Logical modelling of cellular decisions
|
|
- Annabella Alaina Benson
- 5 years ago
- Views:
Transcription
1 Logical modelling of cellular decisions Denis Thieffry Contents Introduction Logical modelling T-helper cell differentiation MAPK network Porquerolles, June 25th, 2013
2 Cell proliferation, differentiation or death... How are decisions taken?
3 Key biological questions How does a cell decide which differentiation pathway to follow? When and to what extend cells become committed? To what extend and how is it possible to force cell to change their differentiation states? => Investigations using dynamical modelling
4 Dynamical modelling Why? To gain rigourous, global, functional understanding of the (complex) underlying networks To predict the behaviour of the system in novel situations To design novel experiments How? Regulatory charts/maps/graphs (CellDesigner, Cytoscape) Qualitative modelling: Boolean / multilevel discrete networks Quantitative modelling: ODE, PDE, Stochastic equations
5 Boolean networks - Stuart Kauffman (1969) x t +1 = B(x t ) The Boolean vector x represents the state of the system Random connections, nodes with predefined degree Canalizing Boolean functions Focus on asymptotic behaviour Two types of attractors: stable states and (simple) cycles Deterministic behaviour (only one possible following state)
6 Kinetic logic - René Thomas (1973) X = B(x) Xi (image or logical function) specifies whether gene i is currently transcribed xi (logical variable) denotes the presence (above a threshold of the functional product of gene i Gene i switched ON Gene i switched OFF 1 Xi 0 1 xi 0 t Delay don Delay doff
7 Logical modelling of regulatory networks [1] B A [2] C A graph describes the interactions between genes or regulatory products Discrete levels of expression associated to each regulatory component and interaction Logical rules/parameters K A = 2 IFF (C=0) K A = 0 otherwise C K B = 1 IFF (A=1) K B = 0 otherwise A K C = 1 IFF (B=1) AND (C=0) K C = 0 otherwise B C= C C Decision trees
8 Logical modelling of regulatory networks [1] B A [2] C A graph describes the interactions between genes or regulatory products Discrete levels of expression associated to each regulatory component and interaction Logical rules/parameters K A = 2 IFF (C=0) K A = 0 otherwise C C=0 1 K B = 1 IFF (A=1) K B = 0 otherwise A K C = 1 IFF (B=1) AND (C=0) K C = 0 otherwise B C Decision diagrams 1 0
9 Logical state transition graphs [1] B A [2] C Regulatory graph + Logical rules => simulations / dynamical analysis Asynchronous updating (R Thomas) ABC C C A State transition graph B B Stable state
10 Logical state transition graphs A [1] [2] C Synchronous updating (S Kauffman) B + Logical rules ABC A C State transition graph B C Cycle Cycle C A Stable state
11 Logical state transition graphs [1] A [2] C Mixed a/synchronous updating: 2 priority classes: (1) synchronous fast decays (2) synchronous slow syntheses B + Logical rules ABC A C State transition graph B B C A B B A Stable state Fauré et al (2006) Bioinformatics 22: e124-31
12 GINsim (Gene Interaction Networks simulation) Aurélien NALDI Fabrice LOPEZ Duncan BERENGIER Claudine CHAOUIYA analysis toolbox core simulator State transition graph GINML parser Regulatory graph graph editor user interface graph analysis simulation Available at Naldi et al (2009) BioSystems 97: Chaouiya et al (2013) Meth Mol Biol 804:
13 Development of dynamical analysis tools Decision diagrams Identification of attractors State transition graph compression Analysis of regulatory circuits Model reduction Priority classes Mixed a/synchronous simulations Petri nets Standard Petri nets Coloured Petri nets Model checking Verification of dynamical properties (temporal logic) Logical programming Attractor identification and reachability analysis
14 Efficient identification of stable states C => 2 stable states : 001 et 110 A B KA= 1 IFF A KB =1 IFF A &!C KC =1 IFF!A A A A 0 1 C
15 Efficient identification of stable states C => 2 stable states : 001 et 110 A B KA A KB A KC A A 1 1 * B B * B B stable 1 C C C C C 0 C stable unstable Stability condition Naldi, Chaouiya & Thieffry (2007) LNCS 4695:
16 Coping with the exponential growth of logical state transition graphs Attractor identification Compaction of state transition graphs Model reduction Temporisation (e.g. priorities, delays, etc.) Model checking
17 Bacteriophage lambda: regulatory graph Thieffry & Thomas (1995)
18 Phage lambda model : logical rules Node => target value CI => 2 CI => 0 Cro => 3 Cro => 2 Cro => 0 CII => 1 CII => 0 N => 1 N => 0 Logical Rule!Cro CII Otherwise!CI &!Cro!CI & Cro CI!CI &!Cro & N Otherwise!CI &!Cro Otherwise Thieffry & Thomas (1995)
19 Lambda phage model: state transition graph (STG) Lysogeny (only CI expressed) [CI, Cro, CII, N]
20 Lambda phage model: state transition graph (STG) coloration according to strongly connected components lysogeny only CI expressed cyclic attractor for lysis only CRO (homeostatically) expressed
21 Lambda phage model: graph of strongly connected components (SCCG) lysogeny lysis
22 Lambda phage model: hierarchical state transition graphs (HTG) transient pathways stable state CI CII N CI lysogeny only CI expressed Cro Cro Cro Cro CII N CI Cro CII N transient cycle CI cyclic attractor lysis only CRO (homeostatically) expressed HTG computation on the fly using Tarjan algorithm + decision diagrams
23 Content of HTG components (schemata) Component Type Number of states Schemata CI Cro CII N i-7 transient paths * * * i-3 transient paths * ct-31 transient cycles * * 1 1 * * * * * ct-2 transient cycle ss-2000 Stable state ca-2 cyclic attractor
24 Regulatory circuits: dynamics in isolation Positive circuit Negative circuit A A D B D B C C attracting cycle stable states Remy et al (2003) Bioinformatics 10: ii172-8
25 Regulatory circuits & Thomas' rules A positive regulatory circuit is necessary to generate multiple stable states or attractors A negative regulatory circuit is necessary to generate sustained oscillatory behaviour Thomas R (1988). Springer Series in Synergics 9: Mathematical theorems and demonstrations: In the differential framework: Thomas (+, 1994), Plathe et al. (±, 1995), Snoussi (±, 1998), Gouzé (±, 1998), Cinquin & Demongeot (+, 2002), Soulé (+, 2003). In the discrete framework: Aracena et al. (+, 2001), Remy et al. (±, 2005), Richard & Comet (+, 2005).
