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Kinetic Modeling Using Biopax Ontology
Kinetic Modeling using BioPAX ontology Oliver Ruebenacker, Ion. I. Moraru, James C. Schaff, and Michael L. Blinov Center for Cell Analysis and Modeling, University of Connecticut Health Center Farmington, CT, 06030 {oruebenacker,moraru,schaff,blinov}@exchange.uchc.edu Abstract – e.g. Cytoscape (http://cytoscape.org, [5]), cPath database http://cbio.mskcc.org/software/cpath, [6]), Thousands of biochemical interactions are PathCase (http://nashua.case.edu/PathwaysWeb), available for download from curated databases such VisANT (http://visant.bu.edu, [7]). However, the as Reactome, Pathway Interaction Database and other current standard for kinetic modeling is Systems sources in the Biological Pathways Exchange Biology Markup Language, SBML ([8], (BioPAX) format. However, the BioPAX ontology http://sbml.org). Both BioPAX and SBML are used to does not encode the necessary information for kinetic encode key information about the participants in modeling and simulation. The current standard for biochemical pathways, their modifications, locations kinetic modeling is the System Biology Markup and interactions, but only SBML can be used directly Language (SBML), but only a small number of models for kinetic modeling, because elements are included in are available in SBML format in public repositories. SBML specifically for the context of a quantitative Additionally, reusing and merging SBML models theory. In contrast, concepts in BioPAX are more presents a significant challenge, because often each abstract. SBML-encoded models typically contain all element has a value only in the context of the given data necessary for simulations, such as molecular model, and information encoding biological meaning species and their concentrations, reactions among is absent. We describe a software system that enables these species, and kinetic laws for these reactions. -
Quantitative Analysis of Amino Acid Metabolism in Liver Cancer Links Glutamate Excretion to Nucleotide Synthesis
Quantitative analysis of amino acid metabolism in liver cancer links glutamate excretion to nucleotide synthesis Avlant Nilssona,1, Jurgen R. Haanstrab,1, Martin Engqvista, Albert Gerdingc,d, Barbara M. Bakkerb,c, Ursula Klingmüllere, Bas Teusinkb, and Jens Nielsena,f,2 aDepartment of Biology and Biological Engineering, Chalmers University of Technology, SE41296 Gothenburg, Sweden; bSystems Biology Lab, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, NL1081HZ Amsterdam, The Netherlands; cLaboratory of Pediatrics, Systems Medicine of Metabolism and Signaling, University of Groningen, University Medical Center Groningen, NL-9713AV Groningen, The Netherlands dDepartment of Laboratory Medicine, University of Groningen, University Medical Center Groningen, NL-9713AV Groningen, The Netherlands; eDivision of Systems Biology and Signal Transduction, German Cancer Research Center, D-69120 Heidelberg, Germany; and fNovo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, DK2800, Denmark Contributed by Jens Nielsen, March 9, 2020 (sent for review November 4, 2019; reviewed by Eytan Ruppin and Matthew G. Vander Heiden) Many cancer cells consume glutamine at high rates; counterintu- the excretion of lactate, glutamate, alanine, and glycine (5). How- itively, they simultaneously excrete glutamate, the first interme- ever, a full genome-wide modeling approach is required to expand diate in glutamine metabolism. Glutamine consumption has been our knowledge of how metabolism functions beyond the canonical linked to replenishment of tricarboxylic acid cycle (TCA) interme- pathways and, in particular, to understand the intricate effects of diates and synthesis of adenosine triphosphate (ATP), but the metabolic compartmentalization and the interplay between ex- reason for glutamate excretion is unclear. Here, we dynamically change fluxes and synthesis of biomass. -
Flux Balance Analysis to Model Microbial Metabolism for Electricity Generation
Lincoln University Digital Thesis Copyright Statement The digital copy of this thesis is protected by the Copyright Act 1994 (New Zealand). This thesis may be consulted by you, provided you comply with the provisions of the Act and the following conditions of use: you will use the copy only for the purposes of research or private study you will recognise the author's right to be identified as the author of the thesis and due acknowledgement will be made to the author where appropriate you will obtain the author's permission before publishing any material from the thesis. Flux balance analysis to model microbial metabolism for electricity generation A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy in Computational Biology and Bioengineering at Lincoln University by Longfei Mao Lincoln University 2013 Abstract of a thesis submitted