DOCSLIB.ORG

Computational Modeling of Genetic and Biochemical Networks. Edited by James M. Bower and Hamid Bolouri Computational Molecular Biology, a Bradford Book, the MIT Press, Cambridge

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Computational Modeling of Genetic and Biochemical Networks. Edited by James M. Bower and Hamid Bolouri Computational Molecular Biology, a Bradford Book, the MIT Press, Cambridge BRIEFINGS IN BIOINFORMATICS. VOL 7. NO 2. 204 ^206 doi:10.1093/bib/bbl001 Advance Access publication March 9, 2006 Book Review Computational Modeling of Genetic stochastic modelling are inevitable due to the small and Biochemical Networks number of molecules in a cell [1]. These intrinsic Edited by James M. Bower and noise effects have been measured in gene expression Hamid Bolouri using fluorescent probes [2, 3]. Chapter 3, ‘A Logical Model of cis-Regulatory Control in Eukaryotic Computational Molecular Biology, Downloaded from https://academic.oup.com/bib/article/7/2/204/304421 by guest on 23 September 2021 A Bradford Book, The MIT Press, Systems’ by Chiou-Hwa Yuh and others, builds Cambridge, Massachusetts; 2004; up on the theoretical framework introduced in ISBN: 0 262 52423 6; Paperback; 390pp.; Chapter 1 and presents a detailed characterization of £22.95/$35.00. a developmental biology gene network with a large number of regulatory factors. Chapters 1–3 deal with individual gene regulations. Eric Mjolsness intro- ‘Computational Modeling of Genetic and duces and reviews computational techniques, such as Biochemical Networks’ arose from a graduate neural network approaches, to study gene networks course taught by the editors in the California in Chapter 4 ‘Trainable Gene Regulation Networks Institute of Technology in 1998. The aim of the with Application to Drosophila Pattern Formation’. book is to provide instruction in the application of Special emphasis is made in the activity patterns modelling techniques in molecular and cell biology during the fruit-fly development. High-throughput to graduate students and postdoctoral researchers. experimental assays play a major role in the current It is also intended as a primer in the subject for shift from reductionist to systems biology approa- both theoretical and experimental biologists. ches. The data sets generated by these experiments The preface discusses the interplay between promise to identify the components and interactions experiments, theory and modelling by addressing of biochemical networks. We increasingly require two important questions: Why is modelling neces- computational approaches suited to the analysis of sary and what type of modelling is appropriate? these data sets, in particular techniques which require It also explains the structure and organization of little prior knowledge of the interactions involved the book, as well as how it should be used. The [4]. In Chapter 5, ‘Genetic Network Inference in book is divided into two parts: Modeling Genetic Computational Models and Applications to Large- Networks (Chapters 1–5) and Modeling Scale Gene Expression Data’, Roland Somogyi and Biochemical Networks (Chapters 6–10). The book others review a number of potentially useful concludes with a chapter on multiscale modelling techniques for uncovering the structure of gene (Chapter 11). networks and also present several studies that used The first part of the book deals with models of these methods. gene regulation. Chapter 1, ‘Modeling the Activity The second part of the book deals with the of Single Genes’ by Michael Gibson and Eric modelling of biochemical networks. The editors Mjolsness, is an introduction to modelling gene have considered these networks as the interactions regulation. It is perhaps one of the best tutorials among the proteins produced by genetic regulation. I have seen in the different modelling techniques It starts with Chapter 6 ‘Atomic-Level Simulation for gene activation and inhibition. In Chapter 2 and Modeling of Biomacromolecules’ by Nagarajan ‘A Probabilistic Model of Prokaryotic Gene and its Vaidehi and William Goddard. This chapter deals Regulation’, Michael Gibson and Jehoshua Bruck with the molecular dynamics techniques that help us focus exclusively on the stochastic modelling of gene to predict the structure of molecules and understand regulation. It was a wise decision to dedicate a the interactions between them. It also presents a few chapter to the stochastic modelling of gene regula- examples of drug design and discovery. Although, tion. At