WorldWideScience

Sample records for biological models

  1. Laboratory of Biological Modeling

    Data.gov (United States)

    Federal Laboratory Consortium — The Laboratory of Biological Modeling is defined by both its methodologies and its areas of application. We use mathematical modeling in many forms and apply it to...

  2. Laboratory of Biological Modeling

    Data.gov (United States)

    Federal Laboratory Consortium — The Laboratory of Biological Modeling is defined by both its methodologies and its areas of application. We use mathematical modeling in many forms and apply it to a...

  3. ECO-BIOLOGICAL SYSTEM MODELING

    Directory of Open Access Journals (Sweden)

    T. I. Burak

    2015-01-01

    Full Text Available The methodology for computer modeling of complex eco-biological models is presented in this paper. It is based on system approach of J. Forrester. Developed methodology is universal for complex ecological and biological systems. Modeling algorithm considers specialties of eco-biological systems and shows adequate and accurate results in practice. 

  4. Validation of systems biology models

    NARCIS (Netherlands)

    Hasdemir, D.

    2015-01-01

    The paradigm shift from qualitative to quantitative analysis of biological systems brought a substantial number of modeling approaches to the stage of molecular biology research. These include but certainly are not limited to nonlinear kinetic models, static network models and models obtained by the

  5. Issues in Biological Shape Modelling

    DEFF Research Database (Denmark)

    Hilger, Klaus Baggesen

    This talk reflects parts of the current research at informatics and Mathematical Modelling at the Technical University of Denmark within biological shape modelling. We illustrate a series of generalizations, modifications, and applications of the elements of constructing models of shape or appear......This talk reflects parts of the current research at informatics and Mathematical Modelling at the Technical University of Denmark within biological shape modelling. We illustrate a series of generalizations, modifications, and applications of the elements of constructing models of shape...

  6. Mathematical modeling of biological processes

    CERN Document Server

    Friedman, Avner

    2014-01-01

    This book on mathematical modeling of biological processes includes a wide selection of biological topics that demonstrate the power of mathematics and computational codes in setting up biological processes with a rigorous and predictive framework. Topics include: enzyme dynamics, spread of disease, harvesting bacteria, competition among live species, neuronal oscillations, transport of neurofilaments in axon, cancer and cancer therapy, and granulomas. Complete with a description of the biological background and biological question that requires the use of mathematics, this book is developed for graduate students and advanced undergraduate students with only basic knowledge of ordinary differential equations and partial differential equations; background in biology is not required. Students will gain knowledge on how to program with MATLAB without previous programming experience and how to use codes in order to test biological hypothesis.

  7. [Biological mutualism, concepts and models].

    Science.gov (United States)

    Perru, Olivier

    2011-01-01

    Mutualism is a biological association for a mutual benefit between two different species. In this paper, firstly, we examine the history and signification of mutualism in relation to symbiosis. Then, we consider the link between concepts and models of mutualism. Models of mutualism depend on different concepts we use: If mutualism is situated at populations' level, it will be expressed by Lotka-Volterra models, concerning exclusively populations' size. If mutualism is considered as a resources' exchange or a biological market increasing the fitness of these organisms, it will be described at an individual level by a cost-benefit model. Our analysis will be limited to the history and epistemology of Lotka-Volterra models and we hypothesize that these models are adapted at first to translate dynamic evolutions of mutualism. They render stability or variations of size and assume that there are clear distinctions and a state of equilibrium between populations of different species. Italian mathematician Vito Volterra demonstrated that biological associations consist in a constant relation between some species. In 1931 and 1935, Volterra described the general form of antagonistic or mutualistic biological associations by the same differential equations. We recognize that these equations have been more used to model competition or prey-predator interactions, but a simple sign change allows describing mutualism. The epistemological problem is the following: Volterra's equations help us to conceptualize a global phenomenon. However, mutualistic interactions may have stronger effects away from equilibrium and these effects may be better understood at individual level. We conclude that, between 1985 and 2000, some researchers carried on working and converting Lotka-Volterra models but this description appeared as insufficient. So, other researchers adopted an economical viewpoint, considering mutualism as a biological market.

  8. Kinetic Modeling of Biological Systems

    Energy Technology Data Exchange (ETDEWEB)

    Resat, Haluk; Petzold, Linda; Pettigrew, Michel F.

    2009-04-21

    The dynamics of how its constituent components interact define the spatio-temporal response of a natural system to stimuli. Modeling the kinetics of the processes that represent a biophysical system has long been pursued with the aim of improving our understanding of the studied system. Due to the unique properties of biological systems, in addition to the usual difficulties faced in modeling the dynamics of physical or chemical systems, biological simulations encounter difficulties that result from intrinsic multiscale and stochastic nature of the biological processes. This chapter discusses the implications for simulation of models involving interacting species with very low copy numbers, which often occur in biological systems and give rise to significant relative fluctuations. The conditions necessitating the use of stochastic kinetic simulation methods and the mathematical foundations of the stochastic simulation algorithms are presented. How the well-organized structural hierarchies often seen in biological systems can lead to multiscale problems, and possible ways to address the encountered computational difficulties are discussed. We present the details of the existing kinetic simulation methods, and discuss their strengths and shortcomings. A list of the publicly available kinetic simulation tools and our reflections for future prospects are also provided.

  9. Mathematical models in biological discovery

    CERN Document Server

    Walter, Charles

    1977-01-01

    When I was asked to help organize an American Association for the Advancement of Science symposium about how mathematical models have con­ tributed to biology, I agreed immediately. The subject is of immense importance and wide-spread interest. However, too often it is discussed in biologically sterile environments by "mutual admiration society" groups of "theoreticians", many of whom have never seen, and most of whom have never done, an original scientific experiment with the biolog­ ical materials they attempt to describe in abstract (and often prejudiced) terms. The opportunity to address the topic during an annual meeting of the AAAS was irresistable. In order to try to maintain the integrity ;,f the original intent of the symposium, it was entitled, "Contributions of Mathematical Models to Biological Discovery". This symposium was organized by Daniel Solomon and myself, held during the 141st annual meeting of the AAAS in New York during January, 1975, sponsored by sections G and N (Biological and Medic...

  10. Teaching biology with model organisms

    Science.gov (United States)

    Keeley, Dolores A.

    The purpose of this study is to identify and use model organisms that represent each of the kingdoms biologists use to classify organisms, while experiencing the process of science through guided inquiry. The model organisms will be the basis for studying the four high school life science core ideas as identified by the Next Generation Science Standards (NGSS): LS1-From molecules to organisms, LS2-Ecosystems, LS3- Heredity, and LS4- Biological Evolution. NGSS also have identified four categories of science and engineering practices which include developing and using models and planning and carrying out investigations. The living organisms will be utilized to increase student interest and knowledge within the discipline of Biology. Pre-test and posttest analysis utilizing student t-test analysis supported the hypothesis. This study shows increased student learning as a result of using living organisms as models for classification and working in an inquiry-based learning environment.

  11. Time lags in biological models

    CERN Document Server

    MacDonald, Norman

    1978-01-01

    In many biological models it is necessary to allow the rates of change of the variables to depend on the past history, rather than only the current values, of the variables. The models may require discrete lags, with the use of delay-differential equations, or distributed lags, with the use of integro-differential equations. In these lecture notes I discuss the reasons for including lags, especially distributed lags, in biological models. These reasons may be inherent in the system studied, or may be the result of simplifying assumptions made in the model used. I examine some of the techniques available for studying the solution of the equations. A large proportion of the material presented relates to a special method that can be applied to a particular class of distributed lags. This method uses an extended set of ordinary differential equations. I examine the local stability of equilibrium points, and the existence and frequency of periodic solutions. I discuss the qualitative effects of lags, and how these...

  12. Integrating systems biology models and biomedical ontologies

    Directory of Open Access Journals (Sweden)

    de Bono Bernard

    2011-08-01

    Full Text Available Abstract Background 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. Results 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. Conclusions 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.

  13. Track structure in biological models.

    Science.gov (United States)

    Curtis, S B

    1986-01-01

    High-energy heavy ions in the galactic cosmic radiation (HZE particles) may pose a special risk during long term manned space flights outside the sheltering confines of the earth's geomagnetic field. These particles are highly ionizing, and they and their nuclear secondaries can penetrate many centimeters of body tissue. The three dimensional patterns of ionizations they create as they lose energy are referred to as their track structure. Several models of biological action on mammalian cells attempt to treat track structure or related quantities in their formulation. The methods by which they do this are reviewed. The proximity function is introduced in connection with the theory of Dual Radiation Action (DRA). The ion-gamma kill (IGK) model introduces the radial energy-density distribution, which is a smooth function characterizing both the magnitude and extension of a charged particle track. The lethal, potentially lethal (LPL) model introduces lambda, the mean distance between relevant ion clusters or biochemical species along the track. Since very localized energy depositions (within approximately 10 nm) are emphasized, the proximity function as defined in the DRA model is not of utility in characterizing track structure in the LPL formulation.

  14. Building multivariate systems biology models

    NARCIS (Netherlands)

    Kirwan, G.M.; Johansson, E.; Kleemann, R.; Verheij, E.R.; Wheelock, A.M.; Goto, S.; Trygg, J.; Wheelock, C.E.

    2012-01-01

    Systems biology methods using large-scale "omics" data sets face unique challenges: integrating and analyzing near limitless data space, while recognizing and removing systematic variation or noise. Herein we propose a complementary multivariate analysis workflow to both integrate "omics" data from

  15. Asmparts: assembly of biological model parts.

    Science.gov (United States)

    Rodrigo, Guillermo; Carrera, Javier; Jaramillo, Alfonso

    2007-12-01

    We propose a new computational tool to produce models of biological systems by assembling models from biological parts. Our software not only takes advantage of modularity, but it also enforces standardisation in part characterisation by considering a model of each part. We have used model parts in SBML to design transcriptional networks. Our software is open source, it works in linux and windows platforms, and it could be used to automatically produce models in a server. Our tool not only facilitates model design, but it will also help to promote the establishment of a registry of model parts.

  16. Integer Programming Models for Computational Biology Problems

    Institute of Scientific and Technical Information of China (English)

    Giuseppe Lancia

    2004-01-01

    The recent years have seen an impressive increase in the use of Integer Programming models for the solution of optimization problems originating in Molecular Biology. In this survey, some of the most successful Integer Programming approaches are described, while a broad overview of application areas being is given in modern Computational Molecular Biology.

  17. Integrating interactive computational modeling in biology curricula.

    Directory of Open Access Journals (Sweden)

    Tomáš Helikar

    2015-03-01

    Full Text Available While the use of computer tools to simulate complex processes such as computer circuits is normal practice in fields like engineering, the majority of life sciences/biological sciences courses continue to rely on the traditional textbook and memorization approach. To address this issue, we explored the use of the Cell Collective platform as a novel, interactive, and evolving pedagogical tool to foster student engagement, creativity, and higher-level thinking. Cell Collective is a Web-based platform used to create and simulate dynamical models of various biological processes. Students can create models of cells, diseases, or pathways themselves or explore existing models. This technology was implemented in both undergraduate and graduate courses as a pilot study to determine the feasibility of such software at the university level. First, a new (In Silico Biology class was developed to enable students to learn biology by "building and breaking it" via computer models and their simulations. This class and technology also provide a non-intimidating way to incorporate mathematical and computational concepts into a class with students who have a limited mathematical background. Second, we used the technology to mediate the use of simulations and modeling modules as a learning tool for traditional biological concepts, such as T cell differentiation or cell cycle regulation, in existing biology courses. Results of this pilot application suggest that there is promise in the use of computational modeling and software tools such as Cell Collective to provide new teaching methods in biology and contribute to the implementation of the "Vision and Change" call to action in undergraduate biology education by providing a hands-on approach to biology.

  18. Integrating interactive computational modeling in biology curricula.

    Science.gov (United States)

    Helikar, Tomáš; Cutucache, Christine E; Dahlquist, Lauren M; Herek, Tyler A; Larson, Joshua J; Rogers, Jim A

    2015-03-01

    While the use of computer tools to simulate complex processes such as computer circuits is normal practice in fields like engineering, the majority of life sciences/biological sciences courses continue to rely on the traditional textbook and memorization approach. To address this issue, we explored the use of the Cell Collective platform as a novel, interactive, and evolving pedagogical tool to foster student engagement, creativity, and higher-level thinking. Cell Collective is a Web-based platform used to create and simulate dynamical models of various biological processes. Students can create models of cells, diseases, or pathways themselves or explore existing models. This technology was implemented in both undergraduate and graduate courses as a pilot study to determine the feasibility of such software at the university level. First, a new (In Silico Biology) class was developed to enable students to learn biology by "building and breaking it" via computer models and their simulations. This class and technology also provide a non-intimidating way to incorporate mathematical and computational concepts into a class with students who have a limited mathematical background. Second, we used the technology to mediate the use of simulations and modeling modules as a learning tool for traditional biological concepts, such as T cell differentiation or cell cycle regulation, in existing biology courses. Results of this pilot application suggest that there is promise in the use of computational modeling and software tools such as Cell Collective to provide new teaching methods in biology and contribute to the implementation of the "Vision and Change" call to action in undergraduate biology education by providing a hands-on approach to biology.

  19. Quantum Biological Channel Modeling and Capacity Calculation

    Directory of Open Access Journals (Sweden)

    Ivan B. Djordjevic

    2012-12-01

    Full Text Available Quantum mechanics has an important role in photosynthesis, magnetoreception, and evolution. There were many attempts in an effort to explain the structure of genetic code and transfer of information from DNA to protein by using the concepts of quantum mechanics. The existing biological quantum channel models are not sufficiently general to incorporate all relevant contributions responsible for imperfect protein synthesis. Moreover, the problem of determination of quantum biological channel capacity is still an open problem. To solve these problems, we construct the operator-sum representation of biological channel based on codon basekets (basis vectors, and determine the quantum channel model suitable for study of the quantum biological channel capacity and beyond. The transcription process, DNA point mutations, insertions, deletions, and translation are interpreted as the quantum noise processes. The various types of quantum errors are classified into several broad categories: (i storage errors that occur in DNA itself as it represents an imperfect storage of genetic information, (ii replication errors introduced during DNA replication process, (iii transcription errors introduced during DNA to mRNA transcription, and (iv translation errors introduced during the translation process. By using this model, we determine the biological quantum channel capacity and compare it against corresponding classical biological channel capacity. We demonstrate that the quantum biological channel capacity is higher than the classical one, for a coherent quantum channel model, suggesting that quantum effects have an important role in biological systems. The proposed model is of crucial importance towards future study of quantum DNA error correction, developing quantum mechanical model of aging, developing the quantum mechanical models for tumors/cancer, and study of intracellular dynamics in general.

  20. Biological transportation networks: Modeling and simulation

    KAUST Repository

    Albi, Giacomo

    2015-09-15

    We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation and angiogenesis) and ion transportation networks (e.g., neural networks) is explained in detail and basic analytical features like the gradient flow structure of the fluid transportation network model and the impact of the model parameters on the geometry and topology of network formation are analyzed. We also present a numerical finite-element based discretization scheme and discuss sample cases of network formation simulations.

  1. Modeling growth in biological materials

    OpenAIRE

    Jones, Gareth Wyn; Chapman, S. Jonathan

    2012-01-01

    The biomechanical modeling of growing tissues has recently become an area of intense interest. In particular, the interplay between growth patterns and mechanical stress is of great importance, with possible applications to arterial mechanics, embryo morphogenesis, tumor development, and bone remodeling. This review aims to give an overview of the theories that have been used to model these phenomena, categorized according to whether the tissue is considered as a continuum object or a collect...

  2. From biological membranes to biomimetic model membranes

    Directory of Open Access Journals (Sweden)

    Eeman, M.

    2010-01-01

    Full Text Available Biological membranes play an essential role in the cellular protection as well as in the control and the transport of nutrients. Many mechanisms such as molecular recognition, enzymatic catalysis, cellular adhesion and membrane fusion take place into the biological membranes. In 1972, Singer et al. provided a membrane model, called fluid mosaic model, in which each leaflet of the bilayer is formed by a homogeneous environment of lipids in a fluid state including globular assembling of proteins and glycoproteins. Since its conception in 1972, many developments were brought to this model in terms of composition and molecular organization. The main development of the fluid mosaic model was made by Simons et al. (1997 and Brown et al. (1997 who suggested that membrane lipids are organized into lateral microdomains (or lipid rafts with a specific composition and a molecular dynamic that are different to the composition and the dynamic of the surrounding liquid crystalline phase. The discovery of a phase separation in the plane of the membrane has induced an explosion in the research efforts related to the biology of cell membranes but also in the development of new technologies for the study of these biological systems. Due to the high complexity of biological membranes and in order to investigate the biological processes that occur on the membrane surface or within the membrane lipid bilayer, a large number of studies are performed using biomimicking model membranes. This paper aims at revisiting the fundamental properties of biological membranes in terms of membrane composition, membrane dynamic and molecular organization, as well as at describing the most common biomimicking models that are frequently used for investigating biological processes such as membrane fusion, membrane trafficking, pore formation as well as membrane interactions at a molecular level.

  3. Structured population models in biology and epidemiology

    CERN Document Server

    Ruan, Shigui

    2008-01-01

    This book consists of six chapters written by leading researchers in mathematical biology. These chapters present recent and important developments in the study of structured population models in biology and epidemiology. Topics include population models structured by age, size, and spatial position; size-structured models for metapopulations, macroparasitc diseases, and prion proliferation; models for transmission of microparasites between host populations living on non-coincident spatial domains; spatiotemporal patterns of disease spread; method of aggregation of variables in population dynamics; and biofilm models. It is suitable as a textbook for a mathematical biology course or a summer school at the advanced undergraduate and graduate level. It can also serve as a reference book for researchers looking for either interesting and specific problems to work on or useful techniques and discussions of some particular problems.

  4. Bridging Physics and Biology Teaching through Modeling

    CERN Document Server

    Hoskinson, Anne-Marie; Zwickl, Benjamin M; Hinko, Kathleen; Caballero, Marcos D

    2013-01-01

    As the frontiers of biology become increasingly interdisciplinary, the physics education community has engaged in ongoing efforts to make physics classes more relevant to life sciences majors. These efforts are complicated by the many apparent differences between these fields, including the types of systems that each studies, the behavior of those systems, the kinds of measurements that each makes, and the role of mathematics in each field. Nonetheless, physics and biology are both fundamental sciences that rely on observations and measurements to construct models of the natural world. In the present theoretical article, we propose that efforts to bridge the teaching of these two disciplines must emphasize shared scientific practices, particularly scientific modeling. We define modeling using language common to both disciplines and highlight how an understanding of the modeling process can help reconcile apparent differences between physics and biology. We elaborate how models can be used for explanatory, pre...

  5. The Strategies of Modeling in Biology Education

    Science.gov (United States)

    Svoboda, Julia; Passmore, Cynthia

    2013-01-01

    Modeling, like inquiry more generally, is not a single method, but rather a complex suite of strategies. Philosophers of biology, citing the diverse aims, interests, and disciplinary cultures of biologists, argue that modeling is best understood in the context of its epistemic aims and cognitive payoffs. In the science education literature, modeling has been discussed in a variety of ways, but often without explicit reference to the diversity of roles models play in scientific practice. We aim to expand and bring clarity to the myriad uses of models in science by presenting a framework from philosopher of biology Jay Odenbaugh that describes five pragmatic strategies of model use in the biological sciences. We then present illustrative examples of each of these roles from an empirical study of an undergraduate biological modeling curriculum, which highlight how students used models to help them frame their research question, explore ideas, and refine their conceptual understanding in an educational setting. Our aim is to begin to explicate the definition of modeling in science in a way that will allow educators and curriculum developers to make informed choices about how and for what purpose modeling enters science classrooms.

  6. Laser interaction with biological material mathematical modeling

    CERN Document Server

    Kulikov, Kirill

    2014-01-01

    This book covers the principles of laser interaction with biological cells and tissues of varying degrees of organization. The problems of biomedical diagnostics are considered. Scattering of laser irradiation of blood cells is modeled for biological structures (dermis, epidermis, vascular plexus). An analytic theory is provided which is based on solving the wave equation for the electromagnetic field. It allows the accurate analysis of interference effects arising from the partial superposition of scattered waves. Treated topics of mathematical modeling are: optical characterization of biological tissue with large-scale and small-scale inhomogeneities in the layers, heating blood vessel under laser irradiation incident on the outer surface of the skin and thermo-chemical denaturation of biological structures at the example of human skin.

  7. Mesoscopic models of biological membranes

    DEFF Research Database (Denmark)

    Venturoli, M.; Sperotto, Maria Maddalena; Kranenburg, M.;

    2006-01-01

    , as model systems to understand the fundamental properties of biomembranes. The properties of lipid bilayers can be studied at different time and length scales. For some properties it is sufficient to envision a membrane as an elastic sheet, while for others it is important to take into account the details...... of the individual atoms. In this review, we focus on an intermediate level, where groups of atoms are lumped into pseudo-particles to arrive at a coarse-grained, or mesoscopic, description of a bilayer, which is subsequently studied using molecular simulation. The aim of this review is to compare various strategies...

  8. Introduction to stochastic models in biology

    DEFF Research Database (Denmark)

    Ditlevsen, Susanne; Samson, Adeline

    2013-01-01

    be exposed to influences that are not completely understood or not feasible to model explicitly. Ignoring these phenomena in the modeling may affect the analysis of the studied biological systems. Therefore there is an increasing need to extend the deterministic models to models that embrace more complex...... variations in the dynamics. A way of modeling these elements is by including stochastic influences or noise. A natural extension of a deterministic differential equations model is a system of stochastic differential equations (SDEs), where relevant parameters are modeled as suitable stochastic processes......, or stochastic processes are added to the driving system equations. This approach assumes that the dynamics are partly driven by noise....

  9. Notions of similarity for computational biology models

    KAUST Repository

    Waltemath, Dagmar

    2016-03-21

    Computational models used in biology are rapidly increasing in complexity, size, and numbers. To build such large models, researchers need to rely on software tools for model retrieval, model combination, and version control. These tools need to be able to quantify the differences and similarities between computational models. However, depending on the specific application, the notion of similarity may greatly vary. A general notion of model similarity, applicable to various types of models, is still missing. Here, we introduce a general notion of quantitative model similarities, survey the use of existing model comparison methods in model building and management, and discuss potential applications of model comparison. To frame model comparison as a general problem, we describe a theoretical approach to defining and computing similarities based on different model aspects. Potentially relevant aspects of a model comprise its references to biological entities, network structure, mathematical equations and parameters, and dynamic behaviour. Future similarity measures could combine these model aspects in flexible, problem-specific ways in order to mimic users\\' intuition about model similarity, and to support complex model searches in databases.

  10. Functional model of biological neural networks.

    Science.gov (United States)

    Lo, James Ting-Ho

    2010-12-01

    A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.

  11. Inferring Biologically Relevant Models: Nested Canalyzing Functions

    CERN Document Server

    Hinkelmann, Franziska

    2010-01-01

    Inferring dynamic biochemical networks is one of the main challenges in systems biology. Given experimental data, the objective is to identify the rules of interaction among the different entities of the network. However, the number of possible models fitting the available data is huge and identifying a biologically relevant model is of great interest. Nested canalyzing functions, where variables in a given order dominate the function, have recently been proposed as a framework for modeling gene regulatory networks. Previously we described this class of functions as an algebraic toric variety. In this paper, we present an algorithm that identifies all nested canalyzing models that fit the given data. We demonstrate our methods using a well-known Boolean model of the cell cycle in budding yeast.

  12. Modelling biological complexity: a physical scientist's perspective.

    Science.gov (United States)

    Coveney, Peter V; Fowler, Philip W

    2005-09-22

    We discuss the modern approaches of complexity and self-organization to understanding dynamical systems and how these concepts can inform current interest in systems biology. From the perspective of a physical scientist, it is especially interesting to examine how the differing weights given to philosophies of science in the physical and biological sciences impact the application of the study of complexity. We briefly describe how the dynamics of the heart and circadian rhythms, canonical examples of systems biology, are modelled by sets of nonlinear coupled differential equations, which have to be solved numerically. A major difficulty with this approach is that all the parameters within these equations are not usually known. Coupled models that include biomolecular detail could help solve this problem. Coupling models across large ranges of length- and time-scales is central to describing complex systems and therefore to biology. Such coupling may be performed in at least two different ways, which we refer to as hierarchical and hybrid multiscale modelling. While limited progress has been made in the former case, the latter is only beginning to be addressed systematically. These modelling methods are expected to bring numerous benefits to biology, for example, the properties of a system could be studied over a wider range of length- and time-scales, a key aim of systems biology. Multiscale models couple behaviour at the molecular biological level to that at the cellular level, thereby providing a route for calculating many unknown parameters as well as investigating the effects at, for example, the cellular level, of small changes at the biomolecular level, such as a genetic mutation or the presence of a drug. The modelling and simulation of biomolecular systems is itself very computationally intensive; we describe a recently developed hybrid continuum-molecular model, HybridMD, and its associated molecular insertion algorithm, which point the way towards the

  13. Post-16 Biology--Some Model Approaches?

    Science.gov (United States)

    Lock, Roger

    1997-01-01

    Outlines alternative approaches to the teaching of difficult concepts in A-level biology which may help student learning by making abstract ideas more concrete and accessible. Examples include models, posters, and poems for illustrating meiosis, mitosis, genetic mutations, and protein synthesis. (DDR)

  14. Ultrafast spectroscopy of model biological membranes

    NARCIS (Netherlands)

    Ghosh, Avishek

    2009-01-01

    In this PhD thesis, I have described the novel time-resolved sum-frequency generation (TR-SFG) spectroscopic technique that I developed during the course of my PhD research and used it study the ultrafast vibrational, structural and orientational dynamics of water molecules at model biological membr

  15. Institute for Multiscale Modeling of Biological Interactions

    Energy Technology Data Exchange (ETDEWEB)

    Paulaitis, Michael E; Garcia-Moreno, Bertrand; Lenhoff, Abraham

    2009-12-26

    The Institute for Multiscale Modeling of Biological Interactions (IMMBI) has two primary goals: Foster interdisciplinary collaborations among faculty and their research laboratories that will lead to novel applications of multiscale simulation and modeling methods in the biological sciences and engineering; and Building on the unique biophysical/biology-based engineering foundations of the participating faculty, train scientists and engineers to apply computational methods that collectively span multiple time and length scales of biological organization. The success of IMMBI will be defined by the following: Size and quality of the applicant pool for pre-doctoral and post-doctoral fellows; Academic performance; Quality of the pre-doctoral and post-doctoral research; Impact of the research broadly and to the DOE (ASCR program) mission; Distinction of the next career step for pre-doctoral and post-doctoral fellows; and Faculty collaborations that result from IMMBI activities. Specific details about accomplishments during the three years of DOE support for IMMBI have been documented in Annual Progress Reports (April 2005, June 2006, and March 2007) and a Report for a National Academy of Sciences Review (October 2005) that were submitted to DOE on the dates indicated. An overview of these accomplishments is provided.

  16. Computational Biology: Modeling Chronic Renal Allograft Injury.

    Science.gov (United States)

    Stegall, Mark D; Borrows, Richard

    2015-01-01

    New approaches are needed to develop more effective interventions to prevent long-term rejection of organ allografts. Computational biology provides a powerful tool to assess the large amount of complex data that is generated in longitudinal studies in this area. This manuscript outlines how our two groups are using mathematical modeling to analyze predictors of graft loss using both clinical and experimental data and how we plan to expand this approach to investigate specific mechanisms of chronic renal allograft injury.

  17. From biology to mathematical models and back: teaching modeling to biology students, and biology to math and engineering students.

    Science.gov (United States)

    Chiel, Hillel J; McManus, Jeffrey M; Shaw, Kendrick M

    2010-01-01

    We describe the development of a course to teach modeling and mathematical analysis skills to students of biology and to teach biology to students with strong backgrounds in mathematics, physics, or engineering. The two groups of students have different ways of learning material and often have strong negative feelings toward the area of knowledge that they find difficult. To give students a sense of mastery in each area, several complementary approaches are used in the course: 1) a "live" textbook that allows students to explore models and mathematical processes interactively; 2) benchmark problems providing key skills on which students make continuous progress; 3) assignment of students to teams of two throughout the semester; 4) regular one-on-one interactions with instructors throughout the semester; and 5) a term project in which students reconstruct, analyze, extend, and then write in detail about a recently published biological model. Based on student evaluations and comments, an attitude survey, and the quality of the students' term papers, the course has significantly increased the ability and willingness of biology students to use mathematical concepts and modeling tools to understand biological systems, and it has significantly enhanced engineering students' appreciation of biology.

  18. Stochasticity in cell biology: Modeling across levels

    Science.gov (United States)

    Pedraza, Juan Manuel

    2009-03-01

    Effective modeling of biological processes requires focusing on a particular level of description, and this requires summarizing de details of lower levels into effective variables and properly accounting for the constrains that other levels impose. In the context of stochasticity in gene expression, I will show how the details of the stochastic process can be characterized by a few effective parameters, which facilitates modeling but complicates interpretation of current experiments. I will show how the resulting noise can provide advantageous or deleterious phenotypic fluctuation and how noise control in the copy number control system of plasmids can change the selective pressures. This system illustrates the direct connection between molecular dynamics and evolutionary dynamics.

  19. Biological Aging - Criteria for Modeling and a New Mechanistic Model

    Science.gov (United States)

    Pletcher, Scott D.; Neuhauser, Claudia

    To stimulate interaction and collaboration across scientific fields, we introduce a minimum set of biological criteria that theoretical models of aging should satisfy. We review results of several recent experiments that examined changes in age-specific mortality rates caused by genetic and environmental manipulation. The empirical data from these experiments is then used to test mathematical models of aging from several different disciplines, including molecular biology, reliability theory, physics, and evolutionary biology/population genetics. We find that none of the current models are consistent with all of the published experimental findings. To provide an example of how our criteria might be applied in practice, we develop a new conceptual model of aging that is consistent with our observations.

  20. Unit testing, model validation, and biological simulation

    Science.gov (United States)

    Watts, Mark D.; Ghayoomie, S. Vahid; Larson, Stephen D.; Gerkin, Richard C.

    2016-01-01

    The growth of the software industry has gone hand in hand with the development of tools and cultural practices for ensuring the reliability of complex pieces of software. These tools and practices are now acknowledged to be essential to the management of modern software. As computational models and methods have become increasingly common in the biological sciences, it is important to examine how these practices can accelerate biological software development and improve research quality. In this article, we give a focused case study of our experience with the practices of unit testing and test-driven development in OpenWorm, an open-science project aimed at modeling Caenorhabditis elegans. We identify and discuss the challenges of incorporating test-driven development into a heterogeneous, data-driven project, as well as the role of model validation tests, a category of tests unique to software which expresses scientific models. PMID:27635225

  1. Heuristic approaches to models and modeling in systems biology

    NARCIS (Netherlands)

    MacLeod, Miles

    2016-01-01

    Prediction and control sufficient for reliable medical and other interventions are prominent aims of modeling in systems biology. The short-term attainment of these goals has played a strong role in projecting the importance and value of the field. In this paper I identify the standard models must m

  2. Topological data analysis of biological aggregation models.

    Science.gov (United States)

    Topaz, Chad M; Ziegelmeier, Lori; Halverson, Tom

    2015-01-01

    We apply tools from topological data analysis to two mathematical models inspired by biological aggregations such as bird flocks, fish schools, and insect swarms. Our data consists of numerical simulation output from the models of Vicsek and D'Orsogna. These models are dynamical systems describing the movement of agents who interact via alignment, attraction, and/or repulsion. Each simulation time frame is a point cloud in position-velocity space. We analyze the topological structure of these point clouds, interpreting the persistent homology by calculating the first few Betti numbers. These Betti numbers count connected components, topological circles, and trapped volumes present in the data. To interpret our results, we introduce a visualization that displays Betti numbers over simulation time and topological persistence scale. We compare our topological results to order parameters typically used to quantify the global behavior of aggregations, such as polarization and angular momentum. The topological calculations reveal events and structure not captured by the order parameters.

  3. Modeling delayed processes in biological systems

    Science.gov (United States)

    Feng, Jingchen; Sevier, Stuart A.; Huang, Bin; Jia, Dongya; Levine, Herbert

    2016-09-01

    Delayed processes are ubiquitous in biological systems and are often characterized by delay differential equations (DDEs) and their extension to include stochastic effects. DDEs do not explicitly incorporate intermediate states associated with a delayed process but instead use an estimated average delay time. In an effort to examine the validity of this approach, we study systems with significant delays by explicitly incorporating intermediate steps. We show that such explicit models often yield significantly different equilibrium distributions and transition times as compared to DDEs with deterministic delay values. Additionally, different explicit models with qualitatively different dynamics can give rise to the same DDEs revealing important ambiguities. We also show that DDE-based predictions of oscillatory behavior may fail for the corresponding explicit model.

  4. An electrostatic model for biological cell division

    CERN Document Server

    Faraggi, Eshel

    2010-01-01

    Probably the most fundamental processes for biological systems is their ability to create themselves through the use of cell division and cell differentiation. In this work a simple physical model is proposed for biological cell division. The model consists of a positive ionic gradient across the cell membrane, and concentration of charge at the nodes of the spindle and on the chromosomes. A simple calculation, based on Coulomb's Law, shows that under such circumstances a chromosome will tend to break up to its constituent chromatids and that the chromatids will be separated by a distance that is an order of thirty percent of the distance between the spindle nodes. Further repulsion between the nodes will tend to stretch the cell and eventually break the cell membrane between the separated chromatids, leading to cell division. The importance of this work is in continuing the understanding of the electromagnetic basis of cell division and providing it with an analytical model. A central implication of this and...

  5. Spherical Cancer Models in Tumor Biology

    Directory of Open Access Journals (Sweden)

    Louis-Bastien Weiswald

    2015-01-01

    Full Text Available Three-dimensional (3D in vitro models have been used in cancer research as an intermediate model between in vitro cancer cell line cultures and in vivo tumor. Spherical cancer models represent major 3D in vitro models that have been described over the past 4 decades. These models have gained popularity in cancer stem cell research using tumorospheres. Thus, it is crucial to define and clarify the different spherical cancer models thus far described. Here, we focus on in vitro multicellular spheres used in cancer research. All these spherelike structures are characterized by their well-rounded shape, the presence of cancer cells, and their capacity to be maintained as free-floating cultures. We propose a rational classification of the four most commonly used spherical cancer models in cancer research based on culture methods for obtaining them and on subsequent differences in sphere biology: the multicellular tumor spheroid model, first described in the early 70s and obtained by culture of cancer cell lines under nonadherent conditions; tumorospheres, a model of cancer stem cell expansion established in a serum-free medium supplemented with growth factors; tissue-derived tumor spheres and organotypic multicellular spheroids, obtained by tumor tissue mechanical dissociation and cutting. In addition, we describe their applications to and interest in cancer research; in particular, we describe their contribution to chemoresistance, radioresistance, tumorigenicity, and invasion and migration studies. Although these models share a common 3D conformation, each displays its own intrinsic properties. Therefore, the most relevant spherical cancer model must be carefully selected, as a function of the study aim and cancer type.

  6. Multiscale mechanical modeling of soft biological tissues

    Science.gov (United States)

    Stylianopoulos, Triantafyllos

    2008-10-01

    Soft biological tissues include both native and artificial tissues. In the human body, tissues like the articular cartilage, arterial wall, and heart valve leaflets are examples of structures composed of an underlying network of collagen fibers, cells, proteins and molecules. Artificial tissues are less complex than native tissues and mainly consist of a fiber polymer network with the intent of replacing lost or damaged tissue. Understanding of the mechanical function of these materials is essential for many clinical treatments (e.g. arterial clamping, angioplasty), diseases (e.g. arteriosclerosis) and tissue engineering applications (e.g. engineered blood vessels or heart valves). This thesis presents the derivation and application of a multiscale methodology to describe the macroscopic mechanical function of soft biological tissues incorporating directly their structural architecture. The model, which is based on volume averaging theory, accounts for structural parameters such as the network volume fraction and orientation, the realignment of the fibers in response to strain, the interactions among the fibers and the interactions between the fibers and the interstitial fluid in order to predict the overall tissue behavior. Therefore, instead of using a constitutive equation to relate strain to stress, the tissue microstructure is modeled within a representative volume element (RVE) and the macroscopic response at any point in the tissue is determined by solving a micromechanics problem in the RVE. The model was applied successfully to acellular collagen gels, native blood vessels, and electrospun polyurethane scaffolds and provided accurate predictions for permeability calculations in isotropic and oriented fiber networks. The agreement of model predictions with experimentally determined mechanical properties provided insights into the mechanics of tissues and tissue constructs, while discrepancies revealed limitations of the model framework.

  7. Model Checking the Biological Model of Membrane Computing with Probabilistic Symbolic Model Checker by Using Two Biological Systems

    Directory of Open Access Journals (Sweden)

    Ravie c. Muniyandi

    2010-01-01

    Full Text Available Problem statement: Membrane computing formalism has provided better modeling capabilities for biological systems in comparison to conventional mathematical models. Model checking could be used to reason about the biological system in detail and with precision by verifying formally whether membrane computing model meets the properties of the system. Approach: This study was carried to investigate the preservation of properties of two biological systems that had been modeled and simulated in membrane computing by a method of model checking using PRISM. The two biological systems were prey-predator population and signal processing in the legend-receptor networks of protein TGF-ß. Results: The model checking of membrane computing model of the biological systems with five different properties showed that the properties of the biological systems could be preserved in the membrane computing model. Conclusion: Membrane computing model not only provides a better approach in representing and simulating a biological system but also able to sustain the basic properties of the system.

  8. Documentation of TRU biological transport model (BIOTRAN)

    Energy Technology Data Exchange (ETDEWEB)

    Gallegos, A.F.; Garcia, B.J.; Sutton, C.M.

    1980-01-01

    Inclusive of Appendices, this document describes the purpose, rationale, construction, and operation of a biological transport model (BIOTRAN). This model is used to predict the flow of transuranic elements (TRU) through specified plant and animal environments using biomass as a vector. The appendices are: (A) Flows of moisture, biomass, and TRU; (B) Intermediate variables affecting flows; (C) Mnemonic equivalents (code) for variables; (D) Variable library (code); (E) BIOTRAN code (Fortran); (F) Plants simulated; (G) BIOTRAN code documentation; (H) Operating instructions for BIOTRAN code. The main text is presented with a specific format which uses a minimum of space, yet is adequate for tracking most relationships from their first appearance to their formulation in the code. Because relationships are treated individually in this manner, and rely heavily on Appendix material for understanding, it is advised that the reader familiarize himself with these materials before proceeding with the main text.

  9. Modeling the Biological Diversity of Pig Carcasses

    DEFF Research Database (Denmark)

    Erbou, Søren Gylling Hemmingsen

    for extracting and modeling meaningful information from the vast amount of information available from non-invasive imaging data. The lean meat percentage (LMP) is a common standard for measuring the quality of pig carcasses. Measuring the LMP using CT and using this as a reference for calibration of online......This thesis applies methods from medical image analysis for modeling the biological diversity of pig carcasses. The Danish meat industry is very focused on improving product quality and productivity by optimizing the use of the carcasses and increasing productivity in the abattoirs. In order...... to achieve these goals there is a need for more detailed information about pig carcasses in relation to measures of quality. Non-invasive imaging such as X-ray Computed Tomography (CT) can provide this very detailed information discerning the major tissue types. Medical image analysis provides the tools...

  10. ACTIVE AND PARTICIPATORY METHODS IN BIOLOGY: MODELING

    Directory of Open Access Journals (Sweden)

    Brînduşa-Antonela SBÎRCEA

    2011-01-01

    Full Text Available By using active and participatory methods it is hoped that pupils will not only come to a deeper understanding of the issues involved, but also that their motivation will be heightened. Pupil involvement in their learning is essential. Moreover, by using a variety of teaching techniques, we can help students make sense of the world in different ways, increasing the likelihood that they will develop a conceptual understanding. The teacher must be a good facilitator, monitoring and supporting group dynamics. Modeling is an instructional strategy in which the teacher demonstrates a new concept or approach to learning and pupils learn by observing. In the teaching of biology the didactic materials are fundamental tools in the teaching-learning process. Reading about scientific concepts or having a teacher explain them is not enough. Research has shown that modeling can be used across disciplines and in all grade and ability level classrooms. Using this type of instruction, teachers encourage learning.

  11. Computational Modeling of Biological Systems From Molecules to Pathways

    CERN Document Server

    2012-01-01

    Computational modeling is emerging as a powerful new approach for studying and manipulating biological systems. Many diverse methods have been developed to model, visualize, and rationally alter these systems at various length scales, from atomic resolution to the level of cellular pathways. Processes taking place at larger time and length scales, such as molecular evolution, have also greatly benefited from new breeds of computational approaches. Computational Modeling of Biological Systems: From Molecules to Pathways provides an overview of established computational methods for the modeling of biologically and medically relevant systems. It is suitable for researchers and professionals working in the fields of biophysics, computational biology, systems biology, and molecular medicine.

  12. Model checking biological systems described using ambient calculus

    DEFF Research Database (Denmark)

    Mardare, Radu Iulian; Priami, Corrado; Qualia, Paola;

    2005-01-01

    Model checking biological systems described using ambient calculus. In Proc. of the second International Workshop on Computational Methods in Systems Biology (CMSB04), Lecture Notes in Bioinformatics 3082:85-103, Springer, 2005.......Model checking biological systems described using ambient calculus. In Proc. of the second International Workshop on Computational Methods in Systems Biology (CMSB04), Lecture Notes in Bioinformatics 3082:85-103, Springer, 2005....

  13. Biologically based multistage modeling of radiation effects

    Energy Technology Data Exchange (ETDEWEB)

    William Hazelton; Suresh Moolgavkar; E. Georg Luebeck

    2005-08-30

    This past year we have made substantial progress in modeling the contribution of homeostatic regulation to low-dose radiation effects and carcinogenesis. We have worked to refine and apply our multistage carcinogenesis models to explicitly incorporate cell cycle states, simple and complex damage, checkpoint delay, slow and fast repair, differentiation, and apoptosis to study the effects of low-dose ionizing radiation in mouse intestinal crypts, as well as in other tissues. We have one paper accepted for publication in ''Advances in Space Research'', and another manuscript in preparation describing this work. I also wrote a chapter describing our combined cell-cycle and multistage carcinogenesis model that will be published in a book on stochastic carcinogenesis models edited by Wei-Yuan Tan. In addition, we organized and held a workshop on ''Biologically Based Modeling of Human Health Effects of Low dose Ionizing Radiation'', July 28-29, 2005 at Fred Hutchinson Cancer Research Center in Seattle, Washington. We had over 20 participants, including Mary Helen Barcellos-Hoff as keynote speaker, talks by most of the low-dose modelers in the DOE low-dose program, experimentalists including Les Redpath (and Mary Helen), Noelle Metting from DOE, and Tony Brooks. It appears that homeostatic regulation may be central to understanding low-dose radiation phenomena. The primary effects of ionizing radiation (IR) are cell killing, delayed cell cycling, and induction of mutations. However, homeostatic regulation causes cells that are killed or damaged by IR to eventually be replaced. Cells with an initiating mutation may have a replacement advantage, leading to clonal expansion of these initiated cells. Thus we have focused particularly on modeling effects that disturb homeostatic regulation as early steps in the carcinogenic process. There are two primary considerations that support our focus on homeostatic regulation. First, a number of

  14. Toward university modeling instruction--biology: adapting curricular frameworks from physics to biology.

    Science.gov (United States)

    Manthey, Seth; Brewe, Eric

    2013-06-01

    University Modeling Instruction (UMI) is an approach to curriculum and pedagogy that focuses instruction on engaging students in building, validating, and deploying scientific models. Modeling Instruction has been successfully implemented in both high school and university physics courses. Studies within the physics education research (PER) community have identified UMI's positive impacts on learning gains, equity, attitudinal shifts, and self-efficacy. While the success of this pedagogical approach has been recognized within the physics community, the use of models and modeling practices is still being developed for biology. Drawing from the existing research on UMI in physics, we describe the theoretical foundations of UMI and how UMI can be adapted to include an emphasis on models and modeling for undergraduate introductory biology courses. In particular, we discuss our ongoing work to develop a framework for the first semester of a two-semester introductory biology course sequence by identifying the essential basic models for an introductory biology course sequence.

  15. Oscillation and stability of delay models in biology

    CERN Document Server

    Agarwal, Ravi P; Saker, Samir H

    2014-01-01

    Environmental variation plays an important role in many biological and ecological dynamical systems. This monograph focuses on the study of oscillation and the stability of delay models occurring in biology. The book presents recent research results on the qualitative behavior of mathematical models under different physical and environmental conditions, covering dynamics including the distribution and consumption of food. Researchers in the fields of mathematical modeling, mathematical biology, and population dynamics will be particularly interested in this material.

  16. INTERVAL OBSERVER FOR A BIOLOGICAL REACTOR MODEL

    Directory of Open Access Journals (Sweden)

    T. A. Kharkovskaia

    2014-05-01

    Full Text Available The method of an interval observer design for nonlinear systems with parametric uncertainties is considered. The interval observer synthesis problem for systems with varying parameters consists in the following. If there is the uncertainty restraint for the state values of the system, limiting the initial conditions of the system and the set of admissible values for the vector of unknown parameters and inputs, the interval existence condition for the estimations of the system state variables, containing the actual state at a given time, needs to be held valid over the whole considered time segment as well. Conditions of the interval observers design for the considered class of systems are shown. They are: limitation of the input and state, the existence of a majorizing function defining the uncertainty vector for the system, Lipschitz continuity or finiteness of this function, the existence of an observer gain with the suitable Lyapunov matrix. The main condition for design of such a device is cooperativity of the interval estimation error dynamics. An individual observer gain matrix selection problem is considered. In order to ensure the property of cooperativity for interval estimation error dynamics, a static transformation of coordinates is proposed. The proposed algorithm is demonstrated by computer modeling of the biological reactor. Possible applications of these interval estimation systems are the spheres of robust control, where the presence of various types of uncertainties in the system dynamics is assumed, biotechnology and environmental systems and processes, mechatronics and robotics, etc.

  17. Morphogenesis and pattern formation in biological systems experiments and models

    CERN Document Server

    Noji, Sumihare; Ueno, Naoto; Maini, Philip

    2003-01-01

    A central goal of current biology is to decode the mechanisms that underlie the processes of morphogenesis and pattern formation. Concerned with the analysis of those phenomena, this book covers a broad range of research fields, including developmental biology, molecular biology, plant morphogenesis, ecology, epidemiology, medicine, paleontology, evolutionary biology, mathematical biology, and computational biology. In Morphogenesis and Pattern Formation in Biological Systems: Experiments and Models, experimental and theoretical aspects of biology are integrated for the construction and investigation of models of complex processes. This collection of articles on the latest advances by leading researchers not only brings together work from a wide spectrum of disciplines, but also provides a stepping-stone to the creation of new areas of discovery.

  18. Modelling biological and chemically induced precipitation of calcium phosphate in enhanced biological phosphorus removal systems.

    Science.gov (United States)

    Barat, R; Montoya, T; Seco, A; Ferrer, J

    2011-06-01

    The biologically induced precipitation processes can be important in wastewater treatment, in particular treating raw wastewater with high calcium concentration combined with Enhanced Biological Phosphorus Removal. Currently, there is little information and experience in modelling jointly biological and chemical processes. This paper presents a calcium phosphate precipitation model and its inclusion in the Activated Sludge Model No 2d (ASM2d). The proposed precipitation model considers that aqueous phase reactions quickly achieve the chemical equilibrium and that aqueous-solid change is kinetically governed. The model was calibrated using data from four experiments in a Sequencing Batch Reactor (SBR) operated for EBPR and finally validated with two experiments. The precipitation model proposed was able to reproduce the dynamics of amorphous calcium phosphate (ACP) formation and later crystallization to hydroxyapatite (HAP) under different scenarios. The model successfully characterised the EBPR performance of the SBR, including the biological, physical and chemical processes.

  19. OFFl Models: Novel Schema for Dynamical Modeling of Biological Systems.

    Science.gov (United States)

    Ogbunugafor, C Brandon; Robinson, Sean P

    2016-01-01

    Flow diagrams are a common tool used to help build and interpret models of dynamical systems, often in biological contexts such as consumer-resource models and similar compartmental models. Typically, their usage is intuitive and informal. Here, we present a formalized version of flow diagrams as a kind of weighted directed graph which follow a strict grammar, which translate into a system of ordinary differential equations (ODEs) by a single unambiguous rule, and which have an equivalent representation as a relational database. (We abbreviate this schema of "ODEs and formalized flow diagrams" as OFFL.) Drawing a diagram within this strict grammar encourages a mental discipline on the part of the modeler in which all dynamical processes of a system are thought of as interactions between dynamical species that draw parcels from one or more source species and deposit them into target species according to a set of transformation rules. From these rules, the net rate of change for each species can be derived. The modeling schema can therefore be understood as both an epistemic and practical heuristic for modeling, serving both as an organizational framework for the model building process and as a mechanism for deriving ODEs. All steps of the schema beyond the initial scientific (intuitive, creative) abstraction of natural observations into model variables are algorithmic and easily carried out by a computer, thus enabling the future development of a dedicated software implementation. Such tools would empower the modeler to consider significantly more complex models than practical limitations might have otherwise proscribed, since the modeling framework itself manages that complexity on the modeler's behalf. In this report, we describe the chief motivations for OFFL, carefully outline its implementation, and utilize a range of classic examples from ecology and epidemiology to showcase its features.

  20. OFFl Models: Novel Schema for Dynamical Modeling of Biological Systems.

    Directory of Open Access Journals (Sweden)

    C Brandon Ogbunugafor

    Full Text Available Flow diagrams are a common tool used to help build and interpret models of dynamical systems, often in biological contexts such as consumer-resource models and similar compartmental models. Typically, their usage is intuitive and informal. Here, we present a formalized version of flow diagrams as a kind of weighted directed graph which follow a strict grammar, which translate into a system of ordinary differential equations (ODEs by a single unambiguous rule, and which have an equivalent representation as a relational database. (We abbreviate this schema of "ODEs and formalized flow diagrams" as OFFL. Drawing a diagram within this strict grammar encourages a mental discipline on the part of the modeler in which all dynamical processes of a system are thought of as interactions between dynamical species that draw parcels from one or more source species and deposit them into target species according to a set of transformation rules. From these rules, the net rate of change for each species can be derived. The modeling schema can therefore be understood as both an epistemic and practical heuristic for modeling, serving both as an organizational framework for the model building process and as a mechanism for deriving ODEs. All steps of the schema beyond the initial scientific (intuitive, creative abstraction of natural observations into model variables are algorithmic and easily carried out by a computer, thus enabling the future development of a dedicated software implementation. Such tools would empower the modeler to consider significantly more complex models than practical limitations might have otherwise proscribed, since the modeling framework itself manages that complexity on the modeler's behalf. In this report, we describe the chief motivations for OFFL, carefully outline its implementation, and utilize a range of classic examples from ecology and epidemiology to showcase its features.

  1. Mathematical modeling of the evolution of a simple biological system

    Digital Repository Service at National Institute of Oceanography (India)

    Gonsalves, M.J.B.D.; Neetu, S.; Krishnan, K.P.; Attri, K.; LokaBharathi, P.A.

    Paula, Goa 403 004, India. Phone: +91 0832 2450624, Fax: +91 0832 2450606, e-mail: mjudith@nio.org Introduction In India, classroom education in biology does not generally include an exercise in which the data can be used to develop models.... This has hampered exposure to quantitative tools in biology, much to the disadvantage of students. The purpose of this note is to report an exercise we carried out to expose traditional biologists educated in India to mathematical modelling of biological...

  2. OFFl Models: Novel Schema for Dynamical Modeling of Biological Systems

    Science.gov (United States)

    2016-01-01

    Flow diagrams are a common tool used to help build and interpret models of dynamical systems, often in biological contexts such as consumer-resource models and similar compartmental models. Typically, their usage is intuitive and informal. Here, we present a formalized version of flow diagrams as a kind of weighted directed graph which follow a strict grammar, which translate into a system of ordinary differential equations (ODEs) by a single unambiguous rule, and which have an equivalent representation as a relational database. (We abbreviate this schema of “ODEs and formalized flow diagrams” as OFFL.) Drawing a diagram within this strict grammar encourages a mental discipline on the part of the modeler in which all dynamical processes of a system are thought of as interactions between dynamical species that draw parcels from one or more source species and deposit them into target species according to a set of transformation rules. From these rules, the net rate of change for each species can be derived. The modeling schema can therefore be understood as both an epistemic and practical heuristic for modeling, serving both as an organizational framework for the model building process and as a mechanism for deriving ODEs. All steps of the schema beyond the initial scientific (intuitive, creative) abstraction of natural observations into model variables are algorithmic and easily carried out by a computer, thus enabling the future development of a dedicated software implementation. Such tools would empower the modeler to consider significantly more complex models than practical limitations might have otherwise proscribed, since the modeling framework itself manages that complexity on the modeler’s behalf. In this report, we describe the chief motivations for OFFL, carefully outline its implementation, and utilize a range of classic examples from ecology and epidemiology to showcase its features. PMID:27270918

  3. Modeling of Biological Intelligence for SCM System Optimization

    OpenAIRE

    Shengyong Chen; Yujun Zheng; Carlo Cattani; Wanliang Wang

    2012-01-01

    This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing c...

  4. Modeling Co-evolution of Speech and Biology.

    Science.gov (United States)

    de Boer, Bart

    2016-04-01

    Two computer simulations are investigated that model interaction of cultural evolution of language and biological evolution of adaptations to language. Both are agent-based models in which a population of agents imitates each other using realistic vowels. The agents evolve under selective pressure for good imitation. In one model, the evolution of the vocal tract is modeled; in the other, a cognitive mechanism for perceiving speech accurately is modeled. In both cases, biological adaptations to using and learning speech evolve, even though the system of speech sounds itself changes at a more rapid time scale than biological evolution. However, the fact that the available acoustic space is used maximally (a self-organized result of cultural evolution) is constant, and therefore biological evolution does have a stable target. This work shows that when cultural and biological traits are continuous, their co-evolution may lead to cognitive adaptations that are strong enough to detect empirically.

  5. Multi-scale modelling and simulation in systems biology.

    Science.gov (United States)

    Dada, Joseph O; Mendes, Pedro

    2011-02-01

    The aim of systems biology is to describe and understand biology at a global scale where biological functions are recognised as a result of complex mechanisms that happen at several scales, from the molecular to the ecosystem. Modelling and simulation are computational tools that are invaluable for description, prediction and understanding these mechanisms in a quantitative and integrative way. Therefore the study of biological functions is greatly aided by multi-scale methods that enable the coupling and simulation of models spanning several spatial and temporal scales. Various methods have been developed for solving multi-scale problems in many scientific disciplines, and are applicable to continuum based modelling techniques, in which the relationship between system properties is expressed with continuous mathematical equations or discrete modelling techniques that are based on individual units to model the heterogeneous microscopic elements such as individuals or cells. In this review, we survey these multi-scale methods and explore their application in systems biology.

  6. Computer Models and Automata Theory in Biology and Medicine

    CERN Document Server

    Baianu, I C

    2004-01-01

    The applications of computers to biological and biomedical problem solving goes back to the very beginnings of computer science, automata theory [1], and mathematical biology [2]. With the advent of more versatile and powerful computers, biological and biomedical applications of computers have proliferated so rapidly that it would be virtually impossible to compile a comprehensive review of all developments in this field. Limitations of computer simulations in biology have also come under close scrutiny, and claims have been made that biological systems have limited information processing power [3]. Such general conjectures do not, however, deter biologists and biomedical researchers from developing new computer applications in biology and medicine. Microprocessors are being widely employed in biological laboratories both for automatic data acquisition/processing and modeling; one particular area, which is of great biomedical interest, involves fast digital image processing and is already established for rout...

  7. A guide to numerical modelling in systems biology

    CERN Document Server

    Deuflhard, Peter

    2015-01-01

    This book is intended for students of computational systems biology with only a limited background in mathematics. Typical books on systems biology merely mention algorithmic approaches, but without offering a deeper understanding. On the other hand, mathematical books are typically unreadable for computational biologists. The authors of the present book have worked hard to fill this gap. The result is not a book on systems biology, but on computational methods in systems biology. This book originated from courses taught by the authors at Freie Universität Berlin. The guiding idea of the courses was to convey those mathematical insights that are indispensable for systems biology, teaching the necessary mathematical prerequisites by means of many illustrative examples and without any theorems. The three chapters cover the mathematical modelling of biochemical and physiological processes, numerical simulation of the dynamics of biological networks, and identification of model parameters by means of comparisons...

  8. Spatial Modeling Tools for Cell Biology

    Science.gov (United States)

    2006-10-01

    Capillary blood flow is shown circling both sides of the cell and entering from the bottom part of the figure. Species are transported in and out of...replication molecules, mitochondria – in which most of he cell energy metabolism takes place, endoplasmic reticula – build of complex membranes... part of the cell biology problem. Numerical solutions of even large scale ODE systems are very fast (seconds to minutes on powerful PCs). Numerical

  9. Mechanistic modeling confronts the complexity of molecular cell biology.

    Science.gov (United States)

    Phair, Robert D

    2014-11-05

    Mechanistic modeling has the potential to transform how cell biologists contend with the inescapable complexity of modern biology. I am a physiologist-electrical engineer-systems biologist who has been working at the level of cell biology for the past 24 years. This perspective aims 1) to convey why we build models, 2) to enumerate the major approaches to modeling and their philosophical differences, 3) to address some recurrent concerns raised by experimentalists, and then 4) to imagine a future in which teams of experimentalists and modelers build-and subject to exhaustive experimental tests-models covering the entire spectrum from molecular cell biology to human pathophysiology. There is, in my view, no technical obstacle to this future, but it will require some plasticity in the biological research mind-set.

  10. Sustainable model building the role of standards and biological semantics.

    Science.gov (United States)

    Krause, Falko; Schulz, Marvin; Swainston, Neil; Liebermeister, Wolfram

    2011-01-01

    Systems biology models can be reused within new simulation scenarios, as parts of more complex models or as sources of biochemical knowledge. Reusability does not come by itself but has to be ensured while creating a model. Most important, models should be designed to remain valid in different contexts-for example, for different experimental conditions-and be published in a standardized and well-documented form. Creating reusable models is worthwhile, but it requires some efforts when a model is developed, implemented, documented, and published. Minimum requirements for published systems biology models have been formulated by the MIRIAM initiative. Main criteria are completeness of information and documentation, availability of machine-readable models in standard formats, and semantic annotations connecting the model elements with entries in biological Web resources. In this chapter, we discuss the assumptions behind bottom-up modeling; present important standards like MIRIAM, the Systems Biology Markup Language (SBML), and the Systems Biology Graphical Notation (SBGN); and describe software tools and services for handling semantic annotations. Finally, we show how standards can facilitate the construction of large metabolic network models.

  11. Uncertainty in biology a computational modeling approach

    CERN Document Server

    Gomez-Cabrero, David

    2016-01-01

    Computational modeling of biomedical processes is gaining more and more weight in the current research into the etiology of biomedical problems and potential treatment strategies.  Computational modeling allows to reduce, refine and replace animal experimentation as well as to translate findings obtained in these experiments to the human background. However these biomedical problems are inherently complex with a myriad of influencing factors, which strongly complicates the model building and validation process.  This book wants to address four main issues related to the building and validation of computational models of biomedical processes: Modeling establishment under uncertainty Model selection and parameter fitting Sensitivity analysis and model adaptation Model predictions under uncertainty In each of the abovementioned areas, the book discusses a number of key-techniques by means of a general theoretical description followed by one or more practical examples.  This book is intended for graduate stude...

  12. Computational Modeling, Formal Analysis, and Tools for Systems Biology.

    Science.gov (United States)

    Bartocci, Ezio; Lió, Pietro

    2016-01-01

    As the amount of biological data in the public domain grows, so does the range of modeling and analysis techniques employed in systems biology. In recent years, a number of theoretical computer science developments have enabled modeling methodology to keep pace. The growing interest in systems biology in executable models and their analysis has necessitated the borrowing of terms and methods from computer science, such as formal analysis, model checking, static analysis, and runtime verification. Here, we discuss the most important and exciting computational methods and tools currently available to systems biologists. We believe that a deeper understanding of the concepts and theory highlighted in this review will produce better software practice, improved investigation of complex biological processes, and even new ideas and better feedback into computer science.

  13. Recent Applications of Hidden Markov Models in Computational Biology

    Institute of Scientific and Technical Information of China (English)

    Khar Heng Choo; Joo Chuan Tong; Louxin Zhang

    2004-01-01

    This paper examines recent developments and applications of Hidden Markov Models (HMMs) to various problems in computational biology, including multiple sequence alignment, homology detection, protein sequences classification, and genomic annotation.

  14. Mathematical models in biology bringing mathematics to life

    CERN Document Server

    Ferraro, Maria; Guarracino, Mario

    2015-01-01

    This book presents an exciting collection of contributions based on the workshop “Bringing Maths to Life” held October 27-29, 2014 in Naples, Italy.  The state-of-the art research in biology and the statistical and analytical challenges facing huge masses of data collection are treated in this Work. Specific topics explored in depth surround the sessions and special invited sessions of the workshop and include genetic variability via differential expression, molecular dynamics and modeling, complex biological systems viewed from quantitative models, and microscopy images processing, to name several. In depth discussions of the mathematical analysis required to extract insights from complex bodies of biological datasets, to aid development in the field novel algorithms, methods and software tools for genetic variability, molecular dynamics, and complex biological systems are presented in this book. Researchers and graduate students in biology, life science, and mathematics/statistics will find the content...

  15. Mathematical and computational modeling in biology at multiple scales

    OpenAIRE

    Tuszynski, Jack A; Winter, Philip; White, Diana; Tseng, Chih-Yuan; Sahu, Kamlesh K.; Gentile, Francesco; Spasevska, Ivana; Omar, Sara Ibrahim; Nayebi, Niloofar; Churchill, Cassandra DM; Klobukowski, Mariusz; El-Magd, Rabab M Abou

    2014-01-01

    A variety of topics are reviewed in the area of mathematical and computational modeling in biology, covering the range of scales from populations of organisms to electrons in atoms. The use of maximum entropy as an inference tool in the fields of biology and drug discovery is discussed. Mathematical and computational methods and models in the areas of epidemiology, cell physiology and cancer are surveyed. The technique of molecular dynamics is covered, with special attention to force fields f...

  16. Structure learning for Bayesian networks as models of biological networks.

    Science.gov (United States)

    Larjo, Antti; Shmulevich, Ilya; Lähdesmäki, Harri

    2013-01-01

    Bayesian networks are probabilistic graphical models suitable for modeling several kinds of biological systems. In many cases, the structure of a Bayesian network represents causal molecular mechanisms or statistical associations of the underlying system. Bayesian networks have been applied, for example, for inferring the structure of many biological networks from experimental data. We present some recent progress in learning the structure of static and dynamic Bayesian networks from data.

  17. Systematic integration of experimental data and models in systems biology

    Directory of Open Access Journals (Sweden)

    Simeonidis Evangelos

    2010-11-01

    Full Text Available Abstract Background The behaviour of biological systems can be deduced from their mathematical models. However, multiple sources of data in diverse forms are required in the construction of a model in order to define its components and their biochemical reactions, and corresponding parameters. Automating the assembly and use of systems biology models is dependent upon data integration processes involving the interoperation of data and analytical resources. Results Taverna workflows have been developed for the automated assembly of quantitative parameterised metabolic networks in the Systems Biology Markup Language (SBML. A SBML model is built in a systematic fashion by the workflows which starts with the construction of a qualitative network using data from a MIRIAM-compliant genome-scale model of yeast metabolism. This is followed by parameterisation of the SBML model with experimental data from two repositories, the SABIO-RK enzyme kinetics database and a database of quantitative experimental results. The models are then calibrated and simulated in workflows that call out to COPASIWS, the web service interface to the COPASI software application for analysing biochemical networks. These systems biology workflows were evaluated for their ability to construct a parameterised model of yeast glycolysis. Conclusions Distributed information about metabolic reactions that have been described to MIRIAM standards enables the automated assembly of quantitative systems biology models of metabolic networks based on user-defined criteria. Such data integration processes can be implemented as Taverna workflows to provide a rapid overview of the components and their relationships within a biochemical system.

