweden)

Júlia Ariana de Souza Gomes

2013-10-01

Full Text Available The objective of this study was to evaluate the influence of integral and decaffeinated coffee brews (Coffea arabica L and C. canephora Pierre on the metabolic parameters of rats fed with hyperlipidemic diet. Thirty male Wistar rats (initial weight of 270 g ± 20 g were used in the study, which were divided into six groups five each. The treatments were normal diet, hyperlipidemic diet, hyperlipidemic diet associated with integral coffee arabica or canephora brews (7.2 mL/kg/day and hyperlipidemic diet associated to decaffeinated arabica, or canephora brews, using the same dosage. After 41 days, performance analyses were conducted.The rats were then euthanized and the carcasses were used for the analysis of dried ether extract and crude protein. Fractions of adipose tissue were processed for histological analysis. There was a reduction in weight gain and accumulation of lipids in the carcasses, lower diameter of adipocytes and a lower relative weight of the liver and kidneys of rats fed with hyperlipidemic diet associated with integral coffee brew. Integral coffee brew reduced the obesity in the rats receiving hyperlipidemic diet, but the same effect did not occur with the decaffeinated types.

A joint-space numerical model of metabolic energy expenditure for human multibody dynamic system.

Science.gov (United States)

Kim, Joo H; Roberts, Dustyn

2015-09-01

Metabolic energy expenditure (MEE) is a critical performance measure of human motion. In this study, a general joint-space numerical model of MEE is derived by integrating the laws of thermodynamics and principles of multibody system dynamics, which can evaluate MEE without the limitations inherent in experimental measurements (phase delays, steady state and task restrictions, and limited range of motion) or muscle-space models (complexities and indeterminacies from excessive DOFs, contacts and wrapping interactions, and reliance on in vitro parameters). Muscle energetic components are mapped to the joint space, in which the MEE model is formulated. A constrained multi-objective optimization algorithm is established to estimate the model parameters from experimental walking data also used for initial validation. The joint-space parameters estimated directly from active subjects provide reliable MEE estimates with a mean absolute error of 3.6 ± 3.6% relative to validation values, which can be used to evaluate MEE for complex non-periodic tasks that may not be experimentally verifiable. This model also enables real-time calculations of instantaneous MEE rate as a function of time for transient evaluations. Although experimental measurements may not be completely replaced by model evaluations, predicted quantities can be used as strong complements to increase reliability of the results and yield unique insights for various applications. Copyright © 2015 John Wiley & Sons, Ltd.

Mitochondrial metabolism in early neural fate and its relevance for neuronal disease modeling.

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Lorenz, Carmen; Prigione, Alessandro

2017-12-01

Modulation of energy metabolism is emerging as a key aspect associated with cell fate transition. The establishment of a correct metabolic program is particularly relevant for neural cells given their high bioenergetic requirements. Accordingly, diseases of the nervous system commonly involve mitochondrial impairment. Recent studies in animals and in neural derivatives of human pluripotent stem cells (PSCs) highlighted the importance of mitochondrial metabolism for neural fate decisions in health and disease. The mitochondria-based metabolic program of early neurogenesis suggests that PSC-derived neural stem cells (NSCs) may be used for modeling neurological disorders. Understanding how metabolic programming is orchestrated during neural commitment may provide important information for the development of therapies against conditions affecting neural functions, including aging and mitochondrial disorders. Copyright © 2017. Published by Elsevier Ltd.

Metabolic network model guided engineering ethylmalonyl-CoA pathway to improve ascomycin production in Streptomyces hygroscopicus var. ascomyceticus.

Science.gov (United States)