the molecular level, random fluctuations and the molecular dynamics techniques are the closest to ß The Author 2006. Published by Oxford University Press. For Permissions, please email: [email protected] Book Review 205 reality, they are very computationally expensive [5] that in free solution and hence stochastic effects and frequently require parameters which are not easy are important [3]. Dennis Bray is one of the world to acquire from experiments. For this reason, the leaders in chemotaxis, and is one of the first chapter also provides a justification for the phenom- researchers to recognize the importance of modelling enological modelling approaches typically employed stochastically reactions occurring in the intracellular in chemical kinetics, which are used in the rest of the environment. Chapter 10 entitled ‘Analysis of book. Chapter 7 ‘Diffusion’ by Guy Bormann and Complex Dynamics in Cell Cycle Regulation’ uses others shows how diffusion often has a crucial impact numerical simulations and dynamical system techni- on modelling. The reader should not underestimate ques to study the complex dynamics regulating the importance of this chapter. Historically, research- the cell cycle. Among the authors of this chapter, we ers working in the modelling of biochemical net- find John Tyson and Bela Novak. Both have made works have disregarded diffusion. However, it is fundamental contributions to the understanding of Downloaded from https://academic.oup.com/bib/article/7/2/204/304421 by guest on 23 September 2021 now well-known that diffusion processes have a the cell cycle dynamics through modelling. The final profound effect on the reactions by affecting their chapter ‘Simplifying and Reducing Complex local rates. Unfortunately, this chapter does not make Models’ by Bard Ermentrout (Chapter 11) deals reference to the classic book by James Murray with a scaling technique, know as averaging, to ‘Mathematical Biology’ [6, 7] and Keener & Sneyd’s reduce complex models. The complexity of biolo- ‘Mathematical Physiology’ [8]. These two references gical models does not permit us to carry out a proper have excellent textbooks with standard applications analysis to understand the parameter controlling of diffusion process to biology and physiology. A the system under consideration. Scaling is one of the similar omission occurs in Chapter 8 ‘Kinetic Models strategies available to mathematicians to reduce the of Excitable Membranes and Synaptic Interactions’. complexity of biological models, and has been Alain Destexhe looks at the transport of molecules effectively applied in numerous instances by the across the cell membranes, focusing on the mechan- author, and many other people working in mathe- isms of ion transport channels, and their stochastic matical biology. Lee Segel, who has recently passed modelling. This chapter does not refer readers to away, is a pioneer in this area. One of the most the ‘Mathematical Physiology’ book [8], which is a important messages of Segel’s work is that in the use standard text in physiological modelling and deals of scaling techniques it is difficult to identify the quite well with the transport of molecules across scales; thus the reduced systems may not be a valid the membranes. Despite leaving out such a reference, approximation of the phenomenon studied [10–12]. this chapter briefly discusses the importance of the I would like to have seen some of Segel’s integration of ion channels modelling with intracel- contribution to scaling mentioned in the discussion lular biochemical processes. I am very pleased that and caveats section of the last chapter. the author has pointed out this. It is a subject of great The structure of each chapter is homogeneous in importance in biochemistry, physiology and devel- the book. Each chapter begins with an overview of opmental biology, where biochemical pathways are the biological phenomenon under consideration. closely linked to the electrophysiology of the ion Attention is paid to the modelling approaches, channels. Type 2 diabetes mellitus is an exciting but some of the chapters do not give full details problem in which this multiscale linking occurs [9]. on computational and mathematical modelling In Chapters 2 and 8, stochastic modelling techniques techniques. While exercises are not given, chapters were introduced for modelling gene regulation and are extremely useful to advance students and the mechanisms of ion channels. Carl Firth and scientists who are interested in initiating