  18. Bayesian parameter estimation for nonlinear modelling of biological pathways

    Directory of Open Access Journals (Sweden)

    Ghasemi Omid

    2011-12-01

    Full Text Available Abstract Background The availability of temporal measurements on biological experiments has significantly promoted research areas in systems biology. To gain insight into the interaction and regulation of biological systems, mathematical frameworks such as ordinary differential equations have been widely applied to model biological pathways and interpret the temporal data. Hill equations are the preferred formats to represent the reaction rate in differential equation frameworks, due to their simple structures and their capabilities for easy fitting to saturated experimental measurements. However, Hill equations are highly nonlinearly parameterized functions, and parameters in these functions cannot be measured easily. Additionally, because of its high nonlinearity, adaptive parameter estimation algorithms developed for linear parameterized differential equations cannot be applied. Therefore, parameter estimation in nonlinearly parameterized differential equation models for biological pathways is both challenging and rewarding. In this study, we propose a Bayesian parameter estimation algorithm to estimate parameters in nonlinear mathematical models for biological pathways using time series data. Results We used the Runge-Kutta method to transform differential equations to difference equations assuming a known structure of the differential equations. This transformation allowed us to generate predictions dependent on previous states and to apply a Bayesian approach, namely, the Markov chain Monte Carlo (MCMC method. We applied this approach to the biological pathways involved in the left ventricle (LV response to myocardial infarction (MI and verified our algorithm by estimating two parameters in a Hill equation embedded in the nonlinear model. We further evaluated our estimation performance with different parameter settings and signal to noise ratios. Our results demonstrated the effectiveness of the algorithm for both linearly and nonlinearly

  19. In Vivo Models to Study Chemokine Biology.

    Science.gov (United States)

    Amaral, F A; Boff, D; Teixeira, M M

    2016-01-01

    Chemokines are essential mediators of leukocyte movement in vivo. In vitro assays of leukocyte migration cannot mimic the complex interactions with other cell types and matrix needed for cells to extravasate and migrate into tissues. Therefore, in vivo strategies to study the effects and potential relevance of chemokines for the migration of particular leukocyte subsets are necessary. Here, we describe methods to study the effects and endogenous role of chemokine in mice. Advantages and pitfalls of particular models are discussed and we focus on description in model's joint and pleural cavity inflammation and the effects and relevance of CXCR2 and CCR2 ligands on cell migration.

  20. Nematodes: Model Organisms in High School Biology

    Science.gov (United States)

    Bliss, TJ; Anderson, Margery; Dillman, Adler; Yourick, Debra; Jett, Marti; Adams, Byron J.; Russell, RevaBeth

    2007-01-01

    In a collaborative effort between university researchers and high school science teachers, an inquiry-based laboratory module was designed using two species of insecticidal nematodes to help students apply scientific inquiry and elements of thoughtful experimental design. The learning experience and model are described in this article. (Contains 4…

  1. Biological models for automatic target detection

    Science.gov (United States)

    Schachter, Bruce

    2008-04-01

    Humans are better at detecting targets in literal imagery than any known algorithm. Recent advances in modeling visual processes have resulted from f-MRI brain imaging with humans and the use of more invasive techniques with monkeys. There are four startling new discoveries. 1) The visual cortex does not simply process an incoming image. It constructs a physics based model of the image. 2) Coarse category classification and range-to-target are estimated quickly - possibly through the dorsal pathway of the visual cortex, combining rapid coarse processing of image data with expectations and goals. This data is then fed back to lower levels to resize the target and enhance the recognition process feeding forward through the ventral pathway. 3) Giant photosensitive retinal ganglion cells provide data for maintaining circadian rhythm (time-of-day) and modeling the physics of the light source. 4) Five filter types implemented by the neurons of the primary visual cortex have been determined. A computer model for automatic target detection has been developed based upon these recent discoveries. It uses an artificial neural network architecture with multiple feed-forward and feedback paths. Our implementation's efficiency derives from the observation that any 2-D filter kernel can be approximated by a sum of 2-D box functions. And, a 2-D box function easily decomposes into two 1-D box functions. Further efficiency is obtained by decomposing the largest neural filter into a high pass filter and a more sparsely sampled low pass filter.

  2. Statistical Model Checking for Biological Systems

    DEFF Research Database (Denmark)

    David, Alexandre; Larsen, Kim Guldstrand; Legay, Axel

    2014-01-01

    Statistical Model Checking (SMC) is a highly scalable simulation-based verification approach for testing and estimating the probability that a stochastic system satisfies a given linear temporal property. The technique has been applied to (discrete and continuous time) Markov chains, stochastic t...

  3. MODEL ORGANISMS USED IN MOLECULAR BIOLOGY OR MEDICAL RESEARCH

    OpenAIRE

    Pandey Govind

    2011-01-01

    A model organism is a non-human species that is studied to understand specific biological phenomena with the expectation that investigations made in the organism model will provide insight into the workings of other organisms. The model organisms are widely used to explore potential causes and treatments for human as well as animal diseases when experiments on animals or humans would be unfeasible or considered less ethical. Studying model organisms may be informative, but care must be taken ...

  4. Parameter estimation and model selection in computational biology.

    Directory of Open Access Journals (Sweden)

    Gabriele Lillacci

    2010-03-01

    Full Text Available A central challenge in computational modeling of biological systems is the determination of the model parameters. Typically, only a fraction of the parameters (such as kinetic rate constants are experimentally measured, while the rest are often fitted. The fitting process is usually based on experimental time course measurements of observables, which are used to assign parameter values that minimize some measure of the error between these measurements and the corresponding model prediction. The measurements, which can come from immunoblotting assays, fluorescent markers, etc., tend to be very noisy and taken at a limited number of time points. In this work we present a new approach to the problem of parameter selection of biological models. We show how one can use a dynamic recursive estimator, known as extended Kalman filter, to arrive at estimates of the model parameters. The proposed method follows. First, we use a variation of the Kalman filter that is particularly well suited to biological applications to obtain a first guess for the unknown parameters. Secondly, we employ an a posteriori identifiability test to check the reliability of the estimates. Finally, we solve an optimization problem to refine the first guess in case it should not be accurate enough. The final estimates are guaranteed to be statistically consistent with the measurements. Furthermore, we show how the same tools can be used to discriminate among alternate models of the same biological process. We demonstrate these ideas by applying our methods to two examples, namely a model of the heat shock response in E. coli, and a model of a synthetic gene regulation system. The methods presented are quite general and may be applied to a wide class of biological systems where noisy measurements are used for parameter estimation or model selection.

  5. 3D Modelling of Biological Systems for Biomimetics

    Institute of Scientific and Technical Information of China (English)

    Shujun Zhang; Kevin Hapeshi; Ashok K. Bhattacharya

    2004-01-01

    With the advanced development of computer-based enabling technologies, many engineering, medical, biology,chemistry, physics and food science etc have developed to the unprecedented levels, which lead to many research and development interests in various multi-discipline areas. Among them, biomimetics is one of the most promising and attractive branches of study. Biomimetics is a branch of study that uses biological systems as a model to develop synthetic systems.To learn from nature, one of the fundamental issues is to understand the natural systems such animals, insects, plants and human beings etc. The geometrical characterization and representation of natural systems is an important fundamental work for biomimetics research. 3D modeling plays a key role in the geometrical characterization and representation, especially in computer graphical visualization. This paper firstly presents the typical procedure of 3D modelling methods and then reviews the previous work of 3D geometrical modelling techniques and systems developed for industrial, medical and animation applications. Especially the paper discusses the problems associated with the existing techniques and systems when they are applied to 3D modelling of biological systems. Based upon the discussions, the paper proposes some areas of research interests in 3D modelling of biological systems and for Biomimetics.

  6. Preservice Biology Teachers' Conceptions About the Tentative Nature of Theories and Models in Biology

    Science.gov (United States)

    Reinisch, Bianca; Krüger, Dirk

    2016-11-01

    In research on the nature of science, there is a need to investigate the role and status of different scientific knowledge forms. Theories and models are two of the most important knowledge forms within biology and are the focus of this study. During interviews, preservice biology teachers (N = 10) were asked about their understanding of theories and models. They were requested to give reasons why they see theories and models as either tentative or certain constructs. Their conceptions were then compared to philosophers' positions (e.g., Popper, Giere). A category system was developed from the qualitative content analysis of the interviews. These categories include 16 conceptions for theories (n tentative = 11; n certain = 5) and 18 conceptions for models (n tentative = 10; n certain = 8). The analysis of the interviews showed that the preservice teachers gave reasons for the tentativeness or certainty of theories and models either due to their understanding of the terms or due to their understanding of the generation or evaluation of theories and models. Therefore, a variety of different terminology, from different sources, should be used in learning-teaching situations. Additionally, an understanding of which processes lead to the generation, evaluation, and refinement or rejection of theories and models should be discussed with preservice teachers. Within philosophy of science, there has been a shift from theories to models. This should be transferred to educational contexts by firstly highlighting the role of models and also their connections to theories.

  7. BayesMD: flexible biological modeling for motif discovery

    DEFF Research Database (Denmark)

    Tang, Man-Hung Eric; Krogh, Anders; Winther, Ole

    2008-01-01

    We present BayesMD, a Bayesian Motif Discovery model with several new features. Three different types of biological a priori knowledge are built into the framework in a modular fashion. A mixture of Dirichlets is used as prior over nucleotide probabilities in binding sites. It is trained on trans......We present BayesMD, a Bayesian Motif Discovery model with several new features. Three different types of biological a priori knowledge are built into the framework in a modular fashion. A mixture of Dirichlets is used as prior over nucleotide probabilities in binding sites. It is trained...

  8. Adaptive neural-based fuzzy modeling for biological systems.

    Science.gov (United States)

    Wu, Shinq-Jen; Wu, Cheng-Tao; Chang, Jyh-Yeong

    2013-04-01

    The inverse problem of identifying dynamic biological networks from their time-course response data set is a cornerstone of systems biology. Hill and Michaelis-Menten model, which is a forward approach, provides local kinetic information. However, repeated modifications and a large amount of experimental data are necessary for the parameter identification. S-system model, which is composed of highly nonlinear differential equations, provides the direct identification of an interactive network. However, the identification of skeletal-network structure is challenging. Moreover, biological systems are always subject to uncertainty and noise. Are there suitable candidates with the potential to deal with noise-contaminated data sets? Fuzzy set theory is developed for handing uncertainty, imprecision and complexity in the real world; for example, we say "driving speed is high" wherein speed is a fuzzy variable and high is a fuzzy set, which uses the membership function to indicate the degree of a element belonging to the set (words in Italics to denote fuzzy variables or fuzzy sets). Neural network possesses good robustness and learning capability. In this study we hybrid these two together into a neural-fuzzy modeling technique. A biological system is formulated to a multi-input-multi-output (MIMO) Takagi-Sugeno (T-S) fuzzy system, which is composed of rule-based linear subsystems. Two kinds of smooth membership functions (MFs), Gaussian and Bell-shaped MFs, are used. The performance of the proposed method is tested with three biological systems.

  9. Biological implications of the Weibull and Gompertz models of aging.

    Science.gov (United States)

    Ricklefs, Robert E; Scheuerlein, Alex

    2002-02-01

    Gompertz and Weibull functions imply contrasting biological causes of demographic aging. The terms describing increasing mortality with age are multiplicative and additive, respectively, which could result from an increase in the vulnerability of individuals to extrinsic causes in the Gompertz model and the predominance of intrinsic causes at older ages in the Weibull model. Experiments that manipulate extrinsic mortality can distinguish these biological models. To facilitate analyses of experimental data, we defined a single index for the rate of aging (omega) for the Weibull and Gompertz functions. Each function described the increase in aging-related mortality in simulated ages at death reasonably well. However, in contrast to the Weibull omega(W), the Gompertz omega(G) was sensitive to variation in the initial mortality rate independently of aging-related mortality. Comparisons between wild and captive populations appear to support the intrinsic-causes model for birds, but give mixed support for both models in mammals.

  10. Modeling of biological intelligence for SCM system optimization.

    Science.gov (United States)

    Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

    2012-01-01

    This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

  11. Modeling of Biological Intelligence for SCM System Optimization

    Directory of Open Access Journals (Sweden)

    Shengyong Chen

    2012-01-01

    Full Text Available This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

  12. Boolean Models of Biological Processes Explain Cascade-Like Behavior.

    Science.gov (United States)

    Chen, Hao; Wang, Guanyu; Simha, Rahul; Du, Chenghang; Zeng, Chen

    2016-01-29

    Biological networks play a key role in determining biological function and therefore, an understanding of their structure and dynamics is of central interest in systems biology. In Boolean models of such networks, the status of each molecule is either "on" or "off" and along with the molecules interact with each other, their individual status changes from "on" to "off" or vice-versa and the system of molecules in the network collectively go through a sequence of changes in state. This sequence of changes is termed a biological process. In this paper, we examine the common perception that events in biomolecular networks occur sequentially, in a cascade-like manner, and ask whether this is likely to be an inherent property. In further investigations of the budding and fission yeast cell-cycle, we identify two generic dynamical rules. A Boolean system that complies with these rules will automatically have a certain robustness. By considering the biological requirements in robustness and designability, we show that those Boolean dynamical systems, compared to an arbitrary dynamical system, statistically present the characteristics of cascadeness and sequentiality, as observed in the budding and fission yeast cell- cycle. These results suggest that cascade-like behavior might be an intrinsic property of biological processes.

  13. Cancer systems biology and modeling: microscopic scale and multiscale approaches.

    Science.gov (United States)

    Masoudi-Nejad, Ali; Bidkhori, Gholamreza; Hosseini Ashtiani, Saman; Najafi, Ali; Bozorgmehr, Joseph H; Wang, Edwin

    2015-02-01

    Cancer has become known as a complex and systematic disease on macroscopic, mesoscopic and microscopic scales. Systems biology employs state-of-the-art computational theories and high-throughput experimental data to model and simulate complex biological procedures such as cancer, which involves genetic and epigenetic, in addition to intracellular and extracellular complex interaction networks. In this paper, different systems biology modeling techniques such as systems of differential equations, stochastic methods, Boolean networks, Petri nets, cellular automata methods and agent-based systems are concisely discussed. We have compared the mentioned formalisms and tried to address the span of applicability they can bear on emerging cancer modeling and simulation approaches. Different scales of cancer modeling, namely, microscopic, mesoscopic and macroscopic scales are explained followed by an illustration of angiogenesis in microscopic scale of the cancer modeling. Then, the modeling of cancer cell proliferation and survival are examined on a microscopic scale and the modeling of multiscale tumor growth is explained along with its advantages.

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

    DEFF Research Database (Denmark)

    Mardare, Radu Iulian; Ihekwaba, Adoha

    2007-01-01

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

  15. Nonlinear Hyperbolic-Parabolic System Modeling Some Biological Phenomena

    Institute of Scientific and Technical Information of China (English)

    WU Shaohua; CHEN Hua

    2011-01-01

    In this paper, we study a nonlinear hyperbolic-parabolic system modeling some biological phenomena. By semigroup theory and Leray-Schauder fixed point argument, the local existence and uniqueness of the weak solutions for this system are proved. For the spatial dimension N = 1, the global existence of the weak solution will be established by the bootstrap argument.

  16. A methodology to annotate systems biology markup language models with the synthetic biology open language.

    Science.gov (United States)

    Roehner, Nicholas; Myers, Chris J

    2014-02-21

    Recently, we have begun to witness the potential of synthetic biology, noted here in the form of bacteria and yeast that have been genetically engineered to produce biofuels, manufacture drug precursors, and even invade tumor cells. The success of these projects, however, has often failed in translation and application to new projects, a problem exacerbated by a lack of engineering standards that combine descriptions of the structure and function of DNA. To address this need, this paper describes a methodology to connect the systems biology markup language (SBML) to the synthetic biology open language (SBOL), existing standards that describe biochemical models and DNA components, respectively. Our methodology involves first annotating SBML model elements such as species and reactions with SBOL DNA components. A graph is then constructed from the model, with vertices corresponding to elements within the model and edges corresponding to the cause-and-effect relationships between these elements. Lastly, the graph is traversed to assemble the annotating DNA components into a composite DNA component, which is used to annotate the model itself and can be referenced by other composite models and DNA components. In this way, our methodology can be used to build up a hierarchical library of models annotated with DNA components. Such a library is a useful input to any future genetic technology mapping algorithm that would automate the process of composing DNA components to satisfy a behavioral specification. Our methodology for SBML-to-SBOL annotation is implemented in the latest version of our genetic design automation (GDA) software tool, iBioSim.

  17. Noether Symmetries Quantization and Superintegrability of Biological Models

    Directory of Open Access Journals (Sweden)

    Maria Clara Nucci

    2016-12-01

    Full Text Available It is shown that quantization and superintegrability are not concepts that are inherent to classical Physics alone. Indeed, one may quantize and also detect superintegrability of biological models by means of Noether symmetries. We exemplify the method by using a mathematical model that was proposed by Basener and Ross (2005, and that describes the dynamics of growth and sudden decrease in the population of Easter Island.

  18. Modeling drug- and chemical- induced hepatotoxicity with systems biology approaches

    Directory of Open Access Journals (Sweden)

    Sudin eBhattacharya

    2012-12-01

    Full Text Available We provide an overview of computational systems biology approaches as applied to the study of chemical- and drug-induced toxicity. The concept of ‘toxicity pathways’ is described in the context of the 2007 US National Academies of Science report, Toxicity testing in the 21st Century: A Vision and A Strategy. Pathway mapping and modeling based on network biology concepts are a key component of the vision laid out in this report for a more biologically-based analysis of dose-response behavior and the safety of chemicals and drugs. We focus on toxicity of the liver (hepatotoxicity – a complex phenotypic response with contributions from a number of different cell types and biological processes. We describe three case studies of complementary multi-scale computational modeling approaches to understand perturbation of toxicity pathways in the human liver as a result of exposure to environmental contaminants and specific drugs. One approach involves development of a spatial, multicellular virtual tissue model of the liver lobule that combines molecular circuits in individual hepatocytes with cell-cell interactions and blood-mediated transport of toxicants through hepatic sinusoids, to enable quantitative, mechanistic prediction of hepatic dose-response for activation of the AhR toxicity pathway. Simultaneously, methods are being developing to extract quantitative maps of intracellular signaling and transcriptional regulatory networks perturbed by environmental contaminants, using a combination of gene expression and genome-wide protein-DNA interaction data. A predictive physiological model (DILIsymTM to understand drug-induced liver injury (DILI, the most common adverse event leading to termination of clinical development programs and regulatory actions on drugs, is also described. The model initially focuses on reactive metabolite-induced DILI in response to administration of acetaminophen, and spans multiple biological scales.

  19. Methods and models in mathematical biology deterministic and stochastic approaches

    CERN Document Server

    Müller, Johannes

    2015-01-01

    This book developed from classes in mathematical biology taught by the authors over several years at the Technische Universität München. The main themes are modeling principles, mathematical principles for the analysis of these models, and model-based analysis of data. The key topics of modern biomathematics are covered: ecology, epidemiology, biochemistry, regulatory networks, neuronal networks, and population genetics. A variety of mathematical methods are introduced, ranging from ordinary and partial differential equations to stochastic graph theory and  branching processes. A special emphasis is placed on the interplay between stochastic and deterministic models.

  20. Toward efficient riparian restoration: integrating economic, physical, and biological models.

    Science.gov (United States)

    Watanabe, Michio; Adams, Richard M; Wu, Junjie; Bolte, John P; Cox, Matt M; Johnson, Sherri L; Liss, William J; Boggess, William G; Ebersole, Joseph L

    2005-04-01

    This paper integrates economic, biological, and physical models to explore the efficient combination and spatial allocation of conservation efforts to protect water quality and increase salmonid populations in the Grande Ronde basin, Oregon. We focus on the effects of shade on water temperatures and the subsequent impacts on endangered juvenile salmonid populations. The integrated modeling system consists of a physical model that links riparian conditions and hydrological characteristics to water temperature; a biological model that links water temperature and riparian conditions to salmonid abundance, and an economic model that incorporates both physical and biological models to estimate minimum cost allocations of conservation efforts. Our findings indicate that conservation alternatives such as passive and active riparian restoration, the width of riparian restoration zones, and the types of vegetation used in restoration activities should be selected based on the spatial distribution of riparian characteristics in the basin. The relative effectiveness of passive and active restoration plays an important role in determining the efficient allocations of conservation efforts. The time frame considered in the restoration efforts and the magnitude of desired temperature reductions also affect the efficient combinations of restoration activities. If the objective of conservation efforts is to maximize fish populations, then fishery benefits should be directly targeted. Targeting other criterion such as water temperatures would result in different allocations of conservation efforts, and therefore are not generally efficient.

  1. Modeling and Simulation Tools: From Systems Biology to Systems Medicine.

    Science.gov (United States)

    Olivier, Brett G; Swat, Maciej J; Moné, Martijn J

    2016-01-01

    Modeling is an integral component of modern biology. In this chapter we look into the role of the model, as it pertains to Systems Medicine, and the software that is required to instantiate and run it. We do this by comparing the development, implementation, and characteristics of tools that have been developed to work with two divergent methodologies: Systems Biology and Pharmacometrics. From the Systems Biology perspective we consider the concept of "Software as a Medical Device" and what this may imply for the migration of research-oriented, simulation software into the domain of human health.In our second perspective, we see how in practice hundreds of computational tools already accompany drug discovery and development at every stage of the process. Standardized exchange formats are required to streamline the model exchange between tools, which would minimize translation errors and reduce the required time. With the emergence, almost 15 years ago, of the SBML standard, a large part of the domain of interest is already covered and models can be shared and passed from software to software without recoding them. Until recently the last stage of the process, the pharmacometric analysis used in clinical studies carried out on subject populations, lacked such an exchange medium. We describe a new emerging exchange format in Pharmacometrics which covers the non-linear mixed effects models, the standard statistical model type used in this area. By interfacing these two formats the entire domain can be covered by complementary standards and subsequently the according tools.

  2. Childhood trauma and personality disorder: toward a biological model.

    Science.gov (United States)

    Lee, Royce

    2006-02-01

    Cross-sectional and prospective associations of personality disorder with childhood trauma provide an important clue regarding the biological mechanism of personality disorder. In this review, empirical literature from several domains is summarized. These include relevant findings from behavioral genetics, preclinical models of early life parental care, and clinical translational studies of personality disorder. Identification of the biological mechanism by which childhood trauma exerts an effect on personality disorder may require modification of the conceptualization of personality disorder, either as a set of categories or dimensions.

  3. Predictive modeling of nanomaterial exposure effects in biological systems

    Directory of Open Access Journals (Sweden)

    Liu X

    2013-09-01

    Full Text Available Xiong Liu,1 Kaizhi Tang,1 Stacey Harper,2 Bryan Harper,2 Jeffery A Steevens,3 Roger Xu1 1Intelligent Automation, Inc., Rockville, MD, USA; 2Department of Environmental and Molecular Toxicology, School of Chemical, Biological, and Environmental Engineering, Oregon State University, Corvallis, OR, USA; 3ERDC Environmental Laboratory, Vicksburg, MS, USA Background: Predictive modeling of the biological effects of nanomaterials is critical for industry and policymakers to assess the potential hazards resulting from the application of engineered nanomaterials. Methods: We generated an experimental dataset on the toxic effects experienced by embryonic zebrafish due to exposure to nanomaterials. Several nanomaterials were studied, such as metal nanoparticles, dendrimer, metal oxide, and polymeric materials. The embryonic zebrafish metric (EZ Metric was used as a screening-level measurement representative of adverse effects. Using the dataset, we developed a data mining approach to model the toxic endpoints and the overall biological impact of nanomaterials. Data mining techniques, such as numerical prediction, can assist analysts in developing risk assessment models for nanomaterials. Results: We found several important attributes that contribute to the 24 hours post-fertilization (hpf mortality, such as dosage concentration, shell composition, and surface charge. These findings concur with previous studies on nanomaterial toxicity using embryonic zebrafish. We conducted case studies on modeling the overall effect/impact of nanomaterials and the specific toxic endpoints such as mortality, delayed development, and morphological malformations. The results show that we can achieve high prediction accuracy for certain biological effects, such as 24 hpf mortality, 120 hpf mortality, and 120 hpf heart malformation. The results also show that the weighting scheme for individual biological effects has a significant influence on modeling the overall impact of

  4. Analyzing Students' Understanding of Models and Modeling Referring to the Disciplines Biology, Chemistry, and Physics

    Science.gov (United States)

    Krell, Moritz; Reinisch, Bianca; Krüger, Dirk

    2015-01-01

    In this study, secondary school students' (N?=?617; grades 7 to 10) understanding of models and modeling was assessed using tasks which explicitly refer to the scientific disciplines of biology, chemistry, and physics and, as a control, to no scientific discipline. The students' responses are interpreted as their biology-, chemistry-, and…

  5. Dynamics of mathematical models in biology bringing mathematics to life

    CERN Document Server

    Zazzu, Valeria; Guarracino, Mario

    2016-01-01

    This volume focuses on contributions from both the mathematics and life science community surrounding the concepts of time and dynamicity of nature, two significant elements which are often overlooked in modeling process to avoid exponential computations. The book is divided into three distinct parts: dynamics of genomes and genetic variation, dynamics of motifs, and dynamics of biological networks. Chapters included in dynamics of genomes and genetic variation analyze the molecular mechanisms and evolutionary processes that shape the structure and function of genomes and those that govern genome dynamics. The dynamics of motifs portion of the volume provides an overview of current methods for motif searching in DNA, RNA and proteins, a key process to discover emergent properties of cells, tissues, and organisms. The part devoted to the dynamics of biological networks covers networks aptly discusses networks in complex biological functions and activities that interpret processes in cells. Moreover, chapters i...

  6. Green Algae as Model Organisms for Biological Fluid Dynamics.

    Science.gov (United States)

    Goldstein, Raymond E

    2015-01-01

    In the past decade the volvocine green algae, spanning from the unicellular Chlamydomonas to multicellular Volvox, have emerged as model organisms for a number of problems in biological fluid dynamics. These include flagellar propulsion, nutrient uptake by swimming organisms, hydrodynamic interactions mediated by walls, collective dynamics and transport within suspensions of microswimmers, the mechanism of phototaxis, and the stochastic dynamics of flagellar synchronization. Green algae are well suited to the study of such problems because of their range of sizes (from 10 μm to several millimetres), their geometric regularity, the ease with which they can be cultured and the availability of many mutants that allow for connections between molecular details and organism-level behavior. This review summarizes these recent developments and highlights promising future directions in the study of biological fluid dynamics, especially in the context of evolutionary biology, that can take advantage of these remarkable organisms.

  7. Programming biological models in Python using PySB.

    Science.gov (United States)

    Lopez, Carlos F; Muhlich, Jeremy L; Bachman, John A; Sorger, Peter K

    2013-01-01

    Mathematical equations are fundamental to modeling biological networks, but as networks get large and revisions frequent, it becomes difficult to manage equations directly or to combine previously developed models. Multiple simultaneous efforts to create graphical standards, rule-based languages, and integrated software workbenches aim to simplify biological modeling but none fully meets the need for transparent, extensible, and reusable models. In this paper we describe PySB, an approach in which models are not only created using programs, they are programs. PySB draws on programmatic modeling concepts from little b and ProMot, the rule-based languages BioNetGen and Kappa and the growing library of Python numerical tools. Central to PySB is a library of macros encoding familiar biochemical actions such as binding, catalysis, and polymerization, making it possible to use a high-level, action-oriented vocabulary to construct detailed models. As Python programs, PySB models leverage tools and practices from the open-source software community, substantially advancing our ability to distribute and manage the work of testing biochemical hypotheses. We illustrate these ideas using new and previously published models of apoptosis.

  8. On linear models and parameter identifiability in experimental biological systems.

    Science.gov (United States)

    Lamberton, Timothy O; Condon, Nicholas D; Stow, Jennifer L; Hamilton, Nicholas A

    2014-10-07

    A key problem in the biological sciences is to be able to reliably estimate model parameters from experimental data. This is the well-known problem of parameter identifiability. Here, methods are developed for biologists and other modelers to design optimal experiments to ensure parameter identifiability at a structural level. The main results of the paper are to provide a general methodology for extracting parameters of linear models from an experimentally measured scalar function - the transfer function - and a framework for the identifiability analysis of complex model structures using linked models. Linked models are composed by letting the output of one model become the input to another model which is then experimentally measured. The linked model framework is shown to be applicable to designing experiments to identify the measured sub-model and recover the input from the unmeasured sub-model, even in cases that the unmeasured sub-model is not identifiable. Applications for a set of common model features are demonstrated, and the results combined in an example application to a real-world experimental system. These applications emphasize the insight into answering "where to measure" and "which experimental scheme" questions provided by both the parameter extraction methodology and the linked model framework. The aim is to demonstrate the tools' usefulness in guiding experimental design to maximize parameter information obtained, based on the model structure.

  9. Biological exposure models for oil spill impact analysis

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    The oil spill impact analysis (OSIA) software system has been developed to supply a tool for comprehensive, quantitative environmental impact assessments resulting from oil spills. In the system, a biological component evaluates potential effects on exposed organisms based on results from a physico-chemieal fates component, including the extent and characteristics of the surface slick, and dissolved and total concentrations of hydrocarbons in the water column. The component includes a particle-based exposure model for migratory adult fish populations, a particle-based exposure model for spawning planktonic organisms (eggs and larvae), and an exposure model for wildlife species (sea birds or marine mammals). The exposure model for migratory adult fish populations simulates the migration behaviors of fish populations migrating to or staying in their feeding areas, over-wintering areas or spawning areas, and determines the acute effects (mortality) and chronic accumulation (body burdens) from the dissolved contaminant. The exposure model for spawning planktonic organisms simulates the release of eggs and larvae, also as particles, from specific spawning areas during the spawning period, and determines their potential exposure to contaminants in the water or sediment. The exposure model for wild species calculates the exposure to surrace oil of wildlife (bird and marine mammal ) categories inhabiting the contaminated area. Compared with the earlier models in which all kinds of organisms are assumed evenly and randomly distributed, the updated biological exposure models can more realistically estimate potential effects on marine ecological system from oil spill pollution events.

  10. Integrative biological systems modeling:challenges and opportunities

    Institute of Scientific and Technical Information of China (English)

    Jialiang WU; Eberhard VOIT

    2009-01-01

    Most biological systems are by nature hybrids consist of interacting discrete and continuous components,which may even operate on different time scales. Therefore," it is desirable to establish modeling frameworks that are capable of combining deterministic and stochastic, discrete and continuous, as well as multi-timescale features. In the context of molecular systems biology, an example for the need of such a combination is the investigation of integrated biological pathways that contain gene regulatory, metabolic and signaling components, which may operate on different time scales and involve on-off switches as well as stochastic effects. The implementation of integrated hybrid systems is not trivial because most software is limited to one or the other of the dichotomies above. In this study, we first review the motivation for hybrid modeling. Secondly, by using the example of a toggle switch model, we illustrate a recently developed modeling framework that is based on the combination of biochemical systems theory (BST) and hybrid functional Petri nets (HFPN). Finally, we discuss remaining challenges and future opportunities.

  11. Towards Modelling and Simulation of Crowded Environments in Cell Biology

    Science.gov (United States)

    Bittig, Arne T.; Jeschke, Matthias; Uhrmacher, Adelinde M.

    2010-09-01

    In modelling and simulation of cell biological processes, spatial homogeneity in the distribution of components is a common but not always valid assumption. Spatial simulation methods differ in computational effort and accuracy, and usually rely on tool-specific input formats for model specification. A clear separation between modelling and simulation allows a declarative model specification thereby facilitating reuse of models and exploiting different simulators. We outline a modelling formalism covering both stochastic spatial simulation at the population level and simulation of individual entities moving in continuous space as well as the combination thereof. A multi-level spatial simulator is presented that combines populations of small particles simulated according to the Next Subvolume Method with individually represented large particles following Brownian motion. This approach entails several challenges that need to be overcome, but nicely balances between calculation effort and required levels of detail.

  12. An Abstraction Theory for Qualitative Models of Biological Systems

    CERN Document Server

    Banks, Richard; 10.4204/EPTCS.40.3

    2010-01-01

    Multi-valued network models are an important qualitative modelling approach used widely by the biological community. In this paper we consider developing an abstraction theory for multi-valued network models that allows the state space of a model to be reduced while preserving key properties of the model. This is important as it aids the analysis and comparison of multi-valued networks and in particular, helps address the well-known problem of state space explosion associated with such analysis. We also consider developing techniques for efficiently identifying abstractions and so provide a basis for the automation of this task. We illustrate the theory and techniques developed by investigating the identification of abstractions for two published MVN models of the lysis-lysogeny switch in the bacteriophage lambda.

  13. Thermal model of local ultrasound heating of biological tissue

    Science.gov (United States)

    Nedogovor, V. A.; Sigal, V. L.; Popsuev, E. I.

    1996-09-01

    Possibilities of creation of controlled temperature fields in deep-seated biological tissue with the use of an endocavity ultrasound applicator with surface cooling are considered. Mathematical models are proposed and calculated that make it possible to construct acoustic and thermal fields in biotissues depending on the thermophysical and ultrasound characteristics of the medium being irradiated and to reveal situations and effects that are important for solving problems of practical medicine in the field of local ultrasound hyperthermia and thermotherapy of tissue.

  14. Cellular systems biology profiling applied to cellular models of disease.

    Science.gov (United States)

    Giuliano, Kenneth A; Premkumar, Daniel R; Strock, Christopher J; Johnston, Patricia; Taylor, Lansing

    2009-11-01

    Building cellular models of disease based on the approach of Cellular Systems Biology (CSB) has the potential to improve the process of creating drugs as part of the continuum from early drug discovery through drug development and clinical trials and diagnostics. This paper focuses on the application of CSB to early drug discovery. We discuss the integration of protein-protein interaction biosensors with other multiplexed, functional biomarkers as an example in using CSB to optimize the identification of quality lead series compounds.

  15. A nude mouse model of endometriosis and its biological behaviors

    Institute of Scientific and Technical Information of China (English)

    WANG Dan-bo; ZHANG Shu-lan; NIU Hui-yan; LU Jing-ming

    2005-01-01

    @@ Endometriosis (EM) as a common and intractable gynecological disease is characterized by unknown etiology and complex pathologic changes. Many factors of the disease are uncertain at the molecular level and it is difficult to study clinically. In this study, we attempted to establish a nude mice model of EM for dynamical observation of the genesis and development of the disease, morphological changes in tissue, and biological behaviors.

  16. Biological Jumping Mechanism Analysis and Modeling for Frog Robot

    Institute of Scientific and Technical Information of China (English)

    Meng Wang; Xi-zhe Zang; Ji-zhuang Fan; Jie Zhao

    2008-01-01

    This paper presents a mechanical model of jumping robot based on the biological mechanism analysis of frog. By biological observation and kinematic analysis the frog jump is divided into take-off phase, aerial phase and landing phase. We find the similar trajectories of hindlimb joints during jump, the important effect of foot during take-off and the role of forelimb in supporting the body. Based on the observation, the frog jump is simplified and a mechanical model is put forward. The robot leg is represented by a 4-bar spring/linkage mechanism model, which has three Degrees of Freedom (DOF) at hip joint and one DOF (passive) at tarsometatarsal joint on the foot. The shoulder and elbow joints each has one DOF for the balancing function of arm.The ground reaction force of the model is analyzed and compared with that of frog during take-off. The results show that the model has the same advantages of low likelihood of premature lift-off and high efficiency as the frog. Analysis results and the model can be employed to develop and control a robot capable of mimicking the jumping behavior of flog.

  17. Continuous Modeling of Calcium Transport Through Biological Membranes

    Science.gov (United States)

    Jasielec, J. J.; Filipek, R.; Szyszkiewicz, K.; Sokalski, T.; Lewenstam, A.

    2016-08-01

    In this work an approach to the modeling of the biological membranes where a membrane is treated as a continuous medium is presented. The Nernst-Planck-Poisson model including Poisson equation for electric potential is used to describe transport of ions in the mitochondrial membrane—the interface which joins mitochondrial matrix with cellular cytosis. The transport of calcium ions is considered. Concentration of calcium inside the mitochondrion is not known accurately because different analytical methods give dramatically different results. We explain mathematically these differences assuming the complexing reaction inside mitochondrion and the existence of the calcium set-point (concentration of calcium in cytosis below which calcium stops entering the mitochondrion).

  18. Mass Extinction in a Simple Mathematical Biological Model

    CERN Document Server

    Tokita, K; Tokita, Kei; Yasutomi, Ayumu

    1997-01-01

    Introducing the effect of extinction into the so-called replicator equations in mathematical biology, we construct a general model of ecosystems. The present model shows mass extinction by its own extinction dynamics when the system initially has a large number of species ( diversity). The extinction dynamics shows several significant features such as a power law in basin size distribution, induction time, etc. The present theory can be a mathematical foundation of the species-area effect in the paleontologic theory for mass extinction.

  19. Ferrokinetics: a biologic model for plasma iron exchange in man.

    Science.gov (United States)

    Cook, J D; Marsaglia, G; Eschbach, J W; Funk, D D; Finch, C A

    1970-02-01

    A method is presented for calculating internal iron kinetics. An early reflux associated with extravascular exchange and a late reflux associated with erythropoiesis are described. A biologic model of iron exchange is proposed in which erythron iron turnover is divided into an effective portion (iron fixed in circulating red cells) and wastage iron of erythropoiesis (late reflux). Nonerythroid iron exchange also has a fixed portion (parenchymal uptake) and an early reflux (lymphatic circuit), both of which correlate in amount with the amount of plasma iron. Ferrokinetic measurements in normal subjects and in various pathologic states are presented to validate the model.

  20. Experimental model of arteriovenous malformation in vitro using biological grafts

    Directory of Open Access Journals (Sweden)

    Sandu Aurelia Mihaela

    2015-06-01

    Full Text Available Introduction: Brain arteriovenous malformations (AVMs represent a serious health problem all around the world. Experimental models help to better understand the pathophysiology of these lesions. Experiment: We performed an experimental model of AVM using biological grafts, arteries and veins harvested from chicken wings at the elbow joint. We used 14 vessels and we performed 20 end-to-end anastomoses to create a nidus with a single feeding artery and a single draining vein. The system was irrigated with colored solution. The experiment was done according with law in force regarding experimental research activity. Conclusions: Experimental models allow us to understand the hemodynamics and predict the outcome of brain AVMs in humans. This experimental model is a useful tool in understanding the hemodynamic properties of brain AVMs. It is very useful in vascular anastomosis training

  1. Exogenous control of biological and ecological systems through evolutionary modelling

    Directory of Open Access Journals (Sweden)

    Alessandro Ferrarini

    2013-09-01

    Full Text Available The controllability of network-like systems is a topical issue in ecology and biology. It relies on the ability to lead a system's behaviour towards the desired state through the appropriate handling of input variables. Up to now, controllability of networks is based on the permanent control of a set of driver nodes that can guide the system's dynamics. This assumption seems motivated by real-world networks observation, where a decentralized control is often applied only to part of the nodes. While in a previous paper I showed that ecological and biological networks can be efficaciously controlled from the inside, here I further introduce a new framework for network controllability based on the employment of exogenous controllers and evolutionary modelling, and provide an exemplification of its application.

  2. Coupled model of physical and biological processes affecting maize pollination

    Science.gov (United States)

    Arritt, R.; Westgate, M.; Riese, J.; Falk, M.; Takle, E.

    2003-04-01

    Controversy over the use of genetically modified (GM) crops has led to increased interest in evaluating and controlling the potential for inadvertent outcrossing in open-pollinated crops such as maize. In response to this problem we have developed a Lagrangian model of pollen dispersion as a component of a coupled end-to-end (anther to ear) physical-biological model of maize pollination. The Lagrangian method is adopted because of its generality and flexibility: first, the method readily accommodates flow fields of arbitrary complexity; second, each element of the material being transported can be identified by its source, time of release, or other properties of interest. The latter allows pollen viability to be estimated as a function of such factors as travel time, temperature, and relative humidity, so that the physical effects of airflow and turbulence on pollen dispersion can be considered together with the biological aspects of pollen release and viability. Predicted dispersion of pollen compares well both to observations and to results from a simpler Gaussian plume model. Ability of the Lagrangian model to handle complex air flows is demonstrated by application to pollen dispersion in the vicinity of an agricultural shelter belt. We also show results indicating that pollen viability can be quantified by an "aging function" that accounts for temperature, humidity, and time of exposure.

  3. Biologic

    CERN Document Server

    Kauffman, L H

    2002-01-01

    In this paper we explore the boundary between biology and the study of formal systems (logic). In the end, we arrive at a summary formalism, a chapter in "boundary mathematics" where there are not only containers but also extainers ><, entities open to interaction and distinguishing the space that they are not. The boundary algebra of containers and extainers is to biologic what boolean algebra is to classical logic. We show how this formalism encompasses significant parts of the logic of DNA replication, the Dirac formalism for quantum mechanics, formalisms for protein folding and the basic structure of the Temperley Lieb algebra at the foundations of topological invariants of knots and links.

  4. MODEL ORGANISMS USED IN MOLECULAR BIOLOGY OR MEDICAL RESEARCH

    Directory of Open Access Journals (Sweden)

    Pandey Govind

    2011-11-01

    Full Text Available A model organism is a non-human species that is studied to understand specific biological phenomena with the expectation that investigations made in the organism model will provide insight into the workings of other organisms. The model organisms are widely used to explore potential causes and treatments for human as well as animal diseases when experiments on animals or humans would be unfeasible or considered less ethical. Studying model organisms may be informative, but care must be taken when generalizing from one organism to another. Often, model organisms are chosen on the basis that they are amenable to experimental manipulation. When researchers look for an organism to use in their studies, they look for several traits. Among these are size, generation time, accessibility, manipulation, genetics, conservation of mechanisms and potential economic benefit. As comparative molecular biology has become more common, some researchers have sought model organisms from a wider assortment of lineages on the tree of life. There are many model organisms, such as viruses (e.g., Phage lambda virus, Tobacco mosaic virus, etc., bacteria (e.g., Bacillus subtilis, Escherichia coli, Pseudomonas fluorescens, Vibrio fischeri, etc., algae (e.g., Chlamydomonas reinhardtii, Emiliania huxleyi, etc., molds (e.g., Aspergillus nidulans, Neurospora crassa, etc., yeasts (e.g., Saccharomyces cerevisiae, Ustilago maydis, etc., higher plants (e.g., Arabidopsis thaliana, Lemna gibba, Lotus japonicus, Nicotiana tabaccum, Oryza sativa, Physcomitrella patens, Zea mays, etc. and animals (e.g., Caenorhabditis elegans, guinea pig, hamster, mouse, rat, cat, chicken, dog, frog, Hydra, Drosophila melanogaster fruit fly, fish, etc..

  5. Lessons learned from quantitative dynamical modeling in systems biology.

    Directory of Open Access Journals (Sweden)

    Andreas Raue

    Full Text Available Due to the high complexity of biological data it is difficult to disentangle cellular processes relying only on intuitive interpretation of measurements. A Systems Biology approach that combines quantitative experimental data with dynamic mathematical modeling promises to yield deeper insights into these processes. Nevertheless, with growing complexity and increasing amount of quantitative experimental data, building realistic and reliable mathematical models can become a challenging task: the quality of experimental data has to be assessed objectively, unknown model parameters need to be estimated from the experimental data, and numerical calculations need to be precise and efficient. Here, we discuss, compare and characterize the performance of computational methods throughout the process of quantitative dynamic modeling using two previously established examples, for which quantitative, dose- and time-resolved experimental data are available. In particular, we present an approach that allows to determine the quality of experimental data in an efficient, objective and automated manner. Using this approach data generated by different measurement techniques and even in single replicates can be reliably used for mathematical modeling. For the estimation of unknown model parameters, the performance of different optimization algorithms was compared systematically. Our results show that deterministic derivative-based optimization employing the sensitivity equations in combination with a multi-start strategy based on latin hypercube sampling outperforms the other methods by orders of magnitude in accuracy and speed. Finally, we investigated transformations that yield a more efficient parameterization of the model and therefore lead to a further enhancement in optimization performance. We provide a freely available open source software package that implements the algorithms and examples compared here.

  6. Revision history aware repositories of computational models of biological systems

    Directory of Open Access Journals (Sweden)

    Nickerson David P

    2011-01-01

    Full Text Available Abstract Background Building repositories of computational models of biological systems ensures that published models are available for both education and further research, and can provide a source of smaller, previously verified models to integrate into a larger model. One problem with earlier repositories has been the limitations in facilities to record the revision history of models. Often, these facilities are limited to a linear series of versions which were deposited in the repository. This is problematic for several reasons. Firstly, there are many instances in the history of biological systems modelling where an 'ancestral' model is modified by different groups to create many different models. With a linear series of versions, if the changes made to one model are merged into another model, the merge appears as a single item in the history. This hides useful revision history information, and also makes further merges much more difficult, as there is no record of which changes have or have not already been merged. In addition, a long series of individual changes made outside of the repository are also all merged into a single revision when they are put back into the repository, making it difficult to separate out individual changes. Furthermore, many earlier repositories only retain the revision history of individual files, rather than of a group of files. This is an important limitation to overcome, because some types of models, such as CellML 1.1 models, can be developed as a collection of modules, each in a separate file. The need for revision history is widely recognised for computer software, and a lot of work has gone into developing version control systems and distributed version control systems (DVCSs for tracking the revision history. However, to date, there has been no published research on how DVCSs can be applied to repositories of computational models of biological systems. Results We have extended the Physiome Model

  7. Human pluripotent stem cells: an emerging model in developmental biology.

    Science.gov (United States)

    Zhu, Zengrong; Huangfu, Danwei

    2013-02-01

    Developmental biology has long benefited from studies of classic model organisms. Recently, human pluripotent stem cells (hPSCs), including human embryonic stem cells and human induced pluripotent stem cells, have emerged as a new model system that offers unique advantages for developmental studies. Here, we discuss how studies of hPSCs can complement classic approaches using model organisms, and how hPSCs can be used to recapitulate aspects of human embryonic development 'in a dish'. We also summarize some of the recently developed genetic tools that greatly facilitate the interrogation of gene function during hPSC differentiation. With the development of high-throughput screening technologies, hPSCs have the potential to revolutionize gene discovery in mammalian development.

  8. Population pharmacokinetic-pharmacodynamic modeling of biological agents: when modeling meets reality.

    Science.gov (United States)

    Mould, Diane R; Frame, Bill

    2010-09-01

    The pharmacokinetics (PK) and pharmacodynamics (PD) of many biological agents (biologics) have inherent complexities requiring specialized approaches to develop reliable, unbiased models. Three cases are covered: preponderance of zero values, nonresponder subpopulations, and adaptive dosing. Engineered biologics exhibit high affinity for target receptors. Biologics can saturate receptors, abolishing free receptor levels for protracted periods. Consequently, the distribution of observations can be heavy at, and near, the boundary. A 2-part model (ie, a truncated δ log-normal distribution) may be appropriate. Mixture models identify subpopulations based on bimodal or multimodal distributions of η values. With biologics, PD may be compromised because of lack of receptors, or the PD may be affected because of other events resulting in erratic excursions. Nonresponders exhibit a random walk-around placebo trajectory, resulting in high residual variability. The distributions of etas are often badly skewed or polymodal. An indescribable mixture model separates subjects who are nonresponders, providing diagnostic pharmacologic information on the drug. Many biologics use PD-based adaptive dosing. During model development, data used for model development include adaptive dosing. For simulation, adaptive dosing must be implemented. Failure to account for dose adjustments results in biased or inflated prediction intervals because subjects in the simulated data undergo inappropriate dose adjustments.

  9. Testing Models: A Key Aspect to Promote Teaching Activities Related to Models and Modelling in Biology Lessons?

    Science.gov (United States)

    Krell, Moritz; Krüger, Dirk

    2016-01-01

    This study investigated biology teachers' (N = 148) understanding of models and modelling (MoMo), their model-related teaching activities and relations between the two. A framework which distinguishes five aspects of MoMo in science ("nature of models," "multiple models," "purpose of models," "testing…

  10. Modeling biological systems with delays in Bio-PEPA

    CERN Document Server

    Caravagna, Giulio; 10.4204/EPTCS.40.7

    2010-01-01

    Delays in biological systems may be used to model events for which the underlying dynamics cannot be precisely observed, or to provide abstraction of some behavior of the system resulting more compact models. In this paper we enrich the stochastic process algebra Bio-PEPA, with the possibility of assigning delays to actions, yielding a new non-Markovian process algebra: Bio-PEPAd. This is a conservative extension meaning that the original syntax of Bio-PEPA is retained and the delay specification which can now be associated with actions may be added to existing Bio-PEPA models. The semantics of the firing of the actions with delays is the delay-as-duration approach, earlier presented in papers on the stochastic simulation of biological systems with delays. These semantics of the algebra are given in the Starting-Terminating style, meaning that the state and the completion of an action are observed as two separate events, as required by delays. Furthermore we outline how to perform stochastic simulation of Bio...

  11. Modeling of biological clogging in unsaturated porous media

    Science.gov (United States)

    Soleimani, Sahar; Van Geel, Paul J.; Isgor, O. Burkan; Mostafa, Mohamed B.

    2009-04-01

    A two-dimensional unsaturated flow and transport model, which includes microbial growth and decay, has been developed to simulate biological clogging in unsaturated soils, specifically biofilters. The bacterial growth and rate of solute reduction due to biodegradation is estimated using the Monod equation. The effect of microbial growth is considered in the proposed conceptual model that relates the relative permeability term for unsaturated flow to the microbial growth. Two applications of the model are presented in this study. Using the model, the clogging mechanism in different soils has been simulated. The results of the model indicate that the time to reach a clogged state is influenced by the hydraulic properties of the soil. Clogging is delayed in soils with higher saturated hydraulic conductivities, and higher porosities. For the relative permeability model proposed, higher van Genuchten n values lead to a delay in clogging. The model was also used to simulate the progressive clogging of a septic bed as the biomat initially forms at the up-gradient end of the distribution pipe, displacing wastewater infiltration and biomat formation further down-gradient over time.

  12. Bridging Mechanistic and Phenomenological Models of Complex Biological Systems

    Science.gov (United States)

    Transtrum, Mark K.; Qiu, Peng

    2016-01-01

    The inherent complexity of biological systems gives rise to complicated mechanistic models with a large number of parameters. On the other hand, the collective behavior of these systems can often be characterized by a relatively small number of phenomenological parameters. We use the Manifold Boundary Approximation Method (MBAM) as a tool for deriving simple phenomenological models from complicated mechanistic models. The resulting models are not black boxes, but remain expressed in terms of the microscopic parameters. In this way, we explicitly connect the macroscopic and microscopic descriptions, characterize the equivalence class of distinct systems exhibiting the same range of collective behavior, and identify the combinations of components that function as tunable control knobs for the behavior. We demonstrate the procedure for adaptation behavior exhibited by the EGFR pathway. From a 48 parameter mechanistic model, the system can be effectively described by a single adaptation parameter τ characterizing the ratio of time scales for the initial response and recovery time of the system which can in turn be expressed as a combination of microscopic reaction rates, Michaelis-Menten constants, and biochemical concentrations. The situation is not unlike modeling in physics in which microscopically complex processes can often be renormalized into simple phenomenological models with only a few effective parameters. The proposed method additionally provides a mechanistic explanation for non-universal features of the behavior. PMID:27187545

  13. A heat transfer model for biological wastewater treatment system

    Science.gov (United States)

    Lin, S. H.

    A heat transfer model for predicting the water temperature of aeration tank in a biological wastewater treatment plant is presented. The heat transfer mechanisms involved in the development of the heat transfer model include heat gains from solar radiation and biochemical reaction and heat losses from evaporation, aeration, wind blowing and conduction through tank walls. Several empirical correlations were adopted and appropriate assumptions made to facilitate the model development. Experiments were conducted in the biological wastewater treatment plant of a chemical fiber company over a year's period. The operational, weather and temperature data were registered. The daily water temperature data were averaged over a month period and compared with the theoretical prediction. Excellent agreement has been obtained between the predicted and measured temperatures, verifying the proposed heat transfer model. Zusammenfassung Es wird ein Wärmeübergangsmodell zur Berechnung der Wassertemperatur im Belüftungstank einer Anlage zur biologischen Abwasserbehandlung vorgestellt. Die in das Modell eingehenden Wärmeübergangsmechanismen umfassen: solare Wärmeeinstrahlung, biochemische Reaktion, Wärmeverluste durch Verdampfung, Belüftung, Windeinfluß und Leitung durch die Behälterwände. Mehrere empirische Beziehungen sowie vertretbare Annahmen tragen zur Modellvereinfachung bei. An der biologischen Abwasser-Kläranlage einer Chemiefaserfirma wurden ein Jahr lang Experimente durchgeführt und dabei Betriebs-, Wetter- und Temperaturdaten aufgezeichnet. Die täglichen Wassertemperaturen, gemittelt über einen Monat, zeigten ausgezeichnete Übereinstimmung mit den theoretischen Vorausberechnungen und bestätigten so die Brauchbarkeit des vorgeschlagenen Wärmeübergangsmodells.

  14. Modeling human risk: Cell & molecular biology in context

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1997-06-01

    It is anticipated that early in the next century manned missions into outer space will occur, with a mission to Mars scheduled between 2015 and 2020. However, before such missions can be undertaken, a realistic estimation of the potential risks to the flight crews is required. One of the uncertainties remaining in this risk estimation is that posed by the effects of exposure to the radiation environment of outer space. Although the composition of this environment is fairly well understood, the biological effects arising from exposure to it are not. The reasons for this are three-fold: (1) A small but highly significant component of the radiation spectrum in outer space consists of highly charged, high energy (HZE) particles which are not routinely experienced on earth, and for which there are insufficient data on biological effects; (2) Most studies on the biological effects of radiation to date have been high-dose, high dose-rate, whereas in space, with the exception of solar particle events, radiation exposures will be low-dose, low dose-rate; (3) Although it has been established that the virtual absence of gravity in space has a profound effect on human physiology, it is not clear whether these effects will act synergistically with those of radiation exposure. A select panel will evaluate the utilizing experiments and models to accurately predict the risks associated with exposure to HZE particles. Topics of research include cellular and tissue response, health effects associated with radiation damage, model animal systems, and critical markers of Radiation response.

  15. Fluctuating Nonlinear Spring Model of Mechanical Deformation of Biological Particles.

    Directory of Open Access Journals (Sweden)

    Olga Kononova

    2016-01-01

    Full Text Available The mechanical properties of virus capsids correlate with local conformational dynamics in the capsid structure. They also reflect the required stability needed to withstand high internal pressures generated upon genome loading and contribute to the success of important events in viral infectivity, such as capsid maturation, genome uncoating and receptor binding. The mechanical properties of biological nanoparticles are often determined from monitoring their dynamic deformations in Atomic Force Microscopy nanoindentation experiments; but a comprehensive theory describing the full range of observed deformation behaviors has not previously been described. We present a new theory for modeling dynamic deformations of biological nanoparticles, which considers the non-linear Hertzian deformation, resulting from an indenter-particle physical contact, and the bending of curved elements (beams modeling the particle structure. The beams' deformation beyond the critical point triggers a dynamic transition of the particle to the collapsed state. This extreme event is accompanied by a catastrophic force drop as observed in the experimental or simulated force (F-deformation (X spectra. The theory interprets fine features of the spectra, including the nonlinear components of the FX-curves, in terms of the Young's moduli for Hertzian and bending deformations, and the structural damage dependent beams' survival probability, in terms of the maximum strength and the cooperativity parameter. The theory is exemplified by successfully describing the deformation dynamics of natural nanoparticles through comparing theoretical curves with experimental force-deformation spectra for several virus particles. This approach provides a comprehensive description of the dynamic structural transitions in biological and artificial nanoparticles, which is essential for their optimal use in nanotechnology and nanomedicine applications.

  16. Separable Watermarking Technique Using the Biological Color Model

    Directory of Open Access Journals (Sweden)

    David Nino

    2009-01-01

    Full Text Available Problem statement: The issue of having robust and fragile watermarking is still main focus for various researchers worldwide. Performance of a watermarking technique depends on how complex as well as how feasible to implement. These issues are tested using various kinds of attacks including geometry and transformation. Watermarking techniques in color images are more challenging than gray images in terms of complexity and information handling. In this study, we focused on implementation of watermarking technique in color images using the biological model. Approach: We proposed a novel method for watermarking using spatial and the Discrete Cosine Transform (DCT domains. The proposed method deled with colored images in the biological color model, the Hue, Saturation and Intensity (HSI. Technique was implemented and used against various colored images including the standard ones such as pepper image. The experiments were done using various attacks such as cropping, transformation and geometry. Results: The method robustness showed high accuracy in retrieval data and technique is fragile against geometric attacks. Conclusion: Watermark security was increased by using the Hadamard transform matrix. The watermarks used were meaningful and of varying sizes and details.

  17. Introduction to mathematical biology modeling, analysis, and simulations

    CERN Document Server

    Chou, Ching Shan

    2016-01-01

    This book is based on a one semester course that the authors have been teaching for several years, and includes two sets of case studies. The first includes chemostat models, predator-prey interaction, competition among species, the spread of infectious diseases, and oscillations arising from bifurcations. In developing these topics, readers will also be introduced to the basic theory of ordinary differential equations, and how to work with MATLAB without having any prior programming experience. The second set of case studies were adapted from recent and current research papers to the level of the students. Topics have been selected based on public health interest. This includes the risk of atherosclerosis associated with high cholesterol levels, cancer and immune interactions, cancer therapy, and tuberculosis. Readers will experience how mathematical models and their numerical simulations can provide explanations that guide biological and biomedical research. Considered to be the undergraduate companion to t...

  18. Modeling of biological doses and mechanical effects on bone transduction

    CERN Document Server

    Rieger, Romain; Jennane, Rachid; 10.1016/j.jtbi.2011.01.003

    2012-01-01

    Shear stress, hormones like parathyroid and mineral elements like calcium mediate the amplitude of stimulus signal which affects the rate of bone remodeling. The current study investigates the theoretical effects of different metabolic doses in stimulus signal level on bone. The model was built considering the osteocyte as the sensing center mediated by coupled mechanical shear stress and some biological factors. The proposed enhanced model was developed based on previously published works dealing with different aspects of bone transduction. It describes the effects of physiological doses variations of Calcium, Parathyroid Hormone, Nitric Oxide and Prostaglandin E2 on the stimulus level sensed by osteocytes in response to applied shear stress generated by interstitial fluid flow. We retained the metabolic factors (Parathyroid Hormone, Nitric Oxide, and Prostaglandin E2) as parameters of bone cell mechanosensitivity because stimulation/inhibition of induced pathways stimulates osteogenic response in vivo. We t...

  19. Fluctuating Nonlinear Spring Model of Mechanical Deformation of Biological Particles

    CERN Document Server

    Kononova, Olga; Marx, Kenneth A; Wuite, Gijs J L; Roos, Wouter H; Barsegov, Valeri

    2015-01-01

    We present a new theory for modeling forced indentation spectral lineshapes of biological particles, which considers non-linear Hertzian deformation due to an indenter-particle physical contact and bending deformations of curved beams modeling the particle structure. The bending of beams beyond the critical point triggers the particle dynamic transition to the collapsed state, an extreme event leading to the catastrophic force drop as observed in the force (F)-deformation (X) spectra. The theory interprets fine features of the spectra: the slope of the FX curves and the position of force-peak signal, in terms of mechanical characteristics --- the Young's moduli for Hertzian and bending deformations E_H and E_b, and the probability distribution of the maximum strength with the strength of the strongest beam F_b^* and the beams' failure rate m. The theory is applied to successfully characterize the $FX$ curves for spherical virus particles --- CCMV, TrV, and AdV.

  20. Quasi – biological model of radiogenic cancer morbidity

    Directory of Open Access Journals (Sweden)

    A. T. Gubin

    2015-01-01

    Full Text Available The methods: Linear differential equations were used to formalize contemporary assumptions of self –sustaining tissue cell kinetics under the impact of adverse factors, on the formation and repairing of cell “pre-cancer” defects, on inheritance and retaining such defects in daughter cells which results in malignant neoplasms, on age-dependent impairment of human body’s function to eliminate such cells.The results: The model reproduces the well-known regularities of radiogenic cancer morbidity increase depending on instantaneous radiation exposure age and on attained age: the relative reduction at increased radiation age which the model attributes to age decrease of stem cells, relative reduction at increased time after radiation induced by “sorting out” of cells with “pre-cancer” defects, absolute increase with age proportional to natural cause mortality rate.The relevance of the developed quasi-biological model is displayed via comparison to the ICRP model for radiogenic increase of solid carcinomas’ morbidity after single radiation exposure. The latter model had been developed after Japanese cohort observations. For both genders high goodness-of-fit was achieved between the models at values of Gompertz’ law factor which had been defined for men and women in this cohort via selecting the value of the only free parameter indicating age-dependent exponential retardation of stem cells’ division.The conclusion: The proposed model suggests that the estimation of radiogenic risk inter-population transfer can be done on the basis of the data on age-dependent mortality intensity increase from all natural causes. The model also creates the premises for inter-species transfer of risk following the well-known parameters of cell populations’ kinetics in animal’s organs and tissues and Gompertz’s law parameters. This model is applicable also for analyses of age-dependent changes of background cancer morbidity. 

  1. Modelling biological behaviours with the unified modelling language: an immunological case study and critique.

    Science.gov (United States)

    Read, Mark; Andrews, Paul S; Timmis, Jon; Kumar, Vipin

    2014-10-01

    We present a framework to assist the diagrammatic modelling of complex biological systems using the unified modelling language (UML). The framework comprises three levels of modelling, ranging in scope from the dynamics of individual model entities to system-level emergent properties. By way of an immunological case study of the mouse disease experimental autoimmune encephalomyelitis, we show how the framework can be used to produce models that capture and communicate the biological system, detailing how biological entities, interactions and behaviours lead to higher-level emergent properties observed in the real world. We demonstrate how the UML can be successfully applied within our framework, and provide a critique of UML's ability to capture concepts fundamental to immunology and biology more generally. We show how specialized, well-explained diagrams with less formal semantics can be used where no suitable UML formalism exists. We highlight UML's lack of expressive ability concerning cyclic feedbacks in cellular networks, and the compounding concurrency arising from huge numbers of stochastic, interacting agents. To compensate for this, we propose several additional relationships for expressing these concepts in UML's activity diagram. We also demonstrate the ambiguous nature of class diagrams when applied to complex biology, and question their utility in modelling such dynamic systems. Models created through our framework are non-executable, and expressly free of simulation implementation concerns. They are a valuable complement and precursor to simulation specifications and implementations, focusing purely on thoroughly exploring the biology, recording hypotheses and assumptions, and serve as a communication medium detailing exactly how a simulation relates to the real biology.

  2. Models for integrated pest control and their biological implications.

    Science.gov (United States)

    Tang, Sanyi; Cheke, Robert A

    2008-09-01

    Successful integrated pest management (IPM) control programmes depend on many factors which include host-parasitoid ratios, starting densities, timings of parasitoid releases, dosages and timings of insecticide applications and levels of host-feeding and parasitism. Mathematical models can help us to clarify and predict the effects of such factors on the stability of host-parasitoid systems, which we illustrate here by extending the classical continuous and discrete host-parasitoid models to include an IPM control programme. The results indicate that one of three control methods can maintain the host level below the economic threshold (ET) in relation to different ET levels, initial densities of host and parasitoid populations and host-parasitoid ratios. The effects of host intrinsic growth rate and parasitoid searching efficiency on host mean outbreak period can be calculated numerically from the models presented. The instantaneous pest killing rate of an insecticide application is also estimated from the models. The results imply that the modelling methods described can help in the design of appropriate control strategies and assist management decision-making. The results also indicate that a high initial density of parasitoids (such as in inundative releases) and high parasitoid inter-generational survival rates will lead to more frequent host outbreaks and, therefore, greater economic damage. The biological implications of this counter intuitive result are discussed.