26 Regulatory circuit functionality A B C D Circuit properties depends on the effect of A on B If A alone is able to switch OFF B: In the presence of A: only one stable state with {A,B,C,D}= 1011 In the absence of A: two stable states 0100 and 0011 The positive cross-inhibitory circuit involving B and C is thus functional only in the absence of A Development of a computational algorithm enabling the analysis of the functionality of regulatory circuits in the discrete case => GINsim
27
28
29 CD4+ T-helper cell differentiation Multiple signalling pathways Various transcriptional factors Specific expression patterns (TFs and lymphokines)
30 Logical modelling of Th activation Klamt S et al (2006). BMC Bioinformatics 7: 56.
31 Logical modelling of Th1/Th2 cell differentiation Mendoza L (2006). BioSystems 84:
32 Towards a comprehensive, modular logical model of the Th differentiation network $% $%& ' $%& (!"#!"#" Yamaoka et al Yamoka et al (2004) ILR 1 if IL and ILR and ILR = 1 IFF IL AND ILR1 AND ILR2
33 Logical modelling of the Th network $%&' $%( ) $%( * $%( ILR = 1 IFF (IL OR IL_e) AND ILR1 AND ILR2 $%!"#"
34 Logical modelling of the Th network $%&' "6( $%*&' $%( ) $%( * $%*(9 $%*(2 $%( 78#" $%*(: $%*( $%!"#"!"#"5 IL = 1 IFF NFAT AND proliferation AND... +,-./01,23/-4
35 Logical modelling of the Th network!-./$%!"3$% Multiple uses of receptor chains!"#$%!-./&,!-./&#!"3&+!"#&'!"#&0!-./&!"3&!"#&(!"#& Converging signals Ternary variables )*+*, )*+*2 )*+*1
36 Current logical model of the Th network IFNB_e IFNG_e IL27_e IL6_e IL21_e IL23_e IL10_e TGFB_e IL12_e IL4_e IL15_e IL2_e APC CGC proliferation IFNGR2 IFNGR1 IL27RA IL6RA GP130 IL10RA IL10RB IL12RB1 IL12RB2 IL15RA IL4RA IL2RB IL2RA IFNBR IFNGR IL27R IL6R IL21R IL23R IL10R TGFBR IL12R IL4R IL15R IL2R CD28 TCR STAT1 STAT3 STAT4 STAT6 STAT5 NFAT IKB IFNG IL21 IL23 IL10 TGFB IL4 IL2 IL17 NFKB SMAD3 IRF1 RUNX3 TBET GATA3 RORGT FOXP3 13 input components, 52 internal components, 339 circuits => too large to perform simulations Naldi et al (2010) PLoS Comput Biol 6: e
37 Model Reduction Detailed model Comprehensive Difficult to analyse Reduced model Easier to analyse Loss of information - Biological (indirect effect) - Dynamical (delays)
38 Implementation of user defined model reductions R1 R3 X T R2 Keep the detailed model Reduction before analysis => New rules for targets of hidden nodes Choice of reduction Dynamical consistency R1 T R2 - No circuit deletion - Same stable states - Reachability may change R3 Naldi et al (2011) Theoretical Computer Science 412:
39 Reduced logical model IFNB_e IFNG_e IL27_e IL6_e IL21_e IL23_e IL10_e TGFB_e IL12_e IL4_e IL15_e IL2_e APC proliferation IL2RA IL2R STAT1 STAT3 STAT4 STAT6 STAT5 NFAT IFNG IL21 IL23 IL10 TGFB IL17 IL4 IL2 TBET GATA3 RORGT FOXP3 13 input components, 21 internal components
40 Selected environments for simulations APC IL2 IL4 IL6 IL10 IL12 IFNG TGFB No input APC Pro-Th1 Pro-Th1 Pro-Th2 Pro-Th17 Pro-Treg Pro-Treg
41 IL2R IL2RA IFNG IL2 Stable signatures IL4 IL10 IL21 IL23 TGFB TBET GATA3 FOXP3 NFAT STAT1 STAT3 STAT4 STAT5 STAT6 proliferation RORGT IL17 Support Th0 [7] Activated Th0 [7] Th1 [7] Activated Th1 [7] Anergic Th1 [78] Anergic Th1 ROR t+ predicted Th1 ROR t+ [44, 45, 70] Th1 Foxp3+ [12] Anergic Th17 Th2 [7] Activated Th2 [7] Anergic Th2 [78] Th2 ROR t+ [49] Activated Treg [79] Treg ROR t+ [46 48] Th1 Foxp3+ ROR t+ predicted Th2 Foxp3+ ROR t+ predicted
42 Simulations (Hierarchical Transition Graphs) APC + IL2 Pro Treg (TGFB) i#25 i#37 IL2+ Proliferation+ IFNG+ IL2- IL2- TGFb+ ss Activated Th0 ss Activated Th0 Pro Th1 (IFNG) i#79 ss Activated Th1 Pro TH2 (IL4, IL6) i#255 IL2- IL4+ IL10+ IL21+ IL23+ ss Activated Th2 Node order: APC, IFNB_e, IFNG_e, IL2_e, IL4_e, IL6_e, IL10_e, IL12_e, IL15_e, IL21_e, IL23_e, IL27_e, TGFB_e, IL2R, IL2RA, IFNG, IL2, IL4, IL10, IL21, IL23, TGFB, TBET, GATA3, FOXP3, NFAT, STAT1, STAT3, STAT4, STAT5, STAT6, Proliferation RORGT and IL17.
