Hill Kinetics Meets P Systems A Case Study on Gene Regulatory - PowerPoint PPT Presentation

Introduction Hill Kinetics Case Study: Computational Units Discussion, Conclusion, Further Work Hill Kinetics Meets P Systems A Case Study on Gene Regulatory Networks as Computing Agents in silico and in vivo Thomas Hinze 1 Sikander Hayat 2 Thorsten Lenser 1 Naoki Matsumaru 1 Peter Dittrich 1 {hinze,thlenser,naoki,dittrich}@minet.uni-jena.de s.hayat@bioinformatik.uni-saarland.de 1 Bio Systems Analysis Group Friedrich Schiller University Jena www.minet.uni-jena.de/csb 2 Computational Biology Group Saarland University www.zbi-saar.de Eight Workshop on Membrane Computing Hill Kinetics Meets P Systems Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

Introduction Hill Kinetics Case Study: Computational Units Discussion, Conclusion, Further Work Outline Hill Kinetics Meets P Systems Introduction • Research Project, Motivation, Intention • Biological Principles of Gene Regulatory Networks (GRNs) • Modelling Approaches, Transformation Strategies, Comparison Hill Kinetics output regulatory circuit Ptrc gfp cl857 lac I PL* PL* pCIRb pTSM b2 • Definition and Discretisation AHL signal pAHLb Plux Lux I lux I lux R lac I PL* • P Systems Π Hill sensor normalised output concentration h 1 0.9 h + m = 2 0.8 Θ = 5 0.7 0.6 • Dynamical Behaviour 0.5 x*x/(x*x+25) 50% 1-x*x/(x*x+25) 0.4 0.3 0.2 h −− 0.1 Θ • Introductory Example 0 0 5 10 15 20 input concentration x x a b z x y z complex formation Case Study: Computational Units x & 0 0 1 z RegGeneX RegGeneY EffGene y 0 1 1 1 0 1 y NAND gate 1 1 0 • Inverter 1 Normalised concentration 0.8 0.6 Input1: 1 Input1: 1 Input1: 0 Input1: 0 • NAND Gate Input2: 0 Input2: 1 Input2: 1 Input2: 0 0.4 0.2 Output 0 • RS Flip-Flop and Its Validation in vivo 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 Time scale Discussion, Conclusion, Further Work Hill Kinetics Meets P Systems Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

Introduction Hill Kinetics Case Study: Computational Units Discussion, Conclusion, Further Work ESIGNET – Research Project Evolving Cell Signalling Networks in silico European interdisciplinary research project • University of Birmingham (Computer Science) • TU Eindhoven (Biomedical Engineering) • Dublin City University (Artificial Life Lab) • University of Jena (Bio Systems Analysis) Objectives • Study the computational properties of GRNs • Develop new ways to model and predict real GRNs • Gain new theoretical perspectives on real GRNs Computing Facilities • Cluster of 33 workstations (two Dual Core AMD Opteron TM 270 processors) • Use of Dresden BIOTEC laboratories for in vivo studies Hill Kinetics Meets P Systems Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

Introduction Hill Kinetics Case Study: Computational Units Discussion, Conclusion, Further Work ESIGNET – Research Project Evolving Cell Signalling Networks in silico European interdisciplinary research project • University of Birmingham (Computer Science) • TU Eindhoven (Biomedical Engineering) • Dublin City University (Artificial Life Lab) • University of Jena (Bio Systems Analysis) Objectives • Study the computational properties of GRNs • Develop new ways to model and predict real GRNs • Gain new theoretical perspectives on real GRNs Computing Facilities • Cluster of 33 workstations (two Dual Core AMD Opteron TM 270 processors) • Use of Dresden BIOTEC laboratories for in vivo studies Hill Kinetics Meets P Systems Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

Introduction Hill Kinetics Case Study: Computational Units Discussion, Conclusion, Further Work ESIGNET – Research Project Evolving Cell Signalling Networks in silico European interdisciplinary research project • University of Birmingham (Computer Science) • TU Eindhoven (Biomedical Engineering) • Dublin City University (Artificial Life Lab) • University of Jena (Bio Systems Analysis) Objectives • Study the computational properties of GRNs • Develop new ways to model and predict real GRNs • Gain new theoretical perspectives on real GRNs Computing Facilities • Cluster of 33 workstations (two Dual Core AMD Opteron TM 270 processors) • Use of Dresden BIOTEC laboratories for in vivo studies Hill Kinetics Meets P Systems Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