research Dennis Bray in Chapter 9, ‘Stochastic Simulation in the area: they are very well-written from a of Cell Signaling Pathways’, revisit stochastic model- pedagogical point of view with extensive references ling techniques in a different context: to simulate and examples carefully cited. In addition, there are bacterial chemotaxis pathways. The intracellular many internal cross-references and comparisons. medium has a high macromolecular content. When References are provided at the end of the each the concentration of obstacles becomes large, chapter and the book includes an extensive index. molecules get easily isolated into compartmentalized The book has been well-received in the regions; under such conditions, the number of community. I have seen open copies of the book reacting molecules is presumably much smaller than in the offices of several of my colleagues. This is the 206 Book Review first
Load more

Recommended publications
  • Metabolic Network Structure Determines Key Aspects of Functionality and Regulation
    letters to nature .............................................................. representative substrates feeding into different parts of metabolism (Table 1). The total number of elementary modes for given Metabolic network structure conditions is here used as a quantitative measure of the degrees of determines key aspects of freedom11, that is, of flexibility. Glucose, for example, can be used in approximately 45 times more different ways than acetate, corre- functionality and regulation sponding to biological intuition. Flux mode number thus directly relates network structure to function. An empty set implies that no Jo¨rg Stelling*, Steffen Klamt*, Katja Bettenbrock*, Stefan Schuster† steady-state flux distribution fulfilling the specifications exists, & Ernst Dieter Gilles* hence predicting an inviable phenotype. For instance, anaerobic use of any of the four substrates except glucose is impossible without * Max Planck Institute for Dynamics of Complex Technical Systems, additional terminal electron acceptors. D-39106 Magdeburg, Germany In particular, we analyse the ability to grow or not to grow of † Max Delbru¨ck Center for Molecular Medicine, D-13092 Berlin, Germany mutants carrying deletions in single genes. For this purpose, the ............................................................................................................................................................................. number of flux modes for a mutant Di using substrate S is The relationship between structure, function and regulation in k 1–3 determined
    [Show full text]
  • Reinhart Heinrich (1946–2006) Pioneer in Systems Biology
    NEWS & VIEWS NATURE|Vol 444|7 December 2006 OBITUARY Reinhart Heinrich (1946–2006) Pioneer in systems biology. In biology, mathematical systems analysis where he showed that the flux of was until recently nearly invisible in the reaction was shared by several the dazzling light of twentieth-century enzymes. Much later, he extended discoveries. But it has emerged from the his ideas to signal-transduction shadows in the field of systems biology, pathways, introducing control a subject buoyed by immense data sets, coefficients to dynamic processes. conveyed by heavy computing power, and Sticking to real examples, such as addressing seemingly incomprehensible the Wnt signalling and MAP kinase forms of complexity. If systems biology has pathways, he again demonstrated heroes, one of them is Reinhart Heinrich, a that new properties and constraints former professor at the Humboldt University emerge when the individual steps in Berlin, who died on 23 October, aged are combined into a complete 60. His most famous accomplishment was pathway. metabolic control theory, published in Heinrich also pointed the way to 1974 with Tom Rapoport and formulated considerations of optimality theory independently by Henrik Kacser and James and evolution that will confront A. Burns in Edinburgh, UK. systems biology for the next From the 1930s to the 1960s, biochemists century. The question of evolution were busy describing metabolic pathways, lies just beneath any effort to just as molecular biologists today are understand biology. Yet in most feverishly trying to inventory the cell’s cases, physiological function and gene-transcription and signalling circuits. evolutionary change are considered The basic kinetic features of the enzymes in distinct and are investigated by the major pathways were studied in great different people.