43 Simulations (HTG) Pro Th17 (TGFB, IL6) RORGT+ i#66 FOXP3+ i#39 i#91 IL2+ IL10+ IL21+ IL23+ RORGT+ TGFB+ RORGT+ IL2- IL10+ IL21+ IL23+ ss Activated Th17 FOXP3- ss Activated Th17 FOXP3+ Node order: APC, IFNB_e, IFNG_e, IL2_e, IL4_e, IL6_e, IL10_e, IL12_e, IL15_e, IL21_e, IL23_e, IL27_e, TGFB_e, IL2R, IL2RA, IFNG, IL2, IL4, IL10, IL21, IL23, TGFB, TBET, GATA3, FOXP3, NFAT, STAT1, STAT3, STAT4, STAT5, STAT6, Proliferation RORGT and IL17.
44 Simulations (HTG) APC + IL4 + IL6 + TGFB (pro Th2 + Th17 cytokines, in the absence of IL2) i#112(1) i#56 STAT5+ STAT5+ i#11 IL2- IL2R- IL2RA+ GATA3+ ss Anergic GATA3+ RORGT+ RORGT+ IL2R- IL2- STAT5+ STAT6+ RORGT+ RORGT+ i#24 RORGT+ i#112(2) IL10+ IL2R- IL4R+ IL21+ IL23+ STAT5+ RORGT+ i#143 ss Activated GATA3+ RORGT+ IL4+ IL10+ IL21+ IL23+ FOXP3+ FOXP3+ FOXP3+ FOXP3+ i#35 IL2R- IL2RA+ GATA3+ RORGT+ IL2R- IL2- STAT5+ STAT6+ IL2- IL2R- IL2- i#54 ss Anergic GATA3+ RORGT+ FOXP3+ STAT5+ i#595 IL2R- IL4- IL10+ IL21+ IL23+ TGFB+ GATA3+ RORGT+ ss Activated GATA3+ RORGT+ FOXP3+ IL10+ IL21+ IL23+ TGFB+ Node order: APC, IFNB_e, IFNG_e, IL2_e, IL4_e, IL6_e, IL10_e, IL12_e, IL15_e, IL21_e, IL23_e, IL27_e, TGFB_e, IL2R, IL2RA, IFNG, IL2, IL4, IL10, IL21, IL23, TGFB, TBET, GATA3, FOXP3, NFAT, STAT1, STAT3, STAT4, STAT5, STAT6, Proliferation RORGT and IL17.
45 Simulations in the absence of stimulation GATA3, Tbet, Foxp3 and RORγt
46 Pro Th2 environment (IL4 & IL6) GATA3, Tbet, Foxp3 and RORγt
47 Pro Treg environment (IL2 & TGFb IL10) GATA3, Tbet, Foxp3 and RORγt
48 Overview of the simulation results for micro-environments Absence of stimulation APC only Pro-Th1 IL2 & IFNg or IL12 Pro-Th2 IL4 & IL6 Pro-Treg IL2 & TGFb or IL10 Pro-Th17 IL6 & TGFb GATA3 Tbet Foxp3 RORγt Naldi et al (2010) PLoS Comput Biol 6: e
49 Use of model checking to assess cell plasticity Export of GINsim models into NuSMV format Specification of perturbation and stable patterns using temporal logic formula Graphical output Monteiro & Chaouiya (2012) Adv Intell Soft Comput 154: Bérenguier et al (2013) Chaos, in press.
50 Regulatory circuit analysis Functional positive circuits Negative circuits
51 Conclusions Model reproducing the main reported Th subtypes (Th0, Th1, Th2, Treg, Th17) in terms of stable states Many more stable states depending on signalling environment, including hybrid subtypes Plasticity of Th subtypes depending on signalling environment Differentiation network rather than lineage tree
52 Prospects Simulations of mutants and other perturbations (e.g. different timing for combinations of external signals) Extension of cellular model (additional pathways, transcription factors, interactions) Incorporation of high-throughput datasets (transcriptomics, proteomics) in collaboration with Vassili Soumelis, Institut Curie Consideration of novel subtypes Quantification of alternative outcomes
53 ENS (Paris) Wassim Abou-Jaoudé Samuel Collombet Jérôme Feret Anna Niarakis Morgane Thomas-Chollier Institut Curie (Paris) Emmanuel Barillot Eric Bonnet Laurence Calzone Philippe Hupé Vassili Soumelis Maxime Touzot Andrei Zinovyev TAGC (Marseille) Luca Grieco Aurélien Naldi Brigitte Kahn-Perlès Jacques van Helden IML (Marseille) Duncan Berenguier Elisabeth Rémy IGC (Lisboa) Claudine Chaouiya Jorge Carneiro Pedro Monteiro Contributors & supports Belgian Inter-university Attraction Pole Bioinformatics and Modelling : from Genomes to Networks
54 Selected references Bérenguier D, Chaouiya C, Monteiro PT, Naldi A, Remy E, Thieffry D, Tichit L (2013).Dynamical modeling and analysis of large cellular regulatory networks. Chaos. In press. Monteiro PT, Chaouiya C (2012). Efficient verification for logical models of regulatory networks. Adv Intell Soft Comput 154: Naldi A, Thieffry D, Chaouiya C (2007). Decision diagrams for the representation and analysis of logical models of genetic networks. Lecture Notes in Bioinformatics 4695: Naldi A, Remy E, Thieffry D, Chaouiya C (2011). Dynamically consistent reduction of logical regulatory graphs. Theoretical Computer Science 412: Naldi A, Carneiro J, Chaouiya C, Thieffry D (2010). Diversity and plasticity of Th cell types predicted from regulatory network modelling. PLoS Computational Biology 6: e
MINISYMPOSIUM LOGICAL MODELLING OF (MULTI)CELLULAR NETWORKS
Friday, July 27th, 11:00 MINISYMPOSIUM LOGICAL MODELLING OF (MULTI)CELLULAR NETWORKS Organizer Pedro T. Monteiro INESC-ID/IST - Univ. de Lisboa 1000-029 Lisboa, PT Pedro.Tiago.Monteiro@tecnico.ulisboa.pt
More informationSynchronous state transition graph
Heike Siebert, FU Berlin, Molecular Networks WS10/11 2-1 Synchronous state transition graph (0, 2) (1, 2) vertex set X (state space) edges (x,f(x)) every state has only one successor attractors are fixed
More informationQualitative Petri Net Modelling of Genetic Networks
Qualitative Petri Net Modelling of Genetic Networks Claudine Chaouiya 1, Elisabeth Remy 2, and Denis Thieffry 1 1 Institut de biologie du Développement de Marseille Luminy UMR 6216, Case 907 - Luminy,
More informationSupplementary Materials
Electronic Supplementary Material (ESI) for Integrative Biology. This journal is The Royal Society of Chemistry 2015 Predicting genetic interactions from Boolean models of biological networks Supplementary
More informationLogical modelling of genetic regulatory networks Contents
Logical modelling of genetic regulatory networks Contents Boolean modelling of gene networks Multilevel logical modelling Regulatory circuits Application to Drosophila segmentation Biological regulatory
More informationBasins of Attraction, Commitment Sets and Phenotypes of Boolean Networks arxiv: v1 [math.ds] 26 Jul 2018