  3. A Color-Opponency Based Biological Model for Color Constancy

    Directory of Open Access Journals (Sweden)

    Yongjie Li

    2011-05-01

    Full Text Available Color constancy is the ability of the human visual system to adaptively correct color-biased scenes under different illuminants. Most of the existing color constancy models are nonphysiologically plausible. Among the limited biological models, the great majority is Retinex and its variations, and only two or three models directly simulate the feature of color-opponency, but only of the very earliest stages of visual pathway, i.e., the single-opponent mechanisms involved at the levels of retinal ganglion cells and lateral geniculate nucleus (LGN neurons. Considering the extensive physiological evidences supporting that both the single-opponent cells in retina and LGN and the double-opponent neurons in primary visual cortex (V1 are the building blocks for color constancy, in this study we construct a color-opponency based color constancy model by simulating the opponent fashions of both the single-opponent and double-opponent cells in a forward manner. As for the spatial structure of the receptive fields (RF, both the classical RF (CRF center and the nonclassical RF (nCRF surround are taken into account for all the cells. The proposed model was tested on several typical image databases commonly used for performance evaluation of color constancy methods, and exciting results were achieved.

  4. First steps in computational systems biology: A practical session in metabolic modeling and simulation.

    Science.gov (United States)

    Reyes-Palomares, Armando; Sánchez-Jiménez, Francisca; Medina, Miguel Ángel

    2009-05-01

    A comprehensive understanding of biological functions requires new systemic perspectives, such as those provided by systems biology. Systems biology approaches are hypothesis-driven and involve iterative rounds of model building, prediction, experimentation, model refinement, and development. Developments in computer science are allowing for ever faster numerical simulations of mathematical models. Mathematical modeling plays an essential role in new systems biology approaches. As a complex, integrated system, metabolism is a suitable topic of study for systems biology approaches. However, up until recently, this topic has not been properly covered in biochemistry courses. This communication reports the development and implementation of a practical lesson plan on metabolic modeling and simulation.

  5. Micrasterias as a Model System in Plant Cell Biology

    Science.gov (United States)

    Lütz-Meindl, Ursula

    2016-01-01

    The unicellular freshwater alga Micrasterias denticulata is an exceptional organism due to its complex star-shaped, highly symmetric morphology and has thus attracted the interest of researchers for many decades. As a member of the Streptophyta, Micrasterias is not only genetically closely related to higher land plants but shares common features with them in many physiological and cell biological aspects. These facts, together with its considerable cell size of about 200 μm, its modest cultivation conditions and the uncomplicated accessibility particularly to any microscopic techniques, make Micrasterias a very well suited cell biological plant model system. The review focuses particularly on cell wall formation and composition, dictyosomal structure and function, cytoskeleton control of growth and morphogenesis as well as on ionic regulation and signal transduction. It has been also shown in the recent years that Micrasterias is a highly sensitive indicator for environmental stress impact such as heavy metals, high salinity, oxidative stress or starvation. Stress induced organelle degradation, autophagy, adaption and detoxification mechanisms have moved in the center of interest and have been investigated with modern microscopic techniques such as 3-D- and analytical electron microscopy as well as with biochemical, physiological and molecular approaches. This review is intended to summarize and discuss the most important results obtained in Micrasterias in the last 20 years and to compare the results to similar processes in higher plant cells. PMID:27462330

  6. Models to Study NK Cell Biology and Possible Clinical Application.

    Science.gov (United States)

    Zamora, Anthony E; Grossenbacher, Steven K; Aguilar, Ethan G; Murphy, William J

    2015-08-03

    Natural killer (NK) cells are large granular lymphocytes of the innate immune system, responsible for direct targeting and killing of both virally infected and transformed cells. NK cells rapidly recognize and respond to abnormal cells in the absence of prior sensitization due to their wide array of germline-encoded inhibitory and activating receptors, which differs from the receptor diversity found in B and T lymphocytes that is due to the use of recombination-activation gene (RAG) enzymes. Although NK cells have traditionally been described as natural killers that provide a first line of defense prior to the induction of adaptive immunity, a more complex view of NK cells is beginning to emerge, indicating they may also function in various immunoregulatory roles and have the capacity to shape adaptive immune responses. With the growing appreciation for the diverse functions of NK cells, and recent technological advancements that allow for a more in-depth understanding of NK cell biology, we can now begin to explore new ways to manipulate NK cells to increase their clinical utility. In this overview unit, we introduce the reader to various aspects of NK cell biology by reviewing topics ranging from NK cell diversity and function, mouse models, and the roles of NK cells in health and disease, to potential clinical applications. © 2015 by John Wiley & Sons, Inc.

  7. Micrasterias as a model system in plant cell biology

    Directory of Open Access Journals (Sweden)

    Ursula Luetz-Meindl

    2016-07-01

    Full Text Available The unicellular freshwater alga Micrasterias denticulata is an exceptional organism due to its extraordinary star-shaped, highly symmetric morphology and has thus attracted the interest of researchers for many decades. As a member of the Streptophyta, Micrasterias is not only genetically closely related to higher land plants but shares common features with them in many physiological and cell biological aspects. These facts, together with its considerable cell size of about 200 µm, its modest cultivation conditions and the uncomplicated accessibility particularly to any microscopic techniques, make Micrasterias a very well suited cell biological plant model system. The review focuses particularly on cell wall formation and composition, dictyosomal structure and function, cytoskeleton control of growth and morphogenesis as well as on ionic regulation and signal transduction. It has been also shown in the recent years that Micrasterias is a highly sensitive indicator for environmental stress impact such as heavy metals, high salinity, oxidative stress or starvation. Stress induced organelle degradation, autophagy, adaption and detoxification mechanisms have moved in the center of interest and have been investigated with modern microscopic techniques such as 3-D- and analytical electron microscopy as well as with biochemical, physiological and molecular approaches. This review is intended to summarize and discuss the most important results obtained in Micrasterias in the last 20 years and to compare the results to similar processes in higher plant cells.

  8. First Steps in Computational Systems Biology: A Practical Session in Metabolic Modeling and Simulation

    Science.gov (United States)

    Reyes-Palomares, Armando; Sanchez-Jimenez, Francisca; Medina, Miguel Angel

    2009-01-01

    A comprehensive understanding of biological functions requires new systemic perspectives, such as those provided by systems biology. Systems biology approaches are hypothesis-driven and involve iterative rounds of model building, prediction, experimentation, model refinement, and development. Developments in computer science are allowing for ever…

  9. A biologically plausible embodied model of action discovery

    Directory of Open Access Journals (Sweden)

    Rufino eBolado-Gomez

    2013-03-01

    Full Text Available During development, animals can spontaneously discover action-outcomepairings enabling subsequent achievement of their goals. We present abiologically plausible embodied model addressing key aspects of thisprocess. The biomimetic model core comprises the basal ganglia and itsloops through cortex and thalamus. We incorporate reinforcementlearning with phasic dopamine supplying a sensory prediction error,signalling 'surprising' outcomes. Phasic dopamine is used in acorticostriatal learning rule which is consistent with recent data. Wealso hypothesised that objects associated with surprising outcomesacquire 'novelty salience' contingent on the predicability of theoutcome. To test this idea we used a simple model of predictiongoverning the dynamics of novelty salience and phasic dopamine. Thetask of the virtual robotic agent mimicked an in vivo counterpart(Gancarz et al., 2011 and involved interaction with a target objectwhich caused a light flash, or a control object which did not.Learning took place according to two schedules. In one, the phasicoutcome was delivered after interaction with the target in anunpredictable way which emulated the in vivo protocol. Without noveltysalience, the model was unable to account for the experimental data.In the other schedule, the phasic outcome was reliably delivered andthe agent showed a rapid increase in the number of interactions withthe target which then decreased over subsequent sessions. We arguethis is precisely the kind of change in behaviour required torepeatedly present representations of context, action and outcome, toneural networks responsible for learning action-outcome contingency.The model also showed corticostriatal plasticity consistent withlearning a new action in basal ganglia. We conclude that actionlearning is underpinned by a complex interplay of plasticity andstimulus salience, and that our model contains many of the elementsfor biological action discovery to take place.

  10. Non-linear rheology in a model biological tissue

    CERN Document Server

    Matoz-Fernandez, D A; Barrat, Jean-Louis; Bertin, Eric; Martens, Kirsten

    2016-01-01

    Mechanical signaling plays a key role in biological processes like embryo development and cancer growth. One prominent way to probe mechanical properties of tissues is to study their response to externally applied forces. Using a particle-based model featuring random apoptosis and environment-dependent division rates, we evidence a crossover from linear flow to a shear-thinning regime with increasing shear rate. To rationalize this non-linear flow we derive a theoretical mean-field scenario that accounts for the interplay of mechanical and active noise in local stresses. These noises are respectively generated by the elastic response of the cell matrix to cell rearrangements and by the internal activity.

  11. Theories and models on the biological of cells in space

    Science.gov (United States)

    Todd, P.; Klaus, D. M.

    1996-01-01

    A wide variety of observations on cells in space, admittedly made under constraining and unnatural conditions in may cases, have led to experimental results that were surprising or unexpected. Reproducibility, freedom from artifacts, and plausibility must be considered in all cases, even when results are not surprising. The papers in symposium on 'Theories and Models on the Biology of Cells in Space' are dedicated to the subject of the plausibility of cellular responses to gravity -- inertial accelerations between 0 and 9.8 m/sq s and higher. The mechanical phenomena inside the cell, the gravitactic locomotion of single eukaryotic and prokaryotic cells, and the effects of inertial unloading on cellular physiology are addressed in theoretical and experimental studies.

  12. EcoCyc: fusing model organism databases with systems biology.

    Science.gov (United States)

    Keseler, Ingrid M; Mackie, Amanda; Peralta-Gil, Martin; Santos-Zavaleta, Alberto; Gama-Castro, Socorro; Bonavides-Martínez, César; Fulcher, Carol; Huerta, Araceli M; Kothari, Anamika; Krummenacker, Markus; Latendresse, Mario; Muñiz-Rascado, Luis; Ong, Quang; Paley, Suzanne; Schröder, Imke; Shearer, Alexander G; Subhraveti, Pallavi; Travers, Mike; Weerasinghe, Deepika; Weiss, Verena; Collado-Vides, Julio; Gunsalus, Robert P; Paulsen, Ian; Karp, Peter D

    2013-01-01

    EcoCyc (http://EcoCyc.org) is a model organism database built on the genome sequence of Escherichia coli K-12 MG1655. Expert manual curation of the functions of individual E. coli gene products in EcoCyc has been based on information found in the experimental literature for E. coli K-12-derived strains. Updates to EcoCyc content continue to improve the comprehensive picture of E. coli biology. The utility of EcoCyc is enhanced by new tools available on the EcoCyc web site, and the development of EcoCyc as a teaching tool is increasing the impact of the knowledge collected in EcoCyc.

  13. Theories and models on the Biology of Cells in Space

    Science.gov (United States)

    Todd, P.; Klaus, D. M.

    A wide variety of observations on cells in space, admittedly made under constraining and unnatural conditions in many cases, have led to experimental results that were surprising or unexpected. Reproducibility, freedom from artifacts, and plausibility must be considered in all cases, even when results are not surprising. The papers in the symposium on ``Theories and Models on the Biology of Cells in Space'' are dedicated to the subject of theplausibility of cellular responses to gravity -- inertial accelerations between 0 and 9.8 m/s^2 and higher. The mechanical phenomena inside the cell, the gravitactic locomotion of single eukaryotic and prokaryotic cells, and the effects of inertial unloading on cellular physiology are addressed in theoretical and experimental studies.

  14. Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems.

    Science.gov (United States)

    Boué, Stéphanie; Talikka, Marja; Westra, Jurjen Willem; Hayes, William; Di Fabio, Anselmo; Park, Jennifer; Schlage, Walter K; Sewer, Alain; Fields, Brett; Ansari, Sam; Martin, Florian; Veljkovic, Emilija; Kenney, Renee; Peitsch, Manuel C; Hoeng, Julia

    2015-01-01

    With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation. Database URL: http://causalbionet.com

  15. Model fit versus biological relevance: Evaluating photosynthesis-temperature models for three tropical seagrass species

    Science.gov (United States)

    Adams, Matthew P.; Collier, Catherine J.; Uthicke, Sven; Ow, Yan X.; Langlois, Lucas; O’Brien, Katherine R.

    2017-01-01

    When several models can describe a biological process, the equation that best fits the data is typically considered the best. However, models are most useful when they also possess biologically-meaningful parameters. In particular, model parameters should be stable, physically interpretable, and transferable to other contexts, e.g. for direct indication of system state, or usage in other model types. As an example of implementing these recommended requirements for model parameters, we evaluated twelve published empirical models for temperature-dependent tropical seagrass photosynthesis, based on two criteria: (1) goodness of fit, and (2) how easily biologically-meaningful parameters can be obtained. All models were formulated in terms of parameters characterising the thermal optimum (Topt) for maximum photosynthetic rate (Pmax). These parameters indicate the upper thermal limits of seagrass photosynthetic capacity, and hence can be used to assess the vulnerability of seagrass to temperature change. Our study exemplifies an approach to model selection which optimises the usefulness of empirical models for both modellers and ecologists alike.

  16. Selection Experiments in the Penna Model for Biological Aging

    Science.gov (United States)

    Medeiros, G.; Idiart, M. A.; de Almeida, R. M. C.

    We consider the Penna model for biological aging to investigate correlations between early fertility and late life survival rates in populations at equilibrium. We consider inherited initial reproduction ages together with a reproduction cost translated in a probability that mother and offspring die at birth, depending on the mother age. For convenient sets of parameters, the equilibrated populations present genetic variability in what regards both genetically programmed death age and initial reproduction age. In the asexual Penna model, a negative correlation between early life fertility and late life survival rates naturally emerges in the stationary solutions. In the sexual Penna model, selection experiments are performed where individuals are sorted by initial reproduction age from the equilibrated populations and the separated populations are evolved independently. After a transient, a negative correlation between early fertility and late age survival rates also emerges in the sense that populations that start reproducing earlier present smaller average genetically programmed death age. These effects appear due to the age structure of populations in the steady state solution of the evolution equations. We claim that the same demographic effects may be playing an important role in selection experiments in the laboratory.

  17. Modelling biological invasions: Individual to population scales at interfaces

    KAUST Repository

    Belmonte-Beitia, J.

    2013-10-01

    Extracting the population level behaviour of biological systems from that of the individual is critical in understanding dynamics across multiple scales and thus has been the subject of numerous investigations. Here, the influence of spatial heterogeneity in such contexts is explored for interfaces with a separation of the length scales characterising the individual and the interface, a situation that can arise in applications involving cellular modelling. As an illustrative example, we consider cell movement between white and grey matter in the brain which may be relevant in considering the invasive dynamics of glioma. We show that while one can safely neglect intrinsic noise, at least when considering glioma cell invasion, profound differences in population behaviours emerge in the presence of interfaces with only subtle alterations in the dynamics at the individual level. Transport driven by local cell sensing generates predictions of cell accumulations along interfaces where cell motility changes. This behaviour is not predicted with the commonly used Fickian diffusion transport model, but can be extracted from preliminary observations of specific cell lines in recent, novel, cryo-imaging. Consequently, these findings suggest a need to consider the impact of individual behaviour, spatial heterogeneity and especially interfaces in experimental and modelling frameworks of cellular dynamics, for instance in the characterisation of glioma cell motility. © 2013 Elsevier Ltd.

  18. Biological control of harmful algal blooms: A modelling study

    Science.gov (United States)

    Solé, Jordi; Estrada, Marta; Garcia-Ladona, Emilio

    2006-07-01

    A multispecies dynamic simulation model (ERSEM) was used to examine the influence of allelopathic and trophic interactions causing feeding avoidance by predators, on the formation of harmful algal blooms, under environmental scenarios typical of a Mediterranean harbour (Barcelona). The biological state variables of the model included four functional groups of phytoplankton (diatoms, toxic and non-toxic flagellates and picophytoplankton), heterotrophic flagellates, micro- and mesozooplankton and bacteria. The physical-chemical forcing (irradiance, temperature and major nutrient concentrations) was based on an actual series of measurements taken along a year cycle in the Barcelona harbour. In order to evaluate potential effects of advection, some runs were repeated after introducing a biomass loss term. Numerical simulations showed that allelopathic effects of a toxic alga on a non-toxic but otherwise similar competitor did not have appreciable influence on the dynamics of the system. However, induction of avoidance of the toxic alga by predators, which resulted on increased predation pressure on other algal groups had a significant effect on the development of algal and predator populations. The presence of advection overrided the effect of these interactions and only allowed organisms with sufficiently high potential growth rates to thrive.

  19. Agent-oriented modeling of the dynamics of complex biological processes I: single agent models

    NARCIS (Netherlands)

    Jonker, C.M.; Treur, J.

    2008-01-01

    In the pair of papers of which this is Part I, the agent-oriented modeling perspective to cope with biological complexity is discussed. Three levels of dynamics are distinguished and related to each other: dynamics of externally observable agent behavior, dynamics of internal agent processes, and dy

  20. 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.

  1. Biology meets Physics: Reductionism and Multi-scale Modeling of Morphogenesis

    DEFF Research Database (Denmark)

    Green, Sara; Batterman, Robert

    2017-01-01

    from philosophers of biology. Specifically, scholars have pointed to the impossibility of deducing biological explanations from physical ones, and to the irreducible nature of distinctively biological processes such as gene regulation and evolution. This paper takes a step back in asking whether bottom......-up modeling is feasible even when modeling simple physical systems across scales. By comparing examples of multi-scale modeling in physics and biology, we argue that the “tyranny of scales” problem present a challenge to reductive explanations in both physics and biology. The problem refers to the scale...... and biology. Contrary to the assumption that physical science approaches provide reductive explanations in biology, we exemplify how inputs from physical science approaches often reveal the importance of macro-scale models and explanations. We illustrate this through an examination of the role of biomechanics...

  2. Illustrations of mathematical modeling in biology: epigenetics, meiosis, and an outlook.

    Science.gov (United States)

    Richards, D; Berry, S; Howard, M

    2012-01-01

    In the past few years, mathematical modeling approaches in biology have begun to fulfill their promise by assisting in the dissection of complex biological systems. Here, we review two recent examples of predictive mathematical modeling in plant biology. The first involves the quantitative epigenetic silencing of the floral repressor gene FLC in Arabidopsis, mediated by a Polycomb-based system. The second involves the spatiotemporal dynamics of telomere bouquet formation in wheat-rye meiosis. Although both the biology and the modeling framework of the two systems are different, both exemplify how mathematical modeling can help to accelerate discovery of the underlying mechanisms in complex biological systems. In both cases, the models that developed were relatively minimal, including only essential features, but both nevertheless yielded fundamental insights. We also briefly review the current state of mathematical modeling in biology, difficulties inherent in its application, and its potential future development.

  3. Long-Term Conceptual Retrieval by College Biology Majors Following Model-Based Instruction

    Science.gov (United States)

    Dauer, Joseph T.; Long, Tammy M.

    2015-01-01

    One of the goals of college-level introductory biology is to establish a foundation of knowledge and skills that can be built upon throughout a biology curriculum. In a reformed introductory biology course, we used iterative model construction as a pedagogical tool to promote students' understanding about conceptual connections, particularly those…

  4. Pharmacology and toxicology of diphenyl diselenide in several biological models

    Directory of Open Access Journals (Sweden)

    R.M. Rosa

    2007-10-01

    Full Text Available The pharmacology of synthetic organoselenium compounds indicates that they can be used as antioxidants, enzyme inhibitors, neuroprotectors, anti-tumor and anti-infectious agents, and immunomodulators. In this review, we focus on the effects of diphenyl diselenide (DPDS in various biological model organisms. DPDS possesses antioxidant activity, confirmed in several in vitro and in vivo systems, and thus has a protective effect against hepatic, renal and gastric injuries, in addition to its neuroprotective activity. The activity of the compound on the central nervous system has been studied since DPDS has lipophilic characteristics, increasing adenylyl cyclase activity and inhibiting glutamate and MK-801 binding to rat synaptic membranes. Systemic administration facilitates the formation of long-term object recognition memory in mice and has a protective effect against brain ischemia and on reserpine-induced orofacial dyskinesia in rats. On the other hand, DPDS may be toxic, mainly because of its interaction with thiol groups. In the yeast Saccharomyces cerevisiae, the molecule acts as a pro-oxidant by depleting free glutathione. Administration to mice during cadmium intoxication has the opposite effect, reducing oxidative stress in various tissues. DPDS is a potent inhibitor of d-aminolevulinate dehydratase and chronic exposure to high doses of this compound has central effects on mouse brain, as well as liver and renal toxicity. Genotoxicity of this compound has been assessed in bacteria, haploid and diploid yeast and in a tumor cell line.

  5. Biologic data, models, and dosimetric methods for internal emitters

    Energy Technology Data Exchange (ETDEWEB)

    Weber, D.A.

    1990-01-01

    The absorbed radiation dose from internal emitters has been and will remain a pivotal factor in assessing risk and therapeutic utility in selecting radiopharmaceuticals for diagnosis and treatment. Although direct measurements of absorbed dose and dose distributions in vivo have been and will continue to be made in limited situations, the measurement of the biodistribution and clearance of radiopharmaceuticals in human subjects and the use of this data is likely to remain the primary means to approach the calculation and estimation of absorbed dose from internal emitters over the next decade. Since several approximations are used in these schema to calculate dose, attention must be given to inspecting and improving the application of this dosimetric method as better techniques are developed to assay body activity and as more experience is gained in applying these schema to calculating absorbed dose. Discussion of the need for considering small scale dosimetry to calculate absorbed dose at the cellular level will be presented in this paper. Other topics include dose estimates for internal emitters, biologic data mathematical models and dosimetric methods employed. 44 refs.

  6. Data to support "Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations & Biological Condition"

    Data.gov (United States)

    U.S. Environmental Protection Agency — Spreadsheets are included here to support the manuscript "Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition". This...

  7. ADAM: Analysis of Discrete Models of Biological Systems Using Computer Algebra

    CERN Document Server

    Hinkelmann, Franziska; Guang, Bonny; McNeill, Rustin; Blekherman, Grigoriy; Veliz-Cuba, Alan; Laubenbacher, Reinhard

    2010-01-01

    Motivation: Many biological systems are modeled qualitatively with discrete models, such as probabilistic Boolean networks, logical models, bounded Petri nets, and agent-based models. Simulation is a common practice for analyzing discrete models, but many systems are far too large to capture all the relevant dynamical features through simulation alone. Results: We convert discrete models into algebraic models and apply tools from computational algebra to analyze their dynamics. The key feature of biological systems that is exploited by our algorithms is their sparsity: while the number of nodes in a biological network may be quite large, each node is affected only by a small number of other nodes. In our experience with models arising in systems biology and random models, this structure leads to fast computations when using algebraic models, and thus efficient analysis. Availability: All algorithms and methods are available in our package Analysis of Dynamic Algebraic Models (ADAM), a user friendly web-interf...

  8. Basic science through engineering? Synthetic modeling and the idea of biology-inspired engineering.

    Science.gov (United States)

    Knuuttila, Tarja; Loettgers, Andrea

    2013-06-01

    Synthetic biology is often understood in terms of the pursuit for well-characterized biological parts to create synthetic wholes. Accordingly, it has typically been conceived of as an engineering dominated and application oriented field. We argue that the relationship of synthetic biology to engineering is far more nuanced than that and involves a sophisticated epistemic dimension, as shown by the recent practice of synthetic modeling. Synthetic models are engineered genetic networks that are implanted in a natural cell environment. Their construction is typically combined with experiments on model organisms as well as mathematical modeling and simulation. What is especially interesting about this combinational modeling practice is that, apart from greater integration between these different epistemic activities, it has also led to the questioning of some central assumptions and notions on which synthetic biology is based. As a result synthetic biology is in the process of becoming more "biology inspired."

  9. 4D-Var data assimilation system for a coupled physical biological model

    Indian Academy of Sciences (India)

    J M Lellouche; M Ouberdous; W Eifler

    2000-12-01

    A 3-compartment model of phytoplankton growth dynamics has been coupled with a primitive-equation circulation model to better understand and quantify physical and biological processes in the Adriatic Sea. This paper presents the development and application of a data assimilation procedure based on optimal control theory. The aim of the procedure is to identify a set of model coefficient values that ensures the best fit between data and model results by minimizing a function that measures model and data discrepancies. In this sense, twin experiments have been successfully implemented in order to have a better estimation of biological model parameters and biological initial conditions.

  10. Photon-tissue interaction model for quantitative assessment of biological tissues

    Science.gov (United States)

    Lee, Seung Yup; Lloyd, William R.; Wilson, Robert H.; Chandra, Malavika; McKenna, Barbara; Simeone, Diane; Scheiman, James; Mycek, Mary-Ann

    2014-02-01

    In this study, we describe a direct fit photon-tissue interaction model to quantitatively analyze reflectance spectra of biological tissue samples. The model rapidly extracts biologically-relevant parameters associated with tissue optical scattering and absorption. This model was employed to analyze reflectance spectra acquired from freshly excised human pancreatic pre-cancerous tissues (intraductal papillary mucinous neoplasm (IPMN), a common precursor lesion to pancreatic cancer). Compared to previously reported models, the direct fit model improved fit accuracy and speed. Thus, these results suggest that such models could serve as real-time, quantitative tools to characterize biological tissues assessed with reflectance spectroscopy.

  11. Using the Unified Modelling Language (UML) to guide the systemic description of biological processes and systems.

    Science.gov (United States)

    Roux-Rouquié, Magali; Caritey, Nicolas; Gaubert, Laurent; Rosenthal-Sabroux, Camille

    2004-07-01

    One of the main issues in Systems Biology is to deal with semantic data integration. Previously, we examined the requirements for a reference conceptual model to guide semantic integration based on the systemic principles. In the present paper, we examine the usefulness of the Unified Modelling Language (UML) to describe and specify biological systems and processes. This makes unambiguous representations of biological systems, which would be suitable for translation into mathematical and computational formalisms, enabling analysis, simulation and prediction of these systems behaviours.

  12. The Role of Model Integration in Complex Systems Modelling An Example from Cancer Biology

    CERN Document Server

    Patel, Manish

    2010-01-01

    Model integration – the process by which different modelling efforts can be brought together to simulate the target system – is a core technology in the field of Systems Biology. In the work presented here model integration was addressed directly taking cancer systems as an example. An in-depth literature review was carried out to survey the model forms and types currently being utilised. This was used to formalise the main challenges that model integration poses, namely that of paradigm (the formalism on which a model is based), focus (the real-world system the model represents) and scale. A two-tier model integration strategy, including a knowledge-driven approach to address model semantics, was developed to tackle these challenges. In the first step a novel description of models at the level of behaviour, rather than the precise mathematical or computational basis of the model, is developed by distilling a set of abstract classes and properties. These can accurately describe model behaviour and hence d...

  13. Differential Equations Models in Biology, Epidemiology and Ecology

    CERN Document Server

    Martelli, Mario

    1991-01-01

    The past forty years have been the stage for the maturation of mathematical biolo~ as a scientific field. The foundations laid by the pioneers of the field during the first half of this century have been combined with advances in ap­ plied mathematics and the computational sciences to create a vibrant area of scientific research with established research journals, professional societies, deep subspecialty areas, and graduate education programs. Mathematical biology is by its very nature cross-disciplinary, and research papers appear in mathemat­ ics, biology and other scientific journals, as well as in the specialty journals devoted to mathematical and theoretical biology. Multiple author papers are common, and so are collaborations between individuals who have academic bases in different traditional departments. Those who seek to keep abreast of current trends and problems need to interact with research workers from a much broader spectrum of fields than is common in the traditional mono-culture discipline...

  14. Unified Modeling of Filtration and Expression of Biological Sludge

    DEFF Research Database (Denmark)

    Sørensen, Peter Borgen

    Dewatering is a costly operation in both industry, e.g . when dewatering drilling mud, harbor sludge or biomass, and at municipal wastewater treatment plants when dewatering biological sludges. In practice, design and operation of dewatering equipment are mostly based on empirical knowledge...

  15. ANIMO: a tool for modeling biological pathway dynamics

    NARCIS (Netherlands)

    Schivo, S.; Scholma, J.; Karperien, M.; Langerak, R.; Pol, van de J.; Post, J.N.

    2014-01-01

    Introduction: Computational methods are applied with increasing success to the analysis of complex biological systems. However, their adoption is sometimes made difficult by requiring prior knowledge about the foundations of such methods, which often come from a different branch of science. The soft

  16. Identifying common components across biological network graphs using a bipartite data model.

    Science.gov (United States)

    Baker, Ej; Culpepper, C; Philips, C; Bubier, J; Langston, M; Chesler, Ej

    2014-01-01

    The GeneWeaver bipartite data model provides an efficient means to evaluate shared molecular components from sets derived across diverse species, disease states and biological processes. In order to adapt this model for examining related molecular components and biological networks, such as pathway or gene network data, we have developed a means to leverage the bipartite data structure to extract and analyze shared edges. Using the Pathway Commons database we demonstrate the ability to rapidly identify shared connected components among a diverse set of pathways. In addition, we illustrate how results from maximal bipartite discovery can be decomposed into hierarchical relationships, allowing shared pathway components to be mapped through various parent-child relationships to help visualization and discovery of emergent kernel driven relationships. Interrogating common relationships among biological networks and conventional GeneWeaver gene lists will increase functional specificity and reliability of the shared biological components. This approach enables self-organization of biological processes through shared biological networks.

  17. Markov Chain-Like Quantum Biological Modeling of Mutations, Aging, and Evolution

    Directory of Open Access Journals (Sweden)

    Ivan B. Djordjevic

    2015-08-01

    Full Text Available Recent evidence suggests that quantum mechanics is relevant in photosynthesis, magnetoreception, enzymatic catalytic reactions, olfactory reception, photoreception, genetics, electron-transfer in proteins, and evolution; to mention few. In our recent paper published in Life, we have derived the operator-sum representation of a biological channel based on codon basekets, and determined the quantum channel model suitable for study of the quantum biological channel capacity. However, this model is essentially memoryless and it is not able to properly model the propagation of mutation errors in time, the process of aging, and evolution of genetic information through generations. To solve for these problems, we propose novel quantum mechanical models to accurately describe the process of creation spontaneous, induced, and adaptive mutations and their propagation in time. Different biological channel models with memory, proposed in this paper, include: (i Markovian classical model, (ii Markovian-like quantum model, and (iii hybrid quantum-classical model. We then apply these models in a study of aging and evolution of quantum biological channel capacity through generations. We also discuss key differences of these models with respect to a multilevel symmetric channel-based Markovian model and a Kimura model-based Markovian process. These models are quite general and applicable to many open problems in biology, not only biological channel capacity, which is the main focus of the paper. We will show that the famous quantum Master equation approach, commonly used to describe different biological processes, is just the first-order approximation of the proposed quantum Markov chain-like model, when the observation interval tends to zero. One of the important implications of this model is that the aging phenotype becomes determined by different underlying transition probabilities in both programmed and random (damage Markov chain-like models of aging, which

  18. Markov Chain-Like Quantum Biological Modeling of Mutations, Aging, and Evolution.

    Science.gov (United States)

    Djordjevic, Ivan B

    2015-08-24

    Recent evidence suggests that quantum mechanics is relevant in photosynthesis, magnetoreception, enzymatic catalytic reactions, olfactory reception, photoreception, genetics, electron-transfer in proteins, and evolution; to mention few. In our recent paper published in Life, we have derived the operator-sum representation of a biological channel based on codon basekets, and determined the quantum channel model suitable for study of the quantum biological channel capacity. However, this model is essentially memoryless and it is not able to properly model the propagation of mutation errors in time, the process of aging, and evolution of genetic information through generations. To solve for these problems, we propose novel quantum mechanical models to accurately describe the process of creation spontaneous, induced, and adaptive mutations and their propagation in time. Different biological channel models with memory, proposed in this paper, include: (i) Markovian classical model, (ii) Markovian-like quantum model, and (iii) hybrid quantum-classical model. We then apply these models in a study of aging and evolution of quantum biological channel capacity through generations. We also discuss key differences of these models with respect to a multilevel symmetric channel-based Markovian model and a Kimura model-based Markovian process. These models are quite general and applicable to many open problems in biology, not only biological channel capacity, which is the main focus of the paper. We will show that the famous quantum Master equation approach, commonly used to describe different biological processes, is just the first-order approximation of the proposed quantum Markov chain-like model, when the observation interval tends to zero. One of the important implications of this model is that the aging phenotype becomes determined by different underlying transition probabilities in both programmed and random (damage) Markov chain-like models of aging, which are mutually

  19. Modelling of environmental impacts from biological treatment of organic municipal waste in EASEWASTE.

    Science.gov (United States)

    Boldrin, Alessio; Neidel, Trine Lund; Damgaard, Anders; Bhander, Gurbakhash S; Møller, Jacob; Christensen, Thomas H

    2011-04-01

    The waste-LCA model EASEWASTE quantifies potential environmental effects from biological treatment of organic waste, based on mass and energy flows, emissions to air, water, soil and groundwater as well as effects from upstream and downstream processes. Default technologies for composting, anaerobic digestion and combinations hereof are available in the model, but the user can change all key parameters in the biological treatment module so that specific local plants and processes can be modelled. EASEWASTE is one of the newest waste LCA models and the biological treatment module was built partly on features of earlier waste-LCA models, but offers additional facilities, more flexibility, transparency and user-friendliness. The paper presents the main features of the module and provides some examples illustrating the capability of the model in environmentally assessing and discriminating the environmental performance of alternative biological treatment technologies in relation to their mass flows, energy consumption, gaseous emissions, biogas recovery and compost/digestate utilization.

  20. Biology meets physics: Reductionism and multi-scale modeling of morphogenesis.

    Science.gov (United States)

    Green, Sara; Batterman, Robert

    2017-02-01

    A common reductionist assumption is that macro-scale behaviors can be described "bottom-up" if only sufficient details about lower-scale processes are available. The view that an "ideal" or "fundamental" physics would be sufficient to explain all macro-scale phenomena has been met with criticism from philosophers of biology. Specifically, scholars have pointed to the impossibility of deducing biological explanations from physical ones, and to the irreducible nature of distinctively biological processes such as gene regulation and evolution. This paper takes a step back in asking whether bottom-up modeling is feasible even when modeling simple physical systems across scales. By comparing examples of multi-scale modeling in physics and biology, we argue that the "tyranny of scales" problem presents a challenge to reductive explanations in both physics and biology. The problem refers to the scale-dependency of physical and biological behaviors that forces researchers to combine different models relying on different scale-specific mathematical strategies and boundary conditions. Analyzing the ways in which different models are combined in multi-scale modeling also has implications for the relation between physics and biology. Contrary to the assumption that physical science approaches provide reductive explanations in biology, we exemplify how inputs from physics often reveal the importance of macro-scale models and explanations. We illustrate this through an examination of the role of biomechanical modeling in developmental biology. In such contexts, the relation between models at different scales and from different disciplines is neither reductive nor completely autonomous, but interdependent.

  1. Not just a theory--the utility of mathematical models in evolutionary biology.

    Directory of Open Access Journals (Sweden)

    Maria R Servedio

    2014-12-01

    Full Text Available Progress in science often begins with verbal hypotheses meant to explain why certain biological phenomena exist. An important purpose of mathematical models in evolutionary research, as in many other fields, is to act as “proof-of-concept” tests of the logic in verbal explanations, paralleling the way in which empirical data are used to test hypotheses. Because not all subfields of biology use mathematics for this purpose, misunderstandings of the function of proof-of-concept modeling are common. In the hope of facilitating communication, we discuss the role of proof-of-concept modeling in evolutionary biology.

  2. Systems Modelling and the Development of Coherent Understanding of Cell Biology

    Science.gov (United States)

    Verhoeff, Roald P.; Waarlo, Arend Jan; Boersma, Kerst Th.

    2008-01-01

    This article reports on educational design research concerning a learning and teaching strategy for cell biology in upper-secondary education introducing "systems modelling" as a key competence. The strategy consists of four modelling phases in which students subsequently develop models of free-living cells, a general two-dimensional model of…

  3. Digital learning material for experimental design and model building in molecular biology

    NARCIS (Netherlands)

    Aegerter-Wilmsen, T.

    2005-01-01

    Designing experimental approaches is a major cognitive skill in molecular biology research, and building models, including quantitative ones, is a cognitive skill which is rapidly gaining importance. Since molecular biology education at university level is aimed at educating future researchers, we c

  4. JSBML 1.0: providing a smorgasbord of options to encode systems biology models

    DEFF Research Database (Denmark)

    Rodriguez, Nicolas; Thomas, Alex; Watanabe, Leandro

    2015-01-01

    JSBML, the official pure Java programming library for the Systems Biology Markup Language (SBML) format, has evolved with the advent of different modeling formalisms in systems biology and their ability to be exchanged and represented via extensions of SBML. JSBML has matured into a major, active...

  5. Analyzing Change in Students' Gene-to-Evolution Models in College-Level Introductory Biology

    Science.gov (United States)

    Dauer, Joseph T.; Momsen, Jennifer L.; Speth, Elena Bray; Makohon-Moore, Sasha C.; Long, Tammy M.

    2013-01-01

    Research in contemporary biology has become increasingly complex and organized around understanding biological processes in the context of systems. To better reflect the ways of thinking required for learning about systems, we developed and implemented a pedagogical approach using box-and-arrow models (similar to concept maps) as a foundational…

  6. Model selection in systems biology depends on experimental design.

    Science.gov (United States)

    Silk, Daniel; Kirk, Paul D W; Barnes, Chris P; Toni, Tina; Stumpf, Michael P H

    2014-06-01

    Experimental design attempts to maximise the information available for modelling tasks. An optimal experiment allows the inferred models or parameters to be chosen with the highest expected degree of confidence. If the true system is faithfully reproduced by one of the models, the merit of this approach is clear - we simply wish to identify it and the true parameters with the most certainty. However, in the more realistic situation where all models are incorrect or incomplete, the interpretation of model selection outcomes and the role of experimental design needs to be examined more carefully. Using a novel experimental design and model selection framework for stochastic state-space models, we perform high-throughput in-silico analyses on families of gene regulatory cascade models, to show that the selected model can depend on the experiment performed. We observe that experimental design thus makes confidence a criterion for model choice, but that this does not necessarily correlate with a model's predictive power or correctness. Finally, in the special case of linear ordinary differential equation (ODE) models, we explore how wrong a model has to be before it influences the conclusions of a model selection analysis.

  7. Development of a General Modeling Framework for Investigating Complex Interactions among Biological and Physical Ecosystem Dynamics

    Science.gov (United States)

    Bennett, C.; Poole, G. C.; Kimball, J. S.; Stanford, J. A.; O'Daniel, S. J.; Mertes, L. A.

    2005-05-01

    Historically, physical scientists have developed models with highly accurate governing equations, while biologists have excelled at abstraction (the strategic simplification of system complexity). These different modeling paradigms yield biological (e.g. food web) and physical (e.g. hydrologic) models that can be difficult to integrate. Complex biological dynamics may be impossible to represent with governing equations. Conversely, physical processes may be oversimplified in biological models. Using agent-based modeling, a technique applied widely in social sciences and economics, we are developing a general modeling system to integrate accurate representations of physical dynamics such as water and heat flux with abstracted biological processes such as nutrient transformations. The modeling system represents an ecosystem as a complex integrated network of intelligent physical and biological "agents" that store, transform, and trade ecosystem resources (e.g., water, heat, nutrients, carbon) using equations that describe either abstracted concepts and/or physical laws. The modular design of the system allows resource submodels to be developed independently and installed into the simulation architecture. The modeling system provides a useful heuristic tool to support integrated physical and biological research topics, such as the influence of hydrologic dynamics and spatio-temporal physical heterogeneity on trophic (food web) dynamics and/or nutrient cycling.

  8. Biologically-motivated system identification: application to microbial growth modeling.

    Science.gov (United States)

    Yan, Jinyao; Deller, J R

    2014-01-01

    This paper presents a new method for identification of system models that are linear in parametric structure, but arbitrarily nonlinear in signal operations. The strategy blends traditional system identification methods with three modeling strategies that are not commonly employed in signal processing: linear-time-invariant-in-parameters models, set-based parameter identification, and evolutionary selection of the model structure. This paper reports recent advances in the theoretical foundation of the methods, then focuses on the operation and performance of the approach, particularly the evolutionary model determination. The method is applied to the modeling of microbial growth by Monod Kinetics.

  9. Caenorhabditis elegans - A model system for space biology studies

    Science.gov (United States)

    Johnson, Thomas E.; Nelson, Gregory A.

    1991-01-01

    The utility of the nematode Caenorhabditis elegans in studies spanning aspects of development, aging, and radiobiology is reviewed. These topics are interrelated via cellular and DNA repair processes especially in the context of oxidative stress and free-radical metabolism. The relevance of these research topics to problems in space biology is discussed and properties of the space environment are outlined. Exposure to the space-flight environment can induce rapid changes in living systems that are similar to changes occurring during aging; manipulation of these environmental parameters may represent an experimental strategy for studies of development and senescence. The current and future opportunities for such space-flight experimentation are presented.

  10. Kinetic modelling of coupled transport across biological membranes.

    Science.gov (United States)

    Korla, Kalyani; Mitra, Chanchal K

    2014-04-01

    In this report, we have modelled a secondary active co-transporter (symport and antiport), based on the classical kinetics model. Michaelis-Menten model of enzyme kinetics for a single substrate, single intermediate enzyme catalyzed reaction was proposed more than a hundred years ago. However, no single model for the kinetics of co-transport of molecules across a membrane is available in the literature We have made several simplifying assumptions and have followed the basic Michaelis-Menten approach. The results have been simulated using GNU Octave. The results will be useful in general kinetic simulations and modelling.

  11. Fuzzy Stochastic Petri Nets for Modeling Biological Systems with Uncertain Kinetic Parameters.

    Science.gov (United States)

    Liu, Fei; Heiner, Monika; Yang, Ming

    2016-01-01

    Stochastic Petri nets (SPNs) have been widely used to model randomness which is an inherent feature of biological systems. However, for many biological systems, some kinetic parameters may be uncertain due to incomplete, vague or missing kinetic data (often called fuzzy uncertainty), or naturally vary, e.g., between different individuals, experimental conditions, etc. (often called variability), which has prevented a wider application of SPNs that require accurate parameters. Considering the strength of fuzzy sets to deal with uncertain information, we apply a specific type of stochastic Petri nets, fuzzy stochastic Petri nets (FSPNs), to model and analyze biological systems with uncertain kinetic parameters. FSPNs combine SPNs and fuzzy sets, thereby taking into account both randomness and fuzziness of biological systems. For a biological system, SPNs model the randomness, while fuzzy sets model kinetic parameters with fuzzy uncertainty or variability by associating each parameter with a fuzzy number instead of a crisp real value. We introduce a simulation-based analysis method for FSPNs to explore the uncertainties of outputs resulting from the uncertainties associated with input parameters, which works equally well for bounded and unbounded models. We illustrate our approach using a yeast polarization model having an infinite state space, which shows the appropriateness of FSPNs in combination with simulation-based analysis for modeling and analyzing biological systems with uncertain information.

  12. Comparative systems biology between human and animal models based on next-generation sequencing methods.

    Science.gov (United States)

    Zhao, Yu-Qi; Li, Gong-Hua; Huang, Jing-Fei

    2013-04-01

    Animal models provide myriad benefits to both experimental and clinical research. Unfortunately, in many situations, they fall short of expected results or provide contradictory results. In part, this can be the result of traditional molecular biological approaches that are relatively inefficient in elucidating underlying molecular mechanism. To improve the efficacy of animal models, a technological breakthrough is required. The growing availability and application of the high-throughput methods make systematic comparisons between human and animal models easier to perform. In the present study, we introduce the concept of the comparative systems biology, which we define as "comparisons of biological systems in different states or species used to achieve an integrated understanding of life forms with all their characteristic complexity of interactions at multiple levels". Furthermore, we discuss the applications of RNA-seq and ChIP-seq technologies to comparative systems biology between human and animal models and assess the potential applications for this approach in the future studies.

  13. Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition

    Science.gov (United States)

    Boosted regression tree (BRT) models were developed to quantify the nonlinear relationships between landscape variables and nutrient concentrations in a mesoscale mixed land cover watershed during base-flow conditions. Factors that affect instream biological components, based on ...

  14. Modelling of environmental impacts from biological treatment of organic municipal waste in EASEWASTE

    DEFF Research Database (Denmark)

    Boldrin, Alessio; Neidel, Trine Lund; Damgaard, Anders

    2011-01-01

    The waste-LCA model EASEWASTE quantifies potential environmental effects from biological treatment of organic waste, based on mass and energy flows, emissions to air, water, soil and groundwater as well as effects from upstream and downstream processes. Default technologies for composting......, anaerobic digestion and combinations hereof are available in the model, but the user can change all key parameters in the biological treatment module so that specific local plants and processes can be modelled. EASEWASTE is one of the newest waste LCA models and the biological treatment module was built...... the environmental performance of alternative biological treatment technologies in relation to their mass flows, energy consumption, gaseous emissions, biogas recovery and compost/digestate utilization....

  15. A model of biological neuron with terminal chaos and quantum-like features

    Energy Technology Data Exchange (ETDEWEB)

    Conte, Elio [Department of Neuroscience, Psychiatric Clinic L. Bini, Bari University, 70100 Bari (Italy); Department of Pharmacology and Human Physiology, TIRES-Center for Innovative Technology for Signal Detection and Processing, Bari University, 70100 Bari (Italy); Pierri, GianPaolo [Department of Neuroscience, Psychiatric Clinic L. Bini, Bari University, 70100 Bari (Italy); Federici, Antonio [Department of Pharmacology and Human Physiology, TIRES-Center for Innovative Technology for Signal Detection and Processing, Bari University, 70100 Bari (Italy); Mendolicchio, Leonardo [Department of Neuroscience, Psychiatric Clinic L. Bini, Bari University, 70100 Bari (Italy); Zbilut, Joseph P. [Department of Molecular Biophysics and Physiology, Rush University, Chicago, IL 60612 (United States)

    2006-11-15

    A model of biological neuron is proposed combining terminal dynamics with quantum-like mechanical features, assuming the spin to be an important entity in neurodynamics, and, in particular, in synaptic transmission.

  16. A Functional Model for Teaching Osmosis-Diffusion to Biology Students

    Science.gov (United States)

    Olsen, Richard W.; Petry, Douglas E.

    1976-01-01

    Described is a maternal-fetal model, operated by the student, to teach osmosis-diffusion to biology students. Included are materials needed, assembly instructions, and student operating procedures. (SL)

  17. Simulation and visualization of coupled hydrodynamical, chemical and biological models

    Directory of Open Access Journals (Sweden)

    Dag Slagstad

    1997-04-01

    Full Text Available This paper briefly describes the principles of hydrodynamical and ecological modelling of marine systems and how model results are presented by use of MATLAB. Two application examples are shown. One refers to modelling and simulation of the carbon vertical transport in the Greenland Sea and the other is a study on the effect of wind pattern for the invasion success of zooplankton from the Norwegian Sea into the North Sea by use of particle tracking.

  18. How computational models can help unlock biological systems.

    Science.gov (United States)

    Brodland, G Wayne

    2015-12-01

    With computation models playing an ever increasing role in the advancement of science, it is important that researchers understand what it means to model something; recognize the implications of the conceptual, mathematical and algorithmic steps of model construction; and comprehend what models can and cannot do. Here, we use examples to show that models can serve a wide variety of roles, including hypothesis testing, generating new insights, deepening understanding, suggesting and interpreting experiments, tracing chains of causation, doing sensitivity analyses, integrating knowledge, and inspiring new approaches. We show that models can bring together information of different kinds and do so across a range of length scales, as they do in multi-scale, multi-faceted embryogenesis models, some of which connect gene expression, the cytoskeleton, cell properties, tissue mechanics, morphogenetic movements and phenotypes. Models cannot replace experiments nor can they prove that particular mechanisms are at work in a given situation. But they can demonstrate whether or not a proposed mechanism is sufficient to produce an observed phenomenon. Although the examples in this article are taken primarily from the field of embryo mechanics, most of the arguments and discussion are applicable to any form of computational modelling.

  19. Modelling, abstraction, and computation in systems biology: A view from computer science.

    Science.gov (United States)

    Melham, Tom

    2013-04-01

    Systems biology is centrally engaged with computational modelling across multiple scales and at many levels of abstraction. Formal modelling, precise and formalised abstraction relationships, and computation also lie at the heart of computer science--and over the past decade a growing number of computer scientists have been bringing their discipline's core intellectual and computational tools to bear on biology in fascinating new ways. This paper explores some of the apparent points of contact between the two fields, in the context of a multi-disciplinary discussion on conceptual foundations of systems biology.

  20. Acclimation, adaptation, traits and trade-offs in plankton functional type models: reconciling terminology for biology and modelling

    DEFF Research Database (Denmark)

    Flynn, Kevin J.; St. John, Michael; Raven, John A.;

    2015-01-01

    ideally only be considered for describing intra-generational interactions; in applications between generations, and certainly between unrelated species, such concepts should be avoided. We suggest that systems biology approaches, through to complex adaptive/acclimative systems modelling, with explicit...

  1. Tangible Models and Haptic Representations Aid Learning of Molecular Biology Concepts

    Science.gov (United States)

    Johannes, Kristen; Powers, Jacklyn; Couper, Lisa; Silberglitt, Matt; Davenport, Jodi

    2016-01-01

    Can novel 3D models help students develop a deeper understanding of core concepts in molecular biology? We adapted 3D molecular models, developed by scientists, for use in high school science classrooms. The models accurately represent the structural and functional properties of complex DNA and Virus molecules, and provide visual and haptic…

  2. A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking.

    Science.gov (United States)

    Pârvu, Ovidiu; Gilbert, David

    2016-01-01

    Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour

  3. A data integration approach for cell cycle analysis oriented to model simulation in systems biology

    Directory of Open Access Journals (Sweden)

    Mosca Ettore

    2007-08-01

    Full Text Available Abstract Background The cell cycle is one of the biological processes most frequently investigated in systems biology studies and it involves the knowledge of a large number of genes and networks of protein interactions. A deep knowledge of the molecular aspect of this biological process can contribute to making cancer research more accurate and innovative. In this context the mathematical modelling of the cell cycle has a relevant role to quantify the behaviour of each component of the systems. The mathematical modelling of a biological process such as the cell cycle allows a systemic description that helps to highlight some features such as emergent properties which could be hidden when the analysis is performed only from a reductionism point of view. Moreover, in modelling complex systems, a complete annotation of all the components is equally important to understand the interaction mechanism inside the network: for this reason data integration of the model components has high relevance in systems biology studies. Description In this work, we present a resource, the Cell Cycle Database, intended to support systems biology analysis on the Cell Cycle process, based on two organisms, yeast and mammalian. The database integrates information about genes and proteins involved in the cell cycle process, stores complete models of the interaction networks and allows the mathematical simulation over time of the quantitative behaviour of each component. To accomplish this task, we developed, a web interface for browsing information related to cell cycle genes, proteins and mathematical models. In this framework, we have implemented a pipeline which allows users to deal with the mathematical part of the models, in order to solve, using different variables, the ordinary differential equation systems that describe the biological process. Conclusion This integrated system is freely available in order to support systems biology research on the cell cycle and

  4. Efficient modeling, simulation and coarse-graining of biological complexity with NFsim.

    Science.gov (United States)

    Sneddon, Michael W; Faeder, James R; Emonet, Thierry

    2011-02-01

    Managing the overwhelming numbers of molecular states and interactions is a fundamental obstacle to building predictive models of biological systems. Here we introduce the Network-Free Stochastic Simulator (NFsim), a general-purpose modeling platform that overcomes the combinatorial nature of molecular interactions. Unlike standard simulators that represent molecular species as variables in equations, NFsim uses a biologically intuitive representation: objects with binding and modification sites acted on by reaction rules. During simulations, rules operate directly on molecular objects to produce exact stochastic results with performance that scales independently of the reaction network size. Reaction rates can be defined as arbitrary functions of molecular states to provide powerful coarse-graining capabilities, for example to merge Boolean and kinetic representations of biological networks. NFsim enables researchers to simulate many biological systems that were previously inaccessible to general-purpose software, as we illustrate with models of immune system signaling, microbial signaling, cytoskeletal assembly and oscillating gene expression.

  5. Parameter estimation method for improper fractional models and its application to molecular biological systems.

    Science.gov (United States)

    Tian, Li-Ping; Liu, Lizhi; Wu, Fang-Xiang

    2010-01-01

    Derived from biochemical principles, molecular biological systems can be described by a group of differential equations. Generally these differential equations contain fractional functions plus polynomials (which we call improper fractional model) as reaction rates. As a result, molecular biological systems are nonlinear in both parameters and states. It is well known that it is challenging to estimate parameters nonlinear in a model. However, in fractional functions both the denominator and numerator are linear in the parameters while polynomials are also linear in parameters. Based on this observation, we develop an iterative linear least squares method for estimating parameters in biological systems modeled by improper fractional functions. The basic idea is to transfer optimizing a nonlinear least squares objective function into iteratively solving a sequence of linear least squares problems. The developed method is applied to the estimation of parameters in a metabolism system. The simulation results show the superior performance of the proposed method for estimating parameters in such molecular biological systems.

  6. Ecosystem Modeling of Biological Processes to Global Budgets

    Science.gov (United States)

    Christopher, Potter S.; Condon, Estelle (Technical Monitor)

    2000-01-01

    From an ecological perspective, the search for life on distant planets begins from several key assumptions. The first of these is that, viewed from a remote location in space, the signature of life on a distant planet will be the result of net gas exchange of organisms with their environment. On the basis of extensive biogeochemical measurements and biogenic trace gas fluxes in modem Earth environments, it is probable that certain groups of organisms both produce and consume the same trace gas(es) within a single bioprofile of Solid (porous) substrate or surface water. The net gas exchange rate with the atmosphere measured at the living surface is frequently the result of competing metabolic reactions, which may carried out by different functional groups of organisms located at dissimilar 'climatic' or chemical microsites within the same bioprofile. Biogenic gases produced at one (deep) level of a bioprofile may be consumed by another functional group of organisms located closer to the level of surface exchange with the atmosphere. A second key assumption is that the net biogenic fluxes of atmospheric gases on Earth can be used to infer relative abundance and functional composition of the major organisms on a distant planet. Examples of this principle include the presence of methanogenic microorganisms abundant today in freshwater ecosystems worldwide, which are major source of atmospheric methane and its seasonal variability in Earth's atmosphere. A third assumption is that scaling up biogenic gas fluxes from a single biological community to the planetary level requires flux measurements at the whole ecosystem level. This implies that measurements of biogenic gas exchange with the global atmosphere cannot be easily inferred from measurements of gas production rates of single organisms, which may have been isolated in some manner from the setting of their native ecosystem. Hence, the unit of biological organization used in modern Earth Science for scaling up to

  7. Bio-logic builder: a non-technical tool for building dynamical, qualitative models.

    Directory of Open Access Journals (Sweden)

    Tomáš Helikar

    Full Text Available Computational modeling of biological processes is a promising tool in biomedical research. While a large part of its potential lies in the ability to integrate it with laboratory research, modeling currently generally requires a high degree of training in mathematics and/or computer science. To help address this issue, we have developed a web-based tool, Bio-Logic Builder, that enables laboratory scientists to define mathematical representations (based on a discrete formalism of biological regulatory mechanisms in a modular and non-technical fashion. As part of the user interface, generalized "bio-logic" modules have been defined to provide users with the building blocks for many biological processes. To build/modify computational models, experimentalists provide purely qualitative information about a particular regulatory mechanisms as is generally found in the laboratory. The Bio-Logic Builder subsequently converts the provided information into a mathematical representation described with Boolean expressions/rules. We used this tool to build a number of dynamical models, including a 130-protein large-scale model of signal transduction with over 800 interactions, influenza A replication cycle with 127 species and 200+ interactions, and mammalian and budding yeast cell cycles. We also show that any and all qualitative regulatory mechanisms can be built using this tool.

  8. Dynamical model for biological functions of DNA molecules

    Institute of Scientific and Technical Information of China (English)

    PANGXiao-fengI; YANGYao

    2004-01-01

    We proposed a dynamic model of DNA to study its nonlinear excitation and duplication and transcription in the basis of molecular structure and changes of conformation of DNA under influence of bioenergy.

  9. Assessing Understanding of Biological Processes: Elucidating Students' Models of Meiosis.

    Science.gov (United States)

    Kindfield, Ann C.

    1994-01-01

    Presents a meiosis reasoning problem that provides direct access to students' current models of chromosomes and meiosis. Also included in the article are tips for classroom implementation and a summary of the solution evaluation. (ZWH)

  10. Biology of Obesity: Lessons from Animal Models of Obesity

    Directory of Open Access Journals (Sweden)

    Keizo Kanasaki

    2011-01-01

    problems, including diabetes, cardiovascular disease, respiratory failure, muscle weakness, and cancer. The precise molecular mechanisms by which obesity induces these health problems are not yet clear. To better understand the pathomechanisms of human disease, good animal models are essential. In this paper, we will analyze animal models of obesity and their use in the research of obesity-associated human health conditions and diseases such as diabetes, cancer, and obstructive sleep apnea syndrome.

  11. On the modeling of internal parameters in hyperelastic biological materials

    CERN Document Server

    Giantesio, Giulia

    2016-01-01

    This paper concerns the behavior of hyperelastic energies depending on an internal parameter. First, the situation in which the internal parameter is a function of the gradient of the deformation is presented. Second, two models where the parameter describes the activation of skeletal muscle tissue are analyzed. In those models, the activation parameter depends on the strain and it is important to consider the derivative of the parameter with respect to the strain in order to capture the proper behavior of the stress.

  12. A biologically plausible model of human shape symmetry perception.

    Science.gov (United States)

    Poirier, Frédéric J A M; Wilson, Hugh R

    2010-01-19

    Symmetry is usually computationally expensive to encode reliably, and yet it is relatively effortless to perceive. Here, we extend F. J. A. M. Poirier and H. R. Wilson's (2006) model for shape perception to account for H. R. Wilson and F. Wilkinson's (2002) data on shape symmetry. Because the model already accounts for shape perception, only minimal neural circuitry is required to enable it to encode shape symmetry as well. The model is composed of three main parts: (1) recovery of object position using large-scale non-Fourier V4-like concentric units that respond at the center of concentric contour segments across orientations, (2) around that recovered object center, curvature mechanisms combine multiplicatively the responses of oriented filters to encode object-centric local shape information, with a preference for convexities, and (3) object-centric symmetry mechanisms. Model and human performances are comparable for symmetry perception of shapes. Moreover, with some improvement of edge recovery, the model can encode symmetry axes in natural images such as faces.

  13. Mathematical models in cell biology and cancer chemotherapy

    CERN Document Server

    Eisen, Martin

    1979-01-01

    The purpose of this book is to show how mathematics can be applied to improve cancer chemotherapy. Unfortunately, most drugs used in treating cancer kill both normal and abnormal cells. However, more cancer cells than normal cells can be destroyed by the drug because tumor cells usually exhibit different growth kinetics than normal cells. To capitalize on this last fact, cell kinetics must be studied by formulating mathematical models of normal and abnormal cell growth. These models allow the therapeutic and harmful effects of cancer drugs to be simulated quantitatively. The combined cell and drug models can be used to study the effects of different methods of administering drugs. The least harmful method of drug administration, according to a given criterion, can be found by applying optimal control theory. The prerequisites for reading this book are an elementary knowledge of ordinary differential equations, probability, statistics, and linear algebra. In order to make this book self-contained, a chapter on...

  14. Dynamic statistical models of biological cognition: insights from communications theory

    Science.gov (United States)

    Wallace, Rodrick

    2014-10-01

    Maturana's cognitive perspective on the living state, Dretske's insight on how information theory constrains cognition, the Atlan/Cohen cognitive paradigm, and models of intelligence without representation, permit construction of a spectrum of dynamic necessary conditions statistical models of signal transduction, regulation, and metabolism at and across the many scales and levels of organisation of an organism and its context. Nonequilibrium critical phenomena analogous to physical phase transitions, driven by crosstalk, will be ubiquitous, representing not only signal switching, but the recruitment of underlying cognitive modules into tunable dynamic coalitions that address changing patterns of need and opportunity at all scales and levels of organisation. The models proposed here, while certainly providing much conceptual insight, should be most useful in the analysis of empirical data, much as are fitted regression equations.

  15. Off-Angle Iris Correction using a Biological Model

    Energy Technology Data Exchange (ETDEWEB)

    Thompson, Joseph T [ORNL; Santos-Villalobos, Hector J [ORNL; Karakaya, Mahmut [ORNL; Barstow, Del R [ORNL; Bolme, David S [ORNL; Boehnen, Chris Bensing [ORNL

    2013-01-01

    This work implements an eye model to simulate corneal refraction effects. Using this model, ray tracing is performed to calculate transforms to remove refractive effects in off-angle iris images when reprojected to a frontal view. The correction process is used as a preprocessing step for off-angle iris images for input to a commercial matcher. With this method, a match score distribution mean improvement of 11.65% for 30 degree images, 44.94% for 40 degree images, and 146.1% improvement for 50 degree images is observed versus match score distributions with unmodi ed images.

  16. Off-Angle Iris Correction using a Biological Model

    Energy Technology Data Exchange (ETDEWEB)

    Santos-Villalobos, Hector J [ORNL; Karakaya, Mahmut [ORNL; Barstow, Del R [ORNL; Boehnen, Chris Bensing [ORNL

    2013-01-01

    This work implements an eye model to simulate corneal refraction effects. Using this model, ray tracing is performed to calculate transforms to remove refractive effects in off-angle iris images when reprojected to a frontal view. The correction process is used as a preprocessing step for off-angle iris images for input to a commercial matcher. With this method, a match score distribution mean improvement of 11.65% for 30 degree images, 44.94% for 40 degree images, and 146.1% improvement for 50 degree images is observed versus match score distributions with unmodified images.

  17. Theoretical biology: Comparing models of species abundance - Brief Communications Arising

    NARCIS (Netherlands)

    Chave, J.; Alonso, D.; Etienne, R.S.

    2006-01-01

    Ecologists are struggling to explain how so many tropical tree species can coexist in tropical forests, and several empirical studies have demonstrated that negative density dependence is an important mechanism of tree-species coexistence1, 2. Volkov et al.3 compare a model incorporating negative de

  18. Teaching the Big Ideas of Biology with Operon Models

    Science.gov (United States)

    Cooper, Robert A.

    2015-01-01

    This paper presents an activity that engages students in model-based reasoning, requiring them to predict the behavior of the trp and lac operons under different environmental conditions. Students are presented six scenarios for the "trp" operon and five for the "lac" operon. In most of the scenarios, specific mutations have…

  19. A Biological Hierarchical Model Based Underwater Moving Object Detection

    Directory of Open Access Journals (Sweden)

    Jie Shen

    2014-01-01

    Full Text Available Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results.