    [Show full text]
  • Implications for Optimal Distributions of Enzyme Concentrations and for the Distribution of flux Control
    BioSystems 54 (1999) 1–14 www.elsevier.com/locate/biosystems Competition for enzymes in metabolic pathways: Implications for optimal distributions of enzyme concentrations and for the distribution of flux control Edda Klipp *, Reinhart Heinrich Humboldt Uni6ersity Berlin, Institute of Biology, Theoretical Biophysics, In6alidenstr. 42, D-10115, Berlin, Germany Received 1 February 1999; accepted 25 April 1999 Abstract The structures of biochemical pathways are assumed to be determined by evolutionary optimization processes. In the framework of mathematical models, these structures should be explained by the formulation of optimization principles. In the present work, the principle of minimal total enzyme concentration at fixed steady state fluxes is applied to metabolic networks. According to this principle there exists a competition of the reactions for the available amount of enzymes such that all biological functions are maintained. In states which fulfil these optimization criteria the enzyme concentrations are distributed in a non-uniform manner among the reactions. This result has conse- quences for the distribution of flux control. It is shown that the flux control matrix c, the elasticity matrix o, and the vector e of enzyme concentrations fulfil in optimal states the relations cTe=e and o Te=0. Starting from a well-balanced distribution of enzymes the minimization of total enzyme concentration leads to a lowering of the SD of the flux control coefficients. © 1999 Elsevier Science Ireland Ltd. All rights reserved. Keywords: Metabolic control theory; Evolutionary optimization; Competition; Enzyme concentrations; Flux control coefficient; Metabolic pathway; Mathematical modeling 1. Introduction optimal manner. On this basis one can draw conclusions about the actual states of metabolic The structural properties of metabolic systems systems using optimization criteria.
    [Show full text]
  • Uva-DARE (Digital Academic Repository)
    UvA-DARE (Digital Academic Repository) Henrik Kacser 1918-1995: Metabolism of control. Westerhoff, H.V. DOI 10.1016/S0167-7799(00)88956-8 Publication date 1995 Published in Trends in Biotechnology Link to publication Citation for published version (APA): Westerhoff, H. V. (1995). Henrik Kacser 1918-1995: Metabolism of control. Trends in Biotechnology, 13, 245. https://doi.org/10.1016/S0167-7799(00)88956-8 General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl) Download date:25 Sep 2021 245 f orum Henrik Kacser (19184995): metabolism of control Obituary Henrik Kacser has been referred missed the rate-limiting step. He funding agency had allowed much to as the ‘pope’ of Metabolic much appreciated
    [Show full text]
  • Mathematical Models for Explaining the Warburg Effect: a Review Focussed on ATP and Biomass Production
    Metabolic pathways analysis 2015 1187 Mathematical models for explaining the Warburg effect: a review focussed on ATP and biomass production Stefan Schuster*1, Daniel Boley†, Philip Moller*,¨ Heiko Stark*‡ and Christoph Kaleta§ *Department of Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany †Computer Science & Engineering, University of Minnesota, Minneapolis, MN 55455, U.S.A. ‡Institute of Systematic Zoology and Evolutionary Biology, Friedrich Schiller University Jena, Erbertstraße 1, 07737 Jena, Germany §Research Group Medical Systems Biology, Christian-Albrechts-University Kiel, Brunswiker Straße 10, Kiel 24105, Germany Abstract For producing ATP, tumour cells rely on glycolysis leading to lactate to about the same extent as on respiration. Thus, the ATP synthesis flux from glycolysis is considerably higher than in the corresponding healthy cells. This is known as the Warburg effect (named after German biochemist Otto H. Warburg) and also applies to striated muscle cells, activated lymphocytes, microglia, endothelial cells and several other cell types. For similar phenomena in several yeasts and many bacteria, the terms Crabtree effect and overflow metabolism respectively, are used. The Warburg effect is paradoxical at first sight because the molar ATP yield of glycolysis is much lower than that of respiration. Although a straightforward explanation is that glycolysis allows a higher ATP production rate, the question arises why cells do not re-allocate protein to the high-yield pathway of respiration. Mathematical modelling can help explain this phenomenon. Here, we review several models at various scales proposed in the literature for explaining the Warburg effect. These models support the hypothesis that glycolysis allows for a higher proliferation rate due to increased ATP production and precursor supply rates.