Basins of Attraction, Commitment Sets and Phenotypes of Boolean Networks arxiv:1807.10103v1 [math.ds] 26 Jul 2018 Hannes Klarner, Frederike Heinitz, Sarah Nee, Heike Siebert Department of Mathematics,
More informationLogic-Based Modeling in Systems Biology
Logic-Based Modeling in Systems Biology Alexander Bockmayr LPNMR 09, Potsdam, 16 September 2009 DFG Research Center Matheon Mathematics for key technologies Outline A.Bockmayr, FU Berlin/Matheon 2 I. Systems
More informationbiological networks Claudine Chaouiya SBML Extention L3F meeting August
Campus de Luminy - Marseille - France Petri nets and qualitative modelling of biological networks Claudine Chaouiya chaouiya@igc.gulbenkian.pt chaouiya@tagc.univ-mrs.fr SML Extention L3F meeting 1-13 ugust
More informationR. Thomas logical method
R. Thomas logical method Adrien Richard, Jean-Paul Comet and Gilles Bernot Laboratoire I3S, CNRS & Université de Nice-Sophia Antipolis, 2000 route des Lucioles, 06903 Sophia Antipolis, France {richard,comet,bernot}@i3s.unice.fr
More informationSimulation of Gene Regulatory Networks
Simulation of Gene Regulatory Networks Overview I have been assisting Professor Jacques Cohen at Brandeis University to explore and compare the the many available representations and interpretations of
More informationLevel 3 Proposals/Qualitative Models
Proposal title Qualitative Models (qual) Proposal authors a Duncan Berenguier TAGC INSERM U928 and IML CNRS UMR 6206, Luminy, 163 av. de Luminy 13288 Marseille, France Claudine Chaouiya IGC Rua da Quinta
More informationarxiv: v1 [cs.dm] 13 Nov 2014
1 arxiv:1411.3539v1 [cs.dm] 13 Nov 2014 Quantification of reachable attractors in asynchronous discrete dynamics Nuno D. Mendes 1,3,4,, Pedro T. Monteiro 1,3,, Jorge Carneiro 1, Elisabeth Remy 2, Claudine
More informationModelling the cell cycle regulatory network
Chapter 3 Modelling the cell cycle regulatory network 3.1 Dynamical modelling Dynamical properties of a system arise from the interaction of its components. In the case of the cell division cycle, cellular
More informationGINsim: A software suite for the qualitative modelling, simulation and analysis of regulatory networks
BioSystems 84 (2006) 91 100 GINsim: A software suite for the qualitative modelling, simulation and analysis of regulatory networks A. Gonzalez Gonzalez a,b, A. Naldi a,l.sánchez b, D. Thieffry a, C. Chaouiya
More informationPositive and negative cycles in Boolean networks
Positive and negative cycles in Boolean networks In the memory of René Thomas Adrien Richard April 2, 2018 Abstract We review and discuss some results about the influence of positive and negative feedback
More informationRandom Boolean Networks
Random Boolean Networks Boolean network definition The first Boolean networks were proposed by Stuart A. Kauffman in 1969, as random models of genetic regulatory networks (Kauffman 1969, 1993). A Random
More informationAsynchronous Stochastic Boolean Networks as Gene Network Models
Journal of Computational Biology Journal of Computational Biology: http://mc.manuscriptcentral.com/liebert/jcb Asynchronous Stochastic Boolean Networks as Gene Network Models Journal: Journal of Computational
More informationCooperative development of logical modelling standards and tools with CoLoMoTo
Bioinformatics Advance Access published January 25, 2015 Cooperative development of logical modelling standards and tools with CoLoMoTo Aurélien Naldi 1, Pedro T. Monteiro 2,3, Christoph Müssel 4, the
More informationBiological networks CS449 BIOINFORMATICS
CS449 BIOINFORMATICS Biological networks Programming today is a race between software engineers striving to build bigger and better idiot-proof programs, and the Universe trying to produce bigger and better
More informationStochastic simulations
Stochastic simulations Application to molecular networks Literature overview Noise in genetic networks Origins How to measure and distinguish between the two types of noise (intrinsic vs extrinsic)? What
More informationA compact modeling approach for deterministic biological systems
A compact modeling approach for deterministic biological systems Luis M. Torres 1 Annegret K. Wagler 2 1 Ecuadorian Research Center on Mathematical Modeling ModeMat Escuela Politécnica Nacional, Quito
More informationLogical modeling of the mammalian cell cycle
Logical modeling of the mammalian cell cycle Pauline Traynard 1,2, Adrien Fauré 3, François Fages 2 and Denis Thieffry 1,2 1 Computational Systems Biology team, Institut de Biologie de l Ecole rmale Supérieure
More informationANALYSIS OF BIOLOGICAL NETWORKS USING HYBRID SYSTEMS THEORY. Nael H. El-Farra, Adiwinata Gani & Panagiotis D. Christofides
ANALYSIS OF BIOLOGICAL NETWORKS USING HYBRID SYSTEMS THEORY Nael H El-Farra, Adiwinata Gani & Panagiotis D Christofides Department of Chemical Engineering University of California, Los Angeles 2003 AIChE
More informationWritten Exam 15 December Course name: Introduction to Systems Biology Course no
Technical University of Denmark Written Exam 15 December 2008 Course name: Introduction to Systems Biology Course no. 27041 Aids allowed: Open book exam Provide your answers and calculations on separate
More informationAnalysing formal models of genetic regulatory networks with delays. Gilles Bernot, Jean-Paul Comet and Adrien Richard
240 Int. J. Bioinformatics Research and Applications, Vol. 4, No. 3, 2008 Analysing formal models of genetic regulatory networks with delays Jamil Ahmad and Olivier Roux IRCCyN UMR CNRS 6597, BP 92101,
More informationLearning in Bayesian Networks