  20. Average intensity and spreading of partially coherent model beams propagating in a turbulent biological tissue

    Science.gov (United States)

    Wu, Yuqian; Zhang, Yixin; Wang, Qiu; Hu, Zhengda

    2016-11-01

    For Gaussian beams with three different partially coherent models, including Gaussian-Schell model (GSM), Laguerre-Gaussian Schell-model (LGSM) and Bessel-Gaussian Schell-model (BGSM) beams propagating through a biological turbulent tissue, the expression of the spatial coherence radius of a spherical wave propagating in a turbulent biological tissue, and the average intensity and beam spreading for GSM, LGSM and BGSM beams are derived based on the fractal model of power spectrum of refractive-index variations in biological tissue. Effects of partially coherent model and parameters of biological turbulence on such beams are studied in numerical simulations. Our results reveal that the spreading of GSM beams is smaller than LGSM and BGSM beams on the same conditions, and the beam with larger source coherence width has smaller beam spreading than that with smaller coherence width. The results are useful for any applications involved light beam propagation through tissues, especially the cases where the average intensity and spreading properties of the light should be taken into account to evaluate the system performance and investigations in the structures of biological tissue.

  1. Human mammary microenvironment better regulates the biology of human breast cancer in humanized mouse model.

    Science.gov (United States)

    Zheng, Ming-Jie; Wang, Jue; Xu, Lu; Zha, Xiao-Ming; Zhao, Yi; Ling, Li-Jun; Wang, Shui

    2015-02-01

    During the past decades, many efforts have been made in mimicking the clinical progress of human cancer in mouse models. Previously, we developed a human breast tissue-derived (HB) mouse model. Theoretically, it may mimic the interactions between "species-specific" mammary microenvironment of human origin and human breast cancer cells. However, detailed evidences are absent. The present study (in vivo, cellular, and molecular experiments) was designed to explore the regulatory role of human mammary microenvironment in the progress of human breast cancer cells. Subcutaneous (SUB), mammary fat pad (MFP), and HB mouse models were developed for in vivo comparisons. Then, the orthotopic tumor masses from three different mouse models were collected for primary culture. Finally, the biology of primary cultured human breast cancer cells was compared by cellular and molecular experiments. Results of in vivo mouse models indicated that human breast cancer cells grew better in human mammary microenvironment. Cellular and molecular experiments confirmed that primary cultured human breast cancer cells from HB mouse model showed a better proliferative and anti-apoptotic biology than those from SUB to MFP mouse models. Meanwhile, primary cultured human breast cancer cells from HB mouse model also obtained the migratory and invasive biology for "species-specific" tissue metastasis to human tissues. Comprehensive analyses suggest that "species-specific" mammary microenvironment of human origin better regulates the biology of human breast cancer cells in our humanized mouse model of breast cancer, which is more consistent with the clinical progress of human breast cancer.

  2. Generalized Beer-Lambert model for near-infrared light propagation in thick biological tissues

    Science.gov (United States)

    Bhatt, Manish; Ayyalasomayajula, Kalyan R.; Yalavarthy, Phaneendra K.

    2016-07-01

    The attenuation of near-infrared (NIR) light intensity as it propagates in a turbid medium like biological tissue is described by modified the Beer-Lambert law (MBLL). The MBLL is generally used to quantify the changes in tissue chromophore concentrations for NIR spectroscopic data analysis. Even though MBLL is effective in terms of providing qualitative comparison, it suffers from its applicability across tissue types and tissue dimensions. In this work, we introduce Lambert-W function-based modeling for light propagation in biological tissues, which is a generalized version of the Beer-Lambert model. The proposed modeling provides parametrization of tissue properties, which includes two attenuation coefficients μ0 and η. We validated our model against the Monte Carlo simulation, which is the gold standard for modeling NIR light propagation in biological tissue. We included numerous human and animal tissues to validate the proposed empirical model, including an inhomogeneous adult human head model. The proposed model, which has a closed form (analytical), is first of its kind in providing accurate modeling of NIR light propagation in biological tissues.

  3. Integrative multicellular biological modeling: a case study of 3D epidermal development using GPU algorithms

    Directory of Open Access Journals (Sweden)

    Christley Scott

    2010-08-01

    Full Text Available Abstract Background Simulation of sophisticated biological models requires considerable computational power. These models typically integrate together numerous biological phenomena such as spatially-explicit heterogeneous cells, cell-cell interactions, cell-environment interactions and intracellular gene networks. The recent advent of programming for graphical processing units (GPU opens up the possibility of developing more integrative, detailed and predictive biological models while at the same time decreasing the computational cost to simulate those models. Results We construct a 3D model of epidermal development and provide a set of GPU algorithms that executes significantly faster than sequential central processing unit (CPU code. We provide a parallel implementation of the subcellular element method for individual cells residing in a lattice-free spatial environment. Each cell in our epidermal model includes an internal gene network, which integrates cellular interaction of Notch signaling together with environmental interaction of basement membrane adhesion, to specify cellular state and behaviors such as growth and division. We take a pedagogical approach to describing how modeling methods are efficiently implemented on the GPU including memory layout of data structures and functional decomposition. We discuss various programmatic issues and provide a set of design guidelines for GPU programming that are instructive to avoid common pitfalls as well as to extract performance from the GPU architecture. Conclusions We demonstrate that GPU algorithms represent a significant technological advance for the simulation of complex biological models. We further demonstrate with our epidermal model that the integration of multiple complex modeling methods for heterogeneous multicellular biological processes is both feasible and computationally tractable using this new technology. We hope that the provided algorithms and source code will be a

  4. QNS measurements on water in biological and model systems

    Energy Technology Data Exchange (ETDEWEB)

    Trantham, E.C.; Rorschach, H.E.; Clegg, J.C.; Hazlewood, C.F.; Nicklow, R.M.

    1981-01-01

    Results are presented on the quasi-elastic spectra of 0.95 THz neutrons scattered from pure water, a 20% agarose gel and cysts of the brine shrimp (Artemia) of hydration 1.2 gms H/sub 2/O per gm of dry solids. The lines are interpreted with a two-component model in which the hydration water scatters elastically and the free water is described by a jump-diffusion correlation function. The results for the line widths GAMMA(Q/sup 2/) are in good agreement with previous measurements for the water sample but show deviations from pure water at large Q for agarose and the Artemia cysts that suggest an increased value of the residence time in the jump-diffusion model.

  5. QNS measurements of water in biological and model systems

    Energy Technology Data Exchange (ETDEWEB)

    Trantham, E.C.; Rorschach, H.E.; Clegg, J.C.; Hazlewood, C.F.; Nicklow, R.M.

    1982-01-01

    Results are presented on the quasi-elastic spectra of 0.95 THz neutrons scattered from pure water, a 20% agarose gel and cysts of the brine shrimp (Artemia) of hydration 1.2 gms H/sub 2/O per gm of dry solids. The lines are interpreted with a two-component model in which the hydration water scatters elastically and the free water is described by a jump-diffusion correlation function. The results for the line widths GAMMA(Q/sup 2/) are in good agreement with previous measurements for the water sample but show deviations from pure water at large Q for agarose and the Artemia cysts that suggest an increased value of the residence time in the jump-diffusion model.

  6. Biochemical physics modeling of biological nano-motors

    Energy Technology Data Exchange (ETDEWEB)

    Santamaría-Holek, I.; López-Alamilla, N. J. [UMDI-Facultad de Ciencias, Universidad Nacional Autónoma de México, Campus Juriquilla, Boulevard Juriquilla 3001, 76230 Querétaro (Mexico)

    2014-01-14

    We present a biochemical physics model accounting for the dynamics and energetics of both translational and rotational protein motors. A modified version of the hand-over-hand mechanism considering competitive inhibition by ADP is presented. Transition state-like theory is used to reconstruct the time dependent free-energy landscape of the cycle catalyst process that allows to predicting the number of steps or rotations that a single motor can perform. In addition, following the usual approach of chemical kinetics, we calculate the average translational velocity and also the stopping time of processes involving a collectivity of motors, such as exocytosis and endocytosis processes. Finally, we formulate a stochastic model reproducing very well single realizations of kinesin and rotary ATPases.

  7. Untangling statistical and biological models to understand network inference: the need for a genomics network ontology.

    Science.gov (United States)

    Emmert-Streib, Frank; Dehmer, Matthias; Haibe-Kains, Benjamin

    2014-01-01

    In this paper, we shed light on approaches that are currently used to infer networks from gene expression data with respect to their biological meaning. As we will show, the biological interpretation of these networks depends on the chosen theoretical perspective. For this reason, we distinguish a statistical perspective from a mathematical modeling perspective and elaborate their differences and implications. Our results indicate the imperative need for a genomic network ontology in order to avoid increasing confusion about the biological interpretation of inferred networks, which can be even enhanced by approaches that integrate multiple data sets, respectively, data types.

  8. The modeling of the temperature field formed inside multilayered biological tissue under laser emission

    Science.gov (United States)

    Kulikov, Kirill

    2009-07-01

    The mathematical model the hyperthermy of the multilayer biological structure under the effect of laser emission is proposed. One allows to variate the electrophysical parameters of the biological structure (complex parameter of refraction of the blood and blood corpuscles, epidermis, the upper layer of derma, the lower layer of derma), the significant dimensions of the regular elements of the blood and to establish dependencies between them and by the biophysical properties of the blood taking into account heating biological tissue under the influence on its surface flow of the nonpolarized monochromatic radiation for the case in vivo.

  9. After the Greeting: Realizing the Potential of Physical Models in Cell Biology.

    Science.gov (United States)

    Paluch, Ewa K

    2015-12-01

    Biophysics is increasingly taking center stage in cell biology as the tools for precise quantifications of cellular behaviors expand. Interdisciplinary approaches, combining quantitative physical modeling with cell biology, are of growing interest to journal editors, funding agencies, and hiring committees. However, despite an ever-increasing emphasis on the importance of interdisciplinary research, the student trained in biology may still be at a loss as to what it actually means. I discuss here some considerations on how to achieve meaningful and high-quality interdisciplinary work.

  10. Structural Equation Modeling: Applications in ecological and evolutionary biology research

    Science.gov (United States)

    Pugesek, Bruce H.; von Eye, Alexander; Tomer, Adrian

    2003-01-01

    This book presents an introduction to the methodology of structural equation modeling, illustrates its use, and goes on to argue that it has revolutionary implications for the study of natural systems. A major theme of this book is that we have, up to this point, attempted to study systems primarily using methods (such as the univariate model) that were designed only for considering individual processes. Understanding systems requires the capacity to examine simultaneous influences and responses. Structural equation modeling (SEM) has such capabilities. It also possesses many other traits that add strength to its utility as a means of making scientific progress. In light of the capabilities of SEM, it can be argued that much of ecological theory is currently locked in an immature state that impairs its relevance. It is further argued that the principles of SEM are capable of leading to the development and evaluation of multivariate theories of the sort vitally needed for the conservation of natural systems. Supplementary information can be found at the authors website, http://www.jamesbgrace.com/. • Details why multivariate analyses should be used to study ecological systems • Exposes unappreciated weakness in many current popular analyses • Emphasizes the future methodological developments needed to advance our understanding of ecological systems.

  11. Modeling the Drug Discovery Process: The Isolation and Biological Testing of Eugenol from Clove Oil

    Science.gov (United States)

    Miles, William H.; Smiley, Patricia M.

    2002-01-01

    This experiment describes the isolation and biological testing of eugenol and neutral compounds from commercially available clove oil. By coupling the chemical separation of the components of clove oil (an experiment described in many introductory organic laboratory textbooks) with a simple antibiotic test, the students "discover" the biologically active compound in clove oil. This experiment models one of the primary methods used in the discovery of new pharmaceutical agents.

  12. Introductory Biology Students' Conceptual Models and Explanations of the Origin of Variation

    Science.gov (United States)

    Bray Speth, Elena; Shaw, Neil; Momsen, Jennifer; Reinagel, Adam; Le, Paul; Taqieddin, Ranya; Long, Tammy

    2014-01-01

    Mutation is the key molecular mechanism generating phenotypic variation, which is the basis for evolution. In an introductory biology course, we used a model-based pedagogy that enabled students to integrate their understanding of genetics and evolution within multiple case studies. We used student-generated conceptual models to assess…

  13. A Model of How Different Biology Experts Explain Molecular and Cellular Mechanisms

    Science.gov (United States)

    Trujillo, Caleb M.; Anderson, Trevor R.; Pelaez, Nancy J.

    2015-01-01

    Constructing explanations is an essential skill for all science learners. The goal of this project was to model the key components of expert explanation of molecular and cellular mechanisms. As such, we asked: What is an appropriate model of the components of explanation used by biology experts to explain molecular and cellular mechanisms? Do…

  14. The Silicon Trypanosome : A Test Case of Iterative Model Extension in Systems Biology

    NARCIS (Netherlands)

    Achcar, Fiona; Fadda, Abeer; Haanstra, Jurgen R.; Kerkhoven, Eduard J.; Kim, Dong-Hyun; Leroux, Alejandro E.; Papamarkou, Theodore; Rojas, Federico; Bakker, Barbara M.; Barrett, Michael P.; Clayton, Christine; Girolami, Mark; Krauth-Siegel, R. Luise; Matthews, Keith R.; Breitling, Rainer; Poole, RK

    2014-01-01

    The African trypanosome, Ttypanosoma brucei, is a unicellular parasite causing African Trypanosomiasis (sleeping sickness in humans and nagana in animals). Due to some of its unique properties, it has emerged as a popular model organism in systems biology. A predictive quantitative model of glycolys

  15. Benchmarking biological nutrient removal in wastewater treatment plants: influence of mathematical model assumptions

    DEFF Research Database (Denmark)

    Flores-Alsina, Xavier; Gernaey, Krist V.; Jeppsson, Ulf

    2012-01-01

    This paper examines the effect of different model assumptions when describing biological nutrient removal (BNR) by the activated sludge models (ASM) 1, 2d & 3. The performance of a nitrogen removal (WWTP1) and a combined nitrogen and phosphorus removal (WWTP2) benchmark wastewater treatment plant...

  16. PENGEMBANGAN MODEL PEMBELAJARAN BIOKIMIA BERBASIS KOMPUTER UNTUK MEMBEKALI KETERAMPILAN BERPIKIR KREATIF MAHASISWA CALON GURU BIOLOGI

    Directory of Open Access Journals (Sweden)

    H. Rahmatan

    2012-10-01

    Full Text Available Penelitian ini bertujuan untuk mengembangkan model pembelajaran biokima berbasis komputer untuk membekali keterampilan berpikir kreatif mahasiswa calon guru biologi. Penelitian ini merupakan penelitian deskriptif. Hasil pengolahan data diperoleh bahwa hasil validasi oleh ahli terhadap model pembelajaran sudah baik demikian juga dengan keterbacaan software pembelajaran. Dengan demikian model pembelajaran biokimia dengan model drill and practice yang dikemas dalam software sudah dapat digunakan untuk mengukur penguasaan konsep biokimia dan keterampilan berpikir kreatif mahasiswa calon guru biologi.   This study aims to develop computer-based learning model biokima creative thinking skills to equip prospective teachers of biology students. This research is a descriptive study. Data processing results obtained that the results of the validation by experts to have a good learning model as well as the legibility of the learning software. Thus the biochemical model of learning by drill and practice models that can be packaged in software has been used to measure mastery of biochemical concepts and creative thinking skills of prospective teachers of biology students.

  17. Computational modeling of chemo-bio-mechanical coupling: a systems-biology approach toward wound healing.

    Science.gov (United States)

    Buganza Tepole, A; Kuhl, E

    2016-01-01

    Wound healing is a synchronized cascade of chemical, biological, and mechanical phenomena, which act in concert to restore the damaged tissue. An imbalance between these events can induce painful scarring. Despite intense efforts to decipher the mechanisms of wound healing, the role of mechanics remains poorly understood. Here, we establish a computational systems biology model to identify the chemical, biological, and mechanical mechanisms of scar formation. First, we introduce the generic problem of coupled chemo-bio-mechanics. Then, we introduce the model problem of wound healing in terms of a particular chemical signal, inflammation, a particular biological cell type, fibroblasts, and a particular mechanical model, isotropic hyperelasticity. We explore the cross-talk between chemical, biological, and mechanical signals and show that all three fields have a significant impact on scar formation. Our model is the first step toward rigorous multiscale, multifield modeling in wound healing. Our formulation has the potential to improve effective wound management and optimize treatment on an individualized patient-specific basis.

  18. Bio-Logic Builder: A Non-Technical Tool for Building Dynamical, Qualitative Models

    Science.gov (United States)

    Helikar, Tomáš; Kowal, Bryan; Madrahimov, Alex; Shrestha, Manish; Pedersen, Jay; Limbu, Kahani; Thapa, Ishwor; Rowley, Thaine; Satalkar, Rahul; Kochi, Naomi; Konvalina, John; Rogers, Jim A.

    2012-01-01

    Computational modeling of biological processes is a promising tool in biomedical research. While a large part of its potential lies in the ability to integrate it with laboratory research, modeling currently generally requires a high degree of training in mathematics and/or computer science. To help address this issue, we have developed a web-based tool, Bio-Logic Builder, that enables laboratory scientists to define mathematical representations (based on a discrete formalism) of biological regulatory mechanisms in a modular and non-technical fashion. As part of the user interface, generalized “bio-logic” modules have been defined to provide users with the building blocks for many biological processes. To build/modify computational models, experimentalists provide purely qualitative information about a particular regulatory mechanisms as is generally found in the laboratory. The Bio-Logic Builder subsequently converts the provided information into a mathematical representation described with Boolean expressions/rules. We used this tool to build a number of dynamical models, including a 130-protein large-scale model of signal transduction with over 800 interactions, influenza A replication cycle with 127 species and 200+ interactions, and mammalian and budding yeast cell cycles. We also show that any and all qualitative regulatory mechanisms can be built using this tool. PMID:23082121

  19. Polaron assisted charge transfer in model biological systems

    Science.gov (United States)

    Li, Guangqi; Movaghar, Bijan

    2016-11-01

    We use a tight binding Hamiltonian to simulate the electron transfer from an initial charge-separating exciton to a final target state through a two-arm transfer model. The structure is copied from the model frequently used to describe electron harvesting in photosynthesis (photosystems I). We use this network to provide proof of principle for dynamics, in quantum system/bath networks, especially those involving interference pathways, and use these results to make predictions on artificially realizable systems. Each site is coupled to the phonon bath via several electron-phonon couplings. The assumed large energy gaps and weak tunneling integrals linking the last 3 sites give rise to"Stark Wannier like" quantum localization; electron transfer to the target cluster becomes impossible without bath coupling. As a result of the electron-phonon coupling, local electronic energies relax when the site is occupied, and transient polaronic states are formed as photo-generated electrons traverse the system. For a symmetric constructively interfering two pathway network, the population is shared equally between two sets of equivalent sites and therefore the polaron energy shift is smaller. The smaller energy shift however makes the tunnel transfer to the last site slower or blocks it altogether. Slight disorder (or thermal noise) can break the symmetry, permitting essentially a "one path", and correspondingly more efficient transfer.

  20. From systems biology to photosynthesis and whole-plant modeling: a conceptual model for integrating multi-scale networks

    Energy Technology Data Exchange (ETDEWEB)

    Weston, David [ORNL; Hanson, Paul J [ORNL; Norby, Richard J [ORNL; Tuskan, Gerald A [ORNL; Wullschleger, Stan D [ORNL

    2012-01-01

    Network analysis is now a common statistical tool for molecular biologists. Network algorithms are readily used to model gene, protein and metabolic correlations providing insight into pathways driving biological phenomenon. One output from such an analysis is a candidate gene list that can be responsible, in part, for the biological process of interest. The question remains, however, as to whether molecular network analysis can be used to inform process models at higher levels of biological organization. In our previous work, transcriptional networks derived from three plant species were constructed, interrogated for orthology and then correlated to photosynthetic inhibition at elevated temperature. One unique aspect of that study was the link from co-expression networks to net photosynthesis. In this addendum, we propose a conceptual model where traditional network analysis can be linked to whole-plant models thereby informing predictions on key processes such as photosynthesis, nutrient uptake and assimilation, and C partitioning.

  1. Development of a coupled physical-biological ecosystem model ECOSMO - Part I: Model description and validation for the North Sea

    DEFF Research Database (Denmark)

    Schrum, Corinna; Alekseeva, I.; St. John, Michael

    2006-01-01

    A 3-D coupled biophysical model ECOSMO (ECOSystem MOdel) has been developed. The biological module of ECOSMO is based on lower trophic level interactions between two phyto- and two zooplankton components. The dynamics of the different phytoplankton components are governed by the availability of t...... trophic level. (c) 2006 Elsevier B.V All rights reserved....

  2. Pig Brain Mitochondria as a Biological Model for Study of Mitochondrial Respiration.

    Science.gov (United States)

    Fišar, Z; Hroudová, J

    2016-01-01

    Oxidative phosphorylation is a key process of intracellular energy transfer by which mitochondria produce ATP. Isolated mitochondria serve as a biological model for understanding the mitochondrial respiration control, effects of various biologically active substances, and pathophysiology of mitochondrial diseases. The aim of our study was to evaluate pig brain mitochondria as a proper biological model for investigation of activity of the mitochondrial electron transport chain. Oxygen consumption rates of isolated pig brain mitochondria were measured using high-resolution respirometry. Mitochondrial respiration of crude mitochondrial fraction, mitochondria purified in sucrose gradient, and mitochondria purified in Percoll gradient were assayed as a function of storage time. Oxygen flux and various mitochondrial respiratory control ratios were not changed within two days of mitochondria storage on ice. Leak respiration was found higher and Complex I-linked respiration lower in purified mitochondria compared to the crude mitochondrial fraction. Damage to both outer and inner mitochondrial membrane caused by the isolation procedure was the greatest after purification in a sucrose gradient. We confirmed that pig brain mitochondria can serve as a biological model for investigation of mitochondrial respiration. The advantage of this biological model is the stability of respiratory parameters for more than 48 h and the possibility to isolate large amounts of mitochondria from specific brain areas without the need to kill laboratory animals. We suggest the use of high-resolution respirometry of pig brain mitochondria for research of the neuroprotective effects and/or mitochondrial toxicity of new medical drugs.

  3. Simple Empirical Model for Identifying Rheological Properties of Soft Biological Tissues

    CERN Document Server

    Kobayashi, Yo; Miyashita, Tomoyuki; Fujie, Masakatsu G

    2015-01-01

    Understanding the rheological properties of soft biological tissue is a key issue for mechanical systems used in the healthcare field. We propose a simple empirical model using Fractional Dynamics and Exponential Nonlinearity (FDEN) to identify the rheological properties of soft biological tissue. The model is derived from detailed material measurements using samples isolated from porcine liver. We conducted dynamic viscoelastic and creep tests on liver samples using a rheometer. The experimental results indicated that biological tissue has specific properties: i) power law increases in storage elastic modulus and loss elastic modulus with the same slope; ii) power law gain decrease and constant phase delay in the frequency domain over two decades; iii) log-log scale linearity between time and strain relationships under constant force; and iv) linear and log scale linearity between strain and stress relationships. Our simple FDEN model uses only three dependent parameters and represents the specific propertie...

  4. Promoting Coordinated Development of Community-Based Information Standards for Modeling in Biology: The COMBINE Initiative.

    Science.gov (United States)

    Hucka, Michael; Nickerson, David P; Bader, Gary D; Bergmann, Frank T; Cooper, Jonathan; Demir, Emek; Garny, Alan; Golebiewski, Martin; Myers, Chris J; Schreiber, Falk; Waltemath, Dagmar; Le Novère, Nicolas

    2015-01-01

    The Computational Modeling in Biology Network (COMBINE) is a consortium of groups involved in the development of open community standards and formats used in computational modeling in biology. COMBINE's aim is to act as a coordinator, facilitator, and resource for different standardization efforts whose domains of use cover related areas of the computational biology space. In this perspective article, we summarize COMBINE, its general organization, and the community standards and other efforts involved in it. Our goals are to help guide readers toward standards that may be suitable for their research activities, as well as to direct interested readers to relevant communities where they can best expect to receive assistance in how to develop interoperable computational models.

  5. MODEL OF METHODS OF FORMING BIOLOGICAL PICTURE OF THE WORLD OF SECONDARY SCHOOL PUPILS

    Directory of Open Access Journals (Sweden)

    Mikhail A. Yakunchev

    2016-12-01

    Full Text Available Introduction: the problem of development of a model of methods of forming the biological picture of the world of pupils as a multicomponent and integrative expression of the complete educational process is considered in the article. It is stated that the results of the study have theoretical and practical importance for effective subject preparation of senior pupils based on acquiring of systematic and generalized knowledge about wildlife. The correspondence of the main idea of the article to the scientific profile of the journal “Integration of Education” determines the choice of the periodical for publication. Materials and Methods: the results of the analysis of materials on modeling of the educational process, on specific models of the formation of a complete comprehension of the scientific picture of the world and its biological component make it possible to suggest a lack of elaboration of the aspect of pedagogical research under study. Therefore, the search for methods to overcome these gaps and to substantiate a particular model, relevant for its practical application by a teacher, is important. The study was based on the use of methods of theoretical level, including the analysis of pedagogical and methodological literature, modeling and generalized expression of the model of forming the biological picture of the world of secondary school senior pupils, which were of higher priority. Results: the use of models of organization of subject preparation of secondary school pupils takes a priority position, as they help to achieve the desired results of training, education and development. The model of methods of forming a biological picture of the world is represented as a theoretical construct in the unity of objective, substantive, procedural, diagnostic and effective blocks. Discussion and Conclusions: in a generalized form the article expresses the model of methods of forming the biological picture of the world of secondary school

  6. A biological plausible Generalized Leaky Integrate-and-Fire neuron model.

    Science.gov (United States)

    Wang, Zhenzhong; Guo, Lilin; Adjouadi, Malek

    2014-01-01

    This study introduces a new Generalized Leaky Integrate-and-Fire (GLIF) neuron model. Unlike Normal Leaky Integrate-and-Fire (NLIF) models, the leaking resistor in the GLIF model equation is assumed to be variable, and an additional term would have the bias current added to the model equation in order to improve the accuracy. Adjusting the parameters defined for the leaking resistor and bias current, a GLIF model could be accurately matched to any Hodgkin-Huxley (HH) model and be able to reproduce plausible biological neuron behaviors.

  7. Tav4SB: integrating tools for analysis of kinetic models of biological systems

    Directory of Open Access Journals (Sweden)

    Rybiński Mikołaj

    2012-04-01

    Full Text Available Abstract Background Progress in the modeling of biological systems strongly relies on the availability of specialized computer-aided tools. To that end, the Taverna Workbench eases integration of software tools for life science research and provides a common workflow-based framework for computational experiments in Biology. Results The Taverna services for Systems Biology (Tav4SB project provides a set of new Web service operations, which extend the functionality of the Taverna Workbench in a domain of systems biology. Tav4SB operations allow you to perform numerical simulations or model checking of, respectively, deterministic or stochastic semantics of biological models. On top of this functionality, Tav4SB enables the construction of high-level experiments. As an illustration of possibilities offered by our project we apply the multi-parameter sensitivity analysis. To visualize the results of model analysis a flexible plotting operation is provided as well. Tav4SB operations are executed in a simple grid environment, integrating heterogeneous software such as Mathematica, PRISM and SBML ODE Solver. The user guide, contact information, full documentation of available Web service operations, workflows and other additional resources can be found at the Tav4SB project’s Web page: http://bioputer.mimuw.edu.pl/tav4sb/. Conclusions The Tav4SB Web service provides a set of integrated tools in the domain for which Web-based applications are still not as widely available as for other areas of computational biology. Moreover, we extend the dedicated hardware base for computationally expensive task of simulating cellular models. Finally, we promote the standardization of models and experiments as well as accessibility and usability of remote services.

  8. GSK-3: functional insights from cell biology and animal models

    Directory of Open Access Journals (Sweden)

    Oksana eKaidanovich-Beilin

    2011-11-01

    Full Text Available Glycogen synthase kinase-3 (GSK-3 is a widely expressed and highly conserved serine/threonine protein kinase encoded in mammals by two genes that generate two related proteins: GSK-3α and GSK-3β. GSK-3 is active in cells under resting conditions and is primarily regulated through inhibition or diversion of its activity. While GSK-3 is one of the few protein kinases that can be inactivated by phosphorylation, the mechanisms of GSK-3 regulation are more varied and not fully understood. Precise control appears to be achieved by a combination of phosphorylation, localization, and sequestration by a number of GSK-3-binding proteins. GSK-3 lies downstream of several major signaling pathways including the phosphatidylinositol 3’ kinase pathway, the Wnt pathway, Hedgehog signaling and Notch. Specific pools of GSK-3, which differ in intracellular localization, binding partner affinity and relative amount are differentially sensitized to several distinct signaling pathways and these sequestration mechanisms contribute to pathway insulation and signal specificity. Dysregulation of signaling pathways involving GSK-3 is associated with the pathogenesis of numerous neurological and psychiatric disorders and there are data suggesting GSK-3 isoform-selective roles in several of these. Here, we review the current knowledge of GSK-3 regulation and targets and discuss the various animal models that have been employed to dissect the functions of GSK-3 in brain development and function through the use of conventional or conditional knock-out mice as well as transgenic mice. These studies have revealed fundamental roles for these protein kinases in memory, behavior and neuronal fate determination and provide insights into possible therapeutic interventions.

  9. The marmoset monkey: a multi-purpose preclinical and translational model of human biology and disease.

    Science.gov (United States)

    't Hart, Bert A; Abbott, David H; Nakamura, Katsuki; Fuchs, Eberhard

    2012-11-01

    The development of biologic molecules (monoclonal antibodies, cytokines, soluble receptors) as specific therapeutics for human disease creates a need for animal models in which safety and efficacy can be tested. Models in lower animal species are precluded when the reagents fail to recognize their targets, which is often the case in rats and mice. In this Feature article we will highlight the common marmoset, a small-bodied nonhuman primate (NHP), as a useful model in biomedical and preclinical translational research.

  10. Analysis of clinical trials with biologics using dose-time-response models.

    Science.gov (United States)

    Lange, Markus R; Schmidli, Heinz

    2015-09-30

    Biologics such as monoclonal antibodies are increasingly and successfully used for the treatment of many chronic diseases. Unlike conventional small drug molecules, which are commonly given as tablets once daily, biologics are typically injected at much longer time intervals, that is, weeks or months. Hence, both the dose and the time interval have to be optimized during the drug development process for biologics. To identify an adequate regimen for the investigated biologic, the dose-time-response relationship must be well characterized, based on clinical trial data. The proposed approach uses semi-mechanistic nonlinear regression models to describe and predict the time-changing response for complex dosing regimens. Both likelihood-based and Bayesian methods for inference and prediction are discussed. The methodology is illustrated with data from a clinical study in an auto-immune disease.

  11. Code and context: Prochlorococcus as a model for cross-scale biology.

    Science.gov (United States)

    Coleman, Maureen L; Chisholm, Sallie W

    2007-09-01

    Prochlorococcus is a simple cyanobacterium that is abundant throughout large regions of the oceans, and has become a useful model for studying the nature and regulation of biological diversity across all scales of complexity. Recent work has revealed that environmental factors such as light, nutrients and predation influence diversity in different ways, changing our image of the structure and dynamics of the global Prochlorococcus population. Advances in metagenomics, transcription profiling and global ecosystem modeling promise to deliver an even greater understanding of this system and further demonstrate the power of cross-scale systems biology.

  12. IMPLEMENTASI MODEL TUTORIAL BERBASIS KOMPUTER FISIOLOGI HEWAN UNTUK MEMBEKALI KEMAMPUAN REKONSTRUKSI KONSEP MAHASISWA CALON GURU BIOLOGI

    Directory of Open Access Journals (Sweden)

    Adeng Slamet

    2015-03-01

    Full Text Available Penelitian ini bertujuan untuk membandingkan model perkuliahan fisiologi hewan yang diharapkan mampu membekali kemampuan rekonstruksi konsep bagi mahasiswa calon guru biologi. Strategi perkuliahan ditempuh melalui implementasi model tutorial berbasis komputer. Sebanyak 80 orang mahasiswa S1 calon guru biologi dibagi ke dalam dua kelompok, 41 mahasiswa mengikuti perkuliahan model tutorial komputer, dan 39 mahasiswa mengikuti perkuliahan konvensional. Kemampuan rekonstruksi konsep diukur dengan membandingkan skor sebelum pembelajaran (pretes dengan setelah implementasi model (postes di antara kedua kelompok belajar. Selain itu, untuk mengungkap pandangan mahasiswa mengenai pengalaman belajarnya, seperangkat angket disebarkan kepada mahasiswa yang mengikuti model perkuliahan.  Efektivitas program perkuliahan dievaluasi dengan tes tertulis bentuk respon terbatas pada mahasiswa yang mengikuti program perkuliahan model tutorial komputer dibandingkan dengan mahasiswa dari kelompok konvensional. Hasil penelitian menunjukkan secara keseluruhan terjadi peningkatan  kemampuan rekonstruksi konsep pada kedua kelompok belajar, namun mahasiswa yang mengikuti perkuliahan model tutorial berbasis komputer menunjukkan peningkatan yang lebih tinggi dibandingkan kelompok mahasiswa peserta perkuliahan konvensional. Mahasiswa menanggapi positif implementasi model tutorial berbasis komputer dalam perkuliahan fisiologi hewan. Dapat disimpulkan bahwa penerapan model tutorial berbasis komputer pada penelitian ini dinyatakan lebih efektif dan mampu  membekali mahasiwa calon guru biologi dalam meningkatkan kemampuan  rekonstruksi konsep.

  13. Quantifying uncertainty in partially specified biological models: how can optimal control theory help us?

    Science.gov (United States)

    Adamson, M W; Morozov, A Y; Kuzenkov, O A

    2016-09-01

    Mathematical models in biology are highly simplified representations of a complex underlying reality and there is always a high degree of uncertainty with regards to model function specification. This uncertainty becomes critical for models in which the use of different functions fitting the same dataset can yield substantially different predictions-a property known as structural sensitivity. Thus, even if the model is purely deterministic, then the uncertainty in the model functions carries through into uncertainty in model predictions, and new frameworks are required to tackle this fundamental problem. Here, we consider a framework that uses partially specified models in which some functions are not represented by a specific form. The main idea is to project infinite dimensional function space into a low-dimensional space taking into account biological constraints. The key question of how to carry out this projection has so far remained a serious mathematical challenge and hindered the use of partially specified models. Here, we propose and demonstrate a potentially powerful technique to perform such a projection by using optimal control theory to construct functions with the specified global properties. This approach opens up the prospect of a flexible and easy to use method to fulfil uncertainty analysis of biological models.

  14. MEMFASILITASI HIGHER ORDER THINKING SKILLS DALAM PERKULIAHAN BIOLOGI SEL MELALUI MODEL INTEGRASI ATRIBUT ASESMEN FORMATIF

    Directory of Open Access Journals (Sweden)

    Sigit Saptono

    2017-02-01

    Full Text Available Abstrak ___________________________________________________________________ Higher order thinking skills sangat dibutuhkan untuk memahami permasalahan dan esensi materi perkuliahan Biologi Sel. Studi dengan desain Research and Development ini bertujuan untuk mengembangkan kemampuan penalaran dan berpikir analitik mahasiswa calon guru biologi melalui penerapan model pembelajaran Integrasi Atribut Asesmen Formatif (IAAF. Sejumlah 61 mahasiswa program studi Pendidikan Biologi Universitas Negeri Semarang semester tiga yang sedang menempuh mata kuliah Biologi Sel menjadi subjek penelitian. Kemampuan penalaran dan berpikir analitik mahasiswa diukur melalui tugas individu, tugas kelompok pembuatan peta konsep dan penyusunan laporan reviu artikel, dan 30 item soal berbentuk selected response questions dan constructed response questions tervalidasi. Hasil analisis data menunjukkan bahwa kemampuan penalaran dan berpikir analitik mahasiswa dapat berkembang secara signifikan, meskipun perkembangan kemampuan argumentasi, salah satu kategori kemampuan berpikir analitik, masih perlu perhatian yang cukup serius.   Abstract ___________________________________________________________________ Higher order thinking skills are needed to understand the problem and the essence of the lecture material Biology Sel. Study design Research and Development aims to develop reasoning skills and analytic thinking biology student teachers through the application of learning models Integration Attributes Formative Assessment (IAAF. Some 61 students of Biology Education Semarang State University who is doing his third semester courses Cell Biology is the subject of research. Analytical reasoning and thinking ability of students is measured through individual assignments, group assignments concept map creation and preparation of the Review articles, and 30 items about the shape of the selected response and constructed response questions, validated questions. The result showed

  15. ADAM: Analysis of Discrete Models of Biological Systems Using Computer Algebra

    Directory of Open Access Journals (Sweden)

    Blekherman Grigoriy

    2011-07-01

    Full Text Available Abstract Background Many biological systems are modeled qualitatively with discrete models, such as probabilistic Boolean networks, logical models, Petri nets, and agent-based models, to gain a better understanding of them. The computational complexity to analyze the complete dynamics of these models grows exponentially in the number of variables, which impedes working with complex models. There exist software tools to analyze discrete models, but they either lack the algorithmic functionality to analyze complex models deterministically or they are inaccessible to many users as they require understanding the underlying algorithm and implementation, do not have a graphical user interface, or are hard to install. Efficient analysis methods that are accessible to modelers and easy to use are needed. Results We propose a method for efficiently identifying attractors and introduce the web-based tool Analysis of Dynamic Algebraic Models (ADAM, which provides this and other analysis methods for discrete models. ADAM converts several discrete model types automatically into polynomial dynamical systems and analyzes their dynamics using tools from computer algebra. Specifically, we propose a method to identify attractors of a discrete model that is equivalent to solving a system of polynomial equations, a long-studied problem in computer algebra. Based on extensive experimentation with both discrete models arising in systems biology and randomly generated networks, we found that the algebraic algorithms presented in this manuscript are fast for systems with the structure maintained by most biological systems, namely sparseness and robustness. For a large set of published complex discrete models, ADAM identified the attractors in less than one second. Conclusions Discrete modeling techniques are a useful tool for analyzing complex biological systems and there is a need in the biological community for accessible efficient analysis tools. ADAM provides

  16. A consilience model to describe N2O production during biological N removal

    DEFF Research Database (Denmark)

    Domingo Felez, Carlos; Smets, Barth F.

    2016-01-01

    Nitrous oxide (N2O), a potent greenhouse gas, is produced during biological nitrogen conversion in wastewater treatment operations. Complex mechanisms underlie N2O production by autotrophic and heterotrophic organisms, which continue to be unravelled. Mathematical models that describe nitric oxide...... (NO) and N2O dynamics have been proposed. Here, a first comprehensive model that considers all relevant NO and N2O production and consumption mechanisms is proposed. The model describes autotrophic NO production by ammonia oxidizing bacteria associated with ammonia oxidation and with nitrite reduction......, followed by NO reduction to N2O. It also considers NO and N2O as intermediates in heterotrophic denitrification in a 4-step model. Three biological NO and N2O production pathways are accounted for, improving the capabilities of existing models while not increasing their complexity. Abiotic contributions...

  17. Fuzzy Logic as a Computational Tool for Quantitative Modelling of Biological Systems with Uncertain Kinetic Data.

    Science.gov (United States)

    Bordon, Jure; Moskon, Miha; Zimic, Nikolaj; Mraz, Miha

    2015-01-01

    Quantitative modelling of biological systems has become an indispensable computational approach in the design of novel and analysis of existing biological systems. However, kinetic data that describe the system's dynamics need to be known in order to obtain relevant results with the conventional modelling techniques. These data are often hard or even impossible to obtain. Here, we present a quantitative fuzzy logic modelling approach that is able to cope with unknown kinetic data and thus produce relevant results even though kinetic data are incomplete or only vaguely defined. Moreover, the approach can be used in the combination with the existing state-of-the-art quantitative modelling techniques only in certain parts of the system, i.e., where kinetic data are missing. The case study of the approach proposed here is performed on the model of three-gene repressilator.

  18. A biologically plausible model of time-scale invariant interval timing.

    Science.gov (United States)

    Almeida, Rita; Ledberg, Anders

    2010-02-01

    The temporal durations between events often exert a strong influence over behavior. The details of this influence have been extensively characterized in behavioral experiments in different animal species. A remarkable feature of the data collected in these experiments is that they are often time-scale invariant. This means that response measurements obtained under intervals of different durations coincide when plotted as functions of relative time. Here we describe a biologically plausible model of an interval timing device and show that it is consistent with time-scale invariant behavior over a substantial range of interval durations. The model consists of a set of bistable units that switch from one state to the other at random times. We first use an abstract formulation of the model to derive exact expressions for some key quantities and to demonstrate time-scale invariance for any range of interval durations. We then show how the model could be implemented in the nervous system through a generic and biologically plausible mechanism. In particular, we show that any system that can display noise-driven transitions from one stable state to another can be used to implement the timing device. Our work demonstrates that a biologically plausible model can qualitatively account for a large body of data and thus provides a link between the biology and behavior of interval timing.

  19. Stochastic and deterministic multiscale models for systems biology: an auxin-transport case study

    Directory of Open Access Journals (Sweden)

    King John R

    2010-03-01

    Full Text Available Abstract Background Stochastic and asymptotic methods are powerful tools in developing multiscale systems biology models; however, little has been done in this context to compare the efficacy of these methods. The majority of current systems biology modelling research, including that of auxin transport, uses numerical simulations to study the behaviour of large systems of deterministic ordinary differential equations, with little consideration of alternative modelling frameworks. Results In this case study, we solve an auxin-transport model using analytical methods, deterministic numerical simulations and stochastic numerical simulations. Although the three approaches in general predict the same behaviour, the approaches provide different information that we use to gain distinct insights into the modelled biological system. We show in particular that the analytical approach readily provides straightforward mathematical expressions for the concentrations and transport speeds, while the stochastic simulations naturally provide information on the variability of the system. Conclusions Our study provides a constructive comparison which highlights the advantages and disadvantages of each of the considered modelling approaches. This will prove helpful to researchers when weighing up which modelling approach to select. In addition, the paper goes some way to bridging the gap between these approaches, which in the future we hope will lead to integrative hybrid models.

  20. Information-theoretic analysis of the dynamics of an executable biological model.

    Directory of Open Access Journals (Sweden)

    Avital Sadot

    Full Text Available To facilitate analysis and understanding of biological systems, large-scale data are often integrated into models using a variety of mathematical and computational approaches. Such models describe the dynamics of the biological system and can be used to study the changes in the state of the system over time. For many model classes, such as discrete or continuous dynamical systems, there exist appropriate frameworks and tools for analyzing system dynamics. However, the heterogeneous information that encodes and bridges molecular and cellular dynamics, inherent to fine-grained molecular simulation models, presents significant challenges to the study of system dynamics. In this paper, we present an algorithmic information theory based approach for the analysis and interpretation of the dynamics of such executable models of biological systems. We apply a normalized compression distance (NCD analysis to the state representations of a model that simulates the immune decision making and immune cell behavior. We show that this analysis successfully captures the essential information in the dynamics of the system, which results from a variety of events including proliferation, differentiation, or perturbations such as gene knock-outs. We demonstrate that this approach can be used for the analysis of executable models, regardless of the modeling framework, and for making experimentally quantifiable predictions.

  1. Identification problem for stochastic models with application to carcinogenesis, cancer detection and radiation biology

    Directory of Open Access Journals (Sweden)

    L. G. Hanin

    2002-01-01

    Full Text Available A general framework for solving identification problem for a broad class of deterministic and stochastic models is discussed. This methodology allows for a unified approach to studying identifiability of various stochastic models arising in biology and medicine including models of spontaneous and induced Carcinogenesis, tumor progression and detection, and randomized hit and target models of irradiated cell survival. A variety of known results on parameter identification for stochastic models is reviewed and several new results are presented with an emphasis on rigorous mathematical development.

  2. Global stability and optimisation of a general impulsive biological control model

    CERN Document Server

    Mailleret, Ludovic

    2008-01-01

    An impulsive model of augmentative biological control consisting of a general continuous predator-prey model in ordinary differential equations augmented by a discrete part describing periodic introductions of predators is considered. It is shown that there exists an invariant periodic solution that corresponds to prey eradication and a condition ensuring its global asymptotic stability is given. An optimisation problem related to the preemptive use of augmentative biological control is then considered. It is assumed that the per time unit budget of biological control (i.e. the number of predators to be released) is fixed and the best deployment of this budget is sought after in terms of release frequency. The cost function to be minimised is the time taken to reduce an unforeseen prey (pest) invasion under some harmless level. The analysis shows that the optimisation problem admits a countable infinite number of solutions. An argumentation considering the required robustness of the optimisation result is the...

  3. Human Development VII: A Spiral Fractal Model of Fine Structure of Physical Energy Could Explain Central Aspects of Biological Information, Biological Organization and Biological Creativity

    Directory of Open Access Journals (Sweden)

    Søren Ventegodt

    2006-01-01

    Full Text Available In this paper we have made a draft of a physical fractal essence of the universe, a sketch of a new cosmology, which we believe to lay at the root of our new holistic biological paradigm. We present the fractal roomy spiraled structures and the energy-rich dancing “infinite strings” or lines of the universe that our hypothesis is based upon. The geometric language of this cosmology is symbolic and both pre-mathematical and pre-philosophical. The symbols are both text and figures, and using these we step by step explain the new model that at least to some extent is able to explain the complex informational system behind morphogenesis, ontogenesis, regeneration and healing. We suggest that it is from this highly dynamic spiraled structure that organization of cells, organs, and the wholeness of the human being including consciousness emerge. The model of ““dancing fractal spirals” carries many similarities to premodern cultures descriptions of the energy of the life and universe. Examples are the Native American shamanistic descriptions of their perception of energy and the old Indian Yogis descriptions of the life-energy within the body and outside. Similar ideas of energy and matter are found in the modern superstring theories. The model of the informational system of the organism gives new meaning to Bateson’s definition of information: “A difference that makes a difference”, and indicates how information-directed self-organization can exist on high structural levels in living organisms, giving birth to their subjectivity and consciousness.

  4. Research on Performance Evaluation of Biological Database based on Layered Queuing Network Model under the Cloud Computing Environment

    OpenAIRE

    Zhengbin Luo; Dongmei Sun

    2013-01-01

    To evaluate the performance of biological database based on layered queuing network model and under cloud computing environment is a premise, as well as an important step for biological database optimization. Based on predecessors’ researches concerning computer software and hardware performance evaluation under cloud environment, the study has further constructed a model system to evaluate the performance of biological database based on layered queuing network model and under cloud environme...

  5. Computational approaches and metrics required for formulating biologically realistic nanomaterial pharmacokinetic models

    Science.gov (United States)

    Riviere, Jim E.; Scoglio, Caterina; Sahneh, Faryad D.; Monteiro-Riviere, Nancy A.

    2013-01-01

    The field of nanomaterial pharmacokinetics is in its infancy, with major advances largely restricted by a lack of biologically relevant metrics, fundamental differences between particles and small molecules of organic chemicals and drugs relative to biological processes involved in disposition, a scarcity of sufficiently rich and characterized in vivo data and a lack of computational approaches to integrating nanomaterial properties to biological endpoints. A central concept that links nanomaterial properties to biological disposition, in addition to their colloidal properties, is the tendency to form a biocorona which modulates biological interactions including cellular uptake and biodistribution. Pharmacokinetic models must take this crucial process into consideration to accurately predict in vivo disposition, especially when extrapolating from laboratory animals to humans since allometric principles may not be applicable. The dynamics of corona formation, which modulates biological interactions including cellular uptake and biodistribution, is thereby a crucial process involved in the rate and extent of biodisposition. The challenge will be to develop a quantitative metric that characterizes a nanoparticle's surface adsorption forces that are important for predicting biocorona dynamics. These types of integrative quantitative approaches discussed in this paper for the dynamics of corona formation must be developed before realistic engineered nanomaterial risk assessment can be accomplished.

  6. Effective identification of conserved pathways in biological networks using hidden Markov models.

    Directory of Open Access Journals (Sweden)

    Xiaoning Qian

    Full Text Available BACKGROUND: The advent of various high-throughput experimental techniques for measuring molecular interactions has enabled the systematic study of biological interactions on a global scale. Since biological processes are carried out by elaborate collaborations of numerous molecules that give rise to a complex network of molecular interactions, comparative analysis of these biological networks can bring important insights into the functional organization and regulatory mechanisms of biological systems. METHODOLOGY/PRINCIPAL FINDINGS: In this paper, we present an effective framework for identifying common interaction patterns in the biological networks of different organisms based on hidden Markov models (HMMs. Given two or more networks, our method efficiently finds the top matching paths in the respective networks, where the matching paths may contain a flexible number of consecutive insertions and deletions. CONCLUSIONS/SIGNIFICANCE: Based on several protein-protein interaction (PPI networks obtained from the Database of Interacting Proteins (DIP and other public databases, we demonstrate that our method is able to detect biologically significant pathways that are conserved across different organisms. Our algorithm has a polynomial complexity that grows linearly with the size of the aligned paths. This enables the search for very long paths with more than 10 nodes within a few minutes on a desktop computer. The software program that implements this algorithm is available upon request from the authors.

  7. Towards a model of non-equilibrium binding of metal ions in biological systems.

    Science.gov (United States)

    Beardmore, James; Exley, Christopher

    2009-02-01

    We have used a systems biology approach to address the hitherto insoluble problem of the quantitative analysis of non-equilibrium binding of aqueous metal ions by competitive ligands in heterogeneous media. To-date, the relative proportions of different metal complexes in aqueous media has only been modelled at chemical equilibrium and there are no quantitative analyses of the approach to equilibrium. While these models have improved our understanding of how metals are used in biological systems they cannot account for the influence of kinetic factors in metal binding, transport and fate. Here we have modelled the binding of aluminium, Al(III), in blood serum by the iron transport protein transferrin (Tf) as it is widely accepted that the biological fate of this non-essential metal is not adequately described by experiments, invitro and insilico, which have consistently demonstrated that at equilibrium 90% of serum Al(III) is bound by Tf. We have coined this paradox 'the blood-aluminium problem' and herein applied a systems biology approach which utilised well-found assumptions to pare away the complexities of the problem such that it was defined by a comparatively simple set of computational rules and, importantly, its solution assumed significant predictive capabilities. Here we show that our novel computational model successfully described the binding of Al(III) by Tf both at equilibrium and as equilibrium for Al(Tf) was approached. The model predicted significant non-equilibrium binding of Al by ligands in competition with Tf and, thereby, provided an explanation of why the distribution of Al(III) in the body cannot be adequately described by its binding and transport by Tf alone. Generically the model highlighted the significance of kinetic in addition to thermodynamic constraints in defining the fate of metal ions in biological systems.

  8. A coupled physical-biological-chemical model for the Indian Ocean

    Indian Academy of Sciences (India)

    P S Swathi; M K Sharada; K S Yajnik

    2000-12-01

    A coupled physical-biological-chemical model has been developed at C-MMACS. for studying the time- variation of primary productivity and air-sea carbon-dioxide exchange in the Indian Ocean. The physical model is based on the Modular Ocean Model, Version 2 (MOM2) and the biological model describes the nonlinear dynamics of a 7-component marine ecosystem. The chemical model includes dynamical equation for the evolution of dissolved inorganic carbon and total alkalinity. The interaction between the biological and chemical model is through the Redfield ratio. The partial pressure of carbon dioxide pCO2 of the surface layer is obtained from the chemical equilibrium equations of Peng et al 1987. Transfer coefficients for air-sea exchange of CO2 are computed dynamically based on the wind speeds. The coupled model reproduces the high productivity observed in the Arabian Sea off the Somali and Omani coasts during the Southwest (SW) monsoon. The entire Arabian Sea is an outgassing region for CO2 in spite of high productivity with transfer rates as high as 80 m-mol C/m2/day during SW monsoon near the Somali Coast on account of strong winds.

  9. A model of how different biology experts explain molecular and cellular mechanisms.

    Science.gov (United States)

    Trujillo, Caleb M; Anderson, Trevor R; Pelaez, Nancy J

    2015-01-01

    Constructing explanations is an essential skill for all science learners. The goal of this project was to model the key components of expert explanation of molecular and cellular mechanisms. As such, we asked: What is an appropriate model of the components of explanation used by biology experts to explain molecular and cellular mechanisms? Do explanations made by experts from different biology subdisciplines at a university support the validity of this model? Guided by the modeling framework of R. S. Justi and J. K. Gilbert, the validity of an initial model was tested by asking seven biologists to explain a molecular mechanism of their choice. Data were collected from interviews, artifacts, and drawings, and then subjected to thematic analysis. We found that biologists explained the specific activities and organization of entities of the mechanism. In addition, they contextualized explanations according to their biological and social significance; integrated explanations with methods, instruments, and measurements; and used analogies and narrated stories. The derived methods, analogies, context, and how themes informed the development of our final MACH model of mechanistic explanations. Future research will test the potential of the MACH model as a guiding framework for instruction to enhance the quality of student explanations.

  10. Mobile Applications in Cell Biology Present New Approaches for Cell Modelling

    Science.gov (United States)

    de Oliveira, Mayara Lustosa; Galembeck, Eduardo

    2016-01-01

    Cell biology apps were surveyed in order to identify whether there are new approaches for modelling cells allowed by the new technologies implemented in tablets and smartphones. A total of 97 apps were identified in 3 stores surveyed (Apple, Google Play and Amazon), they are presented as: education 48.4%, games 26.8% and medicine 15.4%. The apps…

  11. Recursively partitioned mixture model clustering of DNA methylation data using biologically informed correlation structures.

    Science.gov (United States)

    Koestler, Devin C; Christensen, Brock C; Marsit, Carmen J; Kelsey, Karl T; Houseman, E Andres

    2013-03-05

    DNA methylation is a well-recognized epigenetic mechanism that has been the subject of a growing body of literature typically focused on the identification and study of profiles of DNA methylation and their association with human diseases and exposures. In recent years, a number of unsupervised clustering algorithms, both parametric and non-parametric, have been proposed for clustering large-scale DNA methylation data. However, most of these approaches do not incorporate known biological relationships of measured features, and in some cases, rely on unrealistic assumptions regarding the nature of DNA methylation. Here, we propose a modified version of a recursively partitioned mixture model (RPMM) that integrates information related to the proximity of CpG loci within the genome to inform correlation structures from which subsequent clustering analysis is based. Using simulations and four methylation data sets, we demonstrate that integrating biologically informative correlation structures within RPMM resulted in improved goodness-of-fit, clustering consistency, and the ability to detect biologically meaningful clusters compared to methods which ignore such correlation. Integrating biologically-informed correlation structures to enhance modeling techniques is motivated by the rapid increase in resolution of DNA methylation microarrays and the increasing understanding of the biology of this epigenetic mechanism.

  12. The reduction of biological production induced by mesoscale mixing: a modelling study in the Benguela upwelling

    CERN Document Server

    Hernández-Carrasco, Ismael; Hernández-García, Emilio; Garçon, Veronique; López, Cristóbal

    2013-01-01

    Recent studies, both based on remote sensed data and coupled models, showed a reduction of biological productivity due to vigorous horizontal mixing in upwelling systems. In order to better understand this phenomenon, we have considered a system of oceanic flow in the Benguela area coupled with a simple biogeochemical model of Nutrient-Phyto-Zooplankton (NPZ) type. For the flow three different surface velocity fields are considered: one derived from satellite altimetry data, and the other two from a regional numerical model at two different spatial resolutions. We computed horizontal particle dispersion in terms of Lyapunov Exponents, and analyzed their correlations with phytoplankton concentrations. Our modelling approach confirms that in the south Benguela, there is a reduction of biological activity when stirring is increased. Two-dimensional offshore advection seems to be the dominant process involved. In the northern area, other factors not taken into account in our simulation are influencing the ecosyst...

  13. The Sum of the Parts: Large-scale Modeling in Systems Biology

    DEFF Research Database (Denmark)

    Fridolin, Gross; Green, Sara

    2017-01-01

    these questions, we distinguish between two types of reductionism, namely 'modular reductionism' and 'bottom-up reductionism'. Much knowledge in molecular biology has been gained by decomposing living systems into functional modules or through detailed studies of molecular processes. We ask whether systems...... biology provides novel ways to ​ ​recompose these findings in the context of the system as a whole via computational simulations. As an example of computational integration of modules, we analyze the first whole-cell model of the bacterium ​ ​M. genitalium. Secondly, we examine the attempt to recompose...... processes across different spatial scales via multi-scale cardiac models. Although these models also rely on a number of idealizations and simplifying assumptions, we argue that they provide insight into the limitations of reductionist approaches. Whole-cell models can be used to discover properties arising...

  14. Monte Carlo evaluation of biological variation: Random generation of correlated non-Gaussian model parameters

    Science.gov (United States)

    Hertog, Maarten L. A. T. M.; Scheerlinck, Nico; Nicolaï, Bart M.

    2009-01-01

    When modelling the behaviour of horticultural products, demonstrating large sources of biological variation, we often run into the issue of non-Gaussian distributed model parameters. This work presents an algorithm to reproduce such correlated non-Gaussian model parameters for use with Monte Carlo simulations. The algorithm works around the problem of non-Gaussian distributions by transforming the observed non-Gaussian probability distributions using a proposed SKN-distribution function before applying the covariance decomposition algorithm to generate Gaussian random co-varying parameter sets. The proposed SKN-distribution function is based on the standard Gaussian distribution function and can exhibit different degrees of both skewness and kurtosis. This technique is demonstrated using a case study on modelling the ripening of tomato fruit evaluating the propagation of biological variation with time.

  15. Model Order and Identifiability of Non-Linear Biological Systems in Stable Oscillation.

    Science.gov (United States)

    Wigren, Torbjörn

    2015-01-01

    The paper presents a theoretical result that clarifies when it is at all possible to determine the nonlinear dynamic equations of a biological system in stable oscillation, from measured data. As it turns out the minimal order needed for this is dependent on the minimal dimension in which the stable orbit of the system does not intersect itself. This is illustrated with a simulated fourth order Hodgkin-Huxley spiking neuron model, which is identified using a non-linear second order differential equation model. The simulated result illustrates that the underlying higher order model of the spiking neuron cannot be uniquely determined given only the periodic measured data. The result of the paper is of general validity when the dynamics of biological systems in stable oscillation is identified, and illustrates the need to carefully address non-linear identifiability aspects when validating models based on periodic data.

  16. Tuneable resolution as a systems biology approach for multi-scale, multi-compartment computational models.

    Science.gov (United States)

    Kirschner, Denise E; Hunt, C Anthony; Marino, Simeone; Fallahi-Sichani, Mohammad; Linderman, Jennifer J

    2014-01-01

    The use of multi-scale mathematical and computational models to study complex biological processes is becoming increasingly productive. Multi-scale models span a range of spatial and/or temporal scales and can encompass multi-compartment (e.g., multi-organ) models. Modeling advances are enabling virtual experiments to explore and answer questions that are problematic to address in the wet-lab. Wet-lab experimental technologies now allow scientists to observe, measure, record, and analyze experiments focusing on different system aspects at a variety of biological scales. We need the technical ability to mirror that same flexibility in virtual experiments using multi-scale models. Here we present a new approach, tuneable resolution, which can begin providing that flexibility. Tuneable resolution involves fine- or coarse-graining existing multi-scale models at the user's discretion, allowing adjustment of the level of resolution specific to a question, an experiment, or a scale of interest. Tuneable resolution expands options for revising and validating mechanistic multi-scale models, can extend the longevity of multi-scale models, and may increase computational efficiency. The tuneable resolution approach can be applied to many model types, including differential equation, agent-based, and hybrid models. We demonstrate our tuneable resolution ideas with examples relevant to infectious disease modeling, illustrating key principles at work.

  17. Exploring behaviors of stochastic differential equation models of biological systems using change of measures

    Directory of Open Access Journals (Sweden)

    Jha Sumit

    2012-04-01

    Full Text Available Abstract Stochastic Differential Equations (SDE are often used to model the stochastic dynamics of biological systems. Unfortunately, rare but biologically interesting behaviors (e.g., oncogenesis can be difficult to observe in stochastic models. Consequently, the analysis of behaviors of SDE models using numerical simulations can be challenging. We introduce a method for solving the following problem: given a SDE model and a high-level behavioral specification about the dynamics of the model, algorithmically decide whether the model satisfies the specification. While there are a number of techniques for addressing this problem for discrete-state stochastic models, the analysis of SDE and other continuous-state models has received less attention. Our proposed solution uses a combination of Bayesian sequential hypothesis testing, non-identically distributed samples, and Girsanov's theorem for change of measures to examine rare behaviors. We use our algorithm to analyze two SDE models of tumor dynamics. Our use of non-identically distributed samples sampling contributes to the state of the art in statistical verification and model checking of stochastic models by providing an effective means for exposing rare events in SDEs, while retaining the ability to compute bounds on the probability that those events occur.

  18. Exploring behaviors of stochastic differential equation models of biological systems using change of measures.

    Science.gov (United States)

    Jha, Sumit Kumar; Langmead, Christopher James

    2012-04-12

    Stochastic Differential Equations (SDE) are often used to model the stochastic dynamics of biological systems. Unfortunately, rare but biologically interesting behaviors (e.g., oncogenesis) can be difficult to observe in stochastic models. Consequently, the analysis of behaviors of SDE models using numerical simulations can be challenging. We introduce a method for solving the following problem: given a SDE model and a high-level behavioral specification about the dynamics of the model, algorithmically decide whether the model satisfies the specification. While there are a number of techniques for addressing this problem for discrete-state stochastic models, the analysis of SDE and other continuous-state models has received less attention. Our proposed solution uses a combination of Bayesian sequential hypothesis testing, non-identically distributed samples, and Girsanov's theorem for change of measures to examine rare behaviors. We use our algorithm to analyze two SDE models of tumor dynamics. Our use of non-identically distributed samples sampling contributes to the state of the art in statistical verification and model checking of stochastic models by providing an effective means for exposing rare events in SDEs, while retaining the ability to compute bounds on the probability that those events occur.

  19. The Use of Mouse Models for Understanding the Biology of Down Syndrome and Aging

    OpenAIRE

    Vacano, Guido N.; Nathan Duval; David Patterson

    2012-01-01

    Down syndrome is a complex condition caused by trisomy of human chromosome 21. The biology of aging may be different in individuals with Down syndrome; this is not well understood in any organism. Because of its complexity, many aspects of Down syndrome must be studied either in humans or in animal models. Studies in humans are essential but are limited for ethical and practical reasons. Fortunately, genetically altered mice can serve as extremely useful models of Down syndrome, and progress ...

  20. Biological computation

    CERN Document Server

    Lamm, Ehud

    2011-01-01

    Introduction and Biological BackgroundBiological ComputationThe Influence of Biology on Mathematics-Historical ExamplesBiological IntroductionModels and Simulations Cellular Automata Biological BackgroundThe Game of Life General Definition of Cellular Automata One-Dimensional AutomataExamples of Cellular AutomataComparison with a Continuous Mathematical Model Computational UniversalitySelf-Replication Pseudo Code Evolutionary ComputationEvolutionary Biology and Evolutionary ComputationGenetic AlgorithmsExample ApplicationsAnalysis of the Behavior of Genetic AlgorithmsLamarckian Evolution Genet

  1. Research on models of biological systems that can be integrated into mechatronic systems

    Science.gov (United States)

    Pop, P. P.; Pop-Vadean, A.; Barz, C.; Latinovic, T.; Chiver, O.

    2016-02-01

    The models of biological systems that we find on Earth can be the subject of research to develop a few mechatronic systems. Such models are offered by bees, ants, crows, cranes, etc. Article aims to investigate these models and their manifestations. Imitating this behavior and studied him offer ideas for develop models that can be integrated into mechatronic systems. They can be integrated into mechatronic system as algorithms for finding local optimum, to search, to detect an optimal way travel on a network, to find best decision, etc.

  2. Random many-particle systems: applications from biology, and propagation of chaos in abstract models

    CERN Document Server

    Wennberg, Bernt

    2011-01-01

    The paper discusses a family of Markov processes that represent many particle systems, and their limiting behaviour when the number of particles go to infinity. The first part concerns model of biological systems: a model for sympatric speciation, i.e. the process in which a genetically homogeneous population is split in two or more different species sharing the same habitat, and models for swarming animals. The second part of the paper deals with abstract many particle systems, and methods for rigorously deriving mean field models.