    [Show full text]
  • Memories of Reinhart Heinrich
    ARTICLE IN PRESS Journal of Theoretical Biology 252 (2008) 544–545 www.elsevier.com/locate/yjtbi In Memoriam Memories of Reinhart Heinrich I first met Reinhart at the symposium that Athel In April 2000, Reinhart secured lodging for me in the Cornish-Bowden organised at Il Ciocco in April 1989 that lovely house for at Humboldt University, where Marı ´ a brought together numerous researchers interested in the Rosa, my wife, came to spend a few delightful days with control and design of metabolism. We connected immedi- me, and spent time talking with Nana, Reinhart’s wife. We ately, because we had many things in common and were so spent a couple of days in Dresden at Reinhart’s mother’s like-minded. home and heard the concert in Dresden Cathedral for After much discussion of the evolution and optimisation Bach’s St. Matthew’s Passion, which Reinhart loved and of metabolism, our first work was on the optimisation of liked to play on his violin. This was a sensational glycolysis. Paco Montero had the first important idea that performance, but there was no applause at the end, the aim of evolution was to maximize the flux of ATP because, as he explained to us, there is never applause in production, and this has been instrumental for all a church in Germany. There are some things about subsequent work in the field. Germans that are hard for Spanish people to understand! I saw that Reinhart’s theory of the optimization of Reinhart was always worried about world peace. He was enzyme kinetic parameters was very important, but hard very sensitive to human suffering, was especially troubled for students.
    [Show full text]
  • Reasoning About Systems Biology Models in the Semantic Web Era
    ARTICLE IN PRESS Journal of Theoretical Biology 252 (2008) 538–543 www.elsevier.com/locate/yjtbi The markup is the model: Reasoning about systems biology models in the Semantic Web era Douglas B. Kella,c,Ã, Pedro Mendesb,c,d aSchool of Chemistry, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK bSchool of Computer Science, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK cThe Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK dVirginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Washington Street 0477, VA 24061, USA Available online 3 December 2007 Dedicated to the memory of Reinhart Heinrich (1946–2006). Abstract Metabolic control analysis, co-invented by Reinhart Heinrich, is a formalism for the analysis of biochemical networks, and is a highly important intellectual forerunner of modern systems biology. Exchanging ideas and exchanging models are part of the international activities of science and scientists, and the Systems Biology Markup Language (SBML) allows one to perform the latter with great facility. Encoding such models in SBML allows their distributed analysis using loosely coupled workflows, and with the advent of the Internet the various software modules that one might use to analyze biochemical models can reside on entirely different computers and even on different continents. Optimization is at the core of many scientific and biotechnological activities, and Reinhart made many major contributions in this area, stimulating our own activities in the use of the methods of evolutionary computing for optimization. r 2007 Elsevier Ltd.
    [Show full text]
  • German Conference on Bioinformatics 2012
    German Conference on Bioinformatics 2012 Jena, 19-22 September 2012 Conference Program Supporters and Sponsors German Conference on Bioinformatics 2012 Welcome Dear GCB 2012 Attendee, it is a pleasure to welcome you in Jena, to the German Conference on Bioinformatics 2012. This annual, international conference provides a forum for the presentation of current re- search in bioinformatics and computational biology. In addition, five satellite workshops place thematic emphasis on diverse aspects of systems biology: “Systems Biology of Aging” organized by J. Sühnel, “Organ-oriented Systems Biology” by D. Driesch and R. Mrowka, “Network Reconstruction and Analysis in Systems Biology” by W. Wiechert and T. Lengauer, “Computational Proteomics and Metabolomics” by S. Böcker, and “Image- based Systems Biology” by M. Figge. This year we will have 10 highlight papers and 11 regular papers presented at the GCB selected out of 39 submissions. GCB 2012 will also feature keynote presentations by six leading scientists: Claude dePamphilis (Pennsylvania State University, University Park, USA), Oliver Fiehn (University of California, Davis, USA), Arndt von Haeseler (Max F. Perutz Laboratories, Vienna, Austria), Tom Kirkwood (Newcastle University, Newcastle, UK), Erik van Nimwegen (University of Basel, Basel, Switzerland), and Ruth Nussinov (National Cancer Institute, Frederick, USA). With the topics of these talks the meeting indeed succeeded in ‘Joining Evolution, Networks, and Algorithms’, according to this year’s conference motto. Additionally, about 95 poster abstracts were accepted for pres- entation. The GCB 2012 was organized by the Jena Centre of Bioinformatics (JCB) in cooperation with the German Society for Chemical Engineering and Biotechnology (DECHEMA) and the Society for Biochemistry and Molecular Biology (GBM).