Learning in Bayesian Networks Florian Markowetz Max-Planck-Institute for Molecular Genetics Computational Molecular Biology Berlin Berlin: 20.06.2002 1 Overview 1. Bayesian Networks Stochastic Networks
More informationSig2GRN: A Software Tool Linking Signaling Pathway with Gene Regulatory Network for Dynamic Simulation
Sig2GRN: A Software Tool Linking Signaling Pathway with Gene Regulatory Network for Dynamic Simulation Authors: Fan Zhang, Runsheng Liu and Jie Zheng Presented by: Fan Wu School of Computer Science and
More informationMA 777: Topics in Mathematical Biology
MA 777: Topics in Mathematical Biology David Murrugarra Department of Mathematics, University of Kentucky http://www.math.uky.edu/~dmu228/ma777/ Spring 2018 David Murrugarra (University of Kentucky) Lecture
More informationIV121: Computer science applications in biology
IV121: Computer science applications in biology Quantitative Models in Biology David Šafránek March 5, 2012 Obsah Continuous mass action Stochastic mass action Beyond elementary reaction kinetics What
More informationIntroduction to Bioinformatics
CSCI8980: Applied Machine Learning in Computational Biology Introduction to Bioinformatics Rui Kuang Department of Computer Science and Engineering University of Minnesota kuang@cs.umn.edu History of Bioinformatics
More information7.32/7.81J/8.591J. Rm Rm (under construction) Alexander van Oudenaarden Jialing Li. Bernardo Pando. Rm.
Introducing... 7.32/7.81J/8.591J Systems Biology modeling biological networks Lectures: Recitations: ti TR 1:00-2:30 PM W 4:00-5:00 PM Rm. 6-120 Rm. 26-204 (under construction) Alexander van Oudenaarden
More informationarxiv: v1 [math.ag] 13 Oct 2010
arxiv:1010.2669v1 [math.ag] 13 Oct 2010 Fast Gröbner Basis Computation for Boolean Polynomials Franziska Hinkelmann a,b September 24, 2018 Elizabeth Arnold c a Department of Mathematics, Virginia Polytechnic
More informationIntroduction. Dagmar Iber Jörg Stelling. CSB Deterministic, SS 2015, 1.
Introduction Dagmar Iber Jörg Stelling joerg.stelling@bsse.ethz.ch CSB Deterministic, SS 2015, 1 Origins of Systems Biology On this assumption of the passage of blood, made as a basis for argument, and
More informationGReg : a domain specific language for the modeling of genetic regulatory mechanisms
Proceedings of the 2nd International Workshop on Biological Processes & Petri Nets (BioPPN2011) online: http://ceur-ws.org/vol-724 pp.21-35 GReg : a domain specific language for the modeling of genetic
More informationDYNAMIC MODELING OF BIOLOGICAL AND PHYSICAL SYSTEMS
The Pennsylvania State University The Graduate School Eberly College of Science DYNAMIC MODELING OF BIOLOGICAL AND PHYSICAL SYSTEMS A Dissertation in Mathematics by Assieh Saadatpour Moghaddam 2012 Assieh
More informationPositive circuits and d-dimensional spatial differentiation: Application to the formation of sense organs in Drosophila
Positive circuits and d-dimensional spatial differentiation: Application to the formation of sense organs in Drosophila Anne Crumière Institut de Mathématiques de Luminy Campus de Luminy, Case 97, 3288
More informationIntroduction to Bioinformatics
Systems biology Introduction to Bioinformatics Systems biology: modeling biological p Study of whole biological systems p Wholeness : Organization of dynamic interactions Different behaviour of the individual
More informationComputational Genomics. Systems biology. Putting it together: Data integration using graphical models
02-710 Computational Genomics Systems biology Putting it together: Data integration using graphical models High throughput data So far in this class we discussed several different types of high throughput
More informationGene Network Science Diagrammatic Cell Language and Visual Cell
Gene Network Science Diagrammatic Cell Language and Visual Cell Mr. Tan Chee Meng Scientific Programmer, System Biology Group, Bioinformatics Institute Overview Introduction Why? Challenges Diagrammatic
More informationMeasuring TF-DNA interactions
Measuring TF-DNA interactions How is Biological Complexity Achieved? Mediated by Transcription Factors (TFs) 2 Regulation of Gene Expression by Transcription Factors TF trans-acting factors TF TF TF TF
More informationOptimal State Estimation for Boolean Dynamical Systems using a Boolean Kalman Smoother
Optimal State Estimation for Boolean Dynamical Systems using a Boolean Kalman Smoother Mahdi Imani and Ulisses Braga-Neto Department of Electrical and Computer Engineering Texas A&M University College
More informationSupplementary Figures
Supplementary Figures a x 1 2 1.5 1 a 1 = 1.5 a 1 = 1.0 0.5 a 1 = 1.0 b 0 0 20 40 60 80 100 120 140 160 2 t 1.5 x 2 1 0.5 a 1 = 1.0 a 1 = 1.5 a 1 = 1.0 0 0 20 40 60 80 100 120 140 160 t Supplementary Figure
More informationClassification of Random Boolean Networks
Classification of Random Boolean Networks Carlos Gershenson, School of Cognitive and Computer Sciences University of Sussex Brighton, BN1 9QN, U. K. C.Gershenson@sussex.ac.uk http://www.cogs.sussex.ac.uk/users/carlos
More informationBasic modeling approaches for biological systems. Mahesh Bule
Basic modeling approaches for biological systems Mahesh Bule The hierarchy of life from atoms to living organisms Modeling biological processes often requires accounting for action and feedback involving
More informationA New Method to Build Gene Regulation Network Based on Fuzzy Hierarchical Clustering Methods
International Academic Institute for Science and Technology International Academic Journal of Science and Engineering Vol. 3, No. 6, 2016, pp. 169-176. ISSN 2454-3896 International Academic Journal of
More informationRené Thomas Université de Bruxelles. Frontier diagrams: a global view of the structure of phase space.