in partial fulfilment of the requirements for the Degree of Philosophy Flux balance analysis to model microbial metabolism for electricity generation by Longfei Mao Abstract Microbial fuel cells (MFCs) are bioelectrochemcial devices that possess a similar design to a fuel cell, with an anode and a cathode connected through an electrical circuit. Unlike fuel cells, MFCs use microorganisms as biocatalysts to convert organic matter into electrons and protons, of which a portion can be transferred to electrode to generate electricity. Since all microorganisms transfer electrons during metabolism inside the cell, there could potentially be unlimited choices of biocatalyst candidates for MFCs for various applications. However, in reality, the application of MFCs is heavily restricted by their low current outputs. -
Academic Year 2007 Computing Project Group Report SBML-ABC, A
Academic year 2007 Computing project Group report SBML-ABC, a package for data simulation, parameter inference and model selection Imperial College- Division of Molecular Bioscience MSc. Bioinformatics Nathan Harmston, David Knowles, Sarah Langley, Hang Phan Supervisor: Professor Michael Stumpf June 13, 2008 Contents 1 Introduction 1 1.1 Background ...................................... 1 2 Features and dependencies 2 2.1 Project outline ..................................... 2 2.2 Key features ...................................... 2 2.2.1 SBML ..................................... 2 2.2.2 Stochastic simulation ............................. 4 2.2.3 Deterministic simulation ........................... 4 2.2.4 ABC inference ................................ 5 2.3 Interfaces ....................................... 5 2.3.1 CAPI ..................................... 5 2.3.2 Command line interface ........................... 5 2.3.3 Python .................................... 5 2.3.4 R ....................................... 6 2.4 Dependencies ..................................... 6 3 Methods 8 3.1 SBML Adaptor .................................... 8 3.2 Stochastic simulation ................................. 8 3.2.1 Random number generator .......................... 9 3.2.2 Multicompartmental Gillespie algorithm ................... 9 3.2.3 Tau leaping .................................. 10 3.2.4 Chemical Langevin Equation ......................... 11 3.3 Deterministic algorithms ............................... 11 3.3.1 ODE solvers ................................ -
TUTORIAL.Pdf
Systems Biology Toolbox for MATLAB A computational platform for research in Systems Biology Tutorial www.sbtoolbox.org Henning Schmidt, [email protected] Vision ° The Systems Biology Toolbox for MATLAB offers systems biologists an open and user extensible environment, in which to explore ideas, prototype and share new algorithms, and build applications for the analysis and simulation of biological systems. Henning Schmidt, [email protected] www.sbtoolbox.org Tutorial Outline ° General introduction to the toolbox ° Using the toolbox documentation ° Building models and simulation ° Import/Export of models ° Using the toolbox functions - examples ° Mass conservation and simple model reduction ° Steady-state analysis and stability ° Bifurcation analysis ° Parameter sensitivity analysis (metabolic control analysis) ° In silico experiments and the representation of measurement data ° Parameter estimation ° Localization of mechanisms leading to oscillations and bistability ° Network identification ° Writing your own functions for the toolbox ° Modifying existing MATLAB models for use with the toolbox Henning Schmidt, [email protected] www.sbtoolbox.org ° General introduction to the toolbox Henning Schmidt, [email protected] www.sbtoolbox.org Model Development Cycle Graphical Modelling (CellDesigner, PathwayLab, etc.) Model export to graphical Model import modelling tool to SB Toolbox Modelling, Simulation, Analysis, Identification, etc. (SB Toolbox) M e M a o s d u e re l m m e o n Henning Schmidt, [email protected] -
Metabolic Flux Analysis—Linking Isotope Labeling and Metabolic Fluxes
H OH metabolites OH Review Metabolic Flux Analysis—Linking Isotope Labeling and Metabolic Fluxes Yujue Wang 1,2, Fredric E. Wondisford 1, Chi Song 3, Teng Zhang 4 and Xiaoyang Su 1,2,* 1 Department of Medicine, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA; [email protected] (Y.W.); [email protected] (F.E.W.) 2 Metabolomics Shared Resource, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08903, USA 3 Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH 43210, USA; [email protected] 4 Department of Mathematics, University of Central Florida, Orlando, FL 32816, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-732-235-5447 Received: 14 September 2020; Accepted: 4 November 2020; Published: 6 November 2020 Abstract: Metabolic flux analysis (MFA) is an increasingly important tool to