  3. Application of source-receptor models to determine source areas of biological components (pollen and butterflies

    Directory of Open Access Journals (Sweden)

    M. Alarcón

    2010-01-01

    Full Text Available The source-receptor models allow the establishment of relationships between a receptor point (sampling point and the probable source areas (regions of emission through the association of concentration values at the receptor point with the corresponding atmospheric back-trajectories, and, together with other techniques, to interpret transport phenomena on a synoptic scale. These models are generally used in air pollution studies to determine the areas of origin of chemical compounds measured at a sampling point, and thus be able to target actions to reduce pollutants. However, until now, few studies have applied these types of models to describe the source areas of biological organisms. In Catalonia there are very complete records of pollen (data from the Xarxa Aerobiològica de Catalunya, Aerobiology Network of Catalonia and butterflies (data from the Catalan Butterfly Monitoring Scheme, a biological material that is also liable to be transported long distances and whose areas of origin could be interesting to know. This work presents the results of the use of the Seibert et al. model applied to the study of the source regions of: (1 certain pollen of an allergic nature, observed in Catalonia and the Canary Islands, and (2 the migratory butterfly Vanessa cardui, observed in Catalonia. Based on the results obtained we can corroborate the suitability of these models to determine the area of origin of several species, both chemical and biological, therefore expanding the possibilities of applying the original model to the wider field of Aerobiology.

  4. Research on Performance Evaluation of Biological Database based on Layered Queuing Network Model under the Cloud Computing Environment

    Directory of Open Access Journals (Sweden)

    Zhengbin Luo

    2013-06-01

    Full Text Available To evaluate the performance of biological database based on layered queuing network model and under cloud computing environment is a premise, as well as an important step for biological database optimization. Based on predecessors’ researches concerning computer software and hardware performance evaluation under cloud environment, the study has further constructed a model system to evaluate the performance of biological database based on layered queuing network model and under cloud environment. Moreover, traditional layered queuing network model is also optimized and upgraded in this process. After having constructed the performance evaluation system, the study applies laboratory experiment method to test the validity of the constructed performance model. Shown by the test result, this model is effective in evaluating the performance of biological system under cloud environment and the predicted result is quite close to the tested result. This has demonstrated the validity of the model in evaluating the performance of biological database.

  5. A MULTISTAGE BIOLOGICALLY BASED MATHEMATICAL MODEL FOR MOUSE LIVER TUMORS INDUCED BY DICHLOROACETIC ACID (DCA) - EXPLORATION OF THE MODEL

    Science.gov (United States)

    A biologically based mathematical model for the induction of liver tumors in mice by dichloroacetic acid (DCA) has been developed from histopathologic analysis of the livers of exposed mice. This analysis suggests that following chronic exposure to DCA, carcinomas can arise dire...

  6. Modeling Nitrous Oxide Production during Biological Nitrogen Removal via Nitrification and Denitrification: Extensions to the General ASM Models

    DEFF Research Database (Denmark)

    Ni, Bing-Jie; Ruscalleda, Maël; Pellicer i Nàcher, Carles

    2011-01-01

    Nitrous oxide (N2O) can be formed during biological nitrogen (N) removal processes. In this work, a mathematical model is developed that describes N2O production and consumption during activated sludge nitrification and denitrification. The well-known ASM process models are extended to capture N2O...... dynamics during both nitrification and denitrification in biological N removal. Six additional processes and three additional reactants, all involved in known biochemical reactions, have been added. The validity and applicability of the model is demonstrated by comparing simulations with experimental data...... on N2O production from four different mixed culture nitrification and denitrification reactor study reports. Modeling results confirm that hydroxylamine oxidation by ammonium oxidizers (AOB) occurs 10 times slower when NO2– participates as final electron acceptor compared to the oxic pathway. Among...

  7. Biological science learning model based on Turgo's local wisdom on managing biodiversity

    Science.gov (United States)

    Anwari, Nahdi, Maizer Said; Sulistyowati, Eka

    2016-02-01

    Local wisdom as product of local knowledge has been giving a local context in science development. Local wisdom is important to connect scientific theories and local conditions; hence science could be accessed by common people. Using local wisdom as a model for learning science enables students to build contextual learning, hence learning science becomes more meaningful and becomes more accessible for students in a local community. Based on this consideration, therefore, this research developed a model for learning biology based on Turgo's local wisdom on managing biodiversity. For this purpose, Turgo's biodiversity was mapped, and any local values that are co-existing with the biodiversity were recorded. All of these informations were, then, used as a hypohetical model for developing materials for teaching biology in a senior high school adjacent to Turgo. This research employed a qualitative method. We combined questionnaries, interviews and observation to gather the data. We found that Turgo community has been practicing local wisdom on using traditional plants for many uses, including land management and practicing rituals and traditional ceremonies. There were local values that they embrace which enable them to manage the nature wisely. After being cross-referenced with literature regarding educational philoshophy, educational theories and teachings, and biology curriculum for Indonesia's senior high school, we concluded that Turgo's local wisdom on managing biodiversity can be recommended to be used as learning materials and sources for biological learning in schools.

  8. High performance hybrid functional Petri net simulations of biological pathway models on CUDA.

    Science.gov (United States)

    Chalkidis, Georgios; Nagasaki, Masao; Miyano, Satoru

    2011-01-01

    Hybrid functional Petri nets are a wide-spread tool for representing and simulating biological models. Due to their potential of providing virtual drug testing environments, biological simulations have a growing impact on pharmaceutical research. Continuous research advancements in biology and medicine lead to exponentially increasing simulation times, thus raising the demand for performance accelerations by efficient and inexpensive parallel computation solutions. Recent developments in the field of general-purpose computation on graphics processing units (GPGPU) enabled the scientific community to port a variety of compute intensive algorithms onto the graphics processing unit (GPU). This work presents the first scheme for mapping biological hybrid functional Petri net models, which can handle both discrete and continuous entities, onto compute unified device architecture (CUDA) enabled GPUs. GPU accelerated simulations are observed to run up to 18 times faster than sequential implementations. Simulating the cell boundary formation by Delta-Notch signaling on a CUDA enabled GPU results in a speedup of approximately 7x for a model containing 1,600 cells.

  9. A physical-biological coupled model for algal dynamics in lakes.

    Science.gov (United States)

    Franke, U; Hutter, K; Jöhnk, K

    1999-03-01

    A coupled model is presented for simulating physical and biological dynamics in fresh water lakes. The physical model rests upon the assumption that the turbulent kinetic energy in a water column of the lake is fully contained in a mixed layer of variable depth. Below this layer the mechanical energy content is assumed to vanish. Additionally, the horizontal currents are ignored. This one-dimensional two-layered model describes the internal conversion of the mechanical and thermal energy input from the atmosphere into an evolution of the mixed layer depth by entrainment and detrainment mechanisms. It is supposed to form the physical domain in which the simulation of the biological processes takes place. The biological model describes mathematically the typical properties of phyto- and zooplankton, their interactions and their response to the physical environment. This description then allows the study of the behaviour of Lagrangian clusters of virtual plankton that are subjected to such environments. The essence of the model is the dynamical simulation of an arbitrary number of nutrient limited phytoplankton species and one species of zooplankton. The members of the food web above and below affect the model only statically. The model is able to reproduce the typical progression of a predator-prey interaction between phyto- and zooplankton as well as the exploitative competition for nutrients between two phytoplankton species under grazing pressure of Daphnia. It suggests that the influence of the biological system on the physical system results in a weak increase of the surface temperature for coupled simulations, but a considerably higher seasonal thermocline in spring and a lower one in autumn.

  10. The quest for a new modelling framework in mathematical biology. Comment on "On the interplay between mathematics and biology: Hallmarks towards a new systems biology" by N. Bellomo et al.

    Science.gov (United States)

    Eftimie, Raluca

    2015-03-01

    One of the main unsolved problems of modern physics is finding a "theory of everything" - a theory that can explain, with the help of mathematics, all physical aspects of the universe. While the laws of physics could explain some aspects of the biology of living systems (e.g., the phenomenological interpretation of movement of cells and animals), there are other aspects specific to biology that cannot be captured by physics models. For example, it is generally accepted that the evolution of a cell-based system is influenced by the activation state of cells (e.g., only activated and functional immune cells can fight diseases); on the other hand, the evolution of an animal-based system can be influenced by the psychological state (e.g., distress) of animals. Therefore, the last 10-20 years have seen also a quest for a "theory of everything"-approach extended to biology, with researchers trying to propose mathematical modelling frameworks that can explain various biological phenomena ranging from ecology to developmental biology and medicine [1,2,6]. The basic idea behind this approach can be found in a few reviews on ecology and cell biology [6,7,9-11], where researchers suggested that due to the parallel between the micro-scale dynamics and the emerging macro-scale phenomena in both cell biology and in ecology, many mathematical methods used for ecological processes could be adapted to cancer modelling [7,9] or to modelling in immunology [11]. However, this approach generally involved the use of different models to describe different biological aspects (e.g., models for cell and animal movement, models for competition between cells or animals, etc.).

  11. A four-dimensional validation of a coupled physical-biological model of the Arabian Sea

    Science.gov (United States)

    Hood, Raleigh R.; Kohler, Kevin E.; McCreary, Julian P.; Smith, Sharon L.

    2003-11-01

    In this paper, we use a coupled biological/physical model to synthesize and understand observations taken during the US JGOFS Arabian Sea Process Study (ASPS). Its physical component is a variable-density, 4 1/2-layer model; its biological component consists of a set of advective-diffusive equations in each layer that determine nitrogen concentrations in four compartments, namely, nutrients, phytoplankton, zooplankton, and detritus. Solutions are compared to time series and cruise sections from the ASPS data set, including observations of mixed-layer thickness, chlorophyll concentrations, inorganic nitrogen concentrations, particulate nitrogen export flux, zooplankton biomass, and primary production. Through these comparisons, we adjust model parameters to obtain a "best-fit" main-run solution, identify key biological and physical processes, and assess model strengths and weaknesses. Substantial improvements in the model/data comparison are obtained by: (1) adjusting the turbulence-production coefficients in the mixed-layer model to thin the mixed layer; (2) increasing the detrital sinking and remineralization rates to improve the timing and amplitude of the model's export flux; and (3) introducing a parameterization of particle aggregation to lower phytoplankton concentrations in coastal upwelling regions. With these adjustments, the model captures many key aspects of the observed physical and biogeochemical variability in offshore waters, including the near-surface DIN and phytoplankton P concentrations, mesozooplankton biomass, and primary production. Nevertheless, there are still significant model/data discrepancies of P for most of the cruises. Most of them can be attributed to forcing or process errors in the physical model: inaccurate mixed-layer thicknesses, lack of mesoscale eddies and filaments, and differences in the timing and spatial extent of coastal upwelling. Relatively few are clearly related to the simplicity of the biological model, the model

  12. A learning-enabled neuron array IC based upon transistor channel models of biological phenomena.

    Science.gov (United States)

    Brink, S; Nease, S; Hasler, P; Ramakrishnan, S; Wunderlich, R; Basu, A; Degnan, B

    2013-02-01

    We present a single-chip array of 100 biologically-based electronic neuron models interconnected to each other and the outside environment through 30,000 synapses. The chip was fabricated in a standard 350 nm CMOS IC process. Our approach used dense circuit models of synaptic behavior, including biological computation and learning, as well as transistor channel models. We use Address-Event Representation (AER) spike communication for inputs and outputs to this IC. We present the IC architecture and infrastructure, including IC chip, configuration tools, and testing platform. We present measurement of small network of neurons, measurement of STDP neuron dynamics, and measurement from a compiled spiking neuron WTA topology, all compiled into this IC.

  13. A model of electrostatically actuated MEMS and carbon nanotubes resonators for biological mass detection

    KAUST Repository

    Bouchaala, Adam M.

    2015-01-01

    We investigate the dynamics of electrically actuated Micro and Nano (Carbon nanotube (CNT)) cantilever beams implemented as resonant sensors for mass detection of biological elements. The beams are modeled using an Euler-Bernoulli beam theory including the nonlinear electrostatic forces and the added biological elements, which are modeled as a discrete point mass. A multi-mode Galerkin procedure is utilized to derive a reduced-order model, which is used for the dynamic simulations. The frequency shifts due to added mass of Escherichia coli (E. coli) and Prostate Specific Antigen (PSA) are calculated for the primary and higher order modes of vibrations. Also, analytical expressions of the natural frequency shift under dc voltage and added mass have been developed. We found that using higher-order modes of vibration of MEMS beams or miniaturizing the size of the beam to Nano scale leads to significant improved sensitivity. © Springer International Publishing Switzerland 2015.

  14. Mediating objects: scientific and public functions of models in nineteenth-century biology.

    Science.gov (United States)

    Ludwig, David

    2013-01-01

    The aim of this article is to examine the scientific and public functions of two- and three-dimensional models in the context of three episodes from nineteenth-century biology. I argue that these models incorporate both data and theory by presenting theoretical assumptions in the light of concrete data or organizing data through theoretical assumptions. Despite their diverse roles in scientific practice, they all can be characterized as mediators between data and theory. Furthermore, I argue that these different mediating functions often reflect their different audiences that included specialized scientists, students, and the general public. In this sense, models in nineteenth-century biology can be understood as mediators between theory, data, and their diverse audiences.

  15. In silico model-based inference: a contemporary approach for hypothesis testing in network biology.

    Science.gov (United States)

    Klinke, David J

    2014-01-01

    Inductive inference plays a central role in the study of biological systems where one aims to increase their understanding of the system by reasoning backwards from uncertain observations to identify causal relationships among components of the system. These causal relationships are postulated from prior knowledge as a hypothesis or simply a model. Experiments are designed to test the model. Inferential statistics are used to establish a level of confidence in how well our postulated model explains the acquired data. This iterative process, commonly referred to as the scientific method, either improves our confidence in a model or suggests that we revisit our prior knowledge to develop a new model. Advances in technology impact how we use prior knowledge and data to formulate models of biological networks and how we observe cellular behavior. However, the approach for model-based inference has remained largely unchanged since Fisher, Neyman and Pearson developed the ideas in the early 1900s that gave rise to what is now known as classical statistical hypothesis (model) testing. Here, I will summarize conventional methods for model-based inference and suggest a contemporary approach to aid in our quest to discover how cells dynamically interpret and transmit information for therapeutic aims that integrates ideas drawn from high performance computing, Bayesian statistics, and chemical kinetics.

  16. Modeling nitrous oxide production during biological nitrogen removal via nitrification and denitrification: extensions to the general ASM models.

    Science.gov (United States)

    Ni, Bing-Jie; Ruscalleda, Maël; Pellicer-Nàcher, Carles; Smets, Barth F

    2011-09-15

    Nitrous oxide (N(2)O) can be formed during biological nitrogen (N) removal processes. In this work, a mathematical model is developed that describes N(2)O production and consumption during activated sludge nitrification and denitrification. The well-known ASM process models are extended to capture N(2)O dynamics during both nitrification and denitrification in biological N removal. Six additional processes and three additional reactants, all involved in known biochemical reactions, have been added. The validity and applicability of the model is demonstrated by comparing simulations with experimental data on N(2)O production from four different mixed culture nitrification and denitrification reactor study reports. Modeling results confirm that hydroxylamine oxidation by ammonium oxidizers (AOB) occurs 10 times slower when NO(2)(-) participates as final electron acceptor compared to the oxic pathway. Among the four denitrification steps, the last one (N(2)O reduction to N(2)) seems to be inhibited first when O(2) is present. Overall, N(2)O production can account for 0.1-25% of the consumed N in different nitrification and denitrification systems, which can be well simulated by the proposed model. In conclusion, we provide a modeling structure, which adequately captures N(2)O dynamics in autotrophic nitrification and heterotrophic denitrification driven biological N removal processes and which can form the basis for ongoing refinements.

  17. Combining partially ranked data in plant breeding and biology: II. Analysis with Rasch model

    Directory of Open Access Journals (Sweden)

    Ivan Simko

    2010-07-01

    Full Text Available Many years of breeding experiments, germplasm screening, and molecular biologic experimentation have generated volumes of sequence, genotype, and phenotype information that have been stored in public data repositories. These resources afford genetic and genomic researchers the opportunity to handle and analyze raw data from multiple laboratories and study groups whose research interests revolve around a common or closely related trait. However, although such data sets are widely available for secondary analysis, their heterogeneous nature often precludes their direct combination and joint exploration. Integration of phenotype information across multiple studies and databases is challenging due to variations in the measurement instruments, endpoint classifications, and biological material employed by each investigator. In the present work, we demonstrate how Rasch measurement model can surmount these problems. The model allows incorporating data sets with partially overlapping variables, large numbers of missing data points and dissimilar ratings of phenotypic endpoints. The model also enables quantifying the extent of heterogeneity between data sets. Biologists can use the model in a data-mining process to obtain combined ratings from various databases and other sources. Subsequently, these ratings can be used for selecting desirable material or (in combination with genotypic information for mapping genes involved in the particular trait. The model is not limited to genetics and breeding and can be applied in many other areas of biology and agriculture.

  18. Kinetic Modeling of the Arabidopsis Cryptochrome Photocycle: FADHo Accumulation Correlates with Biological Activity

    Science.gov (United States)

    Procopio, Maria; Link, Justin; Engle, Dorothy; Witczak, Jacques; Ritz, Thorsten; Ahmad, Margaret

    2016-01-01

    Cryptochromes are flavoprotein photoreceptors with multiple signaling roles during plant de-etiolation and development. Arabidopsis cryptochromes (cry1 and cry2) absorb light through an oxidized flavin (FADox) cofactor which undergoes reduction to both FADH° and FADH− redox states. Since the FADH° redox state has been linked to biological activity, it is important to estimate its concentration formed upon illumination in vivo. Here we model the photocycle of isolated cry1 and cry2 proteins with a three-state kinetic model. Our model fits the experimental data for flavin photoconversion in vitro for both cry1 and cry2, providing calculated quantum yields which are significantly lower in cry1 than for cry2. The model was applied to the cryptochrome photocycle in vivo using biological activity in plants as a readout for FADH° concentration. The fit to the in vivo data provided quantum yields for cry1 and cry2 flavin reduction similar to those obtained in vitro, with decreased cry1 quantum yield as compared to cry2. These results validate our assumption that FADH° concentration correlates with biological activity. This is the first reported attempt at kinetic modeling of the cryptochrome photocycle in relation to macroscopic signaling events in vivo, and thereby provides a theoretical framework to the components of the photocycle that are necessary for cryptochrome response to environmental signals. PMID:27446119

  19. Kinetic Modeling of the Arabidopsis Cryptochrome Photocycle: FADH(o) Accumulation Correlates with Biological Activity.

    Science.gov (United States)

    Procopio, Maria; Link, Justin; Engle, Dorothy; Witczak, Jacques; Ritz, Thorsten; Ahmad, Margaret

    2016-01-01

    Cryptochromes are flavoprotein photoreceptors with multiple signaling roles during plant de-etiolation and development. Arabidopsis cryptochromes (cry1 and cry2) absorb light through an oxidized flavin (FADox) cofactor which undergoes reduction to both FADH° and FADH(-) redox states. Since the FADH° redox state has been linked to biological activity, it is important to estimate its concentration formed upon illumination in vivo. Here we model the photocycle of isolated cry1 and cry2 proteins with a three-state kinetic model. Our model fits the experimental data for flavin photoconversion in vitro for both cry1 and cry2, providing calculated quantum yields which are significantly lower in cry1 than for cry2. The model was applied to the cryptochrome photocycle in vivo using biological activity in plants as a readout for FADH° concentration. The fit to the in vivo data provided quantum yields for cry1 and cry2 flavin reduction similar to those obtained in vitro, with decreased cry1 quantum yield as compared to cry2. These results validate our assumption that FADH° concentration correlates with biological activity. This is the first reported attempt at kinetic modeling of the cryptochrome photocycle in relation to macroscopic signaling events in vivo, and thereby provides a theoretical framework to the components of the photocycle that are necessary for cryptochrome response to environmental signals.

  20. The philosophy of modelling or does the philosophy of biology have any use?

    Science.gov (United States)

    Orzack, Steven Hecht

    2012-01-19

    Biologists in search of answers to real-world issues such as the ecological consequences of global warming, the design of species' conservation plans, understanding landscape dynamics and understanding gene expression make decisions constantly that are based on a 'philosophical' stance as to how to create and test explanations of an observed phenomenon. For better or for worse, some kind of philosophy is an integral part of the doing of biology. Given this, it is more important than ever to undertake a practical assessment of what philosophy does mean and should mean to biologists. Here, I address three questions: should biologists pay any attention to 'philosophy'; should biologists pay any attention to 'philosophy of biology'; and should biologists pay any attention to the philosophy of biology literature on modelling? I describe why the last question is easily answered affirmatively, with the proviso that the practical benefits to be gained by biologists from this literature will be directly proportional to the extent to which biologists understand 'philosophy' to be a part of biology, not apart from biology.

  1. Heterogeneity of intracellular polymer storage states in enhanced biological phosphorus removal (EBPR)--observation and modeling.

    Science.gov (United States)

    Bucci, Vanni; Majed, Nehreen; Hellweger, Ferdi L; Gu, April Z

    2012-03-20

    A number of agent-based models (ABMs) for biological wastewater treatment processes have been developed, but their skill in predicting heterogeneity of intracellular storage states has not been tested against observations due to the lack of analytical methods for measuring single-cell intracellular properties. Further, several mechanisms can produce and maintain heterogeneity (e.g., different histories, uneven division) and their relative importance has not been explored. This article presents an ABM for the enhanced biological phosphorus removal (EBPR) treatment process that resolves heterogeneity in three intracellular polymer storage compounds (i.e., polyphosphate, polyhydroxybutyrate, and glycogen) in three functional microbial populations (i.e., polyphosphate-accumulating, glycogen-accumulating, and ordinary heterotrophic organisms). Model predicted distributions were compared to those based on single-cell estimates obtained using a Raman microscopy method for a laboratory-scale sequencing batch reactor (SBR) system. The model can reproduce many features of the observed heterogeneity. Two methods for introducing heterogeneity were evaluated. First, biological variability in individual cell behavior was simulated by randomizing model parameters (e.g., maximum acetate uptake rate) at division. This method produced the best fit to the data. An optimization algorithm was used to determine the best variability (i.e., coefficient of variance) for each parameter, which suggests large variability in acetate uptake. Second, biological variability in individual cell states was simulated by randomizing state variables (e.g., internal nutrient) at division, which was not able to maintain heterogeneity because the memory in the internal states is too short. These results demonstrate the ability of ABM to predict heterogeneity and provide insights into the factors that contribute to it. Comparison of the ABM with an equivalent population-level model illustrates the effect

  2. Persistence of biological traces in gun barrels--an approach to an experimental model.

    Science.gov (United States)

    Courts, Cornelius; Madea, Burkhard; Schyma, Christian

    2012-05-01

    Traces of backspatter in gun barrels after homicidal or suicidal contact shots may be a valuable source of forensic evidence. Yet, a systematic investigation of the persistence and durability of DNA from biological traces in gun barrels is lacking. Our aim was to generate a realistic model to emulate blood and tissue spatters in gun barrels generated by contact gunshots at biological targets and to analyse the persistence and typability of DNA recovered from such stains. Herein, we devise and evaluate three different models for the emulation of backspatter from contact shots: a gelatine-based model with embedded blood bags, a model based on a spongious matrix soaked with blood and covered with a thin plastic membrane and a head model consisting of an acrylic half sphere filled with ballistic gelatine and with blood bags attached to the sphere under a 3-mm silicone layer. The sampling procedure for all three models: a first shot was fired with several types of guns at each model construction and subsequently a second shot was fired at a backstop. Blood samples were collected after each shot by probing the inner surface of the front and rear end of the respective gun barrel with a sterile swab. DNA was then extracted and quantified and up to 20 different short tandem repeat (STR) systems were amplified to generate DNA profiles. Although DNA quantity and STR typing results were heterogenous between the models, all models succeeded in delivering full STR profiles even after more than one shot. We conclude that biological traces in gun barrels are robust and accessible to forensic analysis and that systematic examination of the inside of gun barrels may be advisable for forensic casework.

  3. A termination criterion for parameter estimation in stochastic models in systems biology.

    Science.gov (United States)

    Zimmer, Christoph; Sahle, Sven

    2015-11-01

    Parameter estimation procedures are a central aspect of modeling approaches in systems biology. They are often computationally expensive, especially when the models take stochasticity into account. Typically parameter estimation involves the iterative optimization of an objective function that describes how well the model fits some measured data with a certain set of parameter values. In order to limit the computational expenses it is therefore important to apply an adequate stopping criterion for the optimization process, so that the optimization continues at least until a reasonable fit is obtained, but not much longer. In the case of stochastic modeling, at least some parameter estimation schemes involve an objective function that is itself a random variable. This means that plain convergence tests are not a priori suitable as stopping criteria. This article suggests a termination criterion suited to optimization problems in parameter estimation arising from stochastic models in systems biology. The termination criterion is developed for optimization algorithms that involve populations of parameter sets, such as particle swarm or evolutionary algorithms. It is based on comparing the variance of the objective function over the whole population of parameter sets with the variance of repeated evaluations of the objective function at the best parameter set. The performance is demonstrated for several different algorithms. To test the termination criterion we choose polynomial test functions as well as systems biology models such as an Immigration-Death model and a bistable genetic toggle switch. The genetic toggle switch is an especially challenging test case as it shows a stochastic switching between two steady states which is qualitatively different from the model behavior in a deterministic model.

  4. Introductory biology students' conceptual models and explanations of the origin of variation.

    Science.gov (United States)

    Speth, Elena Bray; Shaw, Neil; Momsen, Jennifer; Reinagel, Adam; Le, Paul; Taqieddin, Ranya; Long, Tammy

    2014-01-01

    Mutation is the key molecular mechanism generating phenotypic variation, which is the basis for evolution. In an introductory biology course, we used a model-based pedagogy that enabled students to integrate their understanding of genetics and evolution within multiple case studies. We used student-generated conceptual models to assess understanding of the origin of variation. By midterm, only a small percentage of students articulated complete and accurate representations of the origin of variation in their models. Targeted feedback was offered through activities requiring students to critically evaluate peers' models. At semester's end, a substantial proportion of students significantly improved their representation of how variation arises (though one-third still did not include mutation in their models). Students' written explanations of the origin of variation were mostly consistent with their models, although less effective than models in conveying mechanistic reasoning. This study contributes evidence that articulating the genetic origin of variation is particularly challenging for learners and may require multiple cycles of instruction, assessment, and feedback. To support meaningful learning of the origin of variation, we advocate instruction that explicitly integrates multiple scales of biological organization, assessment that promotes and reveals mechanistic and causal reasoning, and practice with explanatory models with formative feedback.

  5. Biological ensemble modeling to evaluate potential futures of living marine resources

    DEFF Research Database (Denmark)

    Gårdmark, Anna; Lindegren, Martin; Neuenfeldt, Stefan

    2013-01-01

    trajectories carried through to uncertainty of cod responses. Models ignoring the feedback from prey on cod showed large interannual fluctuations in cod dynamics and were more sensitive to the underlying uncertainty of climate forcing than models accounting for such stabilizing predator–prey feedbacks. Yet......Natural resource management requires approaches to understand and handle sources of uncertainty in future responses of complex systems to human activities. Here we present one such approach, the “biological ensemble modeling approach,” using the Eastern Baltic cod (Gadus morhua callarias...

  6. Exposure factors for marine eutrophication impacts assessment based on a mechanistic biological model

    DEFF Research Database (Denmark)

    Cosme, Nuno Miguel Dias; Koski, Marja; Hauschild, Michael Zwicky

    2015-01-01

    ). This pathway is typical of marine eutrophication. A model is proposed to mechanistically estimate the response of coastal marine ecosystems to N inputs. It addresses the biological processes of nutrient-limited primary production (PP), metazoan consumption, and bacterial degradation, in four distinct sinking...... is essential to estimate a marine eutrophication impacts indicator in Life Cycle Impact Assessment (LCIA) of anthropogenic-N emissions. Every relevant process was modelled and the uncertainty of the driving parameters considered low suggesting valid applicability in characterisation modelling in LCIA....

  7. Hybrid neural modelling of an anaerobic digester with respect to biological constraints.

    Science.gov (United States)

    Karama, A; Bernard, O; Gouzé, J L; Benhammou, A; Dochain, D

    2001-01-01

    A hybrid model for an anaerobic digestion process is proposed. The fermentation is assumed to be performed in two steps, acidogenesis and methanogenesis, by two bacterial populations. The model is based on mass balance equations, and the bacterial growth rates are represented by neural networks. In order to guarantee the biological meaning of the hybrid model (positivity of the concentrations, boundedness, saturation or inhibition of the growth rates) outside the training data set, a method that imposes constraints in the neural network is proposed. The method is applied to experimental data from a fixed bed reactor.

  8. On The Construction of Models for Electrical Conduction in Biological Tissues

    Science.gov (United States)

    Gómez-Aguilar, F.; Bernal-Alvarado, J.; Cordova-Fraga, T.; Rosales-García, J.; Guía-Calderón, M.

    2010-12-01

    Applying RC circuit theory, a theoretical representation for the electrical conduction in a biological multilayer system was developed. In particular an equivalent circuit for the epidermis, dermis and the subcutaneous tissue was constructed. This model includes an equivalent circuit, inside the dermis, in order to model a small formation like tumor. This work shows the feasibility to apply superficial electrodes to detect subcutaneous abnormalities. The behavior of the model is shown in the form of a frequency response chart. The Bode and Nyquist plots are also obtained. This theoretical frame is proposed to be a general treatment to describe the bioelectrical transport in a three layer bioelectrical system.

  9. A multiscale theoretical model for diffusive mass transfer in cellular biological media.

    Science.gov (United States)

    Kapellos, George E; Alexiou, Terpsichori S; Payatakes, Alkiviades C

    2007-11-01

    An integrated methodology is developed for the theoretical analysis of solute transport and reaction in cellular biological media, such as tissues, microbial flocs, and biofilms. First, the method of local spatial averaging with a weight function is used to establish the equation which describes solute conservation at the cellular biological medium scale, starting with a continuum-based formulation of solute transport at finer spatial scales. Second, an effective-medium model is developed for the self-consistent calculation of the local diffusion coefficient in the cellular biological medium, including the effects of the structural heterogeneity of the extra-cellular space and the reversible adsorption to extra-cellular polymers. The final expression for the local effective diffusion coefficient is: D(Abeta)=lambda(beta)D(Aupsilon), where D(Aupsilon) is the diffusion coefficient in water, and lambda(beta) is a function of the composition and fundamental geometric and physicochemical system properties, including the size of solute molecules, the size of extra-cellular polymer fibers, and the mass permeability of the cell membrane. Furthermore, the analysis sheds some light on the function of the extra-cellular hydrogel as a diffusive barrier to solute molecules approaching the cell membrane, and its implications on the transport of chemotherapeutic agents within a cellular biological medium. Finally, the model predicts the qualitative trend as well as the quantitative variability of a large number of published experimental data on the diffusion coefficient of oxygen in cell-entrapping gels, microbial flocs, biofilms, and mammalian tissues.

  10. Systems Biology Markup Language (SBML) Level 2 Version 5: Structures and Facilities for Model Definitions.

    Science.gov (United States)

    Hucka, Michael; Bergmann, Frank T; Dräger, Andreas; Hoops, Stefan; Keating, Sarah M; Le Novère, Nicolas; Myers, Chris J; Olivier, Brett G; Sahle, Sven; Schaff, James C; Smith, Lucian P; Waltemath, Dagmar; Wilkinson, Darren J

    2015-09-04

    Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 5 of SBML Level 2. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org.

  11. Modelling the biological performance of a side-stream membrane bioreactor using ASM1

    Institute of Scientific and Technical Information of China (English)

    TIAN Ke-jun; LIU Xin-ai; JIANG Tao; M.D. Kennedy; J.C. Schippers; P.A. Vanrolleghem

    2004-01-01

    Membrane bioreactors(MBRs) are attracting global interest but the mathematical modeling of the biological performance of MBRs remains very limited. This study focuses on the modelling of a side-stream MBR system using Activated Sludge Model No.1(ASM1), and comparing the results with the modelling of traditional activated sludge processes. ASM1 parameters relevant for the long-term biological behaviour in MBR systems were calibrated(i.e. YH=0.72gCOD/gCOD, YA=0.25gCOD/gN, bH=0.25 d-1, bA=0.080 d-1 and fP=0.06), and generally agreed with the parameters in traditional activated sludge processes, with the exception that a higher autotrophic biomass decay rate was observed in the MBR. A sensitivity analysis for steady state operation and DO dynamics suggested that the biological performance of the MBR system(the sludge concentration, effluent quality and the DO dynamics) are very sensitive to the parameters(i.e. YH, YA, bH, bA, (maxH and (maxA), and influent wastewater components(XI, Ss, Xs, SNH).

  12. A computational systems biology software platform for multiscale modeling and simulation: Integrating whole-body physiology, disease biology, and molecular reaction networks

    Directory of Open Access Journals (Sweden)

    Thomas eEissing

    2011-02-01

    Full Text Available Today, in silico studies and trial simulations already complement experimental approaches in pharmaceutical R&D and have become indispensable tools for decision making and communication with regulatory agencies. While biology is multi-scale by nature, project work and software tools usually focus on isolated aspects of drug action, such as pharmacokinetics at the organism scale or pharmacodynamic interaction on the molecular level. We present a modeling and simulation software platform consisting of PK-Sim® and MoBi® capable of building and simulating models that integrate across biological scales. A prototypical multiscale model for the progression of a pancreatic tumor and its response to pharmacotherapy is constructed and virtual patients are treated with a prodrug activated by hepatic metabolization. Tumor growth is driven by signal transduction leading to cell cycle transition and proliferation. Free tumor concentrations of the active metabolite inhibit Raf kinase in the signaling cascade and thereby cell cycle progression. In a virtual clinical study, the individual therapeutic outcome of the chemotherapeutic intervention is simulated for a large population with heterogeneous genomic background. Thereby, the platform allows efficient model building and integration of biological knowledge and prior data from all biological scales. Experimental in vitro model systems can be linked with observations in animal experiments and clinical trials. The interplay between patients, diseases, and drugs and topics with high clinical relevance such as the role of pharmacogenomics, drug-drug or drug-metabolite interactions can be addressed using this mechanistic, insight driven multiscale modeling approach.

  13. A computational systems biology software platform for multiscale modeling and simulation: integrating whole-body physiology, disease biology, and molecular reaction networks.

    Science.gov (United States)

    Eissing, Thomas; Kuepfer, Lars; Becker, Corina; Block, Michael; Coboeken, Katrin; Gaub, Thomas; Goerlitz, Linus; Jaeger, Juergen; Loosen, Roland; Ludewig, Bernd; Meyer, Michaela; Niederalt, Christoph; Sevestre, Michael; Siegmund, Hans-Ulrich; Solodenko, Juri; Thelen, Kirstin; Telle, Ulrich; Weiss, Wolfgang; Wendl, Thomas; Willmann, Stefan; Lippert, Joerg

    2011-01-01

    Today, in silico studies and trial simulations already complement experimental approaches in pharmaceutical R&D and have become indispensable tools for decision making and communication with regulatory agencies. While biology is multiscale by nature, project work, and software tools usually focus on isolated aspects of drug action, such as pharmacokinetics at the organism scale or pharmacodynamic interaction on the molecular level. We present a modeling and simulation software platform consisting of PK-Sim(®) and MoBi(®) capable of building and simulating models that integrate across biological scales. A prototypical multiscale model for the progression of a pancreatic tumor and its response to pharmacotherapy is constructed and virtual patients are treated with a prodrug activated by hepatic metabolization. Tumor growth is driven by signal transduction leading to cell cycle transition and proliferation. Free tumor concentrations of the active metabolite inhibit Raf kinase in the signaling cascade and thereby cell cycle progression. In a virtual clinical study, the individual therapeutic outcome of the chemotherapeutic intervention is simulated for a large population with heterogeneous genomic background. Thereby, the platform allows efficient model building and integration of biological knowledge and prior data from all biological scales. Experimental in vitro model systems can be linked with observations in animal experiments and clinical trials. The interplay between patients, diseases, and drugs and topics with high clinical relevance such as the role of pharmacogenomics, drug-drug, or drug-metabolite interactions can be addressed using this mechanistic, insight driven multiscale modeling approach.

  14. The biological carbon pump in the ocean: Reviewing model representations and its feedbacks on climate perturbations.

    Science.gov (United States)

    Hülse, Dominik; Arndt, Sandra; Ridgwell, Andy; Wilson, Jamie

    2016-04-01

    The ocean-sediment system, as the biggest carbon reservoir in the Earth's carbon cycle, plays a crucial role in regulating atmospheric carbon dioxide concentrations and climate. Therefore, it is essential to constrain the importance of marine carbon cycle feedbacks on global warming and ocean acidification. Arguably, the most important single component of the ocean's carbon cycle is the so-called "biological carbon pump". It transports carbon that is fixed in the light-flooded surface layer of the ocean to the deep ocean and the surface sediment, where it is degraded/dissolved or finally buried in the deep sediments. Over the past decade, progress has been made in understanding different factors that control the efficiency of the biological carbon pump and their feedbacks on the global carbon cycle and climate (i.e. ballasting = ocean acidification feedback; temperature dependant organic matter degradation = global warming feedback; organic matter sulphurisation = anoxia/euxinia feedback). Nevertheless, many uncertainties concerning the interplay of these processes and/or their relative significance remain. In addition, current Earth System Models tend to employ empirical and static parameterisations of the biological pump. As these parametric representations are derived from a limited set of present-day observations, their ability to represent carbon cycle feedbacks under changing climate conditions is limited. The aim of my research is to combine past carbon cycling information with a spatially resolved global biogeochemical model to constrain the functioning of the biological pump and to base its mathematical representation on a more mechanistic approach. Here, I will discuss important aspects that control the efficiency of the ocean's biological carbon pump, review how these processes of first order importance are mathematically represented in existing Earth system Models of Intermediate Complexity (EMIC) and distinguish different approaches to approximate

  15. On the Modelling of Biological Patterns with Mechanochemical Models: Insights from Analysis and Computation

    KAUST Repository

    Moreo, P.

    2009-11-14

    The diversity of biological form is generated by a relatively small number of underlying mechanisms. Consequently, mathematical and computational modelling can, and does, provide insight into how cellular level interactions ultimately give rise to higher level structure. Given cells respond to mechanical stimuli, it is therefore important to consider the effects of these responses within biological self-organisation models. Here, we consider the self-organisation properties of a mechanochemical model previously developed by three of the authors in Acta Biomater. 4, 613-621 (2008), which is capable of reproducing the behaviour of a population of cells cultured on an elastic substrate in response to a variety of stimuli. In particular, we examine the conditions under which stable spatial patterns can emerge with this model, focusing on the influence of mechanical stimuli and the interplay of non-local phenomena. To this end, we have performed a linear stability analysis and numerical simulations based on a mixed finite element formulation, which have allowed us to study the dynamical behaviour of the system in terms of the qualitative shape of the dispersion relation. We show that the consideration of mechanotaxis, namely changes in migration speeds and directions in response to mechanical stimuli alters the conditions for pattern formation in a singular manner. Furthermore without non-local effects, responses to mechanical stimuli are observed to result in dispersion relations with positive growth rates at arbitrarily large wavenumbers, in turn yielding heterogeneity at the cellular level in model predictions. This highlights the sensitivity and necessity of non-local effects in mechanically influenced biological pattern formation models and the ultimate failure of the continuum approximation in their absence. © 2009 Society for Mathematical Biology.

  16. Conceptual modeling in systems biology fosters empirical findings: the mRNA lifecycle.

    Directory of Open Access Journals (Sweden)

    Dov Dori

    Full Text Available One of the main obstacles to understanding complex biological systems is the extent and rapid evolution of information, way beyond the capacity individuals to manage and comprehend. Current modeling approaches and tools lack adequate capacity to model concurrently structure and behavior of biological systems. Here we propose Object-Process Methodology (OPM, a holistic conceptual modeling paradigm, as a means to model both diagrammatically and textually biological systems formally and intuitively at any desired number of levels of detail. OPM combines objects, e.g., proteins, and processes, e.g., transcription, in a way that is simple and easily comprehensible to researchers and scholars. As a case in point, we modeled the yeast mRNA lifecycle. The mRNA lifecycle involves mRNA synthesis in the nucleus, mRNA transport to the cytoplasm, and its subsequent translation and degradation therein. Recent studies have identified specific cytoplasmic foci, termed processing bodies that contain large complexes of mRNAs and decay factors. Our OPM model of this cellular subsystem, presented here, led to the discovery of a new constituent of these complexes, the translation termination factor eRF3. Association of eRF3 with processing bodies is observed after a long-term starvation period. We suggest that OPM can eventually serve as a comprehensive evolvable model of the entire living cell system. The model would serve as a research and communication platform, highlighting unknown and uncertain aspects that can be addressed empirically and updated consequently while maintaining consistency.

  17. Particle-based model to simulate the micromechanics of biological cells

    Science.gov (United States)

    van Liedekerke, P.; Tijskens, E.; Ramon, H.; Ghysels, P.; Samaey, G.; Roose, D.

    2010-06-01

    This paper is concerned with addressing how biological cells react to mechanical impulse. We propose a particle based model to numerically study the mechanical response of these cells with subcellular detail. The model focuses on a plant cell in which two important features are present: (1) the cell’s interior liquidlike phase inducing hydrodynamic phenomena, and (2) the cell wall, a viscoelastic solid membrane that encloses the protoplast. In this particle modeling framework, the cell fluid is modeled by a standard smoothed particle hydrodynamics (SPH) technique. For the viscoelastic solid phase (cell wall), a discrete element method (DEM) is proposed. The cell wall hydraulic conductivity (permeability) is built in through a constitutive relation in the SPH formulation. Simulations show that the SPH-DEM model is in reasonable agreement with compression experiments on an in vitro cell and with analytical models for the basic dynamical modes of a spherical liquid filled shell. We have performed simulations to explore more complex situations such as relaxation and impact, thereby considering two cell types: a stiff plant type and a soft animal-like type. Their particular behavior (force transmission) as a function of protoplasm and cell wall viscosity is discussed. We also show that the mechanics during and after cell failure can be modeled adequately. This methodology has large flexibility and opens possibilities to quantify problems dealing with the response of biological cells to mechanical impulses, e.g., impact, and the prediction of damage on a (sub)cellular scale.

  18. When one model is not enough: combining epistemic tools in systems biology.

    Science.gov (United States)

    Green, Sara

    2013-06-01

    In recent years, the philosophical focus of the modeling literature has shifted from descriptions of general properties of models to an interest in different model functions. It has been argued that the diversity of models and their correspondingly different epistemic goals are important for developing intelligible scientific theories (Leonelli, 2007; Levins, 2006). However, more knowledge is needed on how a combination of different epistemic means can generate and stabilize new entities in science. This paper will draw on Rheinberger's practice-oriented account of knowledge production. The conceptual repertoire of Rheinberger's historical epistemology offers important insights for an analysis of the modelling practice. I illustrate this with a case study on network modeling in systems biology where engineering approaches are applied to the study of biological systems. I shall argue that the use of multiple representational means is an essential part of the dynamic of knowledge generation. It is because of-rather than in spite of-the diversity of constraints of different models that the interlocking use of different epistemic means creates a potential for knowledge production.

  19. Ice Formation in Model Biological Membranes in the Presence of Cryoprotectors

    CERN Document Server

    Kiselev, M A; Kisselev, A M; Ollivon, M

    2000-01-01

    Ice formation in model biological membranes is studied by SAXS and WAXS in the presence of cryoprotectors: dimethyl sulfoxide and glycerol. Three types of phospholipid membranes: DPPC, DMPC, DSPC are chosen for the investigation as well-studied model biological membranes. A special cryostat is used for sample cooling from 14.1C to -55.4C. The ice formation is only detected by WAXS in binary phospholipid/water and ternary phospholipid/cryoprotector/water systems in the condition of excess solvent. Ice formation in a binary phospholipid/water system creates an abrupt decrease of the membrane repeat distance by delta-d, so-called ice-induced dehydration of intermembrane space. The value of delta-d decreases as the cryoprotector concentration increases. The formation of ice does not influence the membrane structure (delta-d = 0) for cryoprotector mole fractions higher than 0.05.

  20. Overshoot in biological systems modelled by Markov chains: a non-equilibrium dynamic phenomenon.

    Science.gov (United States)

    Jia, Chen; Qian, Minping; Jiang, Daquan

    2014-08-01

    A number of biological systems can be modelled by Markov chains. Recently, there has been an increasing concern about when biological systems modelled by Markov chains will perform a dynamic phenomenon called overshoot. In this study, the authors found that the steady-state behaviour of the system will have a great effect on the occurrence of overshoot. They showed that overshoot in general cannot occur in systems that will finally approach an equilibrium steady state. They further classified overshoot into two types, named as simple overshoot and oscillating overshoot. They showed that except for extreme cases, oscillating overshoot will occur if the system is far from equilibrium. All these results clearly show that overshoot is a non-equilibrium dynamic phenomenon with energy consumption. In addition, the main result in this study is validated with real experimental data.

  1. Solving the Advection-Diffusion Equations in Biological Contexts using the Cellular Potts Model

    CERN Document Server

    Dan, D; Chen, K; Glazier, J A; Dan, Debasis; Mueller, Chris; Chen, Kun; Glazier, James A.

    2005-01-01

    The Cellular Potts Model (CPM) is a robust, cell-level methodology for simulation of biological tissues and morphogenesis. Both tissue physiology and morphogenesis depend on diffusion of chemical morphogens in the extra-cellular fluid or matrix (ECM). Standard diffusion solvers applied to the cellular potts model use finite difference methods on the underlying CPM lattice. However, these methods produce a diffusing field tied to the underlying lattice, which is inaccurate in many biological situations in which cell or ECM movement causes advection rapid compared to diffusion. Finite difference schemes suffer numerical instabilities solving the resulting advection-diffusion equations. To circumvent these problems we simulate advection-diffusion within the framework of the CPM using off-lattice finite-difference methods. We define a set of generalized fluid particles which detach advection and diffusion from the lattice. Diffusion occurs between neighboring fluid particles by local averaging rules which approxi...

  2. Inferring Pairwise Interactions from Biological Data Using Maximum-Entropy Probability Models.

    Science.gov (United States)

    Stein, Richard R; Marks, Debora S; Sander, Chris

    2015-07-01

    Maximum entropy-based inference methods have been successfully used to infer direct interactions from biological datasets such as gene expression data or sequence ensembles. Here, we review undirected pairwise maximum-entropy probability models in two categories of data types, those with continuous and categorical random variables. As a concrete example, we present recently developed inference methods from the field of protein contact prediction and show that a basic set of assumptions leads to similar solution strategies for inferring the model parameters in both variable types. These parameters reflect interactive couplings between observables, which can be used to predict global properties of the biological system. Such methods are applicable to the important problems of protein 3-D structure prediction and association of gene-gene networks, and they enable potential applications to the analysis of gene alteration patterns and to protein design.

  3. Inferring Pairwise Interactions from Biological Data Using Maximum-Entropy Probability Models.

    Directory of Open Access Journals (Sweden)

    Richard R Stein

    2015-07-01

    Full Text Available Maximum entropy-based inference methods have been successfully used to infer direct interactions from biological datasets such as gene expression data or sequence ensembles. Here, we review undirected pairwise maximum-entropy probability models in two categories of data types, those with continuous and categorical random variables. As a concrete example, we present recently developed inference methods from the field of protein contact prediction and show that a basic set of assumptions leads to similar solution strategies for inferring the model parameters in both variable types. These parameters reflect interactive couplings between observables, which can be used to predict global properties of the biological system. Such methods are applicable to the important problems of protein 3-D structure prediction and association of gene-gene networks, and they enable potential applications to the analysis of gene alteration patterns and to protein design.

  4. The planarian flatworm: an in vivo model for stem cell biology and nervous system regeneration

    Directory of Open Access Journals (Sweden)

    Luca Gentile

    2011-01-01

    Full Text Available Planarian flatworms are an exception among bilaterians in that they possess a large pool of adult stem cells that enables them to promptly regenerate any part of their body, including the brain. Although known for two centuries for their remarkable regenerative capabilities, planarians have only recently emerged as an attractive model for studying regeneration and stem cell biology. This revival is due in part to the availability of a sequenced genome and the development of new technologies, such as RNA interference and next-generation sequencing, which facilitate studies of planarian regeneration at the molecular level. Here, we highlight why planarians are an exciting tool in the study of regeneration and its underlying stem cell biology in vivo, and discuss the potential promises and current limitations of this model organism for stem cell research and regenerative medicine.

  5. Model for biological communication in a nanofabricated cell-mimic driven by stochastic resonance

    Energy Technology Data Exchange (ETDEWEB)

    Karig, David K [ORNL; Siuti, Piro [ORNL; Dar, Roy D. [University of Tennessee, Knoxville (UTK); Retterer, Scott T [ORNL; Doktycz, Mitchel John [ORNL; Simpson, Michael L [ORNL

    2011-01-01

    Cells offer natural examples of highly efficient networks of nanomachines. Accordingly, both intracellular and intercellular communication mechanisms in nature are looked to as a source of inspiration and instruction for engineered nanocommunication. Harnessing biological functionality in this manner requires an interdisciplinary approach that integrates systems biology, synthetic biology, and nanofabrication. Recent years have seen the amassing of a tremendous wealth of data from the sequencing of new organisms and from high throughput expression experiments. At the same time, a deeper fundamental understanding of individual cell function has been developed, as exemplified by the growth of fields such as noise biology, which seeks to characterize the role of noise in gene expression. The availability of well characterized biological components coupled with a deeper understanding of cell function has led to efforts to engineer both living cells and to create bio-like functionality in non-living substrates in the field of synthetic biology. Here, we present a model system that exemplifies the synergism between these realms of research. We propose a synthetic gene network for operation in a nanofabricated cell mimic array that propagates a biomolecular signal over long distances using the phenomenon of stochastic resonance. Our system consists of a bacterial quorum sensing signal molecule, a bistable genetic switch triggered by this signal, and an array of nanofabricated cell mimic wells that contain the genetic system. An optimal level of noise in the system helps to propagate a time-varying AHL signal over long distances through the array of mimics. This noise level is determined both by the system volume and by the parameters of the genetic network. Our proposed genetically driven stochastic resonance system serves as a testbed for exploring the potential harnessing of gene expression noise to aid in the transmission of a time-varying molecular signal.

  6. Modelling biological processes in WWTP; Modelado de procesos biologicos en las EDAR

    Energy Technology Data Exchange (ETDEWEB)

    Carpes, G.

    2009-07-01

    Biological technologies by active sludges are the most used in wastewater treatments. Multiple variants are affected in the characterization of this process, like wastewater treatment plant (WWTP) design, features and concentration of sludge, dissolved oxygen concentration and characteristics of the wastewater, including temperature and nutrients. Mathematical formula applied to WWTP modelling are presented to design its operation and to test the most important parameters, too. It is necessary to optimize the process in WWTP. (Author) 19 refs.

  7. The female gametophyte: an emerging model for cell type-specific systems biology in plant development

    Directory of Open Access Journals (Sweden)

    Marc William Schmid

    2015-11-01

    Full Text Available Systems biology, a holistic approach describing a system emerging from the interactions of its molecular components, critically depends on accurate qualitative determination and quantitative measurements of these components. Development and improvement of large-scale profiling methods (omics now facilitates comprehensive measurements of many relevant molecules. For multicellular organisms, such as animals, fungi, algae, and plants, the complexity of the system is augmented by the presence of specialized cell types and organs, and a complex interplay within and between them. Cell type-specific analyses are therefore crucial for the understanding of developmental processes and environmental responses. This review first gives an overview of current methods used for large-scale profiling of specific cell types exemplified by recent advances in plant biology. The focus then lies on suitable model systems to study plant development and cell type specification. We introduce the female gametophyte of flowering plants as an ideal model to study fundamental developmental processes. Moreover, the female reproductive lineage is of importance for the emergence of evolutionary novelties such as an unequal parental contribution to the tissue nurturing the embryo or the clonal production of seeds by asexual reproduction (apomixis. Understanding these processes is not only interesting from a developmental or evolutionary perspective, but bears great potential for further crop improvement and the simplification of breeding efforts. We finally highlight novel methods, which are already available or which will likely soon facilitate large-scale profiling of the specific cell types of the female gametophyte in both model and non-model species. We conclude that it may take only few years until an evolutionary systems biology approach toward female gametogenesis may decipher some of its biologically most interesting and economically most valuable processes.

  8. Geomorphic controls on biological soil crust distribution: A conceptual model from the Mojave Desert (USA)

    Science.gov (United States)

    Williams, Amanda J.; Buck, Brenda J.; Soukup, Deborah A.; Merkler, Douglas J.

    2013-08-01

    Biological soil crusts (BSCs) are bio-sedimentary features that play critical geomorphic and ecological roles in arid environments. Extensive mapping, surface characterization, GIS overlays, and statistical analyses explored relationships among BSCs, geomorphology, and soil characteristics in a portion of the Mojave Desert (USA). These results were used to develop a conceptual model that explains the spatial distribution of BSCs. In this model, geologic and geomorphic processes control the ratio of fine sand to rocks, which constrains the development of three surface cover types and biogeomorphic feedbacks across intermontane basins. (1) Cyanobacteria crusts grow where abundant fine sand and negligible rocks form saltating sand sheets. Cyanobacteria facilitate moderate sand sheet activity that reduces growth potential of mosses and lichens. (2) Extensive tall moss-lichen pinnacled crusts are favored on early to late Holocene surfaces composed of mixed rock and fine sand. Moss-lichen crusts induce a dust capture feedback mechanism that promotes further crust propagation and forms biologically-mediated vesicular (Av) horizons. The presence of thick biogenic vesicular horizons supports the interpretation that BSCs are long-lived surface features. (3) Low to moderate density moss-lichen crusts grow on early Holocene and older geomorphic surfaces that display high rock cover and negligible surficial fine sand. Desert pavement processes and abiotic vesicular horizon formation dominate these surfaces and minimize bioturbation potential. The biogeomorphic interactions that sustain these three surface cover trajectories support unique biological communities and soil conditions, thereby sustaining ecological stability. The proposed conceptual model helps predict BSC distribution within intermontane basins to identify biologically sensitive areas, set reference conditions for ecological restoration, and potentially enhance arid landscape models, as scientists address impacts

  9. Positive Almost Periodic Solution on a Nonlinear Logistic Biological Model with Grazing Rates

    Institute of Scientific and Technical Information of China (English)

    NI Hua; TIAN Li-xin

    2013-01-01

    In this paper,we study the following nonlinear biological model dx(t)/dt =x(t)[a(t)-b(t)xα(t)] + f(t,xt),by using fixed pointed theorem,the sufficient conditions of the existence of unique positive almost periodic solution for the above system are obtained,by using the theories of stability,the sufficient conditions which guarantee the stability of the positive almost periodic solution are derived.

  10. Apomixis in Achnanthes (Bacillariophyceae); development of a model system for diatom reproductive biology

    OpenAIRE

    Sabbe, K; Chepurnov, V.A.; Vyverman, W.; Mann, D. G.

    2004-01-01

    The availability of extensive experimental data and remarkable intra- and interspecific variation in breeding behaviour make Achnanthes Bory sensu stricto an especially good model for studying the reproductive and population biology of pennate diatoms. In most Achnanthes species studied, auxospore formation is accompanied by biparental sexual reproduction, but we found uniparental auxosporulation in Achnanthes cf. subsessilis. Auxosporulation appears to be apomictic and follows contraction of...

  11. Comparison of Model Calculations of Biological Damage from Exposure to Heavy Ions with Measurements

    Science.gov (United States)

    Kim, Myung-Hee Y.; Hada, Megumi; Cucinotta, Francis A.; Wu, Honglu

    2014-01-01

    The space environment consists of a varying field of radiation particles including high-energy ions, with spacecraft shielding material providing the major protection to astronauts from harmful exposure. Unlike low-LET gamma or X rays, the presence of shielding does not always reduce the radiation risks for energetic charged-particle exposure. Dose delivered by the charged particle increases sharply at the Bragg peak. However, the Bragg curve does not necessarily represent the biological damage along the particle path since biological effects are influenced by the track structures of both primary and secondary particles. Therefore, the ''biological Bragg curve'' is dependent on the energy and the type of the primary particle and may vary for different biological end points. Measurements of the induction of micronuclei (MN) have made across the Bragg curve in human fibroblasts exposed to energetic silicon and iron ions in vitro at two different energies, 300 MeV/nucleon and 1 GeV/nucleon. Although the data did not reveal an increased yield of MN at the location of the Bragg peak, the increased inhibition of cell progression, which is related to cell death, was found at the Bragg peak location. These results are compared to the calculations of biological damage using a stochastic Monte-Carlo track structure model, Galactic Cosmic Ray Event-based Risk Model (GERM) code (Cucinotta, et al., 2011). The GERM code estimates the basic physical properties along the passage of heavy ions in tissue and shielding materials, by which the experimental set-up can be interpreted. The code can also be used to describe the biophysical events of interest in radiobiology, cancer therapy, and space exploration. The calculation has shown that the severely damaged cells at the Bragg peak are more likely to go through reproductive death, the so called "overkill".

  12. The Biological Big Bang model for the major transitions in evolution

    Science.gov (United States)

    Koonin, Eugene V

    2007-01-01

    Background Major transitions in biological evolution show the same pattern of sudden emergence of diverse forms at a new level of complexity. The relationships between major groups within an emergent new class of biological entities are hard to decipher and do not seem to fit the tree pattern that, following Darwin's original proposal, remains the dominant description of biological evolution. The cases in point include the origin of complex RNA molecules and protein folds; major groups of viruses; archaea and bacteria, and the principal lineages within each of these prokaryotic domains; eukaryotic supergroups; and animal phyla. In each of these pivotal nexuses in life's history, the principal "types" seem to appear rapidly and fully equipped with the signature features of the respective new level of biological organization. No intermediate "grades" or intermediate forms between different types are detectable. Usually, this pattern is attributed to cladogenesis compressed in time, combined with the inevitable erosion of the phylogenetic signal. Hypothesis I propose that most or all major evolutionary transitions that show the "explosive" pattern of emergence of new types of biological entities correspond to a boundary between two qualitatively distinct evolutionary phases. The first, inflationary phase is characterized by extremely rapid evolution driven by various processes of genetic information exchange, such as horizontal gene transfer, recombination, fusion, fission, and spread of mobile elements. These processes give rise to a vast diversity of forms from which the main classes of entities at the new level of complexity emerge independently, through a sampling process. In the second phase, evolution dramatically slows down, the respective process of genetic information exchange tapers off, and multiple lineages of the new type of entities emerge, each of them evolving in a tree-like fashion from that point on. This biphasic model of evolution incorporates the

  13. The Biological Big Bang model for the major transitions in evolution

    Directory of Open Access Journals (Sweden)

    Koonin Eugene V

    2007-08-01

    Full Text Available Abstract Background Major transitions in biological evolution show the same pattern of sudden emergence of diverse forms at a new level of complexity. The relationships between major groups within an emergent new class of biological entities are hard to decipher and do not seem to fit the tree pattern that, following Darwin's original proposal, remains the dominant description of biological evolution. The cases in point include the origin of complex RNA molecules and protein folds; major groups of viruses; archaea and bacteria, and the principal lineages within each of these prokaryotic domains; eukaryotic supergroups; and animal phyla. In each of these pivotal nexuses in life's history, the principal "types" seem to appear rapidly and fully equipped with the signature features of the respective new level of biological organization. No intermediate "grades" or intermediate forms between different types are detectable. Usually, this pattern is attributed to cladogenesis compressed in time, combined with the inevitable erosion of the phylogenetic signal. Hypothesis I propose that most or all major evolutionary transitions that show the "explosive" pattern of emergence of new types of biological entities correspond to a boundary between two qualitatively distinct evolutionary phases. The first, inflationary phase is characterized by extremely rapid evolution driven by various processes of genetic information exchange, such as horizontal gene transfer, recombination, fusion, fission, and spread of mobile elements. These processes give rise to a vast diversity of forms from which the main classes of entities at the new level of complexity emerge independently, through a sampling process. In the second phase, evolution dramatically slows down, the respective process of genetic information exchange tapers off, and multiple lineages of the new type of entities emerge, each of them evolving in a tree-like fashion from that point on. This biphasic model

  14. Determination of Biological Treatability Processes of Textile Wastewater and Implementation of a Fuzzy Logic Model

    Directory of Open Access Journals (Sweden)

    Harun Akif Kabuk

    2015-01-01

    Full Text Available This study investigated the biological treatability of textile wastewater. For this purpose, a membrane bioreactor (MBR was utilized for biological treatment after the ozonation process. Due to the refractory organic contents of textile wastewater that has a low biodegradability capacity, ozonation was implemented as an advanced oxidation process prior to the MBR system to increase the biodegradability of the wastewater. Textile wastewater, oxidized by ozonation, was fed to the MBR at different hydraulic retention times (HRT. During the process, color, chemical oxygen demand (COD, and biochemical oxygen demand (BOD removal efficiencies were monitored for 24-hour, 12-hour, 6-hour, and 3-hour retention times. Under these conditions, 94% color, 65% COD, and 55% BOD removal efficiencies were obtained in the MBR system. The experimental outputs were modeled with multiple linear regressions (MLR and fuzzy logic. MLR results suggested that color removal is more related to COD removal relative to BOD removal. A surface map of this issue was prepared with a fuzzy logic model. Furthermore, fuzzy logic was employed to the whole modeling of the biological system treatment. Determination coefficients for COD, BOD, and color removal efficiencies were 0.96, 0.97, and 0.92, respectively.

  15. 己烯雌酚的仿生合成%Dtethyl Stilboestrol Biological Modeling Synthesis

    Institute of Scientific and Technical Information of China (English)

    张艺川; 林楸晨; 雷江; 缪舒; 张李华

    2011-01-01

    The biological modeling synthesis utilized in the organic synthesis reduced the traditional organic synthesis complex tedious, caused its efficiency to be high, wastes few and the green environmental protection and so on, thus enabled it to obtain the widespread approval and the promotion. This article takes the catalyst biological modeling synthesis diethyl stilboestrol by Vitamin B1. Through the improvement,enables the new building-up reactions to have raw material to be easy, non-toxic, the response condition to be temperate, production rate higher characteristic. Thus applies successfully the biological modeling synthesis in the organic synthesis.%仿生合成运用于有机合成中减少了传统有机合成的复杂繁琐,使其效率高,浪费少和绿色环保等,从而使其得到广泛认可与推广.文章以维生素B1作为催化剂仿生合成己烯雌酚,通过改进,使得新合成反应具有原料易得、无毒、反应条件温和、产率较高等特点,从而成功地把仿生合成应用于有机合成中.

  16. Fixed-point bifurcation analysis in biological models using interval polynomials theory.

    Science.gov (United States)

    Rigatos, Gerasimos G

    2014-06-01

    The paper proposes a systematic method for fixed-point bifurcation analysis in circadian cells and similar biological models using interval polynomials theory. The stages for performing fixed-point bifurcation analysis in such biological systems comprise (i) the computation of fixed points as functions of the bifurcation parameter and (ii) the evaluation of the type of stability for each fixed point through the computation of the eigenvalues of the Jacobian matrix that is associated with the system's nonlinear dynamics model. Stage (ii) requires the computation of the roots of the characteristic polynomial of the Jacobian matrix. This problem is nontrivial since the coefficients of the characteristic polynomial are functions of the bifurcation parameter and the latter varies within intervals. To obtain a clear view about the values of the roots of the characteristic polynomial and about the stability features they provide to the system, the use of interval polynomials theory and particularly of Kharitonov's stability theorem is proposed. In this approach, the study of the stability of a characteristic polynomial with coefficients that vary in intervals is equivalent to the study of the stability of four polynomials with crisp coefficients computed from the boundaries of the aforementioned intervals. The efficiency of the proposed approach for the analysis of fixed-point bifurcations in nonlinear models of biological neurons is tested through numerical and simulation experiments.