René Thomas Université de Bruxelles Frontier diagrams: a global view of the structure of phase space. We have the tools to identify and characterise steady states and trajectories. But WHY several steady
More informationsimplified Petri nets and analysis uk/gnapn/
Supplementary information S1 (table) Databases and tools Logical Booleannet GINsim 1 GNaPN 2 MetaReg 3-5 Continuous JigCell 6 Narrator 7 Oscill8 PET Modelling and analysis of RNs. Tools for editing and
More informationNetworks in systems biology
Networks in systems biology Matthew Macauley Department of Mathematical Sciences Clemson University http://www.math.clemson.edu/~macaule/ Math 4500, Spring 2017 M. Macauley (Clemson) Networks in systems
More informationClassification of Random Boolean Networks
in Artificial Life VIII, Standish, Abbass, Bedau (eds)(mit Press) 2002. pp 1 8 1 Classification of Random Boolean Networks Carlos Gershenson, School of Cognitive and Computer Sciences University of Sussex
More informationCellular Automata. ,C ) (t ) ,..., C i +[ K / 2] Cellular Automata. x > N : C x ! N. = C x. x < 1: C x. = C N+ x.
and beyond Lindenmayer Systems The World of Simple Programs Christian Jacob Department of Computer Science Department of Biochemistry & Molecular Biology University of Calgary CPSC 673 Winter 2004 Random
More informationIdentifying Signaling Pathways
These slides, excluding third-party material, are licensed under CC BY-NC 4.0 by Anthony Gitter, Mark Craven, Colin Dewey Identifying Signaling Pathways BMI/CS 776 www.biostat.wisc.edu/bmi776/ Spring 2018
More informationPOSITIVE CIRCUITS AND MAXIMAL NUMBER OF FIXED
LABORATOIRE INFORMATIQUE, SIGNAUX ET SYSTÈMES DE SOPHIA ANTIPOLIS UMR 6070 POSITIVE CIRCUITS AND MAXIMAL NUMBER OF FIXED POINTS IN DISCRETE DYNAMICAL SYSTEMS Adrien Richard Equipe BIOINFO Rapport de recherche
More informationAccepted Manuscript. Boolean Modeling of Biological Regulatory Networks: A Methodology Tutorial. Assieh Saadatpour, Réka Albert
Accepted Manuscript Boolean Modeling of Biological Regulatory Networks: A Methodology Tutorial Assieh Saadatpour, Réka Albert PII: S1046-2023(12)00277-0 DOI: http://dx.doi.org/10.1016/j.ymeth.2012.10.012
More informationAn LTL Model Checking Approach for Biological Parameter Inference
An LTL Model Checking Approach for Biological Parameter Inference E. Gallet 1, M. Manceny 2, P. Le Gall 1, and P. Ballarini 1 1 Laboratoire MAS, Ecole Centrale Paris, 92195 Châtenay-Malabry, France email:
More informationApplications of Petri Nets
Applications of Petri Nets Presenter: Chung-Wei Lin 2010.10.28 Outline Revisiting Petri Nets Application 1: Software Syntheses Theory and Algorithm Application 2: Biological Networks Comprehensive Introduction
More informationQualitative dynamics semantics for SBGN process description
Qualitative dynamics semantics for SBGN process description Adrien Rougny, Christine Froidevaux, Laurence Calzone, Loïc Paulevé To cite this version: Adrien Rougny, Christine Froidevaux, Laurence Calzone,
More informationPetri net models. tokens placed on places define the state of the Petri net
Petri nets Petri net models Named after Carl Adam Petri who, in the early sixties, proposed a graphical and mathematical formalism suitable for the modeling and analysis of concurrent, asynchronous distributed
More informationA REDUCTION METHOD FOR BOOLEAN NETWORK MODELS PROVEN TO CONSERVE ATTRACTORS
A REDUCTION METHOD FOR BOOLEAN NETWORK MODELS PROVEN TO CONSERVE ATTRACTORS ASSIEH SAADATPOUR, RÉKA ALBERT, AND TIMOTHY C. RELUGA Abstract. Boolean models, wherein each component is characterized with
More informationDynamical-Systems Perspective to Stem-cell Biology: Relevance of oscillatory gene expression dynamics and cell-cell interaction
Dynamical-Systems Perspective to Stem-cell Biology: Relevance of oscillatory gene expression dynamics and cell-cell interaction Kunihiko Kaneko Universal Biology Inst., Center for Complex-Systems Biology,
More information5.3 METABOLIC NETWORKS 193. P (x i P a (x i )) (5.30) i=1
5.3 METABOLIC NETWORKS 193 5.3 Metabolic Networks 5.4 Bayesian Networks Let G = (V, E) be a directed acyclic graph. We assume that the vertices i V (1 i n) represent for example genes and correspond to
More informationModeling of Multiple Valued Gene Regulatory Networks
Proceedings of the 29th Annual International Conference of the IEEE EMBS Cité Internationale, Lyon, France August 23 26, 2007. ThD06.5 Modeling of Multiple Valued Gene Regulatory Networks Abhishek Garg,
More informationMathematical Biology - Lecture 1 - general formulation
Mathematical Biology - Lecture 1 - general formulation course description Learning Outcomes This course is aimed to be accessible both to masters students of biology who have a good understanding of the
More informationFUNDAMENTALS of SYSTEMS BIOLOGY From Synthetic Circuits to Whole-cell Models
FUNDAMENTALS of SYSTEMS BIOLOGY From Synthetic Circuits to Whole-cell Models Markus W. Covert Stanford University 0 CRC Press Taylor & Francis Group Boca Raton London New York Contents /... Preface, xi
More informationOn the convergence of Boolean automata networks without negative cycles