Wang, Junhua; Wang, Cheng; Song, Kejing; Wen, Jianping

2017-10-03

Ascomycin is a 23-membered polyketide macrolide with high immunosuppressant and antifungal activity. As the lower production in bio-fermentation, global metabolic analysis is required to further explore its biosynthetic network and determine the key limiting steps for rationally engineering. To achieve this goal, an engineering approach guided by a metabolic network model was implemented to better understand ascomycin biosynthesis and improve its production. The metabolic conservation of Streptomyces species was first investigated by comparing the metabolic enzymes of Streptomyces coelicolor A3(2) with those of 31 Streptomyces strains, the results showed that more than 72% of the examined proteins had high sequence similarity with counterparts in every surveyed strain. And it was found that metabolic reactions are more highly conserved than the enzymes themselves because of its lower diversity of metabolic functions than that of genes. The main source of the observed metabolic differences was from the diversity of secondary metabolism. According to the high conservation of primary metabolic reactions in Streptomyces species, the metabolic network model of Streptomyces hygroscopicus var. ascomyceticus was constructed based on the latest reported metabolic model of S. coelicolor A3(2) and validated experimentally. By coupling with flux balance analysis and using minimization of metabolic adjustment algorithm, potential targets for ascomycin overproduction were predicted. Since several of the preferred targets were highly associated with ethylmalonyl-CoA biosynthesis, two target genes hcd (encoding 3-hydroxybutyryl-CoA dehydrogenase) and ccr (encoding crotonyl-CoA carboxylase/reductase) were selected for overexpression in S. hygroscopicus var. ascomyceticus FS35. Both the mutants HA-Hcd and HA-Ccr showed higher ascomycin titer, which was consistent with the model predictions. Furthermore, the combined effects of the two genes were evaluated and the strain HA

Data requirements for integrated near field models

International Nuclear Information System (INIS)

Wilems, R.E.; Pearson, F.J. Jr.; Faust, C.R.; Brecher, A.

1981-01-01

The coupled nature of the various processes in the near field require that integrated models be employed to assess long term performance of the waste package and repository. The nature of the integrated near field models being compiled under the SCEPTER program are discussed. The interfaces between these near field models and far field models are described. Finally, near field data requirements are outlined in sufficient detail to indicate overall programmatic guidance for data gathering activities

Effect of Combined Exercise Versus Aerobic-Only Training on Skeletal Muscle Lipid Metabolism in a Rodent Model of Type1 Diabetes.

Science.gov (United States)

Dotzert, Michelle S; McDonald, Matthew W; Murray, Michael R; Nickels, J Zachary; Noble, Earl G; Melling, C W James

2017-12-04

Abnormal skeletal muscle lipid metabolism is associated with insulin resistance in people with type 1 diabetes. Although lipid metabolism is restored with aerobic exercise training, the risk for postexercise hypoglycemia is increased with this modality. Integrating resistance and aerobic exercise is associated with reduced hypoglycemic risk; however, the effects of this exercise modality on lipid metabolism and insulin resistance remain unknown. We compared the effects of combined (aerobic + resistance) versus aerobic exercise training on oxidative capacity and muscle lipid metabolism in a rat model of type 1 diabetes. Male Sprague-Dawley rats were divided into 4 groups: sedentary control (C), sedentary control + diabetes (CD), diabetes + high-intensity aerobic exercise (DAE) and diabetes + combined aerobic and resistance exercise (DARE). Following diabetes induction (20 mg/kg streptozotocin over five days), DAE rats ran for 12 weeks (5 days/week for 1 hour) on a motorized treadmill (27 m/min at a 6-degree grade), and DARE rats alternated daily between running and incremental weighted ladder climbing. After training, DAE showed reduced muscle CD36 protein content and lipid content compared to CD (p≤0.05). DAE rats also had significantly increased citrate synthase (CS) activity compared to CD (p≤0.05). DARE rats showed reduced CD36 protein content compared to CD and increased CS activity compared to CD and DAE rats (p≤0.05). DARE rats demonstrated increased skeletal muscle lipid staining, elevated lipin-1 protein content and insulin sensitivity (p≤0.05). Integration of aerobic and resistance exercise may exert a synergistic effect, producing adaptations characteristic of the "athlete's paradox," including increased capacity to store and oxidize lipids. Crown Copyright © 2017. Published by Elsevier Inc. All rights reserved.

Metabolic-dopaminergic mapping of the 6-hydroxydopamine rat model for Parkinson's disease

International Nuclear Information System (INIS)

Casteels, Cindy; Lauwers, Erwin; Baekelandt, Veerle; Bormans, Guy; Laere, Koen van