Introduction Hill Kinetics Case Study: Computational Units Discussion, Conclusion, Further Work Motivation and Intention Exploring Dynamical Behaviour of Gene Regulatory Networks • Understanding biological reaction networks: essential task in systems biology output regulatory circuit • Many coexisting approaches: Ptrc gfp cl857 lac I PL* PL* pCIRb pTSM b2 analytic, stochastic, algebraic AHL signal pAHLb Plux Lux I lux I lux R lac I PL* • Each specifically emphasises sensor certain modelling aspects ? ? • Emulating dynamical system behaviour based on reaction kinetics 1 0 0 1 1 0 0 0 1 1 0 0 1 0 = ⇒ often key to network functions 1 0 1 1 0 1 0 1 0 0 1 0 0 1 • Reaction kinetics mostly specified for # analytic models based on ODE • Combining advantages of approaches: transformation strategies, model shifting ! • Example: Transformation of Hill Kinetics to P Systems time Hill Kinetics Meets P Systems Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

Introduction Hill Kinetics Case Study: Computational Units Discussion, Conclusion, Further Work Motivation and Intention Exploring Dynamical Behaviour of Gene Regulatory Networks • Understanding biological reaction networks: essential task in systems biology output regulatory circuit • Many coexisting approaches: Ptrc gfp cl857 lac I PL* PL* pCIRb pTSM b2 analytic, stochastic, algebraic AHL signal pAHLb Plux Lux I lux I lux R lac I PL* • Each specifically emphasises sensor certain modelling aspects ? ? • Emulating dynamical system behaviour based on reaction kinetics 1 0 0 1 1 0 0 0 1 1 0 0 1 0 = ⇒ often key to network functions 1 0 1 1 0 1 0 1 0 0 1 0 0 1 • Reaction kinetics mostly specified for # analytic models based on ODE • Combining advantages of approaches: transformation strategies, model shifting ! • Example: Transformation of Hill Kinetics to P Systems time Hill Kinetics Meets P Systems Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

Introduction Hill Kinetics Case Study: Computational Units Discussion, Conclusion, Further Work Biological Principles of Gene Regulation Intercellular Information Processing of Spatial Globality within Organisms gene product Activation (positive gene regulation) signalling substances activation pathway can amplify activation genomic DNA regulator gene effector gene gene expression transcription factor enables gene expression no/few gene product Inhibition (negative gene regulation) signalling substances (inducers) repression pathway can weak repression genomic DNA regulator gene effector gene gene expression transcription factor inhibits gene expression Feedback loops: gene products can act as transcription factors and signalling substances forming gene regulatory networks Hill Kinetics Meets P Systems Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich

Introduction Hill Kinetics Case Study: Computational Units Discussion, Conclusion, Further Work Modelling Approaches and Transformation Strategies and modular compositions, identifying functional units predicting dynamical behaviour of species about discrete signal carriers, hierarchical P systems grammars evaluating structural information recursion steps term correspond to rewriting transitions systems cellular autom. sentential forms X machines difference resp. intermediate state−based equations terms correspond to states machines / automata differentiate / algebraic discretise analytic states correspond to with respect to molecular configurations time and/or space whose trace inter− pretable as process phenotypic differential Petri nets representation process equations pi−calculus calculi / ambient calc. of GRNs models normalised parameters of reaction stochastic nodes correspond to kinetics correspond processes or constraints; to probabilities edges correspond to depen− dencies between processes stochastic statistically Bayesian modelling simulation cumulate / networks algorithms extract simulation case studies master eqn. Markov chains verification Gillespie wetlab experimental data considering randomness and probabilities to study ranges of possible scenarios Hill Kinetics Meets P Systems Thomas Hinze, Sikander Hayat, Thorsten Lenser, Naoki Matsumaru, Peter Dittrich