On the convergence of Boolean automata networks without negative cycles Tarek Melliti, Damien Regnault, Adrien Richard, and Sylvain Sené,3 Laboratoire IBISC, EA456, Université d Évry Val-d Essonne, 9000
More informationAlgorithms and methods of the BoolNet R package
Algorithms and methods of the BoolNet R package Christoph Müssel, Martin Hopfensitz, Hans A. Kestler Abstract This document describes the algorithms and methods that were developed or partially adapted
More information86 Part 4 SUMMARY INTRODUCTION
86 Part 4 Chapter # AN INTEGRATION OF THE DESCRIPTIONS OF GENE NETWORKS AND THEIR MODELS PRESENTED IN SIGMOID (CELLERATOR) AND GENENET Podkolodny N.L. *1, 2, Podkolodnaya N.N. 1, Miginsky D.S. 1, Poplavsky
More informationLectures on Medical Biophysics Department of Biophysics, Medical Faculty, Masaryk University in Brno. Biocybernetics
Lectures on Medical Biophysics Department of Biophysics, Medical Faculty, Masaryk University in Brno Norbert Wiener 26.11.1894-18.03.1964 Biocybernetics Lecture outline Cybernetics Cybernetic systems Feedback
More informationnatural development from this collection of knowledge: it is more reliable to predict the property
1 Chapter 1 Introduction As the basis of all life phenomena, the interaction of biomolecules has been under the scrutiny of scientists and cataloged meticulously [2]. The recent advent of systems biology
More informationOn Timed Models of Gene Networks
On Timed Models of Gene Networks Gregory Batt, Ramzi Ben Salah and Oded Maler VERIMAG, 2, av. de Vignate, 38610 Gieres, France Gregory.Batt@imag.fr Ramzi.Salah@imag.fr Oded.Maler@imag.fr Abstract. We present
More informationProbabilistic Gene Network
Probabilistic Gene Network Kristine Joy E. Carpio,3, Gilles Bernot 2, Jean-Paul Comet 2 and Francine Diener Laboratoire J.A. Dieudonné, Université de Nice - Sophia Antipolis, France 2 CNRS UMR 727, Laboratoire
More informationInferring Protein-Signaling Networks II
Inferring Protein-Signaling Networks II Lectures 15 Nov 16, 2011 CSE 527 Computational Biology, Fall 2011 Instructor: Su-In Lee TA: Christopher Miles Monday & Wednesday 12:00-1:20 Johnson Hall (JHN) 022
More informationFCModeler: Dynamic Graph Display and Fuzzy Modeling of Regulatory and Metabolic Maps
FCModeler: Dynamic Graph Display and Fuzzy Modeling of Regulatory and Metabolic Maps Julie Dickerson 1, Zach Cox 1 and Andy Fulmer 2 1 Iowa State University and 2 Proctor & Gamble. FCModeler Goals Capture
More informationAn introduction to SYSTEMS BIOLOGY
An introduction to SYSTEMS BIOLOGY Paolo Tieri CNR Consiglio Nazionale delle Ricerche, Rome, Italy 10 February 2015 Universidade Federal de Minas Gerais, Belo Horizonte, Brasil Course outline Day 1: intro
More informationCS-E5880 Modeling biological networks Parameter estimation for biological networks
CS-E5880 Modeling biological networks Parameter estimation for biological networks Jukka Intosalmi Department of Computer Science Aalto University January 16, 2018 Outline ODE model calibration as an optimization
More informationBioinformatics 3. V18 Kinetic Motifs. Fri, Jan 8, 2016
Bioinformatics 3 V18 Kinetic Motifs Fri, Jan 8, 2016 Modelling of Signalling Pathways Curr. Op. Cell Biol. 15 (2003) 221 1) How do the magnitudes of signal output and signal duration depend on the kinetic
More informationBioinformatics 3! V20 Kinetic Motifs" Mon, Jan 13, 2014"
Bioinformatics 3! V20 Kinetic Motifs" Mon, Jan 13, 2014" Modelling of Signalling Pathways" Curr. Op. Cell Biol. 15 (2003) 221" 1) How do the magnitudes of signal output and signal duration depend on the
More informationCOMPRESSED STATE SPACE REPRESENTATIONS - BINARY DECISION DIAGRAMS
QUALITATIVE ANALYIS METHODS, OVERVIEW NET REDUCTION STRUCTURAL PROPERTIES COMPRESSED STATE SPACE REPRESENTATIONS - BINARY DECISION DIAGRAMS LINEAR PROGRAMMING place / transition invariants state equation
More informationTheoretical distribution of PSSM scores
Regulatory Sequence Analysis Theoretical distribution of PSSM scores Jacques van Helden Jacques.van-Helden@univ-amu.fr Aix-Marseille Université, France Technological Advances for Genomics and Clinics (TAGC,
More informationGeorg Frey ANALYSIS OF PETRI NET BASED CONTROL ALGORITHMS
Georg Frey ANALYSIS OF PETRI NET BASED CONTROL ALGORITHMS Proceedings SDPS, Fifth World Conference on Integrated Design and Process Technologies, IEEE International Conference on Systems Integration, Dallas,
More informationControlling chaos in random Boolean networks
EUROPHYSICS LETTERS 20 March 1997 Europhys. Lett., 37 (9), pp. 597-602 (1997) Controlling chaos in random Boolean networks B. Luque and R. V. Solé Complex Systems Research Group, Departament de Fisica
More informationBoolean genetic network model for the control of C. elegans early embryonic cell cycles
RESEARCH Open Access Boolean genetic network model for the control of C. elegans early embryonic cell cycles Xiaotai Huang 1*, Long Chen 1, Hung Chim 1, Leanne Lai Hang Chan 1, Zhongying Zhao 2, Hong Yan
More informationBioinformatics 3 V10 Simulating the Dynamics of Gene Regulatory Networks by Boolean Networks. Fri, Nov 27, Bioinformatics 3 WS 15/16 V 10
Bioinformatics 3 V10 Simulating the Dynamics of Gene Regulatory Networks by Boolean Networks Fri, Nov 27, 2015 1 Quorum sensing of Vibrio fischeri This luminescent bacterium exists in small amounts in