2008-01-01

The unilateral 6-hydroxydopamine (6-OHDA) lesion rat model is a well-known acute model for Parkinson's disease (PD). Its validity has been supported by invasive histology, behavioral studies and electrophysiology. Here, we have characterized this model in vivo by multitracer imaging [glucose metabolism and dopamine transporter (DAT)] in relation to behavioral and histological parameters. Eighteen female adult Wistar rats (eight 6-OHDA-lesioned, ten controls) were investigated using multitracer [ 18 F]-fluoro-2-deoxy-D-glucose (FDG) and [ 18 F]-FECT 2'-[ 18 F]-fluoroethyl-(1R-2-exo-3-exe)-8-methyl-3-(4-chlorophenyl)- 8-azabicyclo (3.2.1)-octane-2-carboxylate small animal positron emission tomography (PET). Relative glucose metabolism and parametric DAT binding images were anatomically standardized to Paxinos space and analyzed on a voxel-basis using SPM2, supplemented by a template-based predefined volumes-of-interest approach. Behavior was characterized by the limb-use asymmetry test; dopaminergic innervation was validated by in vitro tyrosine hydroxylase staining. In the 6-OHDA model, significant glucose hypometabolism is present in the ipsilateral sensory-motor cortex (-6.3%; p = 4 x 10 -6 ). DAT binding was severely decreased in the ipsilateral caudate-putamen, nucleus accumbens and substantia nigra (all p -9 ), as confirmed by the behavioral and histological outcomes. Correlation analysis revealed a positive relationship between the degree of DAT impairment and the change in glucose metabolism in the ipsilateral hippocampus (p = 3 x 10 -5 ), while cerebellar glucose metabolism was inversely correlated to the level of DAT impairment (p -4 ). In vivo cerebral mapping of 6-OHDA-lesioned rats using [ 18 F ]-FDG and [ 18 F ]-FECT small animal PET shows molecular-functional correspondence to the cortico-subcortical network impairments observed in PD patients. This provides a further molecular validation supporting the validity of the 6-OHDA lesion model to mimic

Ruminant Metabolic Systems Biology: Reconstruction and Integration of Transcriptome Dynamics Underlying Functional Responses of Tissues to Nutrition and Physiological Statea

Science.gov (United States)

Bionaz, Massimo; Loor, Juan J.

2012-01-01

High-throughput ‘omics’ data analysis via bioinformatics is one key component of the systems biology approach. The systems approach is particularly well-suited for the study of the interactions between nutrition and physiological state with tissue metabolism and functions during key life stages of organisms such as the transition from pregnancy to lactation in mammals, ie, the peripartal period. In modern dairy cows with an unprecedented genetic potential for milk synthesis, the nature of the physiologic and metabolic adaptations during the peripartal period is multifaceted and involves key tissues such as liver, adipose, and mammary. In order to understand such adaptation, we have reviewed several works performed in our and other labs. In addition, we have used a novel bioinformatics approach, Dynamic Impact Approach (DIA), in combination with partly previously published data to help interpret longitudinal biological adaptations of bovine liver, adipose, and mammary tissue to lactation using transcriptomics datasets. Use of DIA with transcriptomic data from those tissues during normal physiological adaptations and in animals fed different levels of energy prepartum allowed visualization and integration of most-impacted metabolic pathways around the time of parturition. The DIA is a suitable tool for applying the integrative systems biology approach. The ultimate goal is to visualize the complexity of the systems at study and uncover key molecular players involved in the tissue’s adaptations to physiological state or nutrition. PMID:22807626

Simultaneous Parameters Identifiability and Estimation of an E. coli Metabolic Network Model

Directory of Open Access Journals (Sweden)

Kese Pontes Freitas Alberton

2015-01-01

Full Text Available This work proposes a procedure for simultaneous parameters identifiability and estimation in metabolic networks in order to overcome difficulties associated with lack of experimental data and large number of parameters, a common scenario in the modeling of such systems. As case study, the complex real problem of parameters identifiability of the Escherichia coli K-12 W3110 dynamic model was investigated, composed by 18 differential ordinary equations and 35 kinetic rates, containing 125 parameters. With the procedure, model fit was improved for most of the measured metabolites, achieving 58 parameters estimated, including 5 unknown initial conditions. The results indicate that simultaneous parameters identifiability and estimation approach in metabolic networks is appealing, since model fit to the most of measured metabolites was possible even when important measures of intracellular metabolites and good initial estimates of parameters are not available.