study metabolism quantitatively. Unlike the concentrations of metabolites, the fluxes, which are the rates at which intracellular metabolites interconvert, are not directly measurable. MFA uses stable isotope labeled tracers to reveal information related to the fluxes. The conceptual idea of MFA is that in tracer experiments the isotope labeling patterns of intracellular metabolites are determined by the fluxes, therefore by measuring the labeling patterns we can infer the fluxes in the network. In this review, we will discuss the basic concept of MFA using a simplified upper glycolysis network as an example. We will show how the fluxes are reflected in the isotope labeling patterns. The central idea we wish to deliver is that under metabolic and isotopic steady-state the labeling pattern of a metabolite is the flux-weighted average of the substrates’ labeling patterns. -
An SBML Overview
An SBML Overview Michael Hucka, Ph.D. Control and Dynamical Systems Division of Engineering and Applied Science California Institute of Technology Pasadena, CA, USA 1 So much is known, and yet, not nearly enough... 2 Must weave solutions using different methods & tools 3 Common side-effect: compatibility problems 4 Models represent knowledge to be exchanged 5 SBML 6 SBML = Systems Biology Markup Language Format for representing quantitative models • Defines object model + rules for its use - Serialized to XML Neutral with respect to modeling framework • ODE vs. stochastic vs. ... A lingua franca for software • Not procedural 7 Some basics of SBML model encoding ๏ Well-stirred compartments c n 8 Some basics of SBML model encoding c protein A protein B n gene mRNAn mRNAc 9 Some basics of SBML model encoding ๏ Reactions can involve any species anywhere c protein A protein B n gene mRNAn mRNAc 10 Some basics of SBML model encoding ๏ Reactions can cross compartment boundaries c protein A protein B n gene mRNAn mRNAc 11 Some basics of SBML model encoding ๏ Reaction/process rates can be (almost) arbitrary formulas c protein A f1(x) protein B n f5(x) f2(x) gene f4(x) mRNAn f3(x) mRNAc 12 Some basics of SBML model encoding ๏ “Rules”: equations expressing relationships in addition to reaction sys. g1(x) c g (x) 2 protein A f1(x) protein B . n f5(x) f2(x) gene f4(x) mRNAn f3(x) mRNAc 13 Some basics of SBML model encoding ๏ “Events”: discontinuous actions triggered by system conditions g1(x) c g (x) 2 protein A f1(x) protein B . -
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. -
Modeling Using Biopax Standard
Modeling using BioPax standard Oliver Ruebenacker1 and Michael L. Blinov1 biological identification for each element of the model The key issue that should be resolved when making makes BioPax standard a recipe for reusable modeling reusable modeling modules is species recognition: are modules. species S1 of model 1 and species S2 of model 2 identical Currently, several online resources provide and should they be mapped onto the same species in a information about pathways in BioPax format: NetPath – merged model? When merging models, species Signal Transduction Pathways [5], Reactome - a curated recognition should be automated. We address this issue knowledgebase of biological pathways [6], and some others, by providing a modeling process compatible with with more resources aiming at BioPax representation. These Biological Pathways Exchange (BioPax) standard [1]. resources store complete pathways, pathways participants, and individual interactions. Due to principal differences Keywords — Model, Pathway data, BioPax, SBML, the between SBML and BioPax, there are no one-to-one Virtual Cell. translators from BioPax to SBML. We have implemented import of pathway data in BioPax standard into the Virtual he main format currently used for encoding of pathway Cell modeling framework [7], using Jena (Java application Tmodels is Systems Biology Markup Language (SBML) programming interface that provides support for Resource [2], which is designed mainly to enable the exchange of Description Framework). The imported data forms a model biochemical networks between different software packages skeleton where each species being automatically related to with little or no human intervention. SBML model contains some database entry. This model skeleton may lack data necessary for simulation, such as species, interactions simulation-related information (such as concentrations, among species, and kinetic laws for these interactions. -
Effective Dynamic Models of Metabolic Networks Michael Vilkhovoy∗, Mason Minot∗, Jeffrey D