More informationCoupled Random Boolean Network Forming an Artificial Tissue
Coupled Random Boolean Network Forming an Artificial Tissue M. Villani, R. Serra, P.Ingrami, and S.A. Kauffman 2 DSSC, University of Modena and Reggio Emilia, via Allegri 9, I-4200 Reggio Emilia villani.marco@unimore.it,
More informationLecture 1 Modeling in Biology: an introduction
Lecture 1 in Biology: an introduction Luca Bortolussi 1 Alberto Policriti 2 1 Dipartimento di Matematica ed Informatica Università degli studi di Trieste Via Valerio 12/a, 34100 Trieste. luca@dmi.units.it
More informationNetwork Biology: Understanding the cell s functional organization. Albert-László Barabási Zoltán N. Oltvai
Network Biology: Understanding the cell s functional organization Albert-László Barabási Zoltán N. Oltvai Outline: Evolutionary origin of scale-free networks Motifs, modules and hierarchical networks Network
More informationProperty preservation along embedding of biological regulatory networks
Property preservation along embedding of biological regulatory networks Mbarka Mabrouki 12, Marc Aiguier 12, Jean-Paul Comet 3, and Pascale Le Gall 12 1 École Centrale Paris Laboratoire de Mathématiques
More informationComputational Systems Biology
Computational Systems Biology Vasant Honavar Artificial Intelligence Research Laboratory Bioinformatics and Computational Biology Graduate Program Center for Computational Intelligence, Learning, & Discovery
More informationNoisy Attractors and Ergodic Sets in Models. of Genetic Regulatory Networks
Noisy Attractors and Ergodic Sets in Models of Genetic Regulatory Networks Andre S. Ribeiro Institute for Biocomplexity and Informatics, Univ. of Calgary, Canada Department of Physics and Astronomy, Univ.
More informationSystem Modelling of Mammalian Cell Cycle Regulation Using Multi-Level Hybrid Petri Nets
21st International Congress on Modelling and Simulation, Gold Coast, Australia, 29 Nov to 4 Dec 2015 www.mssanz.org.au/modsim2015 System Modelling of Mammalian Cell Cycle Regulation Using Multi-Level A.
More informationSimplicity is Complexity in Masquerade. Michael A. Savageau The University of California, Davis July 2004
Simplicity is Complexity in Masquerade Michael A. Savageau The University of California, Davis July 2004 Complexity is Not Simplicity in Masquerade -- E. Yates Simplicity is Complexity in Masquerade One
More informationPOLYNOMIAL SPACE QSAT. Games. Polynomial space cont d
T-79.5103 / Autumn 2008 Polynomial Space 1 T-79.5103 / Autumn 2008 Polynomial Space 3 POLYNOMIAL SPACE Polynomial space cont d Polynomial space-bounded computation has a variety of alternative characterizations
More informationHow to Build a Living Cell in Software or Can we computerize a bacterium?
How to Build a Living Cell in Software or Can we computerize a bacterium? Tom Henzinger IST Austria Turing Test for E. coli Fictional ultra-high resolution video showing molecular processes inside the
More informationOn the Stability of Hybrid Limit Cycles and Isolated Equilibria in a Genetic Network with Binary Hysteresis
On the Stability of Hybrid Limit Cycles and Isolated Equilibria in a Genetic Network with Binary Hysteresis Qin Shu and Ricardo G. Sanfelice Abstract A mathematical model for a two-gene regulatory network
More informationPredici 11 Quick Overview
Predici 11 Quick Overview PREDICI is the leading simulation package for kinetic, process and property modeling with a major emphasis on macromolecular systems. It has been successfully utilized to model
More informationUnravelling the biochemical reaction kinetics from time-series data
Unravelling the biochemical reaction kinetics from time-series data Santiago Schnell Indiana University School of Informatics and Biocomplexity Institute Email: schnell@indiana.edu WWW: http://www.informatics.indiana.edu/schnell
More informationIntroduction to Systems Biology
Introduction to Systems Biology References: Watson s Molecular Biology of the Gene, Chapter 22 Alberts Molecular Biology of the Cell, Chapter 7 Yousof Gheisari ygheisari@med.mui.ac.ir Why is this picture
More informationImmunetworks, intersecting circuits and dynamics
Immunetworks, intersecting circuits and dynamics Jacques Demongeot a,d, Adrien Elena a, Mathilde Noual b,d, Sylvain Sené c,d,, Florence Thuderoz a a Université Joseph Fourier de Grenoble, AGIM, CNRS FRE
More informationSPA for quantitative analysis: Lecture 6 Modelling Biological Processes
1/ 223 SPA for quantitative analysis: Lecture 6 Modelling Biological Processes Jane Hillston LFCS, School of Informatics The University of Edinburgh Scotland 7th March 2013 Outline 2/ 223 1 Introduction
More informationFrom cell biology to Petri nets. Rainer Breitling, Groningen, NL David Gilbert, London, UK Monika Heiner, Cottbus, DE
From cell biology to Petri nets Rainer Breitling, Groningen, NL David Gilbert, London, UK Monika Heiner, Cottbus, DE Biology = Concentrations Breitling / 2 The simplest chemical reaction A B irreversible,
More informationarxiv: v1 [q-bio.mn] 7 Nov 2018
Role of self-loop in cell-cycle network of budding yeast Shu-ichi Kinoshita a, Hiroaki S. Yamada b a Department of Mathematical Engineering, Faculty of Engeneering, Musashino University, -- Ariake Koutou-ku,
More information