Age-dependent metabolic model of radionuclides in Human body

International Nuclear Information System (INIS)

Ye Changqing

1986-01-01

Age-dependent metabolic model of radionuclides in human body was introduced briefly. These data are necessary in setting up the secondary dose limit of internal exposure of the general public. For the gastro-intestinal tract model, it was shown that the dose of various sections of GI tract caused by unsoluble radioactive materials were influenced by the mass of section and mean residence time, both of which are age-dependent, but the absorption fraction f 1 through gastro-intestinal tract should be corrected only for the infant less than 1 year of age. For the lung model, it was indicated that the fraction of deposition or clearance of particles in the different compartments of lung were related to age. The doses of tracheobronchial and pulmonary compartment of adult for 222 Rn or 220 Rn with their decay products were one third of that of 6-years old child who received the maximum dose in comparison with other ages. The age-dependent metabolic models in organ and/or body of Tritium, Iodine-131, Caesium-137, radioactive Strontium, Radium and Plutonium were reported. A generalized approach for estimating the effect of age on deposition fractions and retention half-time were presented. Calculated results indicated that younger ages were characterized by increased deposition fraction and decreased half-time for retention. Representative examples were provided for 21 elements of current interest in health physics

Ontology modeling in physical asset integrity management

CERN Document Server

Yacout, Soumaya

2015-01-01

This book presents cutting-edge applications of, and up-to-date research on, ontology engineering techniques in the physical asset integrity domain. Though a survey of state-of-the-art theory and methods on ontology engineering, the authors emphasize essential topics including data integration modeling, knowledge representation, and semantic interpretation. The book also reflects novel topics dealing with the advanced problems of physical asset integrity applications such as heterogeneity, data inconsistency, and interoperability existing in design and utilization. With a distinctive focus on applications relevant in heavy industry, Ontology Modeling in Physical Asset Integrity Management is ideal for practicing industrial and mechanical engineers working in the field, as well as researchers and graduate concerned with ontology engineering in physical systems life cycles. This book also: Introduces practicing engineers, research scientists, and graduate students to ontology engineering as a modeling techniqu...

Integrated proteomics and metabolomics suggests symbiotic metabolism and multimodal regulation in a fungal-endobacterial system.

Science.gov (United States)

Li, Zhou; Yao, Qiuming; Dearth, Stephen P; Entler, Matthew R; Castro Gonzalez, Hector F; Uehling, Jessie K; Vilgalys, Rytas J; Hurst, Gregory B; Campagna, Shawn R; Labbé, Jessy L; Pan, Chongle

2017-03-01

Many plant-associated fungi host endosymbiotic endobacteria with reduced genomes. While endobacteria play important roles in these tri-partite plant-fungal-endobacterial systems, the active physiology of fungal endobacteria has not been characterized extensively by systems biology approaches. Here, we use integrated proteomics and metabolomics to characterize the relationship between the endobacterium Mycoavidus sp. and the root-associated fungus Mortierella elongata. In nitrogen-poor media, M. elongata had decreased growth but hosted a large and growing endobacterial population. The active endobacterium likely extracted malate from the fungal host as the primary carbon substrate for energy production and biosynthesis of phospho-sugars, nucleobases, peptidoglycan and some amino acids. The endobacterium obtained nitrogen by importing a variety of nitrogen-containing compounds. Further, nitrogen limitation significantly perturbed the carbon and nitrogen flows in the fungal metabolic network. M. elongata regulated many pathways by concordant changes on enzyme abundances, post-translational modifications, reactant concentrations and allosteric effectors. Such multimodal regulations may be a general mechanism for metabolic modulation. © 2016 Society for Applied Microbiology and John Wiley & Sons Ltd.

Integrated Main Propulsion System Performance Reconstruction Process/Models

Science.gov (United States)

Lopez, Eduardo; Elliott, Katie; Snell, Steven; Evans, Michael

2013-01-01

The Integrated Main Propulsion System (MPS) Performance Reconstruction process provides the MPS post-flight data files needed for postflight reporting to the project integration management and key customers to verify flight performance. This process/model was used as the baseline for the currently ongoing Space Launch System (SLS) work. The process utilizes several methodologies, including multiple software programs, to model integrated propulsion system performance through space shuttle ascent. It is used to evaluate integrated propulsion systems, including propellant tanks, feed systems, rocket engine, and pressurization systems performance throughout ascent based on flight pressure and temperature data. The latest revision incorporates new methods based on main engine power balance model updates to model higher mixture ratio operation at lower engine power levels.

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.