  17. The Role of Stochastic Models in Interpreting the Origins of Biological Chirality

    Directory of Open Access Journals (Sweden)

    Gábor Lente

    2010-04-01

    Full Text Available This review summarizes recent stochastic modeling efforts in the theoretical research aimed at interpreting the origins of biological chirality. Stochastic kinetic models, especially those based on the continuous time discrete state approach, have great potential in modeling absolute asymmetric reactions, experimental examples of which have been reported in the past decade. An overview of the relevant mathematical background is given and several examples are presented to show how the significant numerical problems characteristic of the use of stochastic models can be overcome by non-trivial, but elementary algebra. In these stochastic models, a particulate view of matter is used rather than the concentration-based view of traditional chemical kinetics using continuous functions to describe the properties system. This has the advantage of giving adequate description of single-molecule events, which were probably important in the origin of biological chirality. The presented models can interpret and predict the random distribution of enantiomeric excess among repetitive experiments, which is the most striking feature of absolute asymmetric reactions. It is argued that the use of the stochastic kinetic approach should be much more widespread in the relevant literature.

  18. Hidden Markov models and other machine learning approaches in computational molecular biology

    Energy Technology Data Exchange (ETDEWEB)

    Baldi, P. [California Inst. of Tech., Pasadena, CA (United States)

    1995-12-31

    This tutorial was one of eight tutorials selected to be presented at the Third International Conference on Intelligent Systems for Molecular Biology which was held in the United Kingdom from July 16 to 19, 1995. Computational tools are increasingly needed to process the massive amounts of data, to organize and classify sequences, to detect weak similarities, to separate coding from non-coding regions, and reconstruct the underlying evolutionary history. The fundamental problem in machine learning is the same as in scientific reasoning in general, as well as statistical modeling: to come up with a good model for the data. In this tutorial four classes of models are reviewed. They are: Hidden Markov models; artificial Neural Networks; Belief Networks; and Stochastic Grammars. When dealing with DNA and protein primary sequences, Hidden Markov models are one of the most flexible and powerful alignments and data base searches. In this tutorial, attention is focused on the theory of Hidden Markov Models, and how to apply them to problems in molecular biology.

  19. Polarizable Mean-Field Model of Water for Biological Simulations with Amber and Charmm force fields

    CERN Document Server

    Leontyev, Igor

    2015-01-01

    Although a great number of computational models of water are available today, the majority of current biological simulations are done with simple models, such as TIP3P and SPC, developed almost thirty years ago and only slightly modified since then. The reason is that the non-polarizable force fields that are mostly used to describe proteins and other biological molecules are incompatible with more sophisticated modern polarizable models of water. The issue is electronic polarizability: in liquid state, in protein, and in vacuum the water molecule is polarized differently, and therefore has different properties; thus the only way to describe all these different media with the same model is to use a polarizable water model. However, to be compatible with the force field of the rest of the system, e.g. a protein, the latter should be polarizable as well. Here we describe a novel model of water that is in effect polarizable, and yet compatible with the standard non-polarizable force fields such as AMBER, CHARMM,...

  20. Structural identifiability of systems biology models: a critical comparison of methods.

    Directory of Open Access Journals (Sweden)

    Oana-Teodora Chis

    Full Text Available Analysing the properties of a biological system through in silico experimentation requires a satisfactory mathematical representation of the system including accurate values of the model parameters. Fortunately, modern experimental techniques allow obtaining time-series data of appropriate quality which may then be used to estimate unknown parameters. However, in many cases, a subset of those parameters may not be uniquely estimated, independently of the experimental data available or the numerical techniques used for estimation. This lack of identifiability is related to the structure of the model, i.e. the system dynamics plus the observation function. Despite the interest in knowing a priori whether there is any chance of uniquely estimating all model unknown parameters, the structural identifiability analysis for general non-linear dynamic models is still an open question. There is no method amenable to every model, thus at some point we have to face the selection of one of the possibilities. This work presents a critical comparison of the currently available techniques. To this end, we perform the structural identifiability analysis of a collection of biological models. The results reveal that the generating series approach, in combination with identifiability tableaus, offers the most advantageous compromise among range of applicability, computational complexity and information provided.

  1. BSim: an agent-based tool for modeling bacterial populations in systems and synthetic biology.

    Directory of Open Access Journals (Sweden)

    Thomas E Gorochowski

    Full Text Available Large-scale collective behaviors such as synchronization and coordination spontaneously arise in many bacterial populations. With systems biology attempting to understand these phenomena, and synthetic biology opening up the possibility of engineering them for our own benefit, there is growing interest in how bacterial populations are best modeled. Here we introduce BSim, a highly flexible agent-based computational tool for analyzing the relationships between single-cell dynamics and population level features. BSim includes reference implementations of many bacterial traits to enable the quick development of new models partially built from existing ones. Unlike existing modeling tools, BSim fully considers spatial aspects of a model allowing for the description of intricate micro-scale structures, enabling the modeling of bacterial behavior in more realistic three-dimensional, complex environments. The new opportunities that BSim opens are illustrated through several diverse examples covering: spatial multicellular computing, modeling complex environments, population dynamics of the lac operon, and the synchronization of genetic oscillators. BSim is open source software that is freely available from http://bsim-bccs.sf.net and distributed under the Open Source Initiative (OSI recognized MIT license. Developer documentation and a wide range of example simulations are also available from the website. BSim requires Java version 1.6 or higher.

  2. Whack-A-Mole Model: Towards unified description of biological effect caused by radiation-exposure

    CERN Document Server

    Manabe, Yuichiro; Tsunoyama, Yuichi; Nakajima, Hiroo; Nakamura, Issei; Bando, Masako

    2014-01-01

    We present a novel model to estimate biological effects caused by artificial radiation exposure, Whack-a-mole (WAM) model. It is important to take account of the recovery effects during the time course of the cellular reactions. The inclusion of the dose-rate dependence is essential in the risk estimation of low dose radiation, while nearly all the existing theoretical models relies on the total dose dependence only. By analyzing the experimental data of the relation between the radiation dose and the induced mutation frequency of 5 organisms, mouse, drosophila, chrysanthemum, maize and tradescantia, we found that all the data can be reproduced by WAM model. Most remarkably, a scaling function, which is derived from WAM model, consistently accounts for the observed mutation frequencies of 5 organisms. This is the first rationale to account for the dose rate dependence as well as to give a unified understanding of a general feature of organisms.

  3. Whack-A-Mole Model: Towards a Unified Description of Biological Effects Caused by Radiation Exposure

    Science.gov (United States)

    Manabe, Yuichiro; Wada, Takahiro; Tsunoyama, Yuichi; Nakajima, Hiroo; Nakamura, Issei; Bando, Masako

    2015-04-01

    We present a novel model to for estimating biological effects caused by artificial radiation exposure, i.e., the Whack-A-Mole (WAM) model. It is important to take into account the recovery effects during the time course of cellular reactions. The inclusion of dose-rate dependence is essential in the risk estimation of low-dose radiation, while nearly all the existing theoretical models rely on the total dose dependence only. By analyzing experimental data of the relationship between the radiation dose and the induced mutation frequency of five organisms, namely, mouse, Drosophila, chrysanthemum, maize, Tradescantia, we found that all the data can be reproduced by the WAM model. Most remarkably, a scaling function, which is derived from the WAM model, consistently accounts for the observed mutation frequencies of the five organisms. This is the first rationale to account for the dose rate dependence as well as to provide a unified understanding of a general feature of organisms.

  4. Neurospora as a model fungus for studies in cytogenetics and sexual biology at Stanford

    Indian Academy of Sciences (India)

    Namboori B Raju

    2009-03-01

    Dodge’s early work (1927–1940) on Neurospora genetics and sexual biology inspired Beadle and Tatum at Stanford to use N. crassa for their landmark discovery that genes specify enzymes. Neurospora has since become a model organism for numerous genetic, cytogenetic, biochemical, molecular and population biology studies. Neurospora is haploid in the vegetative phase with a transient diploid sexual phase. Its meiotic cells (asci) are large, allowing easy examination of dividing nuclei and chromosomes under a light microscope. The haploid meiotic products are themselves the sexual progeny that grow into vegetative cultures, thus avoiding the cumbersome testcrosses and complex dominance–recessive relationships, as in diploid organisms. The Perkins’ laboratory at Stanford (1949–2007) played a pivotal role in advancing our knowledge of Neurospora genetics, sexual biology, cytogenetics and population biology. Since 1974, I have taken advantage of various chromosome-staining methods to examine ascus and ascospore development in wild type and in numerous mutant strains. In addition, I have used GFP-tagged genes to visualize the expression or silencing of unpaired genes in a post-transcriptional gene silencing process (meiotic silencing by unpaired DNA) that operates specifically during meiosis. The genome of N. crassa contains over 10 000 protein-coding genes. Gene knockouts or mutations in specific sequences may now be readily correlated with the observed cytological defects in the sexual stage, thus advancing our molecular understanding of complex processes during ascus and ascospore development.

  5. Embodied Learning of a Generative Neural Model for Biological Motion Perception and Inference

    Directory of Open Access Journals (Sweden)

    Fabian eSchrodt

    2015-07-01

    Full Text Available Although an action observation network and mirror neurons for understanding the actions and intentions of others have been under deep, interdisciplinary consideration over recent years, it remains largely unknown how the brain manages to map visually perceived biological motion of others onto its own motor system. This paper shows how such a mapping may be established, even if the biologically motion is visually perceived from a new vantage point. We introduce a learning artificial neural network model and evaluate it on full body motion tracking recordings. The model implements an embodied, predictive inference approach. It first learns to correlate and segment multimodal sensory streams of own bodily motion. In doing so, it becomes able to anticipate motion progression, to complete missing modal information, and to self-generate learned motion sequences. When biological motion of another person is observed, this self-knowledge is utilized to recognize similar motion patterns and predict their progress. Due to the relative encodings, the model shows strong robustness in recognition despite observing rather large varieties of body morphology and posture dynamics. By additionally equipping the model with the capability to rotate its visual frame of reference, it is able to deduce the visual perspective onto the observed person, establishing full consistency to the embodied self-motion encodings by means of active inference. In further support of its neuro-cognitive plausibility, we also model typical bistable perceptions when crucial depth information is missing. In sum, the introduced neural model proposes a solution to the problem of how the human brain may establish correspondence between observed bodily motion and its own motor system, thus offering a mechanism that supports the development of mirror neurons.

  6. Biologically Inspired Model for Visual Cognition Achieving Unsupervised Episodic and Semantic Feature Learning.

    Science.gov (United States)

    Qiao, Hong; Li, Yinlin; Li, Fengfu; Xi, Xuanyang; Wu, Wei

    2016-10-01

    Recently, many biologically inspired visual computational models have been proposed. The design of these models follows the related biological mechanisms and structures, and these models provide new solutions for visual recognition tasks. In this paper, based on the recent biological evidence, we propose a framework to mimic the active and dynamic learning and recognition process of the primate visual cortex. From principle point of view, the main contributions are that the framework can achieve unsupervised learning of episodic features (including key components and their spatial relations) and semantic features (semantic descriptions of the key components), which support higher level cognition of an object. From performance point of view, the advantages of the framework are as follows: 1) learning episodic features without supervision-for a class of objects without a prior knowledge, the key components, their spatial relations and cover regions can be learned automatically through a deep neural network (DNN); 2) learning semantic features based on episodic features-within the cover regions of the key components, the semantic geometrical values of these components can be computed based on contour detection; 3) forming the general knowledge of a class of objects-the general knowledge of a class of objects can be formed, mainly including the key components, their spatial relations and average semantic values, which is a concise description of the class; and 4) achieving higher level cognition and dynamic updating-for a test image, the model can achieve classification and subclass semantic descriptions. And the test samples with high confidence are selected to dynamically update the whole model. Experiments are conducted on face images, and a good performance is achieved in each layer of the DNN and the semantic description learning process. Furthermore, the model can be generalized to recognition tasks of other objects with learning ability.

  7. Modeling a full scale oxidation ditch system, coupling hydrodynamics and biological kinetics using ASM1 model

    Energy Technology Data Exchange (ETDEWEB)

    Haouech, L.; Sperandio, M.; Cock, A.; Shayeb, H.

    2009-07-01

    Optimising the aeration in oxidation ditch aims on one hand, a better wastewater quality and on the other hand, a reduction of the energy expenses of the treatment. given that the energy expenses relative to the aeration represents 60 to 80% of the operating costs of a wastewater treatment plant and given that the biological activity is strictly dependent on dissolved oxygen, the transfer of oxygen is considered as one of the key parameters of the process. (Author) 8 refs.

  8. How molecular should your molecular model be? On the level of molecular detail required to simulate biological networks in systems and synthetic biology.

    Science.gov (United States)

    Gonze, Didier; Abou-Jaoudé, Wassim; Ouattara, Djomangan Adama; Halloy, José

    2011-01-01

    The recent advance of genetic studies and the rapid accumulation of molecular data, together with the increasing performance of computers, led researchers to design more and more detailed mathematical models of biological systems. Many modeling approaches rely on ordinary differential equations (ODE) which are based on standard enzyme kinetics. Michaelis-Menten and Hill functions are indeed commonly used in dynamical models in systems and synthetic biology because they provide the necessary nonlinearity to make the dynamics nontrivial (i.e., limit-cycle oscillations or multistability). For most of the systems modeled, the actual molecular mechanism is unknown, and the enzyme equations should be regarded as phenomenological. In this chapter, we discuss the validity and accuracy of these approximations. In particular, we focus on the validity of the Michaelis-Menten function for open systems and on the use of Hill kinetics to describe transcription rates of regulated genes. Our discussion is illustrated by numerical simulations of prototype systems, including the Repressilator (a genetic oscillator) and the Toggle Switch model (a bistable system). We systematically compare the results obtained with the compact version (based on Michaelis-Menten and Hill functions) with its corresponding developed versions (based on "elementary" reaction steps and mass action laws). We also discuss the use of compact approaches to perform stochastic simulations (Gillespie algorithm). On the basis of these results, we argue that using compact models is suitable to model qualitatively biological systems.

  9. What controls biological productivity in coastal upwelling systems? Insights from a comparative modeling study

    Science.gov (United States)

    Lachkar, Z.; Gruber, N.

    2011-06-01

    The magnitude of the biological productivity in Eastern Boundary Upwelling Systems (EBUS) is traditionally viewed as directly reflecting the upwelling intensity. Yet, different EBUS show different sensitivities of productivity to upwelling-favorable winds (Carr and Kearns, 2003). Here, using a comparative modeling study of the California Current System (California CS) and Canary Current System (Canary CS), we show how physical and environmental factors, such as light, temperature and cross-shore circulation modulate the response of biological productivity to upwelling strength. To this end, we made a series of eddy-resolving simulations of the California CS and Canary CS using the Regional Ocean Modeling System (ROMS), coupled to a nitrogen based Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) ecosystem model. We find the nutrient content of the euphotic zone to be 20 % smaller in the Canary CS relative to the California CS. Yet, the biological productivity is 50 % smaller in the latter. This is due to: (1) a faster nutrient-replete growth in the Canary CS relative to the California CS, related to a more favorable light and temperature conditions in the Canary CS, and (2) the longer nearshore water residence times in the Canary CS which lead to larger buildup of biomass in the upwelling zone, thereby enhancing the productivity. The longer residence times in the Canary CS appear to be associated with the wider continental shelves and the lower eddy activity characterizing this upwelling system. This results in a weaker offshore export of nutrients and organic matter, thereby increasing local nutrient recycling and enhancing the coupling between new and export production in the Northwest African system. Our results suggest that climate change induced perturbations such as upwelling favorable wind intensification might lead to contrasting biological responses in the California CS and the Canary CS, with major implications for the biogeochemical cycles and fisheries

  10. What controls biological productivity in coastal upwelling systems? Insights from a comparative modeling study

    Directory of Open Access Journals (Sweden)

    Z. Lachkar

    2011-06-01

    Full Text Available The magnitude of the biological productivity in Eastern Boundary Upwelling Systems (EBUS is traditionally viewed as directly reflecting the upwelling intensity. Yet, different EBUS show different sensitivities of productivity to upwelling-favorable winds (Carr and Kearns, 2003. Here, using a comparative modeling study of the California Current System (California CS and Canary Current System (Canary CS, we show how physical and environmental factors, such as light, temperature and cross-shore circulation modulate the response of biological productivity to upwelling strength. To this end, we made a series of eddy-resolving simulations of the California CS and Canary CS using the Regional Ocean Modeling System (ROMS, coupled to a nitrogen based Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD ecosystem model. We find the nutrient content of the euphotic zone to be 20 % smaller in the Canary CS relative to the California CS. Yet, the biological productivity is 50 % smaller in the latter. This is due to: (1 a faster nutrient-replete growth in the Canary CS relative to the California CS, related to a more favorable light and temperature conditions in the Canary CS, and (2 the longer nearshore water residence times in the Canary CS which lead to larger buildup of biomass in the upwelling zone, thereby enhancing the productivity. The longer residence times in the Canary CS appear to be associated with the wider continental shelves and the lower eddy activity characterizing this upwelling system. This results in a weaker offshore export of nutrients and organic matter, thereby increasing local nutrient recycling and enhancing the coupling between new and export production in the Northwest African system. Our results suggest that climate change induced perturbations such as upwelling favorable wind intensification might lead to contrasting biological responses in the California CS and the Canary CS, with major implications for the biogeochemical cycles

  11. Proteomics-based systems biology modeling of bovine germinal vesicle stage oocyte and cumulus cell interaction.

    Directory of Open Access Journals (Sweden)

    Divyaswetha Peddinti

    Full Text Available BACKGROUND: Oocytes are the female gametes which establish the program of life after fertilization. Interactions between oocyte and the surrounding cumulus cells at germinal vesicle (GV stage are considered essential for proper maturation or 'programming' of oocytes, which is crucial for normal fertilization and embryonic development. However, despite its importance, little is known about the molecular events and pathways involved in this bidirectional communication. METHODOLOGY/PRINCIPAL FINDINGS: We used differential detergent fractionation multidimensional protein identification technology (DDF-Mud PIT on bovine GV oocyte and cumulus cells and identified 811 and 1247 proteins in GV oocyte and cumulus cells, respectively; 371 proteins were significantly differentially expressed between each cell type. Systems biology modeling, which included Gene Ontology (GO and canonical genetic pathway analysis, showed that cumulus cells have higher expression of proteins involved in cell communication, generation of precursor metabolites and energy, as well as transport than GV oocytes. Our data also suggests a hypothesis that oocytes may depend on the presence of cumulus cells to generate specific cellular signals to coordinate their growth and maturation. CONCLUSIONS/SIGNIFICANCE: Systems biology modeling of bovine oocytes and cumulus cells in the context of GO and protein interaction networks identified the signaling pathways associated with the proteins involved in cell-to-cell signaling biological process that may have implications in oocyte competence and maturation. This first comprehensive systems biology modeling of bovine oocytes and cumulus cell proteomes not only provides a foundation for signaling and cell physiology at the GV stage of oocyte development, but are also valuable for comparative studies of other stages of oocyte development at the molecular level.

  12. Monte Carlo modeling in CT-based geometries: dosimetry for biological modeling experiments with particle beam radiation.

    Science.gov (United States)

    Diffenderfer, Eric S; Dolney, Derek; Schaettler, Maximilian; Sanzari, Jenine K; McDonough, James; Cengel, Keith A

    2014-03-01

    The space radiation environment imposes increased dangers of exposure to ionizing radiation, particularly during a solar particle event (SPE). These events consist primarily of low energy protons that produce a highly inhomogeneous dose distribution. Due to this inherent dose heterogeneity, experiments designed to investigate the radiobiological effects of SPE radiation present difficulties in evaluating and interpreting dose to sensitive organs. To address this challenge, we used the Geant4 Monte Carlo simulation framework to develop dosimetry software that uses computed tomography (CT) images and provides radiation transport simulations incorporating all relevant physical interaction processes. We found that this simulation accurately predicts measured data in phantoms and can be applied to model dose in radiobiological experiments with animal models exposed to charged particle (electron and proton) beams. This study clearly demonstrates the value of Monte Carlo radiation transport methods for two critically interrelated uses: (i) determining the overall dose distribution and dose levels to specific organ systems for animal experiments with SPE-like radiation, and (ii) interpreting the effect of random and systematic variations in experimental variables (e.g. animal movement during long exposures) on the dose distributions and consequent biological effects from SPE-like radiation exposure. The software developed and validated in this study represents a critically important new tool that allows integration of computational and biological modeling for evaluating the biological outcomes of exposures to inhomogeneous SPE-like radiation dose distributions, and has potential applications for other environmental and therapeutic exposure simulations.

  13. Biological Effectiveness and Application of Heavy Ions in Radiation Therapy Described by a Physical and Biological Model

    DEFF Research Database (Denmark)

    Olsen, Kjeld J.; Hansen, Johnny W.

    A description is given of the physical basis for applying track structure theory in the determination of the effectiveness of heavy-ion irradiation of single- and multi-hit target systems. It will be shown that for applying the theory to biological systems the effectiveness of heavy-ion irradiation...... simultaneously in therapy....

  14. Modeling and interpreting biological effects of mixtures in the environment: introduction to the metal mixture modeling evaluation project.

    Science.gov (United States)

    Van Genderen, Eric; Adams, William; Dwyer, Robert; Garman, Emily; Gorsuch, Joseph

    2015-04-01

    The fate and biological effects of chemical mixtures in the environment are receiving increased attention from the scientific and regulatory communities. Understanding the behavior and toxicity of metal mixtures poses unique challenges for incorporating metal-specific concepts and approaches, such as bioavailability and metal speciation, in multiple-metal exposures. To avoid the use of oversimplified approaches to assess the toxicity of metal mixtures, a collaborative 2-yr research project and multistakeholder group workshop were conducted to examine and evaluate available higher-tiered chemical speciation-based metal mixtures modeling approaches. The Metal Mixture Modeling Evaluation project and workshop achieved 3 important objectives related to modeling and interpretation of biological effects of metal mixtures: 1) bioavailability models calibrated for single-metal exposures can be integrated to assess mixture scenarios; 2) the available modeling approaches perform consistently well for various metal combinations, organisms, and endpoints; and 3) several technical advancements have been identified that should be incorporated into speciation models and environmental risk assessments for metals.

  15. Simulation and optimization of a coking wastewater biological treatment process by activated sludge models (ASM).

    Science.gov (United States)

    Wu, Xiaohui; Yang, Yang; Wu, Gaoming; Mao, Juan; Zhou, Tao

    2016-01-01

    Applications of activated sludge models (ASM) in simulating industrial biological wastewater treatment plants (WWTPs) are still difficult due to refractory and complex components in influents as well as diversity in activated sludges. In this study, an ASM3 modeling study was conducted to simulate and optimize a practical coking wastewater treatment plant (CWTP). First, respirometric characterizations of the coking wastewater and CWTP biomasses were conducted to determine the specific kinetic and stoichiometric model parameters for the consecutive aeration-anoxic-aeration (O-A/O) biological process. All ASM3 parameters have been further estimated and calibrated, through cross validation by the model dynamic simulation procedure. Consequently, an ASM3 model was successfully established to accurately simulate the CWTP performances in removing COD and NH4-N. An optimized CWTP operation condition could be proposed reducing the operation cost from 6.2 to 5.5 €/m(3) wastewater. This study is expected to provide a useful reference for mathematic simulations of practical industrial WWTPs.

  16. Bioinformatics for transporter pharmacogenomics and systems biology: data integration and modeling with UML.

    Science.gov (United States)

    Yan, Qing

    2010-01-01

    Bioinformatics is the rational study at an abstract level that can influence the way we understand biomedical facts and the way we apply the biomedical knowledge. Bioinformatics is facing challenges in helping with finding the relationships between genetic structures and functions, analyzing genotype-phenotype associations, and understanding gene-environment interactions at the systems level. One of the most important issues in bioinformatics is data integration. The data integration methods introduced here can be used to organize and integrate both public and in-house data. With the volume of data and the high complexity, computational decision support is essential for integrative transporter studies in pharmacogenomics, nutrigenomics, epigenetics, and systems biology. For the development of such a decision support system, object-oriented (OO) models can be constructed using the Unified Modeling Language (UML). A methodology is developed to build biomedical models at different system levels and construct corresponding UML diagrams, including use case diagrams, class diagrams, and sequence diagrams. By OO modeling using UML, the problems of transporter pharmacogenomics and systems biology can be approached from different angles with a more complete view, which may greatly enhance the efforts in effective drug discovery and development. Bioinformatics resources of membrane transporters and general bioinformatics databases and tools that are frequently used in transporter studies are also collected here. An informatics decision support system based on the models presented here is available at http://www.pharmtao.com/transporter . The methodology developed here can also be used for other biomedical fields.

  17. Dual-porosity model of solute diffusion in biological tissue modified by electroporation.

    Science.gov (United States)

    Mahnič-Kalamiza, Samo; Miklavčič, Damijan; Vorobiev, Eugène

    2014-07-01

    In many electroporation applications mass transport in biological tissue is of primary concern. This paper presents a theoretical advancement in the field and gives some examples of model use in electroporation applications. The study focuses on post-treatment solute diffusion. We use a dual-porosity approach to describe solute diffusion in electroporated biological tissue. The cellular membrane presents a hindrance to solute transport into the extracellular space and is modeled as electroporation-dependent porosity, assigned to the intracellular space (the finite rate of mass transfer within an individual cell is not accounted for, for reasons that we elaborate on). The second porosity is that of the extracellular space, through which solute vacates a block of tissue. The model can be used to study extraction out of or introduction of solutes into tissue, and we give three examples of application, a full account of model construction, validation with experiments, and a parametrical analysis. To facilitate easy implementation and experimentation by the reader, the complete derivation of the analytical solution for a simplified example is presented. Validation is done by comparing model results to experimentally-obtained data; we modeled kinetics of sucrose extraction by diffusion from sugar beet tissue in laboratory-scale experiments. The parametrical analysis demonstrates the importance of selected physicochemical and geometrical properties of the system, illustrating possible outcomes of applying the model to different electroporation applications. The proposed model is a new platform that supports rapid extension by state-of-the-art models of electroporation phenomena, developed as latest achievements in the field of electroporation.

  18. Modeling Dose-response at Low Dose: A Systems Biology Approach for Ionization Radiation.

    Science.gov (United States)

    Zhao, Yuchao; Ricci, Paolo F

    2010-03-18

    For ionization radiation (IR) induced cancer, a linear non-threshold (LNT) model at very low doses is the default used by a number of national and international organizations and in regulatory law. This default denies any positive benefit from any level of exposure. However, experimental observations and theoretical biology have found that both linear and J-shaped IR dose-response curves can exist at those very low doses. We develop low dose J-shaped dose-response, based on systems biology, and thus justify its use regarding exposure to IR. This approach incorporates detailed, molecular and cellular descriptions of biological/toxicological mechanisms to develop a dose-response model through a set of nonlinear, differential equations describing the signaling pathways and biochemical mechanisms of cell cycle checkpoint, apoptosis, and tumor incidence due to IR. This approach yields a J-shaped dose response curve while showing where LNT behaviors are likely to occur. The results confirm the hypothesis of the J-shaped dose response curve: the main reason is that, at low-doses of IR, cells stimulate protective systems through a longer cell arrest time per unit of IR dose. We suggest that the policy implications of this approach are an increasingly correct way to deal with precautionary measures in public health.

  19. Biological model of vision for an artificial system that learns to perceive its environment

    Energy Technology Data Exchange (ETDEWEB)

    Blackburn, M.R.; Nguyen, H.G.

    1989-06-01

    The objective is to design an artificial vision system for use in robotics applications. Because the desired performance is equivalent to that achieved by nature, the authors anticipate that the objective will be accomplished most efficiently through modeling aspects of the neuroanatomy and neurophysiology of the biological visual system. Information enters the biological visual system through the retina and is passed to the lateral geniculate and optic tectum. The lateral geniculate nucleus (LGN) also receives information from the cerebral cortex and the result of these two inflows is returned to the cortex. The optic tectum likewise receives the retinal information in a context of other converging signals and organizes motor responses. A computer algorithm is described which implements models of the biological visual mechanisms of the retina, thalamic lateral geniculate and perigeniculate nuclei, and primary visual cortex. Motion and pattern analyses are performed in parallel and interact in the cortex to construct perceptions. We hypothesize that motion reflexes serve as unconditioned pathways for the learning and recall of pattern information. The algorithm demonstrates this conditioning through a learning function approximating heterosynaptic facilitation.

  20. Estimation of the Biological Half-Life of Methylmercury Using a Population Toxicokinetic Model

    Directory of Open Access Journals (Sweden)

    Seongil Jo

    2015-07-01

    Full Text Available Methylmercury is well known for causing adverse health effects in the brain and nervous system. Estimating the elimination constant derived from the biological half-life of methylmercury in the blood or hair is an important part of calculating guidelines for methylmercury intake. Thus, this study was conducted to estimate the biological half-life of methylmercury in Korean adults. We used a one-compartment model with a direct relationship between methylmercury concentrations in the blood and daily dietary intake of methylmercury. We quantified the between-person variability of the methylmercury half-life in the population, and informative priors were used to estimate the parameters in the model. The population half-life of methylmercury was estimated to be 80.2 ± 8.6 days. The population mean of the methylmercury half-life was 81.6 ± 8.4 days for men and 78.9 ± 8.6 days for women. The standard deviation of the half-life was estimated at 25.0 ± 8.6 days. Using the direct relationship between methylmercury concentrations in blood and methylmercury intake, the biological half-life in this study was estimated to be longer than indicated by the earlier studies that have been used to set guideline values.

  1. Fractional Calculus-Based Modeling of Electromagnetic Field Propagation in Arbitrary Biological Tissue

    Directory of Open Access Journals (Sweden)

    Pietro Bia

    2016-01-01

    Full Text Available The interaction of electromagnetic fields and biological tissues has become a topic of increasing interest for new research activities in bioelectrics, a new interdisciplinary field combining knowledge of electromagnetic theory, modeling, and simulations, physics, material science, cell biology, and medicine. In particular, the feasibility of pulsed electromagnetic fields in RF and mm-wave frequency range has been investigated with the objective to discover new noninvasive techniques in healthcare. The aim of this contribution is to illustrate a novel Finite-Difference Time-Domain (FDTD scheme for simulating electromagnetic pulse propagation in arbitrary dispersive biological media. The proposed method is based on the fractional calculus theory and a general series expansion of the permittivity function. The spatial dispersion effects are taken into account, too. The resulting formulation is explicit, it has a second-order accuracy, and the need for additional storage variables is minimal. The comparison between simulation results and those evaluated by using an analytical method based on the Fourier transformation demonstrates the accuracy and effectiveness of the developed FDTD model. Five numerical examples showing the plane wave propagation in a variety of dispersive media are examined.

  2. A Two-Layer Mathematical Modelling of Drug Delivery to Biological Tissues

    CERN Document Server

    Chakravarty, Koyel

    2016-01-01

    Local drug delivery has received much recognition in recent years, yet it is still unpredictable how drug efficacy depends on physicochemical properties and delivery kinetics. The purpose of the current study is to provide a useful mathematical model for drug release from a drug delivery device and consecutive drug transport in biological tissue, thereby aiding the development of new therapeutic drug by a systemic approach. In order to study the complete process, a two-layer spatio-temporal model depicting drug transport between the coupled media is presented. Drug release is described by considering solubilisation dynamics of drug particle, diffusion of the solubilised drug through porous matrix and also some other processes like reversible dissociation / recrystallization, drug particle-receptor binding and internalization phenomena. The model has led to a system of partial differential equations describing the important properties of drug kinetics. This model contributes towards the perception of the roles...

  3. Self-optimization, community stability, and fluctuations in two individual-based models of biological coevolution

    CERN Document Server

    Rikvold, Per Arne

    2007-01-01

    We compare and contrast the long-time dynamical properties of two individual-based models of biological coevolution. Selection occurs via multispecies, stochastic population dynamics with reproduction probabilities that depend nonlinearly on the population densities of all species resident in the community. New species are introduced through mutation. Both models are amenable to exact linear stability analysis, and we compare the analytic results with large-scale kinetic Monte Carlo simulations, obtaining the population size as a function of an average interspecies interaction strength. Over time, the models self-optimize through mutation and selection to approximately maximize a community fitness function, subject only to constraints internal to the particular model. If the interspecies interactions are randomly distributed on an interval including positive values, the system evolves toward self-sustaining, mutualistic communities. In contrast, for the predator-prey case the matrix of interactions is antisym...

  4. Applications of Systems Genetics and Biology for Obesity Using Pig Models

    DEFF Research Database (Denmark)

    Kogelman, Lisette J. A.; Kadarmideen, Haja N.

    2016-01-01

    In many biomedical research areas, animals have been used as a model to increase the understanding of molecular mechanisms involved in human diseases. One of those areas is human obesity, where porcine models are increasingly used. The pig shows genetic and physiological features that are very...... similar to humans and have shown to be an excellent model for human obesity. Using pig populations, many genetic studies have been performed to unravel the genetic architecture of human obesity. Most of them are pinpointing toward single genes, but more and more studies focus on a systems genetics...... approach, a branch of systems biology. In this chapter, we will describe the state of the art of genetic studies on human obesity, using pig populations. We will describe the features of using the pig as a model for human obesity and briefly discuss the genetics of obesity, and we will focus on systems...

  5. Ideas and perspectives: climate-relevant marine biologically driven mechanisms in Earth system models

    Science.gov (United States)

    Hense, Inga; Stemmler, Irene; Sonntag, Sebastian

    2017-01-01

    The current generation of marine biogeochemical modules in Earth system models (ESMs) considers mainly the effect of marine biota on the carbon cycle. We propose to also implement other biologically driven mechanisms in ESMs so that more climate-relevant feedbacks are captured. We classify these mechanisms in three categories according to their functional role in the Earth system: (1) biogeochemical pumps, which affect the carbon cycling; (2) biological gas and particle shuttles, which affect the atmospheric composition; and (3) biogeophysical mechanisms, which affect the thermal, optical, and mechanical properties of the ocean. To resolve mechanisms from all three classes, we find it sufficient to include five functional groups: bulk phyto- and zooplankton, calcifiers, and coastal gas and surface mat producers. We strongly suggest to account for a larger mechanism diversity in ESMs in the future to improve the quality of climate projections.

  6. Meeting report from the first meetings of the Computational Modeling in Biology Network (COMBINE).

    Science.gov (United States)

    Le Novère, Nicolas; Hucka, Michael; Anwar, Nadia; Bader, Gary D; Demir, Emek; Moodie, Stuart; Sorokin, Anatoly

    2011-11-30

    The Computational Modeling in Biology Network (COMBINE), is an initiative to coordinate the development of the various community standards and formats in computational systems biology and related fields. This report summarizes the activities pursued at the first annual COMBINE meeting held in Edinburgh on October 6-9 2010 and the first HARMONY hackathon, held in New York on April 18-22 2011. The first of those meetings hosted 81 attendees. Discussions covered both official COMBINE standards-(BioPAX, SBGN and SBML), as well as emerging efforts and interoperability between different formats. The second meeting, oriented towards software developers, welcomed 59 participants and witnessed many technical discussions, development of improved standards support in community software systems and conversion between the standards. Both meetings were resounding successes and showed that the field is now mature enough to develop representation formats and related standards in a coordinated manner.

  7. [The importance of model organisms to study cilia and flagella biology].

    Science.gov (United States)

    Vincensini, Laetitia; Blisnick, Thierry; Bastin, Philippe

    2011-01-01

    Cilia and flagella are ubiquitous organelles that protrude from the surfaces of many cells, and whose architecture is highly conserved from protists to humans. These complex organelles, composed of over 500 proteins, can be either immotile or motile. They are involved in a myriad of biological processes, including sensing (non-motile cilia) and/or cell motility or movement of extracellular fluids (motile cilia). The ever-expanding list of human diseases linked to defective cilia illustrates the functional importance of cilia and flagella. These ciliopathies are characterised by an impressive diversity of symptoms and an often complex genetic etiology. A precise knowledge of cilia and flagella biology is thus critical to better understand these pathologies. However, multi-ciliated cells are terminally differentiated and difficult to manipulate, and a primary cilium is assembled only when the cell exits from the cell cycle. In this context the use of model organisms, that relies on the high degree of structural but also of molecular conservation of these organelles across evolution, is instrumental to decipher the many facets of cilia and flagella biology. In this review, we highlight the specific strengths of the main model organisms to investigate the molecular composition, mode of assembly, sensing and motility mechanisms and functions of cilia and flagella. Pioneering studies carried out in the green alga Chlamydomonas established the link between cilia and several genetic diseases. Moreover, multicellular organisms such as mouse, zebrafish, Xenopus, C. elegans or Drosophila, and protists like Paramecium, Tetrahymena and Trypanosoma or Leishmania each bring specific advantages to the study of cilium biology. For example, the function of genes involved in primary ciliary dyskinesia (due to defects in ciliary motility) can be efficiently assessed in trypanosomes.

  8. The Systems Biology Markup Language (SBML) Level 3 Package: Qualitative Models, Version 1, Release 1.

    Science.gov (United States)

    Chaouiya, Claudine; Keating, Sarah M; Berenguier, Duncan; Naldi, Aurélien; Thieffry, Denis; van Iersel, Martijn P; Le Novère, Nicolas; Helikar, Tomáš

    2015-09-04

    Quantitative methods for modelling biological networks require an in-depth knowledge of the biochemical reactions and their stoichiometric and kinetic parameters. In many practical cases, this knowledge is missing. This has led to the development of several qualitative modelling methods using information such as, for example, gene expression data coming from functional genomic experiments. The SBML Level 3 Version 1 Core specification does not provide a mechanism for explicitly encoding qualitative models, but it does provide a mechanism for SBML packages to extend the Core specification and add additional syntactical constructs. The SBML Qualitative Models package for SBML Level 3 adds features so that qualitative models can be directly and explicitly encoded. The approach taken in this package is essentially based on the definition of regulatory or influence graphs. The SBML Qualitative Models package defines the structure and syntax necessary to describe qualitative models that associate discrete levels of activities with entity pools and the transitions between states that describe the processes involved. This is particularly suited to logical models (Boolean or multi-valued) and some classes of Petri net models can be encoded with the approach.

  9. Biologically inspired information theory: Adaptation through construction of external reality models by living systems.

    Science.gov (United States)

    Nakajima, Toshiyuki

    2015-12-01

    Higher animals act in the world using their external reality models to cope with the uncertain environment. Organisms that have not developed such information-processing organs may also have external reality models built in the form of their biochemical, physiological, and behavioral structures, acquired by natural selection through successful models constructed internally. Organisms subject to illusions would fail to survive in the material universe. How can organisms, or living systems in general, determine the external reality from within? This paper starts with a phenomenological model, in which the self constitutes a reality model developed through the mental processing of phenomena. Then, the it-from-bit concept is formalized using a simple mathematical model. For this formalization, my previous work on an algorithmic process is employed to constitute symbols referring to the external reality, called the inverse causality, with additional improvements to the previous work. Finally, as an extension of this model, the cognizers system model is employed to describe the self as one of many material entities in a world, each of which acts as a subject by responding to the surrounding entities. This model is used to propose a conceptual framework of information theory that can deal with both the qualitative (semantic) and quantitative aspects of the information involved in biological processes.

  10. The study of biological effects of electromagnetic mobile phone radiation on experimental animals by combining numerical modeling and experimental research

    Directory of Open Access Journals (Sweden)

    Dejan Krstić

    2012-12-01

    Full Text Available In order to study biological effects of electromagneticradiation, it is essential to know the real values of field componentsthat penetrated the tissue. The study of biological effects is usuallyperformed on experimental animals. The biological effects observedon experimental animals should be linked with penetrating field inthe tissue. The penetrating electromagnetic field is almost impossibleto measure; therefore, modeling process must be carried out and thefield components in models of experimental animals could becalculated. This paper presents an approach to modeling of fieldpenetration and gives contribution to understanding the real effects of the fields and the sensitivity of tissues to electromagnetic radiation generated by mobile phone.

  11. Development of Biological Movement Recognition by Interaction between Active Basis Model and Fuzzy Optical Flow Division

    Directory of Open Access Journals (Sweden)

    Bardia Yousefi

    2014-01-01

    Full Text Available Following the study on computational neuroscience through functional magnetic resonance imaging claimed that human action recognition in the brain of mammalian pursues two separated streams, that is, dorsal and ventral streams. It follows up by two pathways in the bioinspired model, which are specialized for motion and form information analysis (Giese and Poggio 2003. Active basis model is used to form information which is different from orientations and scales of Gabor wavelets to form a dictionary regarding object recognition (human. Also biologically movement optic-flow patterns utilized. As motion information guides share sketch algorithm in form pathway for adjustment plus it helps to prevent wrong recognition. A synergetic neural network is utilized to generate prototype templates, representing general characteristic form of every class. Having predefined templates, classifying performs based on multitemplate matching. As every human action has one action prototype, there are some overlapping and consistency among these templates. Using fuzzy optical flow division scoring can prevent motivation for misrecognition. We successfully apply proposed model on the human action video obtained from KTH human action database. Proposed approach follows the interaction between dorsal and ventral processing streams in the original model of the biological movement recognition. The attained results indicate promising outcome and improvement in robustness using proposed approach.

  12. Biological Surface Adsorption Index of Nanomaterials: Modelling Surface Interactions of Nanomaterials with Biomolecules.

    Science.gov (United States)

    Chen, Ran; Riviere, Jim E

    2017-01-01

    Quantitative analysis of the interactions between nanomaterials and their surrounding environment is crucial for safety evaluation in the application of nanotechnology as well as its development and standardization. In this chapter, we demonstrate the importance of the adsorption of surrounding molecules onto the surface of nanomaterials by forming biocorona and thus impact the bio-identity and fate of those materials. We illustrate the key factors including various physical forces in determining the interaction happening at bio-nano interfaces. We further discuss the mathematical endeavors in explaining and predicting the adsorption phenomena, and propose a new statistics-based surface adsorption model, the Biological Surface Adsorption Index (BSAI), to quantitatively analyze the interaction profile of surface adsorption of a large group of small organic molecules onto nanomaterials with varying surface physicochemical properties, first employing five descriptors representing the surface energy profile of the nanomaterials, then further incorporating traditional semi-empirical adsorption models to address concentration effects of solutes. These Advancements in surface adsorption modelling showed a promising development in the application of quantitative predictive models in biological applications, nanomedicine, and environmental safety assessment of nanomaterials.

  13. A Gaussian mixture model based cost function for parameter estimation of chaotic biological systems

    Science.gov (United States)

    Shekofteh, Yasser; Jafari, Sajad; Sprott, Julien Clinton; Hashemi Golpayegani, S. Mohammad Reza; Almasganj, Farshad

    2015-02-01

    As we know, many biological systems such as neurons or the heart can exhibit chaotic behavior. Conventional methods for parameter estimation in models of these systems have some limitations caused by sensitivity to initial conditions. In this paper, a novel cost function is proposed to overcome those limitations by building a statistical model on the distribution of the real system attractor in state space. This cost function is defined by the use of a likelihood score in a Gaussian mixture model (GMM) which is fitted to the observed attractor generated by the real system. Using that learned GMM, a similarity score can be defined by the computed likelihood score of the model time series. We have applied the proposed method to the parameter estimation of two important biological systems, a neuron and a cardiac pacemaker, which show chaotic behavior. Some simulated experiments are given to verify the usefulness of the proposed approach in clean and noisy conditions. The results show the adequacy of the proposed cost function.

  14. Unit testing, model validation, and biological simulation [version 1; referees: 2 approved, 1 approved with reservations

    Directory of Open Access Journals (Sweden)

    Gopal P. Sarma

    2016-08-01

    Full Text Available The growth of the software industry has gone hand in hand with the development of tools and cultural practices for ensuring the reliability of complex pieces of software. These tools and practices are now acknowledged to be essential to the management of modern software. As computational models and methods have become increasingly common in the biological sciences, it is important to examine how these practices can accelerate biological software development and improve research quality. In this article, we give a focused case study of our experience with the practices of unit testing and test-driven development in OpenWorm, an open-science project aimed at modeling Caenorhabditis elegans. We identify and discuss the challenges of incorporating test-driven development into a heterogeneous, data-driven project, as well as the role of model validation tests, a category of tests unique to software which expresses scientific models.

  15. Applying complex models to poultry production in the future--economics and biology.

    Science.gov (United States)

    Talpaz, H; Cohen, M; Fancher, B; Halley, J

    2013-09-01

    The ability to determine the optimal broiler feed nutrient density that maximizes margin over feeding cost (MOFC) has obvious economic value. To determine optimal feed nutrient density, one must consider ingredient prices, meat values, the product mix being marketed, and the projected biological performance. A series of 8 feeding trials was conducted to estimate biological responses to changes in ME and amino acid (AA) density. Eight different genotypes of sex-separate reared broilers were fed diets varying in ME (2,723-3,386 kcal of ME/kg) and AA (0.89-1.65% digestible lysine with all essential AA acids being indexed to lysine) levels. Broilers were processed to determine carcass component yield at many different BW (1.09-4.70 kg). Trial data generated were used in model constructed to discover the dietary levels of ME and AA that maximize MOFC on a per broiler or per broiler annualized basis (bird × number of cycles/year). The model was designed to estimate the effects of dietary nutrient concentration on broiler live weight, feed conversion, mortality, and carcass component yield. Estimated coefficients from the step-wise regression process are subsequently used to predict the optimal ME and AA concentrations that maximize MOFC. The effects of changing feed or meat prices across a wide spectrum on optimal ME and AA levels can be evaluated via parametric analysis. The model can rapidly compare both biological and economic implications of changing from current practice to the simulated optimal solution. The model can be exploited to enhance decision making under volatile market conditions.

  16. Modeling of the bacterial mechanism of methicillin-resistance by a systems biology approach.

    Directory of Open Access Journals (Sweden)

    Ida Autiero

    Full Text Available BACKGROUND: A microorganism is a complex biological system able to preserve its functional features against external perturbations and the ability of the living systems to oppose to these external perturbations is defined "robustness". The antibiotic resistance, developed by different bacteria strains, is a clear example of robustness and of ability of the bacterial system to acquire a particular functional behaviour in response to environmental changes. In this work we have modeled the whole mechanism essential to the methicillin-resistance through a systems biology approach. The methicillin is a beta-lactamic antibiotic that act by inhibiting the penicillin-binding proteins (PBPs. These PBPs are involved in the synthesis of peptidoglycans, essential mesh-like polymers that surround cellular enzymes and are crucial for the bacterium survival. METHODOLOGY: The network of genes, mRNA, proteins and metabolites was created using CellDesigner program and the data of molecular interactions are stored in Systems Biology Markup Language (SBML. To simulate the dynamic behaviour of this biochemical network, the kinetic equations were associated with each reaction. CONCLUSIONS: Our model simulates the mechanism of the inactivation of the PBP by methicillin, as well as the expression of PBP2a isoform, the regulation of the SCCmec elements (SCC: staphylococcal cassette chromosome and the synthesis of peptidoglycan by PBP2a. The obtained results by our integrated approach show that the model describes correctly the whole phenomenon of the methicillin resistance and is able to respond to the external perturbations in the same way of the real cell. Therefore, this model can be useful to develop new therapeutic approaches for the methicillin control and to understand the general mechanism regarding the cellular resistance to some antibiotics.

  17. A model of cell biological signaling predicts a phase transition of signaling and provides mathematical formulae.

    Science.gov (United States)

    Tsuruyama, Tatsuaki

    2014-01-01

    A biological signal is transmitted by interactions between signaling molecules in the cell. To date, there have been extensive studies regarding signaling pathways using numerical simulation of kinetic equations that are based on equations of continuity and Fick's law. To obtain a mathematical formulation of cell signaling, we propose a stability kinetic model of cell biological signaling of a simple two-parameter model based on the kinetics of the diffusion-limiting step. In the present model, the signaling is regulated by the binding of a cofactor, such as ATP. Non-linearity of the kinetics is given by the diffusion fluctuation in the interaction between signaling molecules, which is different from previous works that hypothesized autocatalytic reactions. Numerical simulations showed the presence of a critical concentration of the cofactor beyond which the cell signaling molecule concentration is altered in a chaos-like oscillation with frequency, which is similar to a discontinuous phase transition in physics. Notably, we found that the frequency is given by the logarithm function of the difference of the outside cofactor concentration from the critical concentration. This implies that the outside alteration of the cofactor concentration is transformed into the oscillatory alteration of cell inner signaling. Further, mathematical stability kinetic analysis predicted a discontinuous dynamic phase transition in the critical state at which the cofactor concentration is equivalent to the critical concentration. In conclusion, the present model illustrates a unique feature of cell signaling, and the stability analysis may provide an analytical framework of the cell signaling system and a novel formulation of biological signaling.

  18. Exploring the MACH Model's Potential as a Metacognitive Tool to Help Undergraduate Students Monitor Their Explanations of Biological Mechanisms

    Science.gov (United States)

    Trujillo, Caleb M.; Anderson, Trevor R.; Pelaez, Nancy J.

    2016-01-01

    When undergraduate biology students learn to explain biological mechanisms, they face many challenges and may overestimate their understanding of living systems. Previously, we developed the MACH model of four components used by expert biologists to explain mechanisms: Methods, Analogies, Context, and How. This study explores the implementation of…

  19. Traveling wave solutions of a biological reaction-convection-diffusion equation model by using $(G'/G$ expansion method

    Directory of Open Access Journals (Sweden)

    Shahnam Javadi

    2013-07-01

    Full Text Available In this paper, the $(G'/G$-expansion method is applied to solve a biological reaction-convection-diffusion model arising in mathematical biology. Exact traveling wave solutions are obtained by this method. This scheme can be applied to a wide class of nonlinear partial differential equations.

  20. Comparison of Model Calculations of Biological Damage from Exposure to Heavy Ions with Measurements

    Science.gov (United States)

    Kim, Myung-Hee Y.; Wu, Honglu; Hada, Megumi; Cucinotta, Francis

    The space environment consists of a varying field of radiation particles including high-energy ions, with spacecraft shielding material providing the major protection to astronauts from harmful exposure. Unlike low-LET g or X rays, the presence of shielding does not always reduce the radiation risks for energetic charged-particle exposure. Dose delivered by the charged particle increases sharply at the Bragg peak. However, the Bragg curve does not necessarily represent the biological damage along the particle path since biological effects are influenced by the track structures of both primary and secondary particles. Therefore, the ‘‘biological Bragg curve’’ is dependent on the energy and the type of the primary particle and may vary for different biological end points. Measurements of the induction of micronuclei (MN) have made across the Bragg curve in human fibroblasts exposed to energetic silicon and iron ions in vitro at two different energies, 300 MeV/nucleon and 1 GeV/nucleon. Although the data did not reveal an increased yield of MN at the location of the Bragg peak, the increased inhibition of cell progression, which is related to cell death, was found at the Bragg peak location. These results are compared to the calculations of biological damage using a stochastic Monte-Carlo track structure model, Galactic Cosmic Ray Event-based Risk Model (GERM) code (Cucinotta et al., 2011). The GERM code estimates the basic physical properties along the passage of heavy ions in tissue and shielding materials, by which the experimental set-up can be interpreted. The code can also be used to describe the biophysical events of interest in radiobiology, cancer therapy, and space exploration. The calculation has shown that the severely damaged cells at the Bragg peak are more likely to go through reproductive death, the so called “overkill”. F. A. Cucinotta, I. Plante, A. L. Ponomarev, and M. Y. Kim, Nuclear Interactions in Heavy Ion Transport and Event

  1. A cooperative strategy for parameter estimation in large scale systems biology models

    Directory of Open Access Journals (Sweden)

    Villaverde Alejandro F

    2012-06-01

    Full Text Available Abstract Background Mathematical models play a key role in systems biology: they summarize the currently available knowledge in a way that allows to make experimentally verifiable predictions. Model calibration consists of finding the parameters that give the best fit to a set of experimental data, which entails minimizing a cost function that measures the goodness of this fit. Most mathematical models in systems biology present three characteristics which make this problem very difficult to solve: they are highly non-linear, they have a large number of parameters to be estimated, and the information content of the available experimental data is frequently scarce. Hence, there is a need for global optimization methods capable of solving this problem efficiently. Results A new approach for parameter estimation of large scale models, called Cooperative Enhanced Scatter Search (CeSS, is presented. Its key feature is the cooperation between different programs (“threads” that run in parallel in different processors. Each thread implements a state of the art metaheuristic, the enhanced Scatter Search algorithm (eSS. Cooperation, meaning information sharing between threads, modifies the systemic properties of the algorithm and allows to speed up performance. Two parameter estimation problems involving models related with the central carbon metabolism of E. coli which include different regulatory levels (metabolic and transcriptional are used as case studies. The performance and capabilities of the method are also evaluated using benchmark problems of large-scale global optimization, with excellent results. Conclusions The cooperative CeSS strategy is a general purpose technique that can be applied to any model calibration problem. Its capability has been demonstrated by calibrating two large-scale models of different characteristics, improving the performance of previously existing methods in both cases. The cooperative metaheuristic presented here

  2. Indoor Air Nuclear, Biological, and Chemical Health Modeling and Assessment System

    Energy Technology Data Exchange (ETDEWEB)

    Stenner, Robert D.; Hadley, Donald L.; Armstrong, Peter R.; Buck, John W.; Hoopes, Bonnie L.; Janus, Michael C.

    2001-03-01

    Indoor air quality effects on human health are of increasing concern to public health agencies and building owners. The prevention and treatment of 'sick building' syndrome and the spread of air-borne diseases in hospitals, for example, are well known priorities. However, increasing attention is being directed to the vulnerability of our public buildings/places, public security and national defense facilities to terrorist attack or the accidental release of air-borne biological pathogens, harmful chemicals, or radioactive contaminants. The Indoor Air Nuclear, Biological, and Chemical Health Modeling and Assessment System (IA-NBC-HMAS) was developed to serve as a health impact analysis tool for use in addressing these concerns. The overall goal was to develop a user-friendly fully functional prototype Health Modeling and Assessment system, which will operate under the PNNL FRAMES system for ease of use and to maximize its integration with other modeling and assessment capabilities accessible within the FRAMES system (e.g., ambient air fate and transport models, water borne fate and transport models, Physiologically Based Pharmacokinetic models, etc.). The prototype IA-NBC-HMAS is designed to serve as a functional Health Modeling and Assessment system that can be easily tailored to meet specific building analysis needs of a customer. The prototype system was developed and tested using an actual building (i.e., the Churchville Building located at the Aberdeen Proving Ground) and release scenario (i.e., the release and measurement of tracer materials within the building) to ensure realism and practicality in the design and development of the prototype system. A user-friendly "demo" accompanies this report to allow the reader the opportunity for a "hands on" review of the prototype system's capability.

  3. N-gram analysis of 970 microbial organisms reveals presence of biological language models

    Directory of Open Access Journals (Sweden)

    Ganapathiraju Madhavi K

    2011-01-01

    Full Text Available Abstract Background It has been suggested previously that genome and proteome sequences show characteristics typical of natural-language texts such as "signature-style" word usage indicative of authors or topics, and that the algorithms originally developed for natural language processing may therefore be applied to genome sequences to draw biologically relevant conclusions. Following this approach of 'biological language modeling', statistical n-gram analysis has been applied for comparative analysis of whole proteome sequences of 44 organisms. It has been shown that a few particular amino acid n-grams are found in abundance in one organism but occurring very rarely in other organisms, thereby serving as genome signatures. At that time proteomes of only 44 organisms were available, thereby limiting the generalization of this hypothesis. Today nearly 1,000 genome sequences and corresponding translated sequences are available, making it feasible to test the existence of biological language models over the evolutionary tree. Results We studied whole proteome sequences of 970 microbial organisms using n-gram frequencies and cross-perplexity employing the Biological Language Modeling Toolkit and Patternix Revelio toolkit. Genus-specific signatures were observed even in a simple unigram distribution. By taking statistical n-gram model of one organism as reference and computing cross-perplexity of all other microbial proteomes with it, cross-perplexity was found to be predictive of branch distance of the phylogenetic tree. For example, a 4-gram model from proteome of Shigellae flexneri 2a, which belongs to the Gammaproteobacteria class showed a self-perplexity of 15.34 while the cross-perplexity of other organisms was in the range of 15.59 to 29.5 and was proportional to their branching distance in the evolutionary tree from S. flexneri. The organisms of this genus, which happen to be pathotypes of E.coli, also have the closest perplexity values with

  4. Assessing Impacts of Climate Change on Forests: The State of Biological Modeling

    Science.gov (United States)

    Dale, V. H.; Rauscher, H. M.

    1993-04-06

    Models that address the impacts to forests of climate change are reviewed by four levels of biological organization: global, regional or landscape, community, and tree. The models are compared as to their ability to assess changes in greenhouse gas flux, land use, maps of forest type or species composition, forest resource productivity, forest health, biodiversity, and wildlife habitat. No one model can address all of these impacts, but landscape transition models and regional vegetation and land-use models consider the largest number of impacts. Developing landscape vegetation dynamics models of functional groups is suggested as a means to integrate the theory of both landscape ecology and individual tree responses to climate change. Risk assessment methodologies can be adapted to deal with the impacts of climate change at various spatial and temporal scales. Four areas of research development are identified: (1) linking socioeconomic and ecologic models, (2) interfacing forest models at different scales, (3) obtaining data on susceptibility of trees and forest to changes in climate and disturbance regimes, and (4) relating information from different scales.

  5. Assessing impacts of climate change on forests: The state of biological modeling

    Energy Technology Data Exchange (ETDEWEB)

    Dale, V.H. [Oak Ridge National Lab., TN (United States); Rauscher, H.M. [Forest Service, Grand Rapids, MI (United States). North Central Forest Experiment Station

    1993-04-06

    Models that address the impacts to forests of climate change are reviewed by four levels of biological organization: global, regional or landscape, community, and tree. The models are compared as to their ability to assess changes in greenhouse gas flux, land use, maps of forest type or species composition, forest resource productivity, forest health, biodiversity, and wildlife habitat. No one model can address all of these impacts, but landscape transition models and regional vegetation and land-use models consider the largest number of impacts. Developing landscape vegetation dynamics models of functional groups is suggested as a means to integrate the theory of both landscape ecology and individual tree responses to climate change. Risk assessment methodologies can be adapted to deal with the impacts of climate change at various spatial and temporal scales. Four areas of research development are identified: (1) linking socioeconomic and ecologic models, (2) interfacing forest models at different scales, (3) obtaining data on susceptibility of trees and forest to changes in climate and disturbance regimes, and (4) relating information from different scales.

  6. Three "quantum" models of competition and cooperation in interacting biological populations and social groups

    CERN Document Server

    Vol, E D

    2012-01-01

    In present paper we propose the consistent statistical approach which appropriate for a number of models describing both behavior of biological populations and various social groups interacting with each other.The approach proposed based on the ideas of quantum theory of open systems (QTOS) and allows one to account explicitly both discreteness of a system variables and their fluctuations near mean values.Therefore this approach can be applied also for the description of small populations where standard dynamical methods are failed. We study in detail three typical models of interaction between populations and groups: 1) antagonistic struggle between two populations 2) cooperation (or, more precisely, obligatory mutualism) between two species 3) the formation of coalition between two feeble groups in their conflict with third one that is more powerful . The models considered in a sense are mutually complementary and include the most types of interaction between populations and groups. Besides this method can ...

  7. Modeling Complex Biological Flows in Multi-Scale Systems using the APDEC Framework

    Energy Technology Data Exchange (ETDEWEB)

    Trebotich, D

    2006-06-24

    We have developed advanced numerical algorithms to model biological fluids in multiscale flow environments using the software framework developed under the SciDAC APDEC ISIC. The foundation of our computational effort is an approach for modeling DNA-laden fluids as ''bead-rod'' polymers whose dynamics are fully coupled to an incompressible viscous solvent. The method is capable of modeling short range forces and interactions between particles using soft potentials and rigid constraints. Our methods are based on higher-order finite difference methods in complex geometry with adaptivity, leveraging algorithms and solvers in the APDEC Framework. Our Cartesian grid embedded boundary approach to incompressible viscous flow in irregular geometries has also been interfaced to a fast and accurate level-sets method within the APDEC Framework for extracting surfaces from volume renderings of medical image data and used to simulate cardio-vascular and pulmonary flows in critical anatomies.

  8. Macroscopic model for biological fixation and its uncover-ing idea in Chinese Mongolian traditional osteopathy

    Institute of Scientific and Technical Information of China (English)

    ZHAO Namula; LI Xue-en; WANG Mei; HU Da-lai

    2009-01-01

    Splintage external fixation in Chinese Mongolian oste-opathy is a biological macroscopic model. In this model, the ideas of self-life "unity of mind and body" and vital natural "correspondence of nature and human" combine the physi-ological and psychological self-fixation with supplementary external fixation of fracture using small splints. This model implies macroscopic ideas of uncovering fixation and healing: structural stability integrating geometrical "dy-namic" stability with mechanical "dynamic" equilibrium and the stability of state integrating statics with dynamics, and osteoblasts with osteoclasts, and psychological stability in-tegrating closed and open systems of human and nature. These ideas indicate a trend of development in modem osteopathy.

  9. A model of the ocean iron cycle and its influence on biological production

    Science.gov (United States)

    Dutkiewicz, S.; Parekh, P.; Follows, M.

    2003-04-01

    Biological productivity in large regions of the ocean, specifically high nutrient, low chlorophyll regions, is limited by the deficit in iron relative to other nutrients. We have developed a parameterization of the iron cycle of the world's oceans which attempts to explicitly represent the processes by which this deficit in iron occurs. We have implemented this parameterization in the context of the MIT three dimensional global ocean model and examined the consequences for nutrient distributions, new production and primary production. The iron model parameterizes the mechanisms of scavenging of iron onto sinking particles and complexation with an organic ligand and is driven by specified aeolian flux patterns. First, using an idealized representation of export production, limited by light, phosphate and iron, the model reproduces the broad features of the observed ocean phosphate and iron distributions. We replace the simplified export parameterization with an explicit, but highly idealized, ecosystem model. The model represents a simplified food web with two phytoplankton size classes and a single grazer. The base currency for this model is phosphorus, but the larger phytoplankton class (i.e. diatoms) is also limited by silica. Both classes are limited by the availability of iron. The results of this model are also generally consistent with the observed patterns of phosphate and iron. In addition, the model captures the broad features of the distributions and cycles of silica, chlorophyll and primary production. We will also explore the sensitivities of this model to the forcing fields (e.g. aeolian iron flux) and parameter choices of the ecosystem model. This model represents a step towards the explicit representation of the ocean iron cycle, and its biogeochemical influences, in global biogeochemical models.