bioRxiv preprint doi: https://doi.org/10.1101/047316; this version posted August 31, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 Effective Dynamic Models of Metabolic Networks Michael Vilkhovoy∗, Mason Minot∗, Jeffrey D. Varner∗ ∗School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14850 USA Abstract—Mathematical models of biochemical networks are by a pseudo enzyme whose expression is controlled by an useful tools to understand and ultimately predict how cells utilize optimal decision) to achieve a physiological objective (Fig. nutrients to produce valuable products. Hybrid cybernetic models 1A). HCMs generate intracellular flux distributions consistent in combination with elementary modes (HCM) is a tool to model cellular metabolism. However, HCM is limited to reduced with other approaches such as metabolic flux analysis (MFA), metabolic networks because of the computational burden of and also describe dynamic extracellular measurements superior calculating elementary modes. In this study, we developed the to dynamic FBA (DFBA) [12]. However, HCMs are restricted hybrid cybernetic modeling with flux balance analysis or HCM- to networks which can be decomposed into EMs (or EPs). FBA technique which uses flux balance solutions instead of In this study, we developed the hybrid cybernetic modeling elementary modes to dynamically model metabolism. We show HCM-FBA has comparable performance to HCM for a proof with flux balance analysis (HCM-FBA) technique. HCM- of concept metabolic network and for a reduced anaerobic E. -
Integrating Biopax Knowledge with SBML Models
Integrating BioPAX knowledge with SBML models O. Ruebenacker, I.I. Moraru, J.S. Schaff, M. L. Blinov Center for cell Analysis and Modeling, University of Connecticut Health Center Farmington, CT, USA E-mail: [email protected] Abstract Online databases store thousands of molecular interactions and pathways, and numerous modeling software tools provide users with an interface to create and simulate mathematical models of such interactions. However, the two most widespread used standards for storing pathway data (Biological Pathway Exchange; BioPAX) and for exchanging mathematical models of pathways (Systems Biology Markup Langiuage; SBML) are structurally and semantically different. Conversion between formats (making data present in one format available in another format) based on simple one-to-one mappings may lead to loss or distortion of data, is difficult to automate, and often impractical and/or erroneous. This seriously limits the integration of knowledge data and models. In this paper we introduce an approach for such integration based on a bridging format that we named Systems Biology Pathway Exchange (SBPAX) alluding to SBML and BioPAX. It facilitates conversion between data in different formats by a combination of one-to- one mappings to and from SBPAX and operations within the SBPAX data. The concept of SBPAX is to provide a flexible description expanding around essential pathway data – basically the common subset of all formats describing processes, the substances participating in these processes and their locations. SBPAX can act as a repository for molecular interaction data from a variety of sources in different formats, and the information about their relative relationships, thus providing a platform for converting between formats and documenting assumptions used during conversion, gluing (identifying related elements across different formats) and merging (creating a coherent set of data from multiple sources) data. -
Biological Pathways Exchange Language Level 3, Release Version 1 Documentation
BioPAX – Biological Pathways Exchange Language Level 3, Release Version 1 Documentation BioPAX Release, July 2010. The BioPAX data exchange format is the joint work of the BioPAX workgroup and Level 3 builds on the work of Level 2 and Level 1. BioPAX Level 3 input from: Mirit Aladjem, Ozgun Babur, Gary D. Bader, Michael Blinov, Burk Braun, Michelle Carrillo, Michael P. Cary, Kei-Hoi Cheung, Julio Collado-Vides, Dan Corwin, Emek Demir, Peter D'Eustachio, Ken Fukuda, Marc Gillespie, Li Gong, Gopal Gopinathrao, Nan Guo, Peter Hornbeck, Michael Hucka, Olivier Hubaut, Geeta Joshi- Tope, Peter Karp, Shiva Krupa, Christian Lemer, Joanne Luciano, Irma Martinez-Flores, Zheng Li, David Merberg, Huaiyu Mi, Ion Moraru, Nicolas Le Novere, Elgar Pichler, Suzanne Paley, Monica Penaloza- Spinola, Victoria Petri, Elgar Pichler, Alex Pico, Harsha Rajasimha, Ranjani Ramakrishnan, Dean Ravenscroft, Jonathan Rees, Liya Ren, Oliver Ruebenacker, Alan Ruttenberg, Matthias Samwald, Chris Sander, Frank Schacherer, Carl Schaefer, James Schaff, Nigam Shah, Andrea Splendiani, Paul Thomas, Imre Vastrik, Ryan Whaley, Edgar Wingender, Guanming Wu, Jeremy Zucker BioPAX Level 2 input from: Mirit Aladjem, Gary D. Bader, Ewan Birney, Michael P. Cary, Dan Corwin, Kam Dahlquist, Emek Demir, Peter D'Eustachio, Ken Fukuda, Frank Gibbons, Marc Gillespie, Michael Hucka, Geeta Joshi-Tope, David Kane, Peter Karp, Christian Lemer, Joanne Luciano, Elgar Pichler, Eric Neumann, Suzanne Paley, Harsha Rajasimha, Jonathan Rees, Alan Ruttenberg, Andrey Rzhetsky, Chris Sander, Frank Schacherer,