Principles of integrated modeling of coal seam mining

Energy Technology Data Exchange (ETDEWEB)

Magda, R

1983-01-01

Mathematical modeling of underground coal mining is discussed. Construction of a mathematical model of an underground mine is analyzed. The model is based on integrating the elementary units (modules). A so-called elementary mining field is defined with the example of a longwall face. A model of an elementary coal seam zone is constructed by integrating the elementary mining fields (in time and space) and supplementing them with a suitable model of mine roadway structure. By integrating the elementary coal seam zones a model of mining level is constructed. Such a mathematical model is used for optimizing the selected mining parameters e.g. structure of mine roadways, size of a coal mine, and organizational scheme of underground mining in a mine or in a mine section using the standardized optimization criterion e.g. investment. Use of the integration model of underground mining for optimizing coal mine construction is evaluated. The following elements of investment and operating cost are considered: shaft excavation, shaft equipment, investment in mining sections, ventilation, mine draining etc. 1 reference.

The Xenopus oocyte: a model for studying the metabolic regulation of cancer cell death.

Science.gov (United States)

Nutt, Leta K

2012-06-01

Abnormal metabolism and the evasion of apoptosis are both considered hallmarks of cancer. A remarkable biochemical model system, the Xenopus laevis oocyte, exhibits altered metabolism coupled to its apoptotic machinery in a similar fashion to cancer cells. This review considers the theory that these two hallmarks of cancer are coupled in tumor cells and provides strong proof that the Xenopus laevis oocyte system is an appropriate model in which to dissect the biochemical events underlying the connection between the two hallmarks. By further elucidating the mechanisms through which metabolism suppresses apoptotic machinery, we may gain a better understanding about how normal cells transform into cancer cells. Copyright © 2012 Elsevier Ltd. All rights reserved.

Integrating systems biology models and biomedical ontologies.

Science.gov (United States)

Hoehndorf, Robert; Dumontier, Michel; Gennari, John H; Wimalaratne, Sarala; de Bono, Bernard; Cook, Daniel L; Gkoutos, Georgios V

2011-08-11

Systems biology is an approach to biology that emphasizes the structure and dynamic behavior of biological systems and the interactions that occur within them. To succeed, systems biology crucially depends on the accessibility and integration of data across domains and levels of granularity. Biomedical ontologies were developed to facilitate such an integration of data and are often used to annotate biosimulation models in systems biology. We provide a framework to integrate representations of in silico systems biology with those of in vivo biology as described by biomedical ontologies and demonstrate this framework using the Systems Biology Markup Language. We developed the SBML Harvester software that automatically converts annotated SBML models into OWL and we apply our software to those biosimulation models that are contained in the BioModels Database. We utilize the resulting knowledge base for complex biological queries that can bridge levels of granularity, verify models based on the biological phenomenon they represent and provide a means to establish a basic qualitative layer on which to express the semantics of biosimulation models. We establish an information flow between biomedical ontologies and biosimulation models and we demonstrate that the integration of annotated biosimulation models and biomedical ontologies enables the verification of models as well as expressive queries. Establishing a bi-directional information flow between systems biology and biomedical ontologies has the potential to enable large-scale analyses of biological systems that span levels of granularity from molecules to organisms.

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...

Photosynthetic Entrainment of the Circadian Clock Facilitates Plant Growth under Environmental Fluctuations: Perspectives from an Integrated Model of Phase Oscillator and Phloem Transportation

Directory of Open Access Journals (Sweden)

Takayuki Ohara

2017-10-01

Full Text Available Plants need to avoid carbon starvation and resultant growth inhibition under fluctuating light environments to ensure optimal growth and reproduction. As diel patterns of carbon metabolism are influenced by the circadian clock, appropriate regulation of the clock is essential for plants to properly manage their carbon resources. For proper adjustment of the circadian phase, higher plants utilize environmental signals such as light or temperature and metabolic signals such as photosynthetic products; the importance of the latter as phase regulators has been recently elucidated. A mutant of Arabidopsis thaliana that is deficient in phase response to sugar has been shown, under fluctuating light conditions, to be unable to adjust starch turnover and to realize carbon homeostasis. Whereas, the effects of light entrainment on growth and survival of higher plants are well studied, the impact of phase regulation by sugar remains unknown. Here we show that endogenous sugar entrainment facilitates plant growth. We integrated two mathematical models, one describing the dynamics of carbon metabolism in A. thaliana source leaves and the other growth of sink tissues dependent on sucrose translocation from the source. The integrated model predicted that sugar-sensitive plants grow faster than sugar-insensitive plants under constant as well as changing photoperiod conditions. We found that sugar entrainment enables efficient carbon investment for growth by stabilizing sucrose supply to sink tissues. Our results highlight the importance of clock entrainment by both exogenous and endogenous signals for optimizing growth and increasing fitness.