  10. Heterotypic mouse models of canine osteosarcoma recapitulate tumor heterogeneity and biological behavior

    Directory of Open Access Journals (Sweden)

    Milcah C. Scott

    2016-12-01

    Full Text Available Osteosarcoma (OS is a heterogeneous and rare disease with a disproportionate impact because it mainly affects children and adolescents. Lamentably, more than half of patients with OS succumb to metastatic disease. Clarification of the etiology of the disease, development of better strategies to manage progression, and methods to guide personalized treatments are among the unmet health needs for OS patients. Progress in managing the disease has been hindered by the extreme heterogeneity of OS; thus, better models that accurately recapitulate the natural heterogeneity of the disease are needed. For this study, we used cell lines derived from two spontaneous canine OS tumors with distinctly different biological behavior (OS-1 and OS-2 for heterotypic in vivo modeling that recapitulates the heterogeneous biology and behavior of this disease. Both cell lines demonstrated stability of the transcriptome when grown as orthotopic xenografts in athymic nude mice. Consistent with the behavior of the original tumors, OS-2 xenografts grew more rapidly at the primary site and had greater propensity to disseminate to lung and establish microscopic metastasis. Moreover, OS-2 promoted formation of a different tumor-associated stromal environment than OS-1 xenografts. OS-2-derived tumors comprised a larger percentage of the xenograft tumors than OS-1-derived tumors. In addition, a robust pro-inflammatory population dominated the stromal cell infiltrates in OS-2 xenografts, whereas a mesenchymal population with a gene signature reflecting myogenic signaling dominated those in the OS-1 xenografts. Our studies show that canine OS cell lines maintain intrinsic features of the tumors from which they were derived and recapitulate the heterogeneous biology and behavior of bone cancer in mouse models. This system provides a resource to understand essential interactions between tumor cells and the stromal environment that drive the progression and metastatic propensity of

  11. A Novel CPU/GPU Simulation Environment for Large-Scale Biologically-Realistic Neural Modeling

    Directory of Open Access Journals (Sweden)

    Roger V Hoang

    2013-10-01

    Full Text Available Computational Neuroscience is an emerging field that provides unique opportunities to studycomplex brain structures through realistic neural simulations. However, as biological details are added tomodels, the execution time for the simulation becomes longer. Graphics Processing Units (GPUs are now being utilized to accelerate simulations due to their ability to perform computations in parallel. As such, they haveshown significant improvement in execution time compared to Central Processing Units (CPUs. Most neural simulators utilize either multiple CPUs or a single GPU for better performance, but still show limitations in execution time when biological details are not sacrificed. Therefore, we present a novel CPU/GPU simulation environment for large-scale biological networks,the NeoCortical Simulator version 6 (NCS6. NCS6 is a free, open-source, parallelizable, and scalable simula-tor, designed to run on clusters of multiple machines, potentially with high performance computing devicesin each of them. It has built-in leaky-integrate-and-fire (LIF and Izhikevich (IZH neuron models, but usersalso have the capability to design their own plug-in interface for different neuron types as desired. NCS6is currently able to simulate one million cells and 100 million synapses in quasi real time by distributing dataacross these heterogeneous clusters of CPUs and GPUs.

  12. Synthetic biology of minimal living cells: primitive cell models and semi-synthetic cells.

    Science.gov (United States)

    Stano, Pasquale

    2010-09-01

    This article summarizes a contribution presented at the ESF 2009 Synthetic Biology focused on the concept of the minimal requirement for life and on the issue of constructive (synthetic) approaches in biological research. The attempts to define minimal life within the framework of autopoietic theory are firstly described, and a short report on the development of autopoietic chemical systems based on fatty acid vesicles, which are relevant as primitive cell models is given. These studies can be used as a starting point for the construction of more complex systems, firstly being inspired by possible origins of life scenarioes (and therefore by considering primitive functions), then by considering an approach based on modern biomacromolecular-encoded functions. At this aim, semi-synthetic minimal cells are defined as those man-made vesicle-based systems that are composed of the minimal number of genes, proteins, biomolecules and which can be defined as living. Recent achievements on minimal sized semi-synthetic cells are then discussed, and the kind of information obtained is recognized as being distinctively derived by a constructive approach. Synthetic biology is therefore a fundamental tool for gaining basic knowledge about biosystems, and it should not be confined at all to the engineering side.

  13. A stochastic multicellular model identifies biological watermarks from disorders in self-organized patterns of phyllotaxis

    Science.gov (United States)

    Refahi, Yassin; Brunoud, Géraldine; Farcot, Etienne; Jean-Marie, Alain; Pulkkinen, Minna; Vernoux, Teva; Godin, Christophe

    2016-01-01

    Exploration of developmental mechanisms classically relies on analysis of pattern regularities. Whether disorders induced by biological noise may carry information on building principles of developmental systems is an important debated question. Here, we addressed theoretically this question using phyllotaxis, the geometric arrangement of plant aerial organs, as a model system. Phyllotaxis arises from reiterative organogenesis driven by lateral inhibitions at the shoot apex. Motivated by recurrent observations of disorders in phyllotaxis patterns, we revisited in depth the classical deterministic view of phyllotaxis. We developed a stochastic model of primordia initiation at the shoot apex, integrating locality and stochasticity in the patterning system. This stochastic model recapitulates phyllotactic patterns, both regular and irregular, and makes quantitative predictions on the nature of disorders arising from noise. We further show that disorders in phyllotaxis instruct us on the parameters governing phyllotaxis dynamics, thus that disorders can reveal biological watermarks of developmental systems. DOI: http://dx.doi.org/10.7554/eLife.14093.001 PMID:27380805

  14. Combination of Neuro-Fuzzy Network Models with Biological Knowledge for Reconstructing Gene Regulatory Networks

    Institute of Scientific and Technical Information of China (English)

    Guixia Liu; Lei Liu; Chunyu Liu; Ming Zheng; Lanying Su; Chunguang Zhou

    2011-01-01

    Inferring gene regulatory networks from large-scale expression data is an important topic in both cellular systems and computational biology. The inference of regulators might be the core factor for understanding actual regulatory conditions in gene regulatory networks, especially when strong regulators do work significantly, in this paper, we propose a novel approach based on combining neuro-fuzzy network models with biological knowledge to infer strong regulators and interrelated fuzzy rules. The hybrid neuro-fuzzy architecture can not only infer the fuzzy rules, which are suitable for describing the regulatory conditions in regulatory networks, but also explain the meaning of nodes and weight value in the neural network. It can get useful rules automatically without factitious judgments. At the same time, it does not add recursive layers to the model, and the model can also strengthen the relationships among genes and reduce calculation. We use the proposed approach to reconstruct a partial gene regulatory network of yeast. The results show that this approach can work effectively.

  15. Network-based analysis of affected biological processes in type 2 diabetes models.

    Directory of Open Access Journals (Sweden)

    Manway Liu

    2007-06-01

    Full Text Available Type 2 diabetes mellitus is a complex disorder associated with multiple genetic, epigenetic, developmental, and environmental factors. Animal models of type 2 diabetes differ based on diet, drug treatment, and gene knockouts, and yet all display the clinical hallmarks of hyperglycemia and insulin resistance in peripheral tissue. The recent advances in gene-expression microarray technologies present an unprecedented opportunity to study type 2 diabetes mellitus at a genome-wide scale and across different models. To date, a key challenge has been to identify the biological processes or signaling pathways that play significant roles in the disorder. Here, using a network-based analysis methodology, we identified two sets of genes, associated with insulin signaling and a network of nuclear receptors, which are recurrent in a statistically significant number of diabetes and insulin resistance models and transcriptionally altered across diverse tissue types. We additionally identified a network of protein-protein interactions between members from the two gene sets that may facilitate signaling between them. Taken together, the results illustrate the benefits of integrating high-throughput microarray studies, together with protein-protein interaction networks, in elucidating the underlying biological processes associated with a complex disorder.

  16. Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE

    Directory of Open Access Journals (Sweden)

    Brentani Helena

    2004-08-01

    Full Text Available Abstract Background An important challenge for transcript counting methods such as Serial Analysis of Gene Expression (SAGE, "Digital Northern" or Massively Parallel Signature Sequencing (MPSS, is to carry out statistical analyses that account for the within-class variability, i.e., variability due to the intrinsic biological differences among sampled individuals of the same class, and not only variability due to technical sampling error. Results We introduce a Bayesian model that accounts for the within-class variability by means of mixture distribution. We show that the previously available approaches of aggregation in pools ("pseudo-libraries" and the Beta-Binomial model, are particular cases of the mixture model. We illustrate our method with a brain tumor vs. normal comparison using SAGE data from public databases. We show examples of tags regarded as differentially expressed with high significance if the within-class variability is ignored, but clearly not so significant if one accounts for it. Conclusion Using available information about biological replicates, one can transform a list of candidate transcripts showing differential expression to a more reliable one. Our method is freely available, under GPL/GNU copyleft, through a user friendly web-based on-line tool or as R language scripts at supplemental web-site.

  17. Ground truth methods for optical cross-section modeling of biological aerosols

    Science.gov (United States)

    Kalter, J.; Thrush, E.; Santarpia, J.; Chaudhry, Z.; Gilberry, J.; Brown, D. M.; Brown, A.; Carter, C. C.

    2011-05-01

    Light detection and ranging (LIDAR) systems have demonstrated some capability to meet the needs of a fastresponse standoff biological detection method for simulants in open air conditions. These systems are designed to exploit various cloud signatures, such as differential elastic backscatter, fluorescence, and depolarization in order to detect biological warfare agents (BWAs). However, because the release of BWAs in open air is forbidden, methods must be developed to predict candidate system performance against real agents. In support of such efforts, the Johns Hopkins University Applied Physics Lab (JHU/APL) has developed a modeling approach to predict the optical properties of agent materials from relatively simple, Biosafety Level 3-compatible bench top measurements. JHU/APL has fielded new ground truth instruments (in addition to standard particle sizers, such as the Aerodynamic particle sizer (APS) or GRIMM aerosol monitor (GRIMM)) to more thoroughly characterize the simulant aerosols released in recent field tests at Dugway Proving Ground (DPG). These instruments include the Scanning Mobility Particle Sizer (SMPS), the Ultraviolet Aerodynamic Particle Sizer (UVAPS), and the Aspect Aerosol Size and Shape Analyser (Aspect). The SMPS was employed as a means of measuring smallparticle concentrations for more accurate Mie scattering simulations; the UVAPS, which measures size-resolved fluorescence intensity, was employed as a path toward fluorescence cross section modeling; and the Aspect, which measures particle shape, was employed as a path towards depolarization modeling.

  18. Bayesian parameter inference and model selection by population annealing in systems biology.

    Science.gov (United States)

    Murakami, Yohei

    2014-01-01

    Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection. Especially, the framework named approximate Bayesian computation is often used for parameter inference and model selection in systems biology. However, Monte Carlo methods needs to be used to compute Bayesian posterior distributions. In addition, the posterior distributions of parameters are sometimes almost uniform or very similar to their prior distributions. In such cases, it is difficult to choose one specific value of parameter with high credibility as the representative value of the distribution. To overcome the problems, we introduced one of the population Monte Carlo algorithms, population annealing. Although population annealing is usually used in statistical mechanics, we showed that population annealing can be used to compute Bayesian posterior distributions in the approximate Bayesian computation framework. To deal with un-identifiability of the representative values of parameters, we proposed to run the simulations with the parameter ensemble sampled from the posterior distribution, named "posterior parameter ensemble". We showed that population annealing is an efficient and convenient algorithm to generate posterior parameter ensemble. We also showed that the simulations with the posterior parameter ensemble can, not only reproduce the data used for parameter inference, but also capture and predict the data which was not used for parameter inference. Lastly, we introduced the marginal likelihood in the approximate Bayesian computation framework for Bayesian model selection. We showed that population annealing enables us to compute the marginal likelihood in the approximate Bayesian computation framework and conduct model selection depending on the Bayes factor.

  19. Formal modeling and analysis of ER-α associated Biological Regulatory Network in breast cancer

    Science.gov (United States)

    Tareen, Samar H.K.; Siddiqa, Amnah; Bibi, Zurah; Ahmad, Jamil

    2016-01-01

    Background Breast cancer (BC) is one of the leading cause of death among females worldwide. The increasing incidence of BC is due to various genetic and environmental changes which lead to the disruption of cellular signaling network(s). It is a complex disease in which several interlinking signaling cascades play a crucial role in establishing a complex regulatory network. The logical modeling approach of René Thomas has been applied to analyze the behavior of estrogen receptor-alpha (ER-α) associated Biological Regulatory Network (BRN) for a small part of complex events that leads to BC metastasis. Methods A discrete model was constructed using the kinetic logic formalism and its set of logical parameters were obtained using the model checking technique implemented in the SMBioNet software which is consistent with biological observations. The discrete model was further enriched with continuous dynamics by converting it into an equivalent Petri Net (PN) to analyze the logical parameters of the involved entities. Results In-silico based discrete and continuous modeling of ER-α associated signaling network involved in BC provides information about behaviors and gene-gene interaction in detail. The dynamics of discrete model revealed, imperative behaviors represented as cyclic paths and trajectories leading to pathogenic states such as metastasis. Results suggest that the increased expressions of receptors ER-α, IGF-1R and EGFR slow down the activity of tumor suppressor genes (TSGs) such as BRCA1, p53 and Mdm2 which can lead to metastasis. Therefore, IGF-1R and EGFR are considered as important inhibitory targets to control the metastasis in BC. Conclusion The in-silico approaches allow us to increase our understanding of the functional properties of living organisms. It opens new avenues of investigations of multiple inhibitory targets (ER-α, IGF-1R and EGFR) for wet lab experiments as well as provided valuable insights in the treatment of cancers such as BC.

  20. The causal pie model: an epidemiological method applied to evolutionary biology and ecology.

    Science.gov (United States)

    Wensink, Maarten; Westendorp, Rudi G J; Baudisch, Annette

    2014-05-01

    A general concept for thinking about causality facilitates swift comprehension of results, and the vocabulary that belongs to the concept is instrumental in cross-disciplinary communication. The causal pie model has fulfilled this role in epidemiology and could be of similar value in evolutionary biology and ecology. In the causal pie model, outcomes result from sufficient causes. Each sufficient cause is made up of a "causal pie" of "component causes". Several different causal pies may exist for the same outcome. If and only if all component causes of a sufficient cause are present, that is, a causal pie is complete, does the outcome occur. The effect of a component cause hence depends on the presence of the other component causes that constitute some causal pie. Because all component causes are equally and fully causative for the outcome, the sum of causes for some outcome exceeds 100%. The causal pie model provides a way of thinking that maps into a number of recurrent themes in evolutionary biology and ecology: It charts when component causes have an effect and are subject to natural selection, and how component causes affect selection on other component causes; which partitions of outcomes with respect to causes are feasible and useful; and how to view the composition of a(n apparently homogeneous) population. The diversity of specific results that is directly understood from the causal pie model is a test for both the validity and the applicability of the model. The causal pie model provides a common language in which results across disciplines can be communicated and serves as a template along which future causal analyses can be made.

  1. Applying Intelligent Computing Techniques to Modeling Biological Networks from Expression Data

    Institute of Scientific and Technical Information of China (English)

    Wei-Po Lee; Kung-Cheng Yang

    2008-01-01

    Constructing biological networks is one of the most important issues in system sbiology. However, constructing a network from data manually takes a considerable large amount of time, therefore an automated procedure is advocated. To automate the procedure of network construction, in this work we use two intelligent computing techniques, genetic programming and neural computation, to infer two kinds of network models that use continuous variables. To verify the presented approaches, experiments have been conducted and the preliminary results show that both approaches can be used to infer networks successfully.

  2. Synthesis, molecular modeling and biological evaluation of two new chicoric acid analogs.

    Science.gov (United States)

    Righi, Giuliana; Pelagalli, Romina; Isoni, Valerio; Tirotta, Ilaria; Dallocchio, Roberto; Dessì, Alessandro; Macchi, Beatrice; Frezza, Caterina; Rossetti, Ilaria; Bovicelli, Paolo

    2017-02-01

    Two conformationally constrained compounds similar to chicoric acid but lacking the catechol and carboxyl groups were prepared. In these analogues, the single bond between the two caffeoyl fragments has been replaced with a chiral oxirane ring and both aromatic residues modified protecting completely or partially the catechol moiety as methyl ether. Preliminary molecular modelling studies carried out on the two analogues showed interactions near the active site of HIV integrase; however, in comparison with raltegravir, the biological evaluation confirmed that CAA-1 and CAA-2 were unable to inhibit infection at lower concentration.

  3. Constructing Soliton and Kink Solutions of PDE Models in Transport and Biology

    Directory of Open Access Journals (Sweden)

    Vsevolod A. Vladimirov

    2006-06-01

    Full Text Available We present a review of our recent works directed towards discovery of a periodic, kink-like and soliton-like travelling wave solutions within the models of transport phenomena and the mathematical biology. Analytical description of these wave patterns is carried out by means of our modification of the direct algebraic balance method. In the case when the analytical description fails, we propose to approximate invariant travelling wave solutions by means of an infinite series of exponential functions. The effectiveness of the method of approximation is demonstrated on a hyperbolic modification of Burgers equation.

  4. Rabi model as a quantum coherent heat engine: From quantum biology to superconducting circuits

    OpenAIRE

    2014-01-01

    PHYSICAL REVIEW A 91, 023816 (2015) Rabi model as a quantum coherent heat engine: From quantum biology to superconducting circuits Ferdi Altintas,1 Ali U¨ . C. Hardal,2 and O¨ zgu¨r E. Mu¨stecaplıog˘lu2,* 1Department of Physics, Abant Izzet Baysal University, Bolu, 14280, Turkey 2Department of Physics, Koc¸ University, Sarıyer, ˙Istanbul, 34450, Turkey (Received 10 November 2014; published 12 February 2015) We propose a multilevel quantum heat engine with a working medium de...

  5. Automated Bayesian model development for frequency detection in biological time series

    Directory of Open Access Journals (Sweden)

    Oldroyd Giles ED

    2011-06-01

    Full Text Available Abstract Background A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. Results In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Conclusions Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and

  6. Mesos-scale modeling of irradiation in pressurized water reactor concrete biological shields

    Energy Technology Data Exchange (ETDEWEB)

    Le Pape, Yann [ORNL; Huang, Hai [Idaho National Laboratory (INL)

    2016-01-01

    Neutron irradiation exposure causes aggregate expansion, namely radiation-induced volumetric expansion (RIVE). The structural significance of RIVE on a portion of a prototypical pressurized water reactor (PWR) concrete biological shield (CBS) is investigated by using a meso- scale nonlinear concrete model with inputs from an irradiation transport code and a coupled moisture transport-heat transfer code. RIVE-induced severe cracking onset appears to be triggered by the ini- tial shrinkage-induced cracking and propagates to a depth of > 10 cm at extended operation of 80 years. Relaxation of the cement paste stresses results in delaying the crack propagation by about 10 years.

  7. Molecular modeling studies, synthesis and biological evaluation of dabigatran analogues as thrombin inhibitors.

    Science.gov (United States)

    Dong, Ming-Hui; Chen, Hai-Feng; Ren, Yu-Jie; Shao, Fang-Ming

    2016-01-15

    In this work, 48 thrombin inhibitors based on the structural scaffold of dabigatran were analyzed using a combination of molecular modeling techniques. We generated three-dimensional quantitative structure-activity relationship (3D-QSAR) models based on three alignments for both comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) to highlight the structural requirements for thrombin protein inhibition. In addition to the 3D-QSAR study, Topomer CoMFA model also was established with a higher leave-one-out cross-validation q(2) and a non-cross-validation r(2), which suggest that the three models have good predictive ability. The results indicated that the steric, hydrophobic and electrostatic fields play key roles in QSAR model. Furthermore, we employed molecular docking and re-docking simulation explored the binding relationship of the ligand and the receptor protein in detail. Molecular docking simulations identified several key interactions that were also indicated through 3D-QSAR analysis. On the basis of the obtained results, two compounds were designed and predicted by three models, the biological evaluation in vitro (IC50) demonstrated that these molecular models were effective for the development of novel potent thrombin inhibitors.

  8. Ancestor of the new archetypal biology: Goethe's dynamic typology as a model for contemporary evolutionary developmental biology.

    Science.gov (United States)

    Riegner, Mark F

    2013-12-01

    As understood historically, typological thinking has no place in evolutionary biology since its conceptual framework is viewed as incompatible with population thinking. In this article, I propose that what I describe as dynamic typological thinking has been confused with, and has been overshadowed by, a static form of typological thinking. This conflation results from an inability to grasp dynamic typological thinking due to the overlooked requirement to engage our cognitive activity in an unfamiliar way. Thus, analytical thinking alone is unsuited to comprehend the nature of dynamic typological thinking. Over 200 years ago, J. W. von Goethe, in his Metamorphosis of Plants (1790) and other writings, introduced a dynamic form of typological thinking that has been traditionally misunderstood and misrepresented. I describe in detail Goethe's phenomenological methodology and its contemporary value in understanding morphological patterns in living organisms. Furthermore, contrary to the implications of static typological thinking, dynamic typological thinking is perfectly compatible with evolutionary dynamics and, if rightly understood, can contribute significantly to the still emerging field of evolutionary developmental biology (evo-devo).

  9. Chaste: A test-driven approach to software development for biological modelling

    KAUST Repository

    Pitt-Francis, Joe

    2009-12-01

    Chaste (\\'Cancer, heart and soft-tissue environment\\') is a software library and a set of test suites for computational simulations in the domain of biology. Current functionality has arisen from modelling in the fields of cancer, cardiac physiology and soft-tissue mechanics. It is released under the LGPL 2.1 licence. Chaste has been developed using agile programming methods. The project began in 2005 when it was reasoned that the modelling of a variety of physiological phenomena required both a generic mathematical modelling framework, and a generic computational/simulation framework. The Chaste project evolved from the Integrative Biology (IB) e-Science Project, an inter-institutional project aimed at developing a suitable IT infrastructure to support physiome-level computational modelling, with a primary focus on cardiac and cancer modelling. Program summary: Program title: Chaste. Catalogue identifier: AEFD_v1_0. Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEFD_v1_0.html. Program obtainable from: CPC Program Library, Queen\\'s University, Belfast, N. Ireland. Licensing provisions: LGPL 2.1. No. of lines in distributed program, including test data, etc.: 5 407 321. No. of bytes in distributed program, including test data, etc.: 42 004 554. Distribution format: tar.gz. Programming language: C++. Operating system: Unix. Has the code been vectorised or parallelized?: Yes. Parallelized using MPI. RAM:< 90   Megabytes for two of the scenarios described in Section 6 of the manuscript (Monodomain re-entry on a slab or Cylindrical crypt simulation). Up to 16 Gigabytes (distributed across processors) for full resolution bidomain cardiac simulation. Classification: 3. External routines: Boost, CodeSynthesis XSD, CxxTest, HDF5, METIS, MPI, PETSc, Triangle, Xerces. Nature of problem: Chaste may be used for solving coupled ODE and PDE systems arising from modelling biological systems. Use of Chaste in two application areas are described in this paper: cardiac

  10. Benchmarking biological nutrient removal in wastewater treatment plants: influence of mathematical model assumptions.

    Science.gov (United States)

    Flores-Alsina, Xavier; Gernaey, Krist V; Jeppsson, Ulf

    2012-01-01

    This paper examines the effect of different model assumptions when describing biological nutrient removal (BNR) by the activated sludge models (ASM) 1, 2d & 3. The performance of a nitrogen removal (WWTP1) and a combined nitrogen and phosphorus removal (WWTP2) benchmark wastewater treatment plant was compared for a series of model assumptions. Three different model approaches describing BNR are considered. In the reference case, the original model implementations are used to simulate WWTP1 (ASM1 & 3) and WWTP2 (ASM2d). The second set of models includes a reactive settler, which extends the description of the non-reactive TSS sedimentation and transport in the reference case with the full set of ASM processes. Finally, the third set of models is based on including electron acceptor dependency of biomass decay rates for ASM1 (WWTP1) and ASM2d (WWTP2). The results show that incorporation of a reactive settler: (1) increases the hydrolysis of particulates; (2) increases the overall plant's denitrification efficiency by reducing the S(NOx) concentration at the bottom of the clarifier; (3) increases the oxidation of COD compounds; (4) increases X(OHO) and X(ANO) decay; and, finally, (5) increases the growth of X(PAO) and formation of X(PHA,Stor) for ASM2d, which has a major impact on the whole P removal system. Introduction of electron acceptor dependent decay leads to a substantial increase of the concentration of X(ANO), X(OHO) and X(PAO) in the bottom of the clarifier. The paper ends with a critical discussion of the influence of the different model assumptions, and emphasizes the need for a model user to understand the significant differences in simulation results that are obtained when applying different combinations of 'standard' models.

  11. Seasonal nutrient and plankton dynamics in a physical-biological model of Crater Lake

    Science.gov (United States)

    Fennel, K.; Collier, R.; Larson, G.; Crawford, G.; Boss, E.

    2007-01-01

    A coupled 1D physical-biological model of Crater Lake is presented. The model simulates the seasonal evolution of two functional phytoplankton groups, total chlorophyll, and zooplankton in good quantitative agreement with observations from a 10-year monitoring study. During the stratified period in summer and early fall the model displays a marked vertical structure: the phytoplankton biomass of the functional group 1, which represents diatoms and dinoflagellates, has its highest concentration in the upper 40 m; the phytoplankton biomass of group 2, which represents chlorophyta, chrysophyta, cryptomonads and cyanobacteria, has its highest concentrations between 50 and 80 m, and phytoplankton chlorophyll has its maximum at 120 m depth. A similar vertical structure is a reoccurring feature in the available data. In the model the key process allowing a vertical separation between biomass and chlorophyll is photoacclimation. Vertical light attenuation (i.e., water clarity) and the physiological ability of phytoplankton to increase their cellular chlorophyll-to-biomass ratio are ultimately determining the location of the chlorophyll maximum. The location of the particle maxima on the other hand is determined by the balance between growth and losses and occurs where growth and losses equal. The vertical particle flux simulated by our model agrees well with flux measurements from a sediment trap. This motivated us to revisit a previously published study by Dymond et al. (1996). Dymond et al. used a box model to estimate the vertical particle flux and found a discrepancy by a factor 2.5-10 between their model-derived flux and measured fluxes from a sediment trap. Their box model neglected the exchange flux of dissolved and suspended organic matter, which, as our model and available data suggests is significant for the vertical exchange of nitrogen. Adjustment of Dymond et al.'s assumptions to account for dissolved and suspended nitrogen yields a flux estimate that is

  12. Combined model of intrinsic and extrinsic variability for computational network design with application to synthetic biology.

    Directory of Open Access Journals (Sweden)

    Tina Toni

    Full Text Available Biological systems are inherently variable, with their dynamics influenced by intrinsic and extrinsic sources. These systems are often only partially characterized, with large uncertainties about specific sources of extrinsic variability and biochemical properties. Moreover, it is not yet well understood how different sources of variability combine and affect biological systems in concert. To successfully design biomedical therapies or synthetic circuits with robust performance, it is crucial to account for uncertainty and effects of variability. Here we introduce an efficient modeling and simulation framework to study systems that are simultaneously subject to multiple sources of variability, and apply it to make design decisions on small genetic networks that play a role of basic design elements of synthetic circuits. Specifically, the framework was used to explore the effect of transcriptional and post-transcriptional autoregulation on fluctuations in protein expression in simple genetic networks. We found that autoregulation could either suppress or increase the output variability, depending on specific noise sources and network parameters. We showed that transcriptional autoregulation was more successful than post-transcriptional in suppressing variability across a wide range of intrinsic and extrinsic magnitudes and sources. We derived the following design principles to guide the design of circuits that best suppress variability: (i high protein cooperativity and low miRNA cooperativity, (ii imperfect complementarity between miRNA and mRNA was preferred to perfect complementarity, and (iii correlated expression of mRNA and miRNA--for example, on the same transcript--was best for suppression of protein variability. Results further showed that correlations in kinetic parameters between cells affected the ability to suppress variability, and that variability in transient states did not necessarily follow the same principles as variability in

  13. SBMLSimulator: A Java Tool for Model Simulation and Parameter Estimation in Systems Biology

    Directory of Open Access Journals (Sweden)

    Alexander Dörr

    2014-12-01

    Full Text Available The identification of suitable model parameters for biochemical reactions has been recognized as a quite difficult endeavor. Parameter values from literature or experiments can often not directly be combined in complex reaction systems. Nature-inspired optimization techniques can find appropriate sets of parameters that calibrate a model to experimentally obtained time series data. We present SBMLsimulator, a tool that combines the Systems Biology Simulation Core Library for dynamic simulation of biochemical models with the heuristic optimization framework EvA2. SBMLsimulator provides an intuitive graphical user interface with various options as well as a fully-featured command-line interface for large-scale and script-based model simulation and calibration. In a parameter estimation study based on a published model and artificial data we demonstrate the capability of SBMLsimulator to identify parameters. SBMLsimulator is useful for both, the interactive simulation and exploration of the parameter space and for the large-scale model calibration and estimation of uncertain parameter values.

  14. Evaluation of iodide deficiency in the lactating rat and pup using a biologically based dose response (BBDR) Model***

    Science.gov (United States)

    A biologically-based dose response (BBDR) model for the hypothalamic-pituitary thyroid (HPT) axis in the lactating rat and nursing pup was developed to describe the perturbations caused by iodide deficiency on the 1-IPT axis. Model calibrations, carried out by adjusting key model...

  15. Evaluation of iodide deficiency in the lactating rat and pup using a biologically based dose-response model

    Science.gov (United States)

    A biologically-based dose response (BBDR) model for the hypothalamic-pituitary thyroid (BPT) axis in the lactating rat and nursing pup was developed to describe the perturbations caused by iodide deficiency on the HPT axis. Model calibrations, carried out by adjusting key model p...

  16. Biologically-directed modeling reflects cytolytic clearance of SIV-infected cells in vivo in macaques.

    Directory of Open Access Journals (Sweden)

    W David Wick

    Full Text Available The disappointing outcomes of cellular immune-based vaccines against HIV-1 despite strong evidence for the protective role of CD8⁺ T lymphocytes (CTLs has prompted revisiting the mechanisms of cellular immunity. Prior data from experiments examining the kinetics of Simian Immunodeficiency Virus (SIV clearance in infected macaques with or without in vivo CD8 depletion were interpreted as refuting the concept that CTLs suppress SIV/HIV by direct killing of infected cells. Here we briefly review the biological evidence for CTL cytolytic activity in viral infections, and utilize biologically-directed modeling to assess the possibility of a killing mechanism for the antiviral effect of CTLs, taking into account the generation, proliferation, and survival of activated CD4⁺ and CD8⁺ T lymphocytes, as well as the life cycle of the virus. Our analyses of the published macaque data using these models support a killing mechanism, when one considers T lymphocyte and HIV-1 lifecycles, and factors such as the eclipse period before release of virions by infected cells, an exponential pattern of virion production by infected cells, and a variable lifespan for acutely infected cells. We conclude that for SIV/HIV pathogenesis, CTLs deserve their reputation as being cytolytic.

  17. Modeling LIDAR Detection of Biological Aerosols to Determine Optimum Implementation Strategy

    Energy Technology Data Exchange (ETDEWEB)

    Sheen, David M.; Aker, Pam M.

    2007-09-19

    This report summarizes work performed for a larger multi-laboratory project named the Background Interferent Measurement and Standards project. While originally tasked to develop algorithms to optimize biological warfare agent detection using UV fluorescence LIDAR, the current uncertainties in the reported fluorescence profiles and cross sections the development of any meaningful models. It was decided that a better approach would be to model the wavelength-dependent elastic backscattering from a number of ambient background aerosol types, and compare this with that generated from representative sporulated and vegetative bacterial systems. Calculations in this report show that a 266, 355, 532 and 1064 nm elastic backscatter LIDAR experiment will allow an operator to immediately recognize when sulfate, VOC-based or road dust (silicate) aerosols are approaching, independent of humidity changes. It will be more difficult to distinguish soot aerosols from biological aerosols, or vegetative bacteria from sporulated bacteria. In these latter cases, the elastic scattering data will most likely have to be combined with UV fluorescence data to enable a more robust categorization.

  18. Evaluation of biological activity of Uncaria tomentosa (Willd.) DC. using the chicken embryo model.

    Science.gov (United States)

    Pilarski, Radosław; Bednarczyk, Marek; Gulewicz, Krzysztof

    2009-01-01

    The biological activity of Uncaria tomentosa (Willd.) DC. (cat's claw) was evaluated by application of the chicken embryo model. Among three groups of eggs (n = 360) with twelve-day old embryos, two were injected with different doses of cat's claw extracts (0.0492 and 0.492 mg/200 lambda). To the third control group 200 lambda of physiological salt was applied. All eggs were incubated in conventional forced-air apparatus until hatched. Hatchability, chicken weight and wholesomeness were analyzed. Selected parameters of blood including number of erythrocytes (RBC), number of leukocytes (WBC), mean red cell volume (MCV), hematocrit (HCT), hemoglobin concentration (HGB), mean amount of cell hemoglobin (MCH), mean cell hemoglobin concentration (MCHC) and embryo weight (MAS) were assayed and compared. Significant differences with ANOVA were observed for MCV (P = 0.002), MCHC (P = 0.00001) and MCH (P = 0.02). Applying the chicken embryo model brought new information about the biological activity of U. tomentosa showing an unfavourable effect on some morphological blood parameters.

  19. PLASMODIUM PRE-ERYTHROCYTIC STAGES: BIOLOGY, WHOLE PARASITE VACCINES AND TRANSGENIC MODELS

    Directory of Open Access Journals (Sweden)

    Kota Arun Kumar

    2012-01-01

    Full Text Available Malaria remains one of the world’s worst health problems, which causes 216 million new cases and approximately 655,000 deaths every year WHO World Malaria Report, 2011. Malaria transmission to the mammalian host is initiated through a mosquito bite that delivers sporozoites into the vertebrate host. The injected sporozoites are selectively targeted to liver which is the first obligatory step in infection thus making this stage an attractive target for both drug and vaccine development. Research using rodent models of malaria has greatly facilitated the understanding of several aspects of pre-erythrocytic parasite biology and immunology. However, translation of this knowledge to combat Plasmodium falciparum infections still offers several challenges. We highlight in this review some of the recent advances in the field of Plasmodium sporozoite and liver stage biology and in the generation of whole organism attenuated vaccines. We also comment on the application of transgenic models central to Circumsporozoite Protein (CSP in understanding the mechanism of pre-erythrocytic immunity.

  20. ScaleNet: a literature-based model of scale insect biology and systematics.

    Science.gov (United States)

    García Morales, Mayrolin; Denno, Barbara D; Miller, Douglass R; Miller, Gary L; Ben-Dov, Yair; Hardy, Nate B

    2016-01-01

    Scale insects (Hemiptera: Coccoidea) are small herbivorous insects found on all continents except Antarctica. They are extremely invasive, and many species are serious agricultural pests. They are also emerging models for studies of the evolution of genetic systems, endosymbiosis and plant-insect interactions. ScaleNet was launched in 1995 to provide insect identifiers, pest managers, insect systematists, evolutionary biologists and ecologists efficient access to information about scale insect biological diversity. It provides comprehensive information on scale insects taken directly from the primary literature. Currently, it draws from 23,477 articles and describes the systematics and biology of 8194 valid species. For 20 years, ScaleNet ran on the same software platform. That platform is no longer viable. Here, we present a new, open-source implementation of ScaleNet. We have normalized the data model, begun the process of correcting invalid data, upgraded the user interface, and added online administrative tools. These improvements make ScaleNet easier to use and maintain and make the ScaleNet data more accurate and extendable. Database URL: http://scalenet.info.

  1. Incorporating biological pathways via a Markov random field model in genome-wide association studies.

    Directory of Open Access Journals (Sweden)

    Min Chen

    2011-04-01

    Full Text Available Genome-wide association studies (GWAS examine a large number of markers across the genome to identify associations between genetic variants and disease. Most published studies examine only single markers, which may be less informative than considering multiple markers and multiple genes jointly because genes may interact with each other to affect disease risk. Much knowledge has been accumulated in the literature on biological pathways and interactions. It is conceivable that appropriate incorporation of such prior knowledge may improve the likelihood of making genuine discoveries. Although a number of methods have been developed recently to prioritize genes using prior biological knowledge, such as pathways, most methods treat genes in a specific pathway as an exchangeable set without considering the topological structure of a pathway. However, how genes are related with each other in a pathway may be very informative to identify association signals. To make use of the connectivity information among genes in a pathway in GWAS analysis, we propose a Markov Random Field (MRF model to incorporate pathway topology for association analysis. We show that the conditional distribution of our MRF model takes on a simple logistic regression form, and we propose an iterated conditional modes algorithm as well as a decision theoretic approach for statistical inference of each gene's association with disease. Simulation studies show that our proposed framework is more effective to identify genes associated with disease than a single gene-based method. We also illustrate the usefulness of our approach through its applications to a real data example.

  2. Interconnection between biological abnormalities in borderline personality disorder: use of the Bayesian networks model.

    Science.gov (United States)

    De la Fuente, José Manuel; Bengoetxea, Endika; Navarro, Felipe; Bobes, Julio; Alarcón, Renato Daniel

    2011-04-30

    There is agreement in that strengthening the sets of neurobiological data would reinforce the diagnostic objectivity of many psychiatric entities. This article attempts to use this approach in borderline personality disorder (BPD). Assuming that most of the biological findings in BPD reflect common underlying pathophysiological processes we hypothesized that most of the data involved in the findings would be statistically interconnected and interdependent, indicating biological consistency for this diagnosis. Prospectively obtained data on scalp and sleep electroencephalography (EEG), clinical neurologic soft signs, the dexamethasone suppression and thyrotropin-releasing hormone stimulation tests of 20 consecutive BPD patients were used to generate a Bayesian network model, an artificial intelligence paradigm that visually illustrates eventual associations (or inter-dependencies) between otherwise seemingly unrelated variables. The Bayesian network model identified relationships among most of the variables. EEG and TSH were the variables that influence most of the others, especially sleep parameters. Neurological soft signs were linked with EEG, TSH, and sleep parameters. The results suggest the possibility of using objective neurobiological variables to strengthen the validity of future diagnostic criteria and nosological characterization of BPD.

  3. Advantages and challenges of using physics curricula as a model for reforming an undergraduate biology course.

    Science.gov (United States)

    Donovan, D A; Atkins, L J; Salter, I Y; Gallagher, D J; Kratz, R F; Rousseau, J V; Nelson, G D

    2013-06-01

    We report on the development of a life sciences curriculum, targeted to undergraduate students, which was modeled after a commercially available physics curriculum and based on aspects of how people learn. Our paper describes the collaborative development process and necessary modifications required to apply a physics pedagogical model in a life sciences context. While some approaches were easily adapted, others provided significant challenges. Among these challenges were: representations of energy, introducing definitions, the placement of Scientists' Ideas, and the replicability of data. In modifying the curriculum to address these challenges, we have come to see them as speaking to deeper differences between the disciplines, namely that introductory physics--for example, Newton's laws, magnetism, light--is a science of pairwise interaction, while introductory biology--for example, photosynthesis, evolution, cycling of matter in ecosystems--is a science of linked processes, and we suggest that this is how the two disciplines are presented in introductory classes. We illustrate this tension through an analysis of our adaptations of the physics curriculum for instruction on the cycling of matter and energy; we show that modifications of the physics curriculum to address the biological framework promotes strong gains in student understanding of these topics, as evidenced by analysis of student work.

  4. Evaluation of blade-strike models for estimating the biological performance of large Kaplan hydro turbines

    Energy Technology Data Exchange (ETDEWEB)

    Deng, Z. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Carlson, T. J. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Ploskey, G. R. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Richmond, M. C. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2005-11-01

    Bio-indexing of hydro turbines has been identified as an important means to optimize passage conditions for fish by identifying operations for existing and new design turbines that minimize the probability of injury. Cost-effective implementation of bio-indexing requires the use of tools such as numerical and physical turbine models to generate hypotheses for turbine operations that can be tested at prototype scales using live fish. Blade strike has been proposed as an index variable for the biological performance of turbines. Report reviews an evaluation of the use of numerical blade-strike models as a means with which to predict the probability of blade strike and injury of juvenile salmon smolt passing through large Kaplan turbines on the mainstem Columbia River.

  5. An individual-based predator-prey model for biological coevolution: Fluctuations, stability, and community structure

    CERN Document Server

    Rikvold, Per Arne

    2007-01-01

    We study an individual-based predator-prey model of biological coevolution, using linear stability analysis and large-scale kinetic Monte Carlo simulations. The model exhibits approximate 1/f noise in diversity and population-size fluctuations, and it generates a sequence of quasi-steady communities in the form of simple food webs. These communities are quite resilient toward the loss of one or a few species, which is reflected in different power-law exponents for the durations of communities and the lifetimes of species. The exponent for the former is near -1, while the latter is close to -2. Statistical characteristics of the evolving communities, including degree (predator and prey) distributions and proportions of basal, intermediate, and top species, compare reasonably with data for real food webs.

  6. A fractal model for nuclear organization: current evidence and biological implications.

    Science.gov (United States)

    Bancaud, Aurélien; Lavelle, Christophe; Huet, Sébastien; Ellenberg, Jan

    2012-10-01

    Chromatin is a multiscale structure on which transcription, replication, recombination and repair of the genome occur. To fully understand any of these processes at the molecular level under physiological conditions, a clear picture of the polymorphic and dynamic organization of chromatin in the eukaryotic nucleus is required. Recent studies indicate that a fractal model of chromatin architecture is consistent with both the reaction-diffusion properties of chromatin interacting proteins and with structural data on chromatin interminglement. In this study, we provide a critical overview of the experimental evidence that support a fractal organization of chromatin. On this basis, we discuss the functional implications of a fractal chromatin model for biological processes and propose future experiments to probe chromatin organization further that should allow to strongly support or invalidate the fractal hypothesis.

  7. Programming with models: modularity and abstraction provide powerful capabilities for systems biology.

    Science.gov (United States)

    Mallavarapu, Aneil; Thomson, Matthew; Ullian, Benjamin; Gunawardena, Jeremy

    2009-03-06

    Mathematical models are increasingly used to understand how phenotypes emerge from systems of molecular interactions. However, their current construction as monolithic sets of equations presents a fundamental barrier to progress. Overcoming this requires modularity, enabling sub-systems to be specified independently and combined incrementally, and abstraction, enabling generic properties of biological processes to be specified independently of specific instances. These, in turn, require models to be represented as programs rather than as datatypes. Programmable modularity and abstraction enables libraries of modules to be created, which can be instantiated and reused repeatedly in different contexts with different components. We have developed a computational infrastructure that accomplishes this. We show here why such capabilities are needed, what is required to implement them and what can be accomplished with them that could not be done previously.

  8. CHANGES IN BIOLOGICAL PROPERTIES OF ORDINARY BLACK SOILS AT GLEYISATION (MODEL EXPERIMENT

    Directory of Open Access Journals (Sweden)

    Kandashova K. A.

    2015-10-01

    Full Text Available The article presents the results of laboratory modeling of gleyisation and its effect on the biological properties of soils with stagnant regime in ordinary black soils. Gleyisation is a complex biochemical process that occurs under oxygen reduction conditions. Anaerobic microorganisms, the presence of organic substances, and the constant or prolonged waterlogging of individual horizons or the entire soil profile promote gleyisation. Model experiments revealed that gleyisation increase the total number of bacteria and suppresses number of actinomycetes, micromycetes and growth of fungal mycelium. Gleyisation decreases the activity of oxidoreductases and increases the hydrolases activity. In addition, the second content of humus slightly increases and active acidity (pH changes to neutral. Accumulation of large amounts of iron oxide (II in soil is revealed

  9. Robust biological nitrogen fixation in a model grass-bacterial association.

    Science.gov (United States)

    Pankievicz, Vânia C S; do Amaral, Fernanda P; Santos, Karina F D N; Agtuca, Beverly; Xu, Youwen; Schueller, Michael J; Arisi, Ana Carolina M; Steffens, Maria B R; de Souza, Emanuel M; Pedrosa, Fábio O; Stacey, Gary; Ferrieri, Richard A

    2015-03-01

    Nitrogen-fixing rhizobacteria can promote plant growth; however, it is controversial whether biological nitrogen fixation (BNF) from associative interaction contributes to growth promotion. The roots of Setaria viridis, a model C4 grass, were effectively colonized by bacterial inoculants resulting in a significant enhancement of growth. Nitrogen-13 tracer studies provided direct evidence for tracer uptake by the host plant and incorporation into protein. Indeed, plants showed robust growth under nitrogen-limiting conditions when inoculated with an ammonium-excreting strain of Azospirillum brasilense. (11)C-labeling experiments showed that patterns in central carbon metabolism and resource allocation exhibited by nitrogen-starved plants were largely reversed by bacterial inoculation, such that they resembled plants grown under nitrogen-sufficient conditions. Adoption of S. viridis as a model should promote research into the mechanisms of associative nitrogen fixation with the ultimate goal of greater adoption of BNF for sustainable crop production.

  10. Modelling and interpreting biologically crusted dryland soil sub-surface structure using automated micropenetrometry

    Science.gov (United States)

    Hoon, Stephen R.; Felde, Vincent J. M. N. L.; Drahorad, Sylvie L.; Felix-Henningsen, Peter

    2015-04-01

    Soil penetrometers are used routinely to determine the shear strength of soils and deformable sediments both at the surface and throughout a depth profile in disciplines as diverse as soil science, agriculture, geoengineering and alpine avalanche-safety (e.g. Grunwald et al. 2001, Van Herwijnen et al. 2009). Generically, penetrometers comprise two principal components: An advancing probe, and a transducer; the latter to measure the pressure or force required to cause the probe to penetrate or advance through the soil or sediment. The force transducer employed to determine the pressure can range, for example, from a simple mechanical spring gauge to an automatically data-logged electronic transducer. Automated computer control of the penetrometer step size and probe advance rate enables precise measurements to be made down to a resolution of 10's of microns, (e.g. the automated electronic micropenetrometer (EMP) described by Drahorad 2012). Here we discuss the determination, modelling and interpretation of biologically crusted dryland soil sub-surface structures using automated micropenetrometry. We outline a model enabling the interpretation of depth dependent penetration resistance (PR) profiles and their spatial differentials using the model equations, σ {}(z) ={}σ c0{}+Σ 1n[σ n{}(z){}+anz + bnz2] and dσ /dz = Σ 1n[dσ n(z) /dz{} {}+{}Frn(z)] where σ c0 and σ n are the plastic deformation stresses for the surface and nth soil structure (e.g. soil crust, layer, horizon or void) respectively, and Frn(z)dz is the frictional work done per unit volume by sliding the penetrometer rod an incremental distance, dz, through the nth layer. Both σ n(z) and Frn(z) are related to soil structure. They determine the form of σ {}(z){} measured by the EMP transducer. The model enables pores (regions of zero deformation stress) to be distinguished from changes in layer structure or probe friction. We have applied this method to both artificial calibration soils in the

  11. Experiment design through dynamical characterisation of non-linear systems biology models utilising sparse grids.

    Science.gov (United States)

    Donahue, M M; Buzzard, G T; Rundell, A E

    2010-07-01

    The sparse grid-based experiment design algorithm sequentially selects an experimental design point to discriminate between hypotheses for given experimental conditions. Sparse grids efficiently screen the global uncertain parameter space to identify acceptable parameter subspaces. Clustering the located acceptable parameter vectors by the similarity of the simulated model trajectories characterises the data-compatible model dynamics. The experiment design algorithm capitalizes on the diversity of the experimentally distinguishable system output dynamics to select the design point that best discerns between competing model-structure and parameter-encoded hypotheses. As opposed to designing the experiments to explicitly reduce uncertainty in the model parameters, this approach selects design points to differentiate between dynamical behaviours. This approach further differs from other experimental design methods in that it simultaneously addresses both parameter- and structural-based uncertainty that is applicable to some ill-posed problems where the number of uncertain parameters exceeds the amount of data, places very few requirements on the model type, available data and a priori parameter estimates, and is performed over the global uncertain parameter space. The experiment design algorithm is demonstrated on a mitogen-activated protein kinase cascade model. The results show that system dynamics are highly uncertain with limited experimental data. Nevertheless, the algorithm requires only three additional experimental data points to simultaneously discriminate between possible model structures and acceptable parameter values. This sparse grid-based experiment design process provides a systematic and computationally efficient exploration over the entire uncertain parameter space of potential model structures to resolve the uncertainty in the non-linear systems biology model dynamics.

  12. ASPASIA: A toolkit for evaluating the effects of biological interventions on SBML model behaviour

    Science.gov (United States)

    Coles, Mark C.; Kullberg, Marika C.; Timmis, Jon

    2017-01-01

    A calibrated computational model reflects behaviours that are expected or observed in a complex system, providing a baseline upon which sensitivity analysis techniques can be used to analyse pathways that may impact model responses. However, calibration of a model where a behaviour depends on an intervention introduced after a defined time point is difficult, as model responses may be dependent on the conditions at the time the intervention is applied. We present ASPASIA (Automated Simulation Parameter Alteration and SensItivity Analysis), a cross-platform, open-source Java toolkit that addresses a key deficiency in software tools for understanding the impact an intervention has on system behaviour for models specified in Systems Biology Markup Language (SBML). ASPASIA can generate and modify models using SBML solver output as an initial parameter set, allowing interventions to be applied once a steady state has been reached. Additionally, multiple SBML models can be generated where a subset of parameter values are perturbed using local and global sensitivity analysis techniques, revealing the model’s sensitivity to the intervention. To illustrate the capabilities of ASPASIA, we demonstrate how this tool has generated novel hypotheses regarding the mechanisms by which Th17-cell plasticity may be controlled in vivo. By using ASPASIA in conjunction with an SBML model of Th17-cell polarisation, we predict that promotion of the Th1-associated transcription factor T-bet, rather than inhibition of the Th17-associated transcription factor RORγt, is sufficient to drive switching of Th17 cells towards an IFN-γ-producing phenotype. Our approach can be applied to all SBML-encoded models to predict the effect that intervention strategies have on system behaviour. ASPASIA, released under the Artistic License (2.0), can be downloaded from http://www.york.ac.uk/ycil/software. PMID:28158307

  13. Development of in vitro models for investigating spatially fractionated irradiation: physics and biological results

    Science.gov (United States)

    Blockhuys, S; Vanhoecke, B; Paelinck, L; Bracke, M; DeWagter, C

    2009-03-01

    We present different in vitro experimental models which allow us to evaluate the effect of spatially fractionated dose distributions on metabolic activity. We irradiated a monolayer of MCF-7/6 human breast cancer cells with a steep and a smooth 6 MV x-ray dose gradient. In the steep gradient model, we irradiated the cells with three separate small fields. We also developed two smooth gradient models. In the first model, the cells are cultured in a T25 flask and irradiated with a smooth dose gradient over the length of the flask, while in the second one, the cells are cultured in a 96-well plate and also irradiated over the length of the plate. In an attempt to correlate the spatially fractionated dose distributions with metabolic activity, the effect of irradiation was evaluated by means of the MTT assay. This assay is used to determine the metabolic activity by measuring the amount of formazan formed after the conversion of MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) by cellular dehydrogenases. The results obtained with our different models suggest a dose-specific effect on metabolic activity, characterized by an increased formazan optical density occurring in the dose range 1.0-4.0 Gy in the steep dose gradient model and in the dose ranges 4.2-6.5 Gy and 2.3-5.1 Gy in the two smooth dose gradient models. The corresponding times for maximal formazan accumulation were 5-7 days in the steep dose gradient model and day 9-13 and day 9-11 in the smooth dose gradient models. Altogether, our results suggest that the MTT assay may be used as a biological dose-response meter to monitor the radiotherapeutic effectiveness.

  14. Development of in vitro models for investigating spatially fractionated irradiation: physics and biological results

    Energy Technology Data Exchange (ETDEWEB)

    Blockhuys, S; Vanhoecke, B; Bracke, M [Laboratory Experimental Cancer Research, Ghent University Hospital, De Pintelaan 185, B-9000 Gent (Belgium); Paelinck, L; De Wagter, C [Department of Radiotherapy, Ghent University Hospital, De Pintelaan 185, B-9000 Gent (Belgium)], E-mail: Stephanie.Blockhuys@ugent.be

    2009-03-21

    We present different in vitro experimental models which allow us to evaluate the effect of spatially fractionated dose distributions on metabolic activity. We irradiated a monolayer of MCF-7/6 human breast cancer cells with a steep and a smooth 6 MV x-ray dose gradient. In the steep gradient model, we irradiated the cells with three separate small fields. We also developed two smooth gradient models. In the first model, the cells are cultured in a T25 flask and irradiated with a smooth dose gradient over the length of the flask, while in the second one, the cells are cultured in a 96-well plate and also irradiated over the length of the plate. In an attempt to correlate the spatially fractionated dose distributions with metabolic activity, the effect of irradiation was evaluated by means of the MTT assay. This assay is used to determine the metabolic activity by measuring the amount of formazan formed after the conversion of MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) by cellular dehydrogenases. The results obtained with our different models suggest a dose-specific effect on metabolic activity, characterized by an increased formazan optical density occurring in the dose range 1.0-4.0 Gy in the steep dose gradient model and in the dose ranges 4.2-6.5 Gy and 2.3-5.1 Gy in the two smooth dose gradient models. The corresponding times for maximal formazan accumulation were 5-7 days in the steep dose gradient model and day 9-13 and day 9-11 in the smooth dose gradient models. Altogether, our results suggest that the MTT assay may be used as a biological dose-response meter to monitor the radiotherapeutic effectiveness.

  15. How can selection of biologically inspired features improve the performance of a robust object recognition model?

    Directory of Open Access Journals (Sweden)

    Masoud Ghodrati

    Full Text Available Humans can effectively and swiftly recognize objects in complex natural scenes. This outstanding ability has motivated many computational object recognition models. Most of these models try to emulate the behavior of this remarkable system. The human visual system hierarchically recognizes objects in several processing stages. Along these stages a set of features with increasing complexity is extracted by different parts of visual system. Elementary features like bars and edges are processed in earlier levels of visual pathway and as far as one goes upper in this pathway more complex features will be spotted. It is an important interrogation in the field of visual processing to see which features of an object are selected and represented by the visual cortex. To address this issue, we extended a hierarchical model, which is motivated by biology, for different object recognition tasks. In this model, a set of object parts, named patches, extracted in the intermediate stages. These object parts are used for training procedure in the model and have an important role in object recognition. These patches are selected indiscriminately from different positions of an image and this can lead to the extraction of non-discriminating patches which eventually may reduce the performance. In the proposed model we used an evolutionary algorithm approach to select a set of informative patches. Our reported results indicate that these patches are more informative than usual random patches. We demonstrate the strength of the proposed model on a range of object recognition tasks. The proposed model outperforms the original model in diverse object recognition tasks. It can be seen from the experiments that selected features are generally particular parts of target images. Our results suggest that selected features which are parts of target objects provide an efficient set for robust object recognition.

  16. How can selection of biologically inspired features improve the performance of a robust object recognition model?

    Science.gov (United States)

    Ghodrati, Masoud; Khaligh-Razavi, Seyed-Mahdi; Ebrahimpour, Reza; Rajaei, Karim; Pooyan, Mohammad

    2012-01-01

    Humans can effectively and swiftly recognize objects in complex natural scenes. This outstanding ability has motivated many computational object recognition models. Most of these models try to emulate the behavior of this remarkable system. The human visual system hierarchically recognizes objects in several processing stages. Along these stages a set of features with increasing complexity is extracted by different parts of visual system. Elementary features like bars and edges are processed in earlier levels of visual pathway and as far as one goes upper in this pathway more complex features will be spotted. It is an important interrogation in the field of visual processing to see which features of an object are selected and represented by the visual cortex. To address this issue, we extended a hierarchical model, which is motivated by biology, for different object recognition tasks. In this model, a set of object parts, named patches, extracted in the intermediate stages. These object parts are used for training procedure in the model and have an important role in object recognition. These patches are selected indiscriminately from different positions of an image and this can lead to the extraction of non-discriminating patches which eventually may reduce the performance. In the proposed model we used an evolutionary algorithm approach to select a set of informative patches. Our reported results indicate that these patches are more informative than usual random patches. We demonstrate the strength of the proposed model on a range of object recognition tasks. The proposed model outperforms the original model in diverse object recognition tasks. It can be seen from the experiments that selected features are generally particular parts of target images. Our results suggest that selected features which are parts of target objects provide an efficient set for robust object recognition.

  17. Multiscale modeling of biological functions: from enzymes to molecular machines (Nobel Lecture).

    Science.gov (United States)

    Warshel, Arieh

    2014-09-15

    A detailed understanding of the action of biological molecules is a pre-requisite for rational advances in health sciences and related fields. Here, the challenge is to move from available structural information to a clear understanding of the underlying function of the system. In light of the complexity of macromolecular complexes, it is essential to use computer simulations to describe how the molecular forces are related to a given function. However, using a full and reliable quantum mechanical representation of large molecular systems has been practically impossible. The solution to this (and related) problems has emerged from the realization that large systems can be spatially divided into a region where the quantum mechanical description is essential (e.g. a region where bonds are being broken), with the remainder of the system being represented on a simpler level by empirical force fields. This idea has been particularly effective in the development of the combined quantum mechanics/molecular mechanics (QM/MM) models. Here, the coupling between the electrostatic effects of the quantum and classical subsystems has been a key to the advances in describing the functions of enzymes and other biological molecules. The same idea of representing complex systems in different resolutions in both time and length scales has been found to be very useful in modeling the action of complex systems. In such cases, starting with coarse grained (CG) representations that were originally found to be very useful in simulating protein folding, and augmenting them with a focus on electrostatic energies, has led to models that are particularly effective in probing the action of molecular machines. The same multiscale idea is likely to play a major role in modeling of even more complex systems, including cells and collections of cells.

  18. Evaluation of biological activities of highly diluted nucleotide sequences by using cellular models

    Directory of Open Access Journals (Sweden)

    Pierre Dorfman

    2012-09-01

    Full Text Available Background: highly diluted specific nucleic acids (SNA®, designed to modulate viral and cytokine genes expression, are currently used in Micro-Immunotherapy to treat viral infections and immune disorders. Although some preliminary studies have showed clinical benefit of these homeopathic preparations [1], no experimental data are available to explain their mechanism of action. Aims: to investigate the in vitro effect of two sets of highly diluted (HD SNA targeting i latent/lytic Epstein-Barr virus (SNA EBV and ii TNF-α and its receptor p55 involved in rheumatoid arthritis (SNA RA on cellular models. Methodology: serial homeopathic dilutions of SNA EBV and SNA RA (15cH-18cH were tested on a EBV-positive B-lymphoblastoid (B95-8 and on a LPS-stimulated macrophage (THP1 cell lines respectively, in comparison with agitated/diluted water and scramble DNA sequences prepared in the same conditions (negative controls. For B95-8 proliferative model, high mobility group box 1 protein (HMGB1 was used as reference. Analyzed biological parameters on B95-8 were i cell proliferation measured after 24 and 48h of incubation with HD SNA and ii expression of the EBV ZEBRA protein in response to TGF-β by Western-blotting (T+24h. For THP1 model, TNF-α synthesis and release were determined by RT-qPCR and ELISA (protein, after stimulation by LPS (1µg/ml and HD SNA co-administration. Results: we demonstrated that HD SNA RA significantly down-regulated TNF-α synthesis and release. This biological activity was showed to be specific (no effect of HD scramble SNA and related to the level of dilution (maximal effect with higher dilutions. Unexpectedly, a biological effect of agitated/diluted water was also detected in both cellular models. For B95-8 model, this effect resulted in a significant decrease of B95-8 proliferation (comparable to the HMGB1 reference and an inhibition of ZEBRA expression. Similarly, a reproducible

  19. Models for estimating the metabolic syndrome biological age as the new index for evaluation and management of metabolic syndrome

    Science.gov (United States)

    Kang, Young Gon; Suh, Eunkyung; Chun, Hyejin; Kim, Sun-Hyun; Kim, Deog Ki; Bae, Chul-Young

    2017-01-01

    Purpose This study aims to propose a metabolic syndrome (MS) biological age model, through which overall evaluation and management of the health status and aging state in MS can be done easily. Through this model, we hope to provide a novel evaluation and management health index that can be utilized in various health care fields. Patient and methods MS parameters from American Heart Association/National Heart, Lung, and Blood Institute guidelines in 2005 were used as biomarkers for the estimation of MS biological age. MS biological age model development was done by analyzing data of 263,828 participants and clinical application of the developed MS biological age was assessed by analyzing the data of 188,886 subjects. Results The principal component accounted for 36.1% in male and 38.9% in female of the total variance in the battery of five variables. The correlation coefficient between corrected biological age and chronological age in males and females were 0.711 and 0.737, respectively. Significant difference for mean MS biological age and chronological age between the three groups, normal, at risk and MS, was seen (P<0.001). Conclusion For the comprehensive approach in MS management, MS biological age is expected to be additionally utilized as a novel evaluation and management index along with the traditional MS diagnosis. PMID:28203066

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

    Science.gov (United States)

    Mardinoglu, Adil; Gatto, Francesco; Nielsen, Jens

    2013-09-01

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

  1. Mouse Tumor Biology (MTB): a database of mouse models for human cancer.

    Science.gov (United States)

    Bult, Carol J; Krupke, Debra M; Begley, Dale A; Richardson, Joel E; Neuhauser, Steven B; Sundberg, John P; Eppig, Janan T

    2015-01-01

    The Mouse Tumor Biology (MTB; http://tumor.informatics.jax.org) database is a unique online compendium of mouse models for human cancer. MTB provides online access to expertly curated information on diverse mouse models for human cancer and interfaces for searching and visualizing data associated with these models. The information in MTB is designed to facilitate the selection of strains for cancer research and is a platform for mining data on tumor development and patterns of metastases. MTB curators acquire data through manual curation of peer-reviewed scientific literature and from direct submissions by researchers. Data in MTB are also obtained from other bioinformatics resources including PathBase, the Gene Expression Omnibus and ArrayExpress. Recent enhancements to MTB improve the association between mouse models and human genes commonly mutated in a variety of cancers as identified in large-scale cancer genomics studies, provide new interfaces for exploring regions of the mouse genome associated with cancer phenotypes and incorporate data and information related to Patient-Derived Xenograft models of human cancers.

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

    Directory of Open Access Journals (Sweden)

    Reifman Jaques

    2009-09-01

    Full Text Available Abstract Background Because metabolism is fundamental in sustaining microbial life, drugs that target pathogen-specific metabolic enzymes and pathways can be very effective. In particular, the metabolic challenges faced by intracellular pathogens, such as Mycobacterium tuberculosis, residing in the infected host provide novel opportunities for therapeutic intervention. Results We developed a mathematical framework to simulate the effects on the growth of a pathogen when enzymes in its metabolic pathways are inhibited. Combining detailed models of enzyme kinetics, a complete metabolic network description as modeled by flux balance analysis, and a dynamic cell population growth model, we quantitatively modeled and predicted the dose-response of the 3-nitropropionate inhibitor on the growth of M. tuberculosis in a medium whose carbon source was restricted to fatty acids, and that of the 5'-O-(N-salicylsulfamoyl adenosine inhibitor in a medium with low-iron concentration. Conclusion The predicted results quantitatively reproduced the experimentally measured dose-response curves, ranging over three orders of magnitude in inhibitor concentration. Thus, by allowing for detailed specifications of the underlying enzymatic kinetics, metabolic reactions/constraints, and growth media, our model captured the essential chemical and biological factors that determine the effects of drug inhibition on in vitro growth of M. tuberculosis cells.

  3. Antiproliferative Pt(IV) complexes: synthesis, biological activity, and quantitative structure-activity relationship modeling.

    Science.gov (United States)

    Gramatica, Paola; Papa, Ester; Luini, Mara; Monti, Elena; Gariboldi, Marzia B; Ravera, Mauro; Gabano, Elisabetta; Gaviglio, Luca; Osella, Domenico

    2010-09-01

    Several Pt(IV) complexes of the general formula [Pt(L)2(L')2(L'')2] [axial ligands L are Cl-, RCOO-, or OH-; equatorial ligands L' are two am(m)ine or one diamine; and equatorial ligands L'' are Cl- or glycolato] were rationally designed and synthesized in the attempt to develop a predictive quantitative structure-activity relationship (QSAR) model. Numerous theoretical molecular descriptors were used alongside physicochemical data (i.e., reduction peak potential, Ep, and partition coefficient, log Po/w) to obtain a validated QSAR between in vitro cytotoxicity (half maximal inhibitory concentrations, IC50, on A2780 ovarian and HCT116 colon carcinoma cell lines) and some features of Pt(IV) complexes. In the resulting best models, a lipophilic descriptor (log Po/w or the number of secondary sp3 carbon atoms) plus an electronic descriptor (Ep, the number of oxygen atoms, or the topological polar surface area expressed as the N,O polar contribution) is necessary for modeling, supporting the general finding that the biological behavior of Pt(IV) complexes can be rationalized on the basis of their cellular uptake, the Pt(IV)-->Pt(II) reduction, and the structure of the corresponding Pt(II) metabolites. Novel compounds were synthesized on the basis of their predicted cytotoxicity in the preliminary QSAR model, and were experimentally tested. A final QSAR model, based solely on theoretical molecular descriptors to ensure its general applicability, is proposed.