    [Show full text]
  • Multifunctional Enzymes and Pathway Modelling
    115 ESCEC, Oct. 5th - 8th 2003, Rüdesheim, Germany MULTIFUNCTIONAL ENZYMES AND PATHWAY MODELLING 1,* 2 STEFAN SCHUSTER AND IONELA ZEVEDEI-OANCEA 1Friedrich Schiller University Jena, Faculty of Biology and Pharmaceutics, Section of Bioinformatics, Ernst-Abbe-Platz 2, D-07743 Jena, Germany 2Humboldt University Berlin, Section of Theoretical Biophysics, Invalidenstr. 42, D-10115 Berlin, Germany E-Mail: *[email protected] Received: 10th March 2004 / Published: 1st October 2004 ABSTRACT The analysis of network properties of metabolic systems has recently attracted increasing interest. While enzymes are usually considered to be specific catalysts, many enzymes in living cells are characterized by broad substrate specificity. Here we discuss some aspects of the treatment of such multifunctional enzymes in metabolic pathway analysis, for example, their suitable representation. The fact that the choice of independent functions of multifunctional enzymes is non- unique is explained. We comment on the annotation of such enzymes in metabolic databases and give some suggestions to improve this. We then explain the proper definition of metabolic pathways (elementary flux modes) for systems involving multifunctional enzymes and discuss some ontological problems. INTRODUCTION Metabolic pathway analysis has become a widely used tool in biochemical modelling [1-5]. It is instrumental in metabolic engineering [6,7] and functional genomics [8,9]. The analysis is based on the decomposition of metabolic networks into their smallest functional entities - the metabolic pathways. One of its major advantages is that it does not require any knowledge of kinetic parameters. It only uses the stoichiometric coefficients and information about the directionality of enzymatic reactions. Published in „Experimental Standard Conditions of Enzyme Characterizations“, M.G.
    [Show full text]
  • ISGSB 2016 Will Be Devoted to the Memory of One of the Founders 7
    General Information General Information Venue ISGSB Organisers 2 016 Friedrich-Schiller-University of Jena Stefan Schuster ISGSB JENA Auditorium maximum Department of Bioinformatics • FSU Jena Fürstengraben 1 Dominik Driesch 07743 Jena, Germany BioControl Jena GmbH INTERNATIONAL STUDY GROUP Oliver Ebenhöh Date Institute for Quantitative and Theoretical Biology FOR SYSTEMS BIOLOGY 4–7 October 2016 HHU Düsseldorf Marc Thilo Figge Conference Website Applied Systems Biology • HKI Jena www.isgsb2016.de Pu Li Institute for Automation and Systems Engineering CALL FOR ABSTRACTS Conference Chair TU Ilmenau Stefan Schuster Manja Marz Friedrich-Schiller-University of Jena RNA Bioinformatics and High Throughput Analysis • FSU Jena Department of Bioinformatics Günter Theißen Ernst-Abbe-Platz 2 Department of Genetics • FSU Jena 07743 Jena, Germany Johannes Woestemeyer Institute of Microbiology • FSU Jena Conference Organisation Conventus Congressmanagement & Marketing GmbH YSGSB Organisers Anja Kreutzmann Kailash Adhikari Carl-Pulfrich-Strasse 1 Oxford Brookes University 07745 Jena, Germany Mathias Bockwoldt Tel. +49 3641 31 16-357 University of Tromsø Fax +49 3641 31 16-243 Sybille Dühring [email protected] Department of Bioinformatics • FSU Jena © Sergey Nivens - fotlia.com www.conventus.de Sabrina Ellenberger Institute of Microbiology • FSU Jena Industrial Exhibition Alexander Heberle The meeting will be accompanied by a specialised industrial exhi- University Medical Centre Groningen GERMANY bition. Interested companies are invited to request information on Sebastian Henkel 04–07 OCTOBER JENA, the exhibition modalities from the conference organisation through BioControl Jena GmbH 2016 Conventus. Franziska Hufsky RNA Bioinformatics and High Throughput Analysis • FSU Jena Registration James Rowe Further information about registration and fees can be found online Durham University at www.isgsb2016.de soon.
    [Show full text]
  • Arxiv:1703.06496V2 [Q-Bio.MN] 4 Feb 2018
    A Graph-Based Approach to Analyze Flux-Balanced Pathways in Metabolic Networks Mona Arabzadeh1, Morteza Saheb Zamani1, Mehdi Sedighi1, and Sayed-Amir Marashi2 1Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran and 2Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran. fm.arabzadeh,szamani,[email protected], [email protected] Abstract An Elementary Flux Mode (EFM) is a pathway with minimum set of reactions that are functional in steady-state constrained space. Due to the high computational complexity of calculating EFMs, different approaches have been proposed to find these flux-balanced pathways. In this paper, an approach to find a subset of EFMs is proposed based on a graph data model. The given metabolic network is mapped to the graph model and decisions for reaction inclusion can be made based on metabolites and their associated reactions. This notion makes the approach more conve- nient to categorize the output pathways. Implications of the proposed method on metabolic networks are discussed. Keywords: Elementary Flux Mode (EFM); Graph Data Model; Metabolic Network 1 Introduction Metabolic network models are among the well-studied models in biotechnology. The reconstruction of these networks is possible by collecting the gene-protein- reaction information from related genomic data and literature [1]. It is impor- tant to explore biologically relevant pathways in metabolic networks. Forcing constraints to a reconstructed biochemical network results in the definition of achievable cellular functions [2]. Mathematical representation of constraints are as flux-balance constraints (e.g., conservation of flux) which means the network should be at the steady-state condition, and flux bounds which limit numer- ical ranges of network parameters and coefficients such as the minimum and maximum range of fluxes for each reaction.
    [Show full text]
  • Isotopomer Subspaces As Indicators of Metabolic-Pathway Structure
    ARTICLE IN PRESS Journal of Theoretical Biology 252 (2008) 391–401 www.elsevier.com/locate/yjtbi Isotopomer subspaces as indicators of metabolic-pathway structure Philip W. Kuchela,b,Ã, David J. Philpc aSchool of Molecular and Microbial Biosciences, University of Sydney, NSW 2006, Australia bCentre for Mathematical Biology, University of Sydney, NSW 2006, Australia cNational Centre for Epidemiology and Population Health, Australian National University, Canberra, ACT 0200, Australia Received 18 April 2007; received in revised form 11 May 2007; accepted 15 May 2007 Available online 5 July 2007 This paper is dedicated to the memory of Professor Reinhart Heinrich of Berlin Abstract The relative abundances and rates of formation of particular isotopic isomers (isotopomers) of metabolic intermediates from 13C-labelled substrates in living cells provide information on the routes taken by the initial 13C-atoms. When a primary substrate such as 13 [U, C] D-glucose is added to human erythrocytes, the pattern of labels in terminal metabolites is determined by a set of carbon-group exchange reactions in both glycolysis and the pentose phosphate pathway (PPP). Of a given terminal metabolite, not all possible isotopomers will be produced from each possible primary substrate isotopomer. There are only 8 different 13C-isotopomers of lactate but not all of these are produced when one of the 64 possible 13C-isotopomers of glucose is used as the input substrate; thus a subset of all 63 glucose isotopomers  8 lactate isotopomers+1 unlabelled glucose  1 unlabelled lactate ¼ 505 pattern associations, would be produced if a complete experimental analysis were performed with all the glucose variants.
    [Show full text]