  4. A Biophysicochemical Model for NO Removal by the Chemical Absorption-Biological Reduction Integrated Process.

    Science.gov (United States)

    Zhao, Jingkai; Xia, Yinfeng; Li, Meifang; Li, Sujing; Li, Wei; Zhang, Shihan

    2016-08-16

    The chemical absorption-biological reduction (CABR) integrated process is regarded as a promising technology for NOx removal from flue gas. To advance the scale-up of the CABR process, a mathematic model based on mass transfer with reaction in the gas, liquid, and biofilm was developed to simulate and predict the NOx removal by the CABR system in a biotrickling filter. The developed model was validated by the experimental results and subsequently was used to predict the system performance under different operating conditions, such as NO and O2 concentration and gas and liquid flow rate. NO distribution in the gas phase along the biotrickling filter was also modeled and predicted. On the basis of the modeling results, the liquid flow rate and total iron concentration were optimized to achieve >90% NO removal efficiency. Furthermore, sensitivity analysis of the model revealed that the performance of the CABR process was controlled by the bioreduction activity of Fe(III)EDTA. This work will provide the guideline for the design and operation of the CABR process in the industrial application.

  5. Modeling marrow damage from response data: Morphallaxis from radiation biology to benzene toxicity

    Energy Technology Data Exchange (ETDEWEB)

    Jones, T.D.; Morris, M.D.; Hasan, J.S.

    1995-12-01

    Consensus principles from radiation biology were used to describe a generic set of nonlinear, first-order differential equations for modeling of toxicity-induced compensatory cell kinetics in terms of sublethal injury, repair, direct killing, killing of cells with unrepaired sublethal injury, and repopulation. This cellular model was linked to a probit model of hematopoietic mortality that describes death from infection and/or hemorrhage between {approximately} 5 and 30 days. Mortality data from 27 experiments with 851 doseresponse groups, in which doses were protracted by rate and/or fractionation, were used to simultaneously estimate all rate constants by maximum-likelihood methods. Data used represented 18,940 test animals distributed according to: (mice, 12,827); (rats, 2,925); (sheep, 1,676); (swine, 829); (dogs, 479); and (burros, 204). Although a long-term, repopulating hematopoietic stem cell is ancestral to all lineages needed to restore normal homeostasis, the dose-response data from the protracted irradiations indicate clearly that the particular lineage that is ``critical`` to hematopoietic recovery does not resemble stem-like cells with regard to radiosensitivity and repopulation rates. Instead, the weakest link in the chain of hematopoiesis was found to have an intrinsic radioresistance equal to or greater than stromal cells and to repopulate at the same rates. Model validation has been achieved by predicting the LD{sub 50} and/or fractional group mortality in 38 protracted-dose experiments (rats and mice) that were not used in the fitting of model coefficients.

  6. Analysis of undergraduate students' conceptual models of a complex biological system across a diverse body of learners

    Science.gov (United States)

    Dirnbeck, Matthew R.

    Biological systems pose a challenge both for learners and teachers because they are complex systems mediated by feedback loops; networks of cause-effect relationships; and non-linear, hierarchical, and emergent properties. Teachers and scientists routinely use models to communicate ideas about complex systems. Model-based pedagogies engage students in model construction as a means of practicing higher-order reasoning skills. One such modeling paradigm describes systems in terms of their structures, behaviors, and functions (SBF). The SBF framework is a simple modeling language that has been used to teach about complex biological systems. Here, we used student-generated SBF models to assess students' causal reasoning in the context of a novel biological problem on an exam. We compared students' performance on the modeling problem, their performance on a set of knowledge/comprehension questions, and their performance on a set of scientific reasoning questions. We found that students who performed well on knowledge and understanding questions also constructed more networked, higher quality models. Previous studies have shown that learners' mental maps increase in complexity with increased expertise. We wanted to investigate if biology students with varying levels of training in biology showed a similar pattern when constructing system models. In a pilot study, we administered the same modeling problem to two additional groups of students: 1) an animal physiology course for students pursuing a major in biology (n=37) and 2) an exercise physiology course for non-majors (n=27). We found that there was no significant difference in model organization across the three student populations, but there was a significant difference in the ability to represent function between the three populations. Between the three groups the non-majors had the lowest function scores, the introductory majors had the middle function scores, and the upper division majors had the highest function

  7. Dynamic neuronal ensembles: Issues in representing structure change in object-oriented, biologically-based brain models

    Energy Technology Data Exchange (ETDEWEB)

    Vahie, S.; Zeigler, B.P.; Cho, H. [Univ. of Arizona, Tucson, AZ (United States)

    1996-12-31

    This paper describes the structure of dynamic neuronal ensembles (DNEs). DNEs represent a new paradigm for learning, based on biological neural networks that use variable structures. We present a computational neural element that demonstrates biological neuron functionality such as neurotransmitter feedback absolute refractory period and multiple output potentials. More specifically, we will develop a network of neural elements that have the ability to dynamically strengthen, weaken, add and remove interconnections. We demonstrate that the DNE is capable of performing dynamic modifications to neuron connections and exhibiting biological neuron functionality. In addition to its applications for learning, DNEs provide an excellent environment for testing and analysis of biological neural systems. An example of habituation and hyper-sensitization in biological systems, using a neural circuit from a snail is presented and discussed. This paper provides an insight into the DNE paradigm using models developed and simulated in DEVS.

  8. Reconstructing Anaximander's biological model unveils a theory of evolution akin to Darwin's, though centuries before the birth of science.

    Science.gov (United States)

    Trevisanato, Siro Igino

    2016-08-01

    Anaximander's fragments on biology report a theory of evolution, which, unlike the development of other biological systems in the ancient Aegean, is naturalistic and is not based on metaphysics. According to Anaximander, evolution affected all living beings, including humans. The first biological systems formed in an aquatic environment, and were encased in a rugged and robust envelope. Evolution progressed with modifications that enabled the formation of more dynamic biological systems. For instance, after reaching land, the robust armors around aquatic beings dried up, and became brittle, This led to the loss of the armor and the development of more mobile life forms. Anaximander's theory combines observations of animals with speculations, and as such mirrors the more famous theory of evolution by Charles Darwin expressed 24 centuries later. The poor reception received by Anaximander's model in his time, illustrates a zeitgeist that would explain the contemporary lag phase in the development of biology and, as a result, medicine, in the ancient western world.

  9. The ASM-NSF Biology Scholars Program: An Evidence-Based Model for Faculty Development.

    Science.gov (United States)

    Chang, Amy L; Pribbenow, Christine M

    2016-05-01

    The American Society for Microbiology (ASM) established its ASM-NSF (National Science Foundation) Biology Scholars Program (BSP) to promote undergraduate education reform by 1) supporting biologists to implement evidence-based teaching practices, 2) engaging life science professional societies to facilitate biologists' leadership in scholarly teaching within the discipline, and 3) participating in a teaching community that fosters disciplinary-level science, technology, engineering, and mathematics (STEM) reform. Since 2005, the program has utilized year-long residency training to provide a continuum of learning and practice centered on principles from the scholarship of teaching and learning (SoTL) to more than 270 participants ("scholars") from biology and multiple other disciplines. Additionally, the program has recruited 11 life science professional societies to support faculty development in SoTL and discipline-based education research (DBER). To identify the BSP's long-term outcomes and impacts, ASM engaged an external evaluator to conduct a study of the program's 2010-2014 scholars (n = 127) and society partners. The study methods included online surveys, focus groups, participant observation, and analysis of various documents. Study participants indicate that the program achieved its proposed goals relative to scholarship, professional society impact, leadership, community, and faculty professional development. Although participants also identified barriers that hindered elements of their BSP participation, findings suggest that the program was essential to their development as faculty and provides evidence of the BSP as a model for other societies seeking to advance undergraduate science education reform. The BSP is the longest-standing faculty development program sponsored by a collective group of life science societies. This collaboration promotes success across a fragmented system of more than 80 societies representing the life sciences and helps

  10. Quantitative utilization of prior biological knowledge in the Bayesian network modeling of gene expression data

    Directory of Open Access Journals (Sweden)

    Gao Shouguo

    2011-08-01

    Full Text Available Abstract Background Bayesian Network (BN is a powerful approach to reconstructing genetic regulatory networks from gene expression data. However, expression data by itself suffers from high noise and lack of power. Incorporating prior biological knowledge can improve the performance. As each type of prior knowledge on its own may be incomplete or limited by quality issues, integrating multiple sources of prior knowledge to utilize their consensus is desirable. Results We introduce a new method to incorporate the quantitative information from multiple sources of prior knowledge. It first uses the Naïve Bayesian classifier to assess the likelihood of functional linkage between gene pairs based on prior knowledge. In this study we included cocitation in PubMed and schematic similarity in Gene Ontology annotation. A candidate network edge reservoir is then created in which the copy number of each edge is proportional to the estimated likelihood of linkage between the two corresponding genes. In network simulation the Markov Chain Monte Carlo sampling algorithm is adopted, and samples from this reservoir at each iteration to generate new candidate networks. We evaluated the new algorithm using both simulated and real gene expression data including that from a yeast cell cycle and a mouse pancreas development/growth study. Incorporating prior knowledge led to a ~2 fold increase in the number of known transcription regulations recovered, without significant change in false positive rate. In contrast, without the prior knowledge BN modeling is not always better than a random selection, demonstrating the necessity in network modeling to supplement the gene expression data with additional information. Conclusion our new development provides a statistical means to utilize the quantitative information in prior biological knowledge in the BN modeling of gene expression data, which significantly improves the performance.

  11. Model-based analysis of the role of biological, hydrological and geochemical factors affecting uranium bioremediation.

    Science.gov (United States)

    Zhao, Jiao; Scheibe, Timothy D; Mahadevan, R

    2011-07-01

    Uranium contamination is a serious concern at several sites motivating the development of novel treatment strategies such as the Geobacter-mediated reductive immobilization of uranium. However, this bioremediation strategy has not yet been optimized for the sustained uranium removal. While several reactive-transport models have been developed to represent Geobacter-mediated bioremediation of uranium, these models often lack the detailed quantitative description of the microbial process (e.g., biomass build-up in both groundwater and sediments, electron transport system, etc.) and the interaction between biogeochemical and hydrological process. In this study, a novel multi-scale model was developed by integrating our recent model on electron capacitance of Geobacter (Zhao et al., 2010) with a comprehensive simulator of coupled fluid flow, hydrologic transport, heat transfer, and biogeochemical reactions. This mechanistic reactive-transport model accurately reproduces the experimental data for the bioremediation of uranium with acetate amendment. We subsequently performed global sensitivity analysis with the reactive-transport model in order to identify the main sources of prediction uncertainty caused by synergistic effects of biological, geochemical, and hydrological processes. The proposed approach successfully captured significant contributing factors across time and space, thereby improving the structure and parameterization of the comprehensive reactive-transport model. The global sensitivity analysis also provides a potentially useful tool to evaluate uranium bioremediation strategy. The simulations suggest that under difficult environments (e.g., highly contaminated with U(VI) at a high migration rate of solutes), the efficiency of uranium removal can be improved by adding Geobacter species to the contaminated site (bioaugmentation) in conjunction with the addition of electron donor (biostimulation). The simulations also highlight the interactive effect of

  12. Biology-based modeling to analyze uranium toxicity data on Daphnia magna in a multigeneration study.

    Science.gov (United States)

    Massarin, Sandrine; Beaudouin, Remy; Zeman, Florence; Floriani, Magali; Gilbin, Rodolphe; Alonzo, Frederic; Pery, Alexandre R R

    2011-05-01

    Recent studies have investigated chronic toxicity of waterborne depleted uranium on the life cycle and physiology of Daphnia magna. In particular, a reduction in food assimilation was observed. Our aims here were to examine whether this reduction could fully account for observed effects on both growth and reproduction, for three successive generations, and to investigate through microscope analyses whether this reduction resulted from direct damage to the intestinal epithelium. We analyzed data obtained by exposing Daphnia magna to uranium over three successive generations. We used energy-based models, which are both able to fit simultaneously growth and reproduction and are biologically relevant. Two possible modes of action were compared - decrease in food assimilation rate and increase in maintenance costs. In our models, effects were related either to internal concentration or to exposure concentration. The model that fitted the data best represented a decrease in food assimilation related to exposure concentration. Furthermore, observations of consequent histological damage to the intestinal epithelium, together with uranium precipitates in the epithelial cells, supported the assumption that uranium has direct effects on the digestive tract. We were able to model the data in all generations and showed that sensitivity increased from one generation to the next, in particular through a significant increase of the intensity of effect, once the threshold for appearance of effects was exceeded.

  13. An investigation of a nonlocal hyperbolic model for self-organization of biological groups.

    Science.gov (United States)

    Fetecau, Razvan C; Eftimie, Raluca

    2010-10-01

    In this article, we introduce and study a new nonlocal hyperbolic model for the formation and movement of animal aggregations. We assume that the nonlocal attractive, repulsive, and alignment interactions between individuals can influence both the speed and the turning rates of group members. We use analytical and numerical techniques to investigate the effect of these nonlocal interactions on the long-time behavior of the patterns exhibited by the model. We establish the local existence and uniqueness and show that the nonlinear hyperbolic system does not develop shock solutions (gradient blow-up). Depending on the relative magnitudes of attraction and repulsion, we show that the solutions of the model either exist globally in time or may exhibit finite-time amplitude blow-up. We illustrate numerically the various patterns displayed by the model: dispersive aggregations, finite-size groups and blow-up patterns, the latter corresponding to aggregations which may collapse to a point. The transition from finite-size to blow-up patterns is governed by the magnitude of the social interactions and the random turning rates. The presence of these types of patterns and the absence of shocks are consequences of the biologically relevant assumptions regarding the form of the speed and the turning rate functions, as well as of the kernels describing the social interactions.

  14. Biologically Inspired Model for Inference of 3D Shape from Texture.

    Science.gov (United States)

    Gomez, Olman; Neumann, Heiko

    2016-01-01

    A biologically inspired model architecture for inferring 3D shape from texture is proposed. The model is hierarchically organized into modules roughly corresponding to visual cortical areas in the ventral stream. Initial orientation selective filtering decomposes the input into low-level orientation and spatial frequency representations. Grouping of spatially anisotropic orientation responses builds sketch-like representations of surface shape. Gradients in orientation fields and subsequent integration infers local surface geometry and globally consistent 3D depth. From the distributions in orientation responses summed in frequency, an estimate of the tilt and slant of the local surface can be obtained. The model suggests how 3D shape can be inferred from texture patterns and their image appearance in a hierarchically organized processing cascade along the cortical ventral stream. The proposed model integrates oriented texture gradient information that is encoded in distributed maps of orientation-frequency representations. The texture energy gradient information is defined by changes in the grouped summed normalized orientation-frequency response activity extracted from the textured object image. This activity is integrated by directed fields to generate a 3D shape representation of a complex object with depth ordering proportional to the fields output, with higher activity denoting larger distance in relative depth away from the viewer.

  15. From Levy to Brownian: a computational model based on biological fluctuation.

    Directory of Open Access Journals (Sweden)

    Surya G Nurzaman

    Full Text Available BACKGROUND: Theoretical studies predict that Lévy walks maximizes the chance of encountering randomly distributed targets with a low density, but Brownian walks is favorable inside a patch of targets with high density. Recently, experimental data reports that some animals indeed show a Lévy and Brownian walk movement patterns when forage for foods in areas with low and high density. This paper presents a simple, Gaussian-noise utilizing computational model that can realize such behavior. METHODOLOGY/PRINCIPAL FINDINGS: We extend Lévy walks model of one of the simplest creature, Escherichia coli, based on biological fluctuation framework. We build a simulation of a simple, generic animal to observe whether Lévy or Brownian walks will be performed properly depends on the target density, and investigate the emergent behavior in a commonly faced patchy environment where the density alternates. CONCLUSIONS/SIGNIFICANCE: Based on the model, animal behavior of choosing Lévy or Brownian walk movement patterns based on the target density is able to be generated, without changing the essence of the stochastic property in Escherichia coli physiological mechanism as explained by related researches. The emergent behavior and its benefits in a patchy environment are also discussed. The model provides a framework for further investigation on the role of internal noise in realizing adaptive and efficient foraging behavior.

  16. A new fluid dynamics model to evaluate the contractile force of a biological spring, Vorticella convallaria

    Science.gov (United States)

    Ryu, Sangjin; Matsudaira, Paul

    2008-11-01

    Vorticella convallaria, a sessile peritrich having a body and spring-like stalk, is a model for a bioinspired actuator because of its remarkably fast (msec) and powerful contractions (nN). An example of a biological spring, the stalk converts biochemical energy to physical motion, but the mechanics of contraction are poorly understood. To evaluate contraction force, past models have assumed the body to be a sphere moving in quiescent water and have equated contraction force to drag force on the body described by Stokes' law. However, flow induced by contracting Vorticella does not satisfy conditions of Stokes' law because the flow is unsteady (Womersley number > 1) and bound with a solid substrate to which the cell is tethered. We develop a more rigorous model for contraction force evaluation by assuming the body to be a sphere unsteadily moving perpendicularly toward a solid surface. The model comprises quasi-steady drag force, added mass force and history force with wall effect correction terms for each force. Vorticella not only generates a maximum contraction force greater than Stokes' drag, but it also experiences drag force in the direction of contraction in the later stage of contraction due to the memory effect of water.

  17. The Use of Mouse Models for Understanding the Biology of Down Syndrome and Aging

    Directory of Open Access Journals (Sweden)

    Guido N. Vacano

    2012-01-01

    Full Text Available Down syndrome is a complex condition caused by trisomy of human chromosome 21. The biology of aging may be different in individuals with Down syndrome; this is not well understood in any organism. Because of its complexity, many aspects of Down syndrome must be studied either in humans or in animal models. Studies in humans are essential but are limited for ethical and practical reasons. Fortunately, genetically altered mice can serve as extremely useful models of Down syndrome, and progress in their production and analysis has been remarkable. Here, we describe various mouse models that have been used to study Down syndrome. We focus on segmental trisomies of mouse chromosome regions syntenic to human chromosome 21, mice in which individual genes have been introduced, or mice in which genes have been silenced by targeted mutagenesis. We selected a limited number of genes for which considerable evidence links them to aspects of Down syndrome, and about which much is known regarding their function. We focused on genes important for brain and cognitive function, and for the altered cancer spectrum seen in individuals with Down syndrome. We conclude with observations on the usefulness of mouse models and speculation on future directions.

  18. The use of mouse models for understanding the biology of down syndrome and aging.

    Science.gov (United States)

    Vacano, Guido N; Duval, Nathan; Patterson, David

    2012-01-01

    Down syndrome is a complex condition caused by trisomy of human chromosome 21. The biology of aging may be different in individuals with Down syndrome; this is not well understood in any organism. Because of its complexity, many aspects of Down syndrome must be studied either in humans or in animal models. Studies in humans are essential but are limited for ethical and practical reasons. Fortunately, genetically altered mice can serve as extremely useful models of Down syndrome, and progress in their production and analysis has been remarkable. Here, we describe various mouse models that have been used to study Down syndrome. We focus on segmental trisomies of mouse chromosome regions syntenic to human chromosome 21, mice in which individual genes have been introduced, or mice in which genes have been silenced by targeted mutagenesis. We selected a limited number of genes for which considerable evidence links them to aspects of Down syndrome, and about which much is known regarding their function. We focused on genes important for brain and cognitive function, and for the altered cancer spectrum seen in individuals with Down syndrome. We conclude with observations on the usefulness of mouse models and speculation on future directions.

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

    Science.gov (United States)

    Bakker, Barbara M; van Eunen, Karen; Jeneson, Jeroen A L; van Riel, Natal A W; Bruggeman, Frank J; Teusink, Bas

    2010-10-01

    Human metabolic diseases are typically network diseases. This holds not only for multifactorial diseases, such as metabolic syndrome or Type 2 diabetes, but even when a single gene defect is the primary cause, where the adaptive response of the entire network determines the severity of disease. The latter may differ between individuals carrying the same mutation. Understanding the adaptive responses of human metabolism naturally requires a systems biology approach. Modelling of metabolic pathways in micro-organisms and some mammalian tissues has yielded many insights, qualitative as well as quantitative, into their control and regulation. Yet, even for a well-known pathway such as glycolysis, precise predictions of metabolite dynamics from experimentally determined enzyme kinetics have been only moderately successful. In the present review, we compare kinetic models of glycolysis in three cell types (African trypanosomes, yeast and skeletal muscle), evaluate their predictive power and identify limitations in our understanding. Although each of these models has its own merits and shortcomings, they also share common features. For example, in each case independently measured enzyme kinetic parameters were used as input. Based on these 'lessons from glycolysis', we will discuss how to make best use of kinetic computer models to advance our understanding of human metabolic diseases.

  20. High-Resolution Gene Flow Model for Assessing Environmental Impacts of Transgene Escape Based on Biological Parameters and Wind Speed.

    Science.gov (United States)

    Wang, Lei; Haccou, Patsy; Lu, Bao-Rong

    2016-01-01

    Environmental impacts caused by transgene flow from genetically engineered (GE) crops to their wild relatives mediated by pollination are longstanding biosafety concerns worldwide. Mathematical modeling provides a useful tool for estimating frequencies of pollen-mediated gene flow (PMGF) that are critical for assessing such environmental impacts. However, most PMGF models are impractical for this purpose because their parameterization requires actual data from field experiments. In addition, most of these models are usually too general and ignored the important biological characteristics of concerned plant species; and therefore cannot provide accurate prediction for PMGF frequencies. It is necessary to develop more accurate PMGF models based on biological and climatic parameters that can be easily measured in situ. Here, we present a quasi-mechanistic PMGF model that only requires the input of biological and wind speed parameters without actual data from field experiments. Validation of the quasi-mechanistic model based on five sets of published data from field experiments showed significant correlations between the model-simulated and field experimental-generated PMGF frequencies. These results suggest accurate prediction for PMGF frequencies using this model, provided that the necessary biological parameters and wind speed data are available. This model can largely facilitate the assessment and management of environmental impacts caused by transgene flow, such as determining transgene flow frequencies at a particular spatial distance, and establishing spatial isolation between a GE crop and its coexisting non-GE counterparts and wild relatives.

  1. Chemical Structure-Biological Activity Models for Pharmacophores’ 3D-Interactions

    Directory of Open Access Journals (Sweden)

    Mihai V. Putz

    2016-07-01

    Full Text Available Within medicinal chemistry nowadays, the so-called pharmaco-dynamics seeks for qualitative (for understanding and quantitative (for predicting mechanisms/models by which given chemical structure or series of congeners actively act on biological sites either by focused interaction/therapy or by diffuse/hazardous influence. To this aim, the present review exposes three of the fertile directions in approaching the biological activity by chemical structural causes: the special computing trace of the algebraic structure-activity relationship (SPECTRAL-SAR offering the full analytical counterpart for multi-variate computational regression, the minimal topological difference (MTD as the revived precursor for comparative molecular field analyses (CoMFA and comparative molecular similarity indices analysis (CoMSIA; all of these methods and algorithms were presented, discussed and exemplified on relevant chemical medicinal systems as proton pump inhibitors belonging to the 4-indolyl,2-guanidinothiazole class of derivatives blocking the acid secretion from parietal cells in the stomach, the 1-[(2-hydroxyethoxy-methyl]-6-(phenylthiothymine congeners’ (HEPT ligands antiviral activity against Human Immunodeficiency Virus of first type (HIV-1 and new pharmacophores in treating severe genetic disorders (like depression and psychosis, respectively, all involving 3D pharmacophore interactions.

  2. Chemical Structure-Biological Activity Models for Pharmacophores’ 3D-Interactions

    Science.gov (United States)

    Putz, Mihai V.; Duda-Seiman, Corina; Duda-Seiman, Daniel; Putz, Ana-Maria; Alexandrescu, Iulia; Mernea, Maria; Avram, Speranta

    2016-01-01

    Within medicinal chemistry nowadays, the so-called pharmaco-dynamics seeks for qualitative (for understanding) and quantitative (for predicting) mechanisms/models by which given chemical structure or series of congeners actively act on biological sites either by focused interaction/therapy or by diffuse/hazardous influence. To this aim, the present review exposes three of the fertile directions in approaching the biological activity by chemical structural causes: the special computing trace of the algebraic structure-activity relationship (SPECTRAL-SAR) offering the full analytical counterpart for multi-variate computational regression, the minimal topological difference (MTD) as the revived precursor for comparative molecular field analyses (CoMFA) and comparative molecular similarity indices analysis (CoMSIA); all of these methods and algorithms were presented, discussed and exemplified on relevant chemical medicinal systems as proton pump inhibitors belonging to the 4-indolyl,2-guanidinothiazole class of derivatives blocking the acid secretion from parietal cells in the stomach, the 1-[(2-hydroxyethoxy)-methyl]-6-(phenylthio)thymine congeners’ (HEPT ligands) antiviral activity against Human Immunodeficiency Virus of first type (HIV-1) and new pharmacophores in treating severe genetic disorders (like depression and psychosis), respectively, all involving 3D pharmacophore interactions. PMID:27399692

  3. Towards the virtual artery: a multiscale model for vascular physiology at the physics-chemistry-biology interface

    Science.gov (United States)

    Hoekstra, Alfons G.; Alowayyed, Saad; Lorenz, Eric; Melnikova, Natalia; Mountrakis, Lampros; van Rooij, Britt; Svitenkov, Andrew; Závodszky, Gábor; Zun, Pavel

    2016-11-01

    This discussion paper introduces the concept of the Virtual Artery as a multiscale model for arterial physiology and pathologies at the physics-chemistry-biology (PCB) interface. The cellular level is identified as the mesoscopic level, and we argue that by coupling cell-based models with other relevant models on the macro- and microscale, a versatile model of arterial health and disease can be composed. We review the necessary ingredients, both models of arteries at many different scales, as well as generic methods to compose multiscale models. Next, we discuss how this can be combined into the virtual artery. Finally, we argue that the concept of models at the PCB interface could or perhaps should become a powerful paradigm, not only as in our case for studying physiology, but also for many other systems that have such PCB interfaces. This article is part of the themed issue 'Multiscale modelling at the physics-chemistry-biology interface'.

  4. Mathematical model of biological order state or syndrome in traditional Chinese medicine: based on electromagnetic radiation within the human body.

    Science.gov (United States)

    Han, Jinxiang; Huang, Jinzhao

    2012-03-01

    In this study, based on the resonator model and exciplex model of electromagnetic radiation within the human body, mathematical model of biological order state, also referred to as syndrome in traditional Chinese medicine, was established and expressed as: "Sy = v/ 1n(6I + 1)". This model provides the theoretical foundation for experimental research addressing the order state of living system, especially the quantitative research syndrome in traditional Chinese medicine.

  5. Proceedings First Workshop on Applications of Membrane computing, Concurrency and Agent-based modelling in POPulation biology

    CERN Document Server

    Milazzo, Paolo; 10.4204/EPTCS.33

    2010-01-01

    This volume contains the papers presented at the first International Workshop on Applications of Membrane Computing, Concurrency and Agent-based Modelling in Population Biology (AMCA-POP 2010) held in Jena, Germany on August 25th, 2010 as a satellite event of the 11th Conference on Membrane Computing (CMC11). The aim of the workshop is to investigate whether formal modelling and analysis techniques could be applied with profit to systems of interest for population biology and ecology. The considered modelling notations include membrane systems, Petri nets, agent-based notations, process calculi, automata-based notations, rewriting systems and cellular automata. Such notations enable the application of analysis techniques such as simulation, model checking, abstract interpretation and type systems to study systems of interest in disciplines such as population biology, ecosystem science, epidemiology, genetics, sustainability science, evolution and other disciplines in which population dynamics and interactions...

  6. Quantitative Modeling of Membrane Transport and Anisogamy by Small Groups Within a Large-Enrollment Organismal Biology Course

    Directory of Open Access Journals (Sweden)

    Eric S. Haag

    2016-12-01

    Full Text Available Quantitative modeling is not a standard part of undergraduate biology education, yet is routine in the physical sciences. Because of the obvious biophysical aspects, classes in anatomy and physiology offer an opportunity to introduce modeling approaches to the introductory curriculum. Here, we describe two in-class exercises for small groups working within a large-enrollment introductory course in organismal biology. Both build and derive biological insights from quantitative models, implemented using spreadsheets. One exercise models the evolution of anisogamy (i.e., small sperm and large eggs from an initial state of isogamy. Groups of four students work on Excel spreadsheets (from one to four laptops per group. The other exercise uses an online simulator to generate data related to membrane transport of a solute, and a cloud-based spreadsheet to analyze them. We provide tips for implementing these exercises gleaned from two years of experience.

  7. SU-E-T-549: Modeling Relative Biological Effectiveness of Protons for Radiation Induced Brain Necrosis

    Energy Technology Data Exchange (ETDEWEB)

    Mirkovic, D; Peeler, C; Grosshans, D; Titt, U; Taleei, R; Mohan, R [UT M.D. Anderson Cancer Center, Houston, TX (United States)

    2015-06-15

    Purpose: To develop a model of the relative biological effectiveness (RBE) of protons as a function of dose and linear energy transfer (LET) for induction of brain necrosis using clinical data. Methods: In this study, treatment planning information was exported from a clinical treatment planning system (TPS) and used to construct a detailed Monte Carlo model of the patient and the beam delivery system. The physical proton dose and LET were computed in each voxel of the patient volume using Monte Carlo particle transport. A follow-up magnetic resonance imaging (MRI) study registered to the treatment planning CT was used to determine the region of the necrosis in the brain volume. Both, the whole brain and the necrosis volumes were segmented from the computed tomography (CT) dataset using the contours drawn by a physician and the corresponding voxels were binned with respect to dose and LET. The brain necrosis probability was computed as a function of dose and LET by dividing the total volume of all necrosis voxels with a given dose and LET with the corresponding total brain volume resulting in a set of NTCP-like curves (probability as a function of dose parameterized by LET). Results: The resulting model shows dependence on both dose and LET indicating the weakness of the constant RBE model for describing the brain toxicity. To the best of our knowledge the constant RBE model is currently used in all clinical applications which may Result in increased rate of brain toxicities in patients treated with protons. Conclusion: Further studies are needed to develop more accurate brain toxicity models for patients treated with protons and other heavy ions.

  8. Mathematical modeling of cancer cell invasion of tissue: biological insight from mathematical analysis and computational simulation.

    Science.gov (United States)

    Andasari, Vivi; Gerisch, Alf; Lolas, Georgios; South, Andrew P; Chaplain, Mark A J

    2011-07-01

    The ability of cancer cells to break out of tissue compartments and invade locally gives solid tumours a defining deadly characteristic. One of the first steps of invasion is the remodelling of the surrounding tissue or extracellular matrix (ECM) and a major part of this process is the over-expression of proteolytic enzymes, such as the urokinase-type plasminogen activator (uPA) and matrix metalloproteinases (MMPs), by the cancer cells to break down ECM proteins. Degradation of the matrix enables the cancer cells to migrate through the tissue and subsequently to spread to secondary sites in the body, a process known as metastasis. In this paper we undertake an analysis of a mathematical model of cancer cell invasion of tissue, or ECM, which focuses on the role of the urokinase plasminogen activation system. The model consists of a system of five reaction-diffusion-taxis partial differential equations describing the interactions between cancer cells, uPA, uPA inhibitors, plasmin and the host tissue. Cancer cells react chemotactically and haptotactically to the spatio-temporal effects of the uPA system. The results obtained from computational simulations carried out on the model equations produce dynamic heterogeneous spatio-temporal solutions and using linear stability analysis we show that this is caused by a taxis-driven instability of a spatially homogeneous steady-state. Finally we consider the biological implications of the model results, draw parallels with clinical samples and laboratory based models of cancer cell invasion using three-dimensional invasion assay, and go on to discuss future development of the model.

  9. Affects as central organising and integrating factors. A new psychosocial/biological model of the psyche.

    Science.gov (United States)

    Ciompi, L

    1991-07-01

    A new psychosocial/biological model of the psyche is proposed, in which the affects play a central role in organising and integrating cognition. The psyche is understood here as a complex hierarchical structure of affective/cognitive systems of reference (or 'programmes for feeling, thinking, and behaviour'), generated by repetitive concrete action. These systems store past experience in their structure, and provide the functional basis for further cognition and communication. Affects endow these programmes with a specific qualitative value (such as motivation), connect cognitive elements synchronically and diachronically, and contribute to their storage and mobilisation according to context. They also participate in differentiating cognitive systems at higher levels of abstraction. These assumptions are supported by recent findings on the role of the limbic and hypothalamic system for the regulation of emotion, on neuronal plasticity, and on the phenomenon of state-dependent learning and memory. Refutable hypotheses are formulated for further research on the interaction of emotion and cognition.

  10. A Polydisperse Sphere Model Describing the Propagation of Light in Biological Tissue

    Institute of Scientific and Technical Information of China (English)

    WANG Qing-Hua; LI Zhen-Hua; LAI Jian-Cheng; HE An-Zhi

    2007-01-01

    A polydisperse sphere model with the complex refractive index is employed to describe the propagation of light in biological tissue.The scattering coefficient,absorption coefficient and scattering phase function are calculated.At the same time,the inverse problem on retrieving the particles size distribution,imaginary part of the refractive index and number density of scatterers is investigated.The result shows that the retrieval scheme together with the Chahine algorithm is effective in dealing with such an inverse problem.IT is also clarified that a group of parameters including the scattering coefficient,absorption coefficient and phase function are associated with another group including the refractive index,particle size distribution and number density of scatterers,which is a problem described in two different ways and the anisotropy factor is not an independent variable,but is determined by the phase function.

  11. Punctuated equilibrium and shock waves in molecular models of biological evolution.

    Science.gov (United States)

    Saakian, David B; Ghazaryan, Makar H; Hu, Chin-Kun

    2014-08-01

    We consider the dynamics in infinite population evolution models with a general symmetric fitness landscape. We find shock waves, i.e., discontinuous transitions in the mean fitness, in evolution dynamics even with smooth fitness landscapes, which means that the search for the optimal evolution trajectory is more complicated. These shock waves appear in the case of positive epistasis and can be used to represent punctuated equilibria in biological evolution during long geological time scales. We find exact analytical solutions for discontinuous dynamics at the large-genome-length limit and derive optimal mutation rates for a fixed fitness landscape to send the population from the initial configuration to some final configuration in the fastest way.

  12. [Experimental models in oncology: contribution of cell culture on understanding the biology of cancer].

    Science.gov (United States)

    Cruz, Mariana; Enes, Margarida; Pereira, Marta; Dourado, Marília; Sarmento Ribeiro, Ana Bela

    2009-01-01

    In the beginning of the 20th century, tissue culture was started with the aim of studying the behaviour of animal cells in normal and stress conditions. The cell study at molecular level depends on their capacity of growing and how they can be manipulated in laboratory. In vitro cell culture allows us the possibility of studying biological key processes, such as growth, differentiation and cell death, and also to do genetic manipulations essential to the knowledge of structure and genes function. Human stem cells culture provides strategies to circumvent other models' deficiencies. It seems that cancer stem cells remain quiescent until activation by appropriated micro-environmental stimulation. Several studies reveal that different cancer types could be due to stem cell malignant transformations. Removal of these cells is essential to the development of more effective cancer therapies for advanced disease. On the other hand, dendritic cells modified in culture may be used as a therapeutic vaccine in order to induce tumour withdraw.

  13. Conceptual Model-based Systems Biology: mapping knowledge and discovering gaps in the mRNA transcription cycle.

    Directory of Open Access Journals (Sweden)

    Judith Somekh

    2012-12-01

    Full Text Available We propose a Conceptual Model-based Systems Biology framework for qualitative modeling, executing, and eliciting knowledge gaps in molecular biology systems. The framework is an adaptation of Object-Process Methodology (OPM, a graphical and textual executable modeling language. OPM enables concurrent representation of the system's structure-the objects that comprise the system, and behavior-how processes transform objects over time. Applying a top-down approach of recursively zooming into processes, we model a case in point-the mRNA transcription cycle. Starting with this high level cell function, we model increasingly detailed processes along with participating objects. Our modeling approach is capable of modeling molecular processes such as complex formation, localization and trafficking, molecular binding, enzymatic stimulation, and environmental intervention. At the lowest level, similar to the Gene Ontology, all biological processes boil down to three basic molecular functions: catalysis, binding/dissociation, and transporting. During modeling and execution of the mRNA transcription model, we discovered knowledge gaps, which we present and classify into various types. We also show how model execution enhances a coherent model construction. Identification and pinpointing knowledge gaps is an important feature of the framework, as it suggests where research should focus and whether conjectures about uncertain mechanisms fit into the already verified model.

  14. Conceptual Model-based Systems Biology: mapping knowledge and discovering gaps in the mRNA transcription cycle.

    Science.gov (United States)

    Somekh, Judith; Choder, Mordechai; Dori, Dov

    2012-12-20

    We propose a Conceptual Model-based Systems Biology framework for qualitative modeling, executing, and eliciting knowledge gaps in molecular biology systems. The framework is an adaptation of Object-Process Methodology (OPM), a graphical and textual executable modeling language. OPM enables concurrent representation of the system's structure-the objects that comprise the system, and behavior-how processes transform objects over time. Applying a top-down approach of recursively zooming into processes, we model a case in point-the mRNA transcription cycle. Starting with this high level cell function, we model increasingly detailed processes along with participating objects. Our modeling approach is capable of modeling molecular processes such as complex formation, localization and trafficking, molecular binding, enzymatic stimulation, and environmental intervention. At the lowest level, similar to the Gene Ontology, all biological processes boil down to three basic molecular functions: catalysis, binding/dissociation, and transporting. During modeling and execution of the mRNA transcription model, we discovered knowledge gaps, which we present and classify into various types. We also show how model execution enhances a coherent model construction. Identification and pinpointing knowledge gaps is an important feature of the framework, as it suggests where research should focus and whether conjectures about uncertain mechanisms fit into the already verified model.

  15. Biological effects of desert dust in respiratory epithelial cells and a murine model

    Science.gov (United States)

    Ghio, Andrew J.; Kummarapurugu, Suryanaren T.; Tong, Haiyan; Soukup, Joleen M.; Dailey, Lisa A.; Boykin, Elizabeth; Gilmour, M. Ian; Ingram, Peter; Roggli, Victor L.; Goldstein, Harland L.; Reynolds, Richard L.

    2014-01-01

    As a result of the challenge of recent dust storms to public health, we tested the postulate that desert dust collected in the southwestern United States imparts a biological effect in respiratory epithelial cells and an animal model. Two samples of surface sediment were collected from separate dust sources in northeastern Arizona. Analysis of the PM20 fraction demonstrated that the majority of both dust samples were quartz and clay minerals (total SiO2 of 52 and 57%). Using respiratory epithelial and monocytic cell lines, the two desert dusts increased oxidant generation, measured by Amplex Red fluorescence, along with carbon black (a control particle), silica, and NIST 1649 (an ambient air pollution particle). Cell oxidant generation was greatest following exposures to silica and the desert dusts. Similarly, changes in RNA for superoxide dismutase-1, heme oxygenase-1, and cyclooxygenase-2 were also greatest after silica and the desert dusts supporting an oxidative stress after cell exposure. Silica, desert dusts, and the ambient air pollution particle NIST 1649 demonstrated a capacity to activate the p38 and ERK1/2 pathways and release pro-inflammatory mediators. Mice, instilled with the same particles, showed the greatest lavage concentrations of pro-inflammatory mediators, neutrophils, and lung injury following silica and desert dusts. We conclude that, comparable to other particles, desert dusts have a capacity to (1) influence oxidative stress and release of pro-inflammatory mediators in respiratory epithelial cells and (2) provoke an inflammatory injury in the lower respiratory tract of an animal model. The biological effects of desert dusts approximated those of silica.

  16. BClass: A Bayesian Approach Based on Mixture Models for Clustering and Classification of Heterogeneous Biological Data

    Directory of Open Access Journals (Sweden)

    Arturo Medrano-Soto

    2004-12-01

    Full Text Available Based on mixture models, we present a Bayesian method (called BClass to classify biological entities (e.g. genes when variables of quite heterogeneous nature are analyzed. Various statistical distributions are used to model the continuous/categorical data commonly produced by genetic experiments and large-scale genomic projects. We calculate the posterior probability of each entry to belong to each element (group in the mixture. In this way, an original set of heterogeneous variables is transformed into a set of purely homogeneous characteristics represented by the probabilities of each entry to belong to the groups. The number of groups in the analysis is controlled dynamically by rendering the groups as 'alive' and 'dormant' depending upon the number of entities classified within them. Using standard Metropolis-Hastings and Gibbs sampling algorithms, we constructed a sampler to approximate posterior moments and grouping probabilities. Since this method does not require the definition of similarity measures, it is especially suitable for data mining and knowledge discovery in biological databases. We applied BClass to classify genes in RegulonDB, a database specialized in information about the transcriptional regulation of gene expression in the bacterium Escherichia coli. The classification obtained is consistent with current knowledge and allowed prediction of missing values for a number of genes. BClass is object-oriented and fully programmed in Lisp-Stat. The output grouping probabilities are analyzed and interpreted using graphical (dynamically linked plots and query-based approaches. We discuss the advantages of using Lisp-Stat as a programming language as well as the problems we faced when the data volume increased exponentially due to the ever-growing number of genomic projects.

  17. {sup 1}H NMR-based metabolic profiling reveals inherent biological variation in yeast and nematode model systems

    Energy Technology Data Exchange (ETDEWEB)

    Szeto, Samuel S. W.; Reinke, Stacey N.; Lemire, Bernard D., E-mail: bernard.lemire@ualberta.ca [University of Alberta, Department of Biochemistry, School of Molecular and Systems Medicine (Canada)

    2011-04-15

    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 {sup 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.

  18. Phenotypic evolutionary models in stem cell biology: replacement, quiescence, and variability.

    Directory of Open Access Journals (Sweden)

    Marc Mangel

    Full Text Available Phenotypic evolutionary models have been used with great success in many areas of biology, but thus far have not been applied to the study of stem cells except for investigations of cancer. We develop a framework that allows such modeling techniques to be applied to stem cells more generally. The fundamental modeling structure is the stochastic kinetics of stem cells in their niche and of transit amplifying and fully differentiated cells elsewhere in the organism, with positive and negative feedback. This formulation allows graded signals to be turned into all or nothing responses, and shows the importance of looking beyond the niche for understanding how stem cells behave. Using the deterministic version of this framework, we show how competition between different stem cell lines can be analyzed, and under what circumstances stem cells in a niche will be replaced by other stem cells with different phenotypic characteristics. Using the stochastic version of our framework and state dependent life history theory, we show that the optimal behavior of a focal stem cell will involve long periods of quiescence and that a population of identical stem cells will show great variability in the times at which activity occurs; we compare our results with classic ones on quiescence and variability in the hematopoietic system.

  19. Coulomb blockade model of permeation and selectivity in biological ion channels

    Science.gov (United States)

    Kaufman, I. Kh; McClintock, P. V. E.; Eisenberg, R. S.

    2015-08-01

    Biological ion channels are protein nanotubes embedded in, and passing through, the bilipid membranes of cells. Physiologically, they are of crucial importance in that they allow ions to pass into and out of cells, fast and efficiently, though in a highly selective way. Here we show that the conduction and selectivity of calcium/sodium ion channels can be described in terms of ionic Coulomb blockade in a simplified electrostatic and Brownian dynamics model of the channel. The Coulomb blockade phenomenon arises from the discreteness of electrical charge, the strong electrostatic interaction, and an electrostatic exclusion principle. The model predicts a periodic pattern of Ca2+ conduction versus the fixed charge Qf at the selectivity filter (conduction bands) with a period equal to the ionic charge. It thus provides provisional explanations of some observed and modelled conduction and valence selectivity phenomena, including the anomalous mole fraction effect and the calcium conduction bands. Ionic Coulomb blockade and resonant conduction are similar to electronic Coulomb blockade and resonant tunnelling in quantum dots. The same considerations may also be applicable to other kinds of channel, as well as to charged artificial nanopores.

  20. AMIGO2, a toolbox for dynamic modeling, optimization and control in systems biology

    Science.gov (United States)

    Balsa-Canto, Eva; Henriques, David; Gábor, Attila; Banga, Julio R.

    2016-01-01

    Motivation: Many problems of interest in dynamic modeling and control of biological systems can be posed as non-linear optimization problems subject to algebraic and dynamic constraints. In the context of modeling, this is the case of, e.g. parameter estimation, optimal experimental design and dynamic flux balance analysis. In the context of control, model-based metabolic engineering or drug dose optimization problems can be formulated as (multi-objective) optimal control problems. Finding a solution to those problems is a very challenging task which requires advanced numerical methods. Results: This work presents the AMIGO2 toolbox: the first multiplatform software tool that automatizes the solution of all those problems, offering a suite of state-of-the-art (multi-objective) global optimizers and advanced simulation approaches. Availability and Implementation: The toolbox and its documentation are available at: sites.google.com/site/amigo2toolbox. Contact: ebalsa@iim.csic.es Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27378288

  1. Modeling systems-level dynamics: Understanding without mechanistic explanation in integrative systems biology.

    Science.gov (United States)

    MacLeod, Miles; Nersessian, Nancy J

    2015-02-01

    In this paper we draw upon rich ethnographic data of two systems biology labs to explore the roles of explanation and understanding in large-scale systems modeling. We illustrate practices that depart from the goal of dynamic mechanistic explanation for the sake of more limited modeling goals. These processes use abstract mathematical formulations of bio-molecular interactions and data fitting techniques which we call top-down abstraction to trade away accurate mechanistic accounts of large-scale systems for specific information about aspects of those systems. We characterize these practices as pragmatic responses to the constraints many modelers of large-scale systems face, which in turn generate more limited pragmatic non-mechanistic forms of understanding of systems. These forms aim at knowledge of how to predict system responses in order to manipulate and control some aspects of them. We propose that this analysis of understanding provides a way to interpret what many systems biologists are aiming for in practice when they talk about the objective of a "systems-level understanding."

  2. The application of Biological-Hydraulic coupled model for Tubificidae-microorganism interaction system

    Science.gov (United States)

    Zhong, Xiao; Sun, Peide; Song, Yingqi; Wang, Ruyi; Fang, Zhiguo

    2010-11-01

    Based on the fully coupled activated sludge model (FCASM), the novel model Tubificidae -Fully Coupled Activated Sludge Model-hydraulic (T-FCASM-Hydro), has been developed in our previous work. T-FCASM-Hydro not only describe the interactive system between Tubificidae and functional microorganisms for the sludge reduction and nutrient removal simultaneously, but also considere the interaction between biological and hydraulic field, After calibration and validation of T-FCASM-Hydro at Zhuji Feida-hongyu Wastewater treatment plant (WWTP) in Zhejiang province, T-FCASM-Hydro was applied for determining optimal operating condition in the WWTP. Simulation results showed that nitrogen and phosphorus could be removed efficiently, and the efficiency of NH4+-N removal enhanced with increase of DO concentration. At a certain low level of DO concentration in the aerobic stage, shortcut nitrification-denitrification dominated in the process of denitrification in the novel system. However, overhigh agitation (>6 mgṡL-1) could result in the unfavorable feeding behavior of Tubificidae because of the strong flow disturbance, which might lead to low rate of sludge reduction. High sludge reduction rate and high removal rate of nitrogen and phosphorus could be obtained in the new-style oxidation ditch when DO concentration at the aerobic stage with Tubificidae was maintained at 3.6 gṡm-3.

  3. Numerical solution of the Penna model of biological aging with age-modified mutation rate

    Science.gov (United States)

    Magdoń-Maksymowicz, M. S.; Maksymowicz, A. Z.

    2009-06-01

    In this paper we present results of numerical calculation of the Penna bit-string model of biological aging, modified for the case of a -dependent mutation rate m(a) , where a is the parent’s age. The mutation rate m(a) is the probability per bit of an extra bad mutation introduced in offspring inherited genome. We assume that m(a) increases with age a . As compared with the reference case of the standard Penna model based on a constant mutation rate m , the dynamics of the population growth shows distinct changes in age distribution of the population. Here we concentrate on mortality q(a) , a fraction of items eliminated from the population when we go from age (a) to (a+1) in simulated transition from time (t) to next time (t+1) . The experimentally observed q(a) dependence essentially follows the Gompertz exponential law for a above the minimum reproduction age. Deviation from the Gompertz law is however observed for the very old items, close to the maximal age. This effect may also result from an increase in mutation rate m with age a discussed in this paper. The numerical calculations are based on analytical solution of the Penna model, presented in a series of papers by Coe [J. B. Coe, Y. Mao, and M. E. Cates, Phys. Rev. Lett. 89, 288103 (2002)]. Results of the numerical calculations are supported by the data obtained from computer simulation based on the solution by Coe

  4. Examples of testing global identifiability of biological and biomedical models with the DAISY software.

    Science.gov (United States)

    Saccomani, Maria Pia; Audoly, Stefania; Bellu, Giuseppina; D'Angiò, Leontina

    2010-04-01

    DAISY (Differential Algebra for Identifiability of SYstems) is a recently developed computer algebra software tool which can be used to automatically check global identifiability of (linear and) nonlinear dynamic models described by differential equations involving polynomial or rational functions. Global identifiability is a fundamental prerequisite for model identification which is important not only for biological or medical systems but also for many physical and engineering systems derived from first principles. Lack of identifiability implies that the parameter estimation techniques may not fail but any obtained numerical estimates will be meaningless. The software does not require understanding of the underlying mathematical principles and can be used by researchers in applied fields with a minimum of mathematical background. We illustrate the DAISY software by checking the a priori global identifiability of two benchmark nonlinear models taken from the literature. The analysis of these two examples includes comparison with other methods and demonstrates how identifiability analysis is simplified by this tool. Thus we illustrate the identifiability analysis of other two examples, by including discussion of some specific aspects related to the role of observability and knowledge of initial conditions in testing identifiability and to the computational complexity of the software. The main focus of this paper is not on the description of the mathematical background of the algorithm, which has been presented elsewhere, but on illustrating its use and on some of its more interesting features. DAISY is available on the web site http://www.dei.unipd.it/ approximately pia/.

  5. Generation of a tightly regulated doxycycline-inducible model for studying mouse intestinal biology.

    Science.gov (United States)

    Roth, Sabrina; Franken, Patrick; van Veelen, Wendy; Blonden, Lau; Raghoebir, Lalini; Beverloo, Berna; van Drunen, Ellen; Kuipers, Ernst J; Rottier, Robbert; Fodde, Riccardo; Smits, Ron

    2009-01-01

    To develop a sensitive and inducible system to study intestinal biology, we generated a transgenic mouse model expressing the reverse tetracycline transactivator rtTA2-M2 under control of the 12.4 kb murine Villin promoter. The newly generated Villin-rtTA2-M2 mice were then bred with the previously developed tetO-HIST1H2BJ/GFP model to assess inducibility and tissue-specificity. Expression of the histone H2B-GFP fusion protein was observed exclusively upon doxycycline induction and was uniformly distributed throughout the intestinal epithelium. The Villin-rtTA2-M2 was also found to drive transgene expression in the developing mouse intestine. Furthermore, we could detect transgene expression in the proximal tubules of the kidney and in a population of alleged gastric progenitor cells. By administering different concentrations of doxycycline, we show that the Villin-rtTA2-M2 system drives transgene expression in a dosage-dependent fashion. Thus, we have generated a novel doxycycline-inducible mouse model, providing a valuable tool to study the effect of different gene dosages on intestinal physiology and pathology.

  6. Modeling biology with HDL languages: a first step toward a genetic design automation tool inspired from microelectronics.

    Science.gov (United States)

    Gendrault, Yves; Madec, Morgan; Lallement, Christophe; Haiech, Jacques

    2014-04-01

    Nowadays, synthetic biology is a hot research topic. Each day, progresses are made to improve the complexity of artificial biological functions in order to tend to complex biodevices and biosystems. Up to now, these systems are handmade by bioengineers, which require strong technical skills and leads to nonreusable development. Besides, scientific fields that share the same design approach, such as microelectronics, have already overcome several issues and designers succeed in building extremely complex systems with many evolved functions. On the other hand, in systems engineering and more specifically in microelectronics, the development of the domain has been promoted by both the improvement of technological processes and electronic design automation tools. The work presented in this paper paves the way for the adaptation of microelectronics design tools to synthetic biology. Considering the similarities and differences between the synthetic biology and microelectronics, the milestones of this adaptation are described. The first one concerns the modeling of biological mechanisms. To do so, a new formalism is proposed, based on an extension of the generalized Kirchhoff laws to biology. This way, a description of all biological mechanisms can be made with languages widely used in microelectronics. Our approach is therefore successfully validated on specific examples drawn from the literature.

  7. Multi-mutational model for cancer based on age-time patterns of radiation effects: 2. Biological aspects

    Energy Technology Data Exchange (ETDEWEB)

    Mendelsohn, M.L.; Pierce, P.A.

    1997-09-04

    Biological properties of relevance when modeling cancers induced in the atom bomb survivors include the wide distribution of the induced cancers across all organs, their biological indistinguishability from background cancers, their rates being proportional to background cancer rates, their rates steadily increasing over at least 50 years as the survivors age, and their radiation dose response being linear. We have successfully described this array of properties with a modified Armitage-Doll model using 5 to 6 somatic mutations, no intermediate growth, and the dose-related replacement of any one of these time-driven mutations by a radiation-induced mutation. Such a model is contrasted to prevailing models that use fewer mutations combined with intervening growth. While the rationale and effectiveness of our model is compelling for carcinogenesis in the atom bomb survivors, the lack of a promotional component may limit the generality of the model for other types of human carcinogenesis.

  8. Modelling Cost-Effectiveness of Biologic Treatments Based on Disease Activity Scores for the Management of Rheumatoid Arthritis in Spain

    Directory of Open Access Journals (Sweden)

    Ariel Beresniak

    2011-01-01

    Full Text Available Background. The objective of this simulation model was to assess the cost-effectiveness of different biological treatment strategies based on levels of disease activity in Spain, in patients with moderate to severe active RA and an insufficient response to at least one anti-TNF agent. Methods. Clinically meaningful effectiveness criteria were defined using DAS28 scores: remission and Low Disease Activity State (LDAS thresholds. Monte-Carlo simulations were conducted to assess cost-effectiveness over 2 years of four biological sequential strategies composed of anti-TNF agents (adalimumab, infliximab, abatacept or rituximab, in patients with moderate to severe active RA and an insufficient response to etanercept as first biological agent. Results. The sequential strategy including etanercept, abatacept and adalimumab appeared more efficacious over 2 years (102 days in LDAS compared to the same sequence including rituximab as second biological option (82 days in LDAS. Cost-effectiveness ratios showed lower costs per day in LDAS with abatacept (427 € compared to rituximab as second biological option (508 €. All comparisons were confirmed when using remission criteria. Conclusion. Model results suggest that in patients with an insufficient response to anti-TNF agents, the biological sequences including abatacept appear more efficacious and cost-effective than similar sequences including rituximab or cycled anti-TNF agents.

  9. Evaluation of Blade-Strike Models for Estimating the Biological Performance of Large Kaplan Hydro Turbines

    Energy Technology Data Exchange (ETDEWEB)

    Deng, Zhiqun; Carlson, Thomas J.; Ploskey, Gene R.; Richmond, Marshall C.

    2005-11-30

    BioIndex testing of hydro-turbines is sought as an analog to the hydraulic index testing conducted on hydro-turbines to optimize their power production efficiency. In BioIndex testing the goal is to identify those operations within the range identified by Index testing where the survival of fish passing through the turbine is maximized. BioIndex testing includes the immediate tailrace region as well as the turbine environment between a turbine's intake trashracks and the exit of its draft tube. The US Army Corps of Engineers and the Department of Energy have been evaluating a variety of means, such as numerical and physical turbine models, to investigate the quality of flow through a hydro-turbine and other aspects of the turbine environment that determine its safety for fish. The goal is to use these tools to develop hypotheses identifying turbine operations and predictions of their biological performance that can be tested at prototype scales. Acceptance of hypotheses would be the means for validation of new operating rules for the turbine tested that would be in place when fish were passing through the turbines. The overall goal of this project is to evaluate the performance of numerical blade strike models as a tool to aid development of testable hypotheses for bioIndexing. Evaluation of the performance of numerical blade strike models is accomplished by comparing predictions of fish mortality resulting from strike by turbine runner blades with observations made using live test fish at mainstem Columbia River Dams and with other predictions of blade strike made using observations of beads passing through a 1:25 scale physical turbine model.

  10. Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method

    Directory of Open Access Journals (Sweden)

    Sette Alessandro

    2005-05-01

    Full Text Available Abstract Background Many processes in molecular biology involve the recognition of short sequences of nucleic-or amino acids, such as the binding of immunogenic peptides to major histocompatibility complex (MHC molecules. From experimental data, a model of the sequence specificity of these processes can be constructed, such as a sequence motif, a scoring matrix or an artificial neural network. The purpose of these models is two-fold. First, they can provide a summary of experimental results, allowing for a deeper understanding of the mechanisms involved in sequence recognition. Second, such models can be used to predict the experimental outcome for yet untested sequences. In the past we reported the development of a method to generate such models called the Stabilized Matrix Method (SMM. This method has been successfully applied to predicting peptide binding to MHC molecules, peptide transport by the transporter associated with antigen presentation (TAP and proteasomal cleavage of protein sequences. Results Herein we report the implementation of the SMM algorithm as a publicly available software package. Specific features determining the type of problems the method is most appropriate for are discussed. Advantageous features of the package are: (1 the output generated is easy to interpret, (2 input and output are both quantitative, (3 specific computational strategies to handle experimental noise are built in, (4 the algorithm is designed to effectively handle bounded experimental data, (5 experimental data from randomized peptide libraries and conventional peptides can easily be combined, and (6 it is possible to incorporate pair interactions between positions of a sequence. Conclusion Making the SMM method publicly available enables bioinformaticians and experimental biologists to easily access it, to compare its performance to other prediction methods, and to extend it to other applications.

  11. Novel recurrent neural network for modelling biological networks: oscillatory p53 interaction dynamics.

    Science.gov (United States)

    Ling, Hong; Samarasinghe, Sandhya; Kulasiri, Don

    2013-12-01

    Understanding the control of cellular networks consisting of gene and protein interactions and their emergent properties is a central activity of Systems Biology research. For this, continuous, discrete, hybrid, and stochastic methods have been proposed. Currently, the most common approach to modelling accurate temporal dynamics of networks is ordinary differential equations (ODE). However, critical limitations of ODE models are difficulty in kinetic parameter estimation and numerical solution of a large number of equations, making them more suited to smaller systems. In this article, we introduce a novel recurrent artificial neural network (RNN) that addresses above limitations and produces a continuous model that easily estimates parameters from data, can handle a large number of molecular interactions and quantifies temporal dynamics and emergent systems properties. This RNN is based on a system of ODEs representing molecular interactions in a signalling network. Each neuron represents concentration change of one molecule represented by an ODE. Weights of the RNN correspond to kinetic parameters in the system and can be adjusted incrementally during network training. The method is applied to the p53-Mdm2 oscillation system - a crucial component of the DNA damage response pathways activated by a damage signal. Simulation results indicate that the proposed RNN can successfully represent the behaviour of the p53-Mdm2 oscillation system and solve the parameter estimation problem with high accuracy. Furthermore, we presented a modified form of the RNN that estimates parameters and captures systems dynamics from sparse data collected over relatively large time steps. We also investigate the robustness of the p53-Mdm2 system using the trained RNN under various levels of parameter perturbation to gain a greater understanding of the control of the p53-Mdm2 system. Its outcomes on robustness are consistent with the current biological knowledge of this system. As more

  12. Comparative study of the biological properties of Trypanosoma cruzi I genotypes in a murine experimental model.

    Science.gov (United States)

    Cruz, Lissa; Vivas, Angie; Montilla, Marleny; Hernández, Carolina; Flórez, Carolina; Parra, Edgar; Ramírez, Juan David

    2015-01-01

    Chagas disease is an endemic zoonosis in Latin America and caused by the parasite Trypanosoma cruzi. This kinetoplastid displays remarkable genetic variability, allowing its classification into six Discrete Typing Units (DTUs) from TcI to TcVI. T. cruzi I presents the broadest geographical distribution in the continent and has been associated to severe forms of cardiomyopathies. Recently, a particular genotype associated to human infections has been reported and named as TcIDOM (previously named TcIa-b). This genotype shows to be clonal and adapted to the domestic cycle but so far no studies have determined the biological properties of domestic (TcIDOM) and sylvatic TcI strains (previously named TcIc-e). Hence, the aim of this study was to untangle the biological features of these genotypes in murine models. We infected ICR-CD1 mice with five TcI strains (two domestic, two sylvatic and one natural mixture) and determined the course of infection during 91 days (acute and chronic phase of the disease) in terms of parasitemia, tissue tropism, immune response (IgG titers) and tissue invasion by means of histopathology studies. Statistically significant differences were observed in terms of parasitemia curves and prepatent period between domestic (TcIDOM) and sylvatic strains. There were no differences in terms of IgG antibodies response across the mice infected with the five strains. Regarding the histopathology, our results indicate that domestic strains present higher parasitemias and low levels of histopathological damage. In contrast, sylvatic strains showed lower parasitemias and high levels of histopathological damage. These results highlight the sympatric and behavioral differences of domestic and sylvatic TcI strains; the clinical and epidemiological implications are herein discussed.

  13. Biological contribution to social influences on alcohol drinking: evidence from animal models.

    Science.gov (United States)

    Anacker, Allison M J; Ryabinin, Andrey E

    2010-02-01

    Social factors have a tremendous influence on instances of heavy drinking and in turn impact public health. However, it is extremely difficult to assess whether this influence is only a cultural phenomenon or has biological underpinnings. Research in non-human primates demonstrates that the way individuals are brought up during early development affects their future predisposition for heavy drinking, and research in rats demonstrates that social isolation, crowding or low social ranking can lead to increased alcohol intake, while social defeat can decrease drinking. Neurotransmitter mechanisms contributing to these effects (i.e., serotonin, GABA, dopamine) have begun to be elucidated. However, these studies do not exclude the possibility that social effects on drinking occur through generalized stress responses to negative social environments. Alcohol intake can also be elevated in positive social situations, for example, in rats following an interaction with an intoxicated peer. Recent studies have also begun to adapt a new rodent species, the prairie vole, to study the role of social environment in alcohol drinking. Prairie voles demonstrate a high degree of social affiliation between individuals, and many of the neurochemical mechanisms involved in regulation of these social behaviors (for example, dopamine, central vasopressin and the corticotropin releasing factor system) are also known to be involved in regulation of alcohol intake. Naltrexone, an opioid receptor antagonist approved as a pharmacotherapy for alcoholic patients, has recently been shown to decrease both partner preference and alcohol preference in voles. These findings strongly suggest that mechanisms by which social factors influence drinking have biological roots, and can be studied using rapidly developing new animal models.

  14. The Mouse Genome Database (MGD): facilitating mouse as a model for human biology and disease.

    Science.gov (United States)

    Eppig, Janan T; Blake, Judith A; Bult, Carol J; Kadin, James A; Richardson, Joel E

    2015-01-01

    The Mouse Genome Database (MGD, http://www.informatics.jax.org) serves the international biomedical research community as the central resource for integrated genomic, genetic and biological data on the laboratory mouse. To facilitate use of mouse as a model in translational studies, MGD maintains a core of high-quality curated data and integrates experimentally and computationally generated data sets. MGD maintains a unified catalog of genes and genome features, including functional RNAs, QTL and phenotypic loci. MGD curates and provides functional and phenotype annotations for mouse genes using the Gene Ontology and Mammalian Phenotype Ontology. MGD integrates phenotype data and associates mouse genotypes to human diseases, providing critical mouse-human