( Agent-based Modelling and Simulation of Complex Systems )


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    MODNC (MOPS)
    mSSB (Agent Based Simul.)
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    Programmation Impérative 		: Context :
Master 2 mSSB
Year 2014/2015


 : Objectives :
The course on agent-based simulation was formerly part of the Integrative Modelling in Physiology course. The aim is to present the basic principles of cellular automata and agent-based simulation and to show how they can be applied to the modelling and simulation in biology at large and more specifically for physiology.


: Slides :
Introduction.pdf Computational Models of Complex Systems, Introductory lecture 1 , René Doursat
Introduction.pdf Computational Models of Complex Systems, Introductory lecture 2 , René Doursat
Introduction.pdf Modelling and Simulation of Complex Biological Systems
Introduction.pdf CellularAutomata
Introduction.pdf Ordinary Differential Equations (ODEs) and the biological switch, Denis Mestivier

: Projects :
The project consists in modelling and simulating cellular differentiation processes. We will be particularly interested in hypothesises postulating a preponderant role for stochastic phenomena. (see attached reference). Make the study of this system by making the modelling choices and the parameters introduced explicit. Simulate the system through the Netlogo platform and show the dynamics obtained. You will also study the parameters by showing the influence of the various parameters on the system dynamics obtained.

Bertrand Laforge, David Guez, Michael Martinez, Jean-Jacques Kupiec, Modeling embryogenesis and cancer: an approach based on an equilibrium between the autostabilization of stochastic gene expression and the interdependence of cells for proliferation, Progress in Biophysics and Molecular Biology, Volume 89, Issue 1, September 2005, Pages 93-120 (download)

: Selected Readings :
  • Amar P., Bernot G., Norris V. Modeling and simulation of large assemblies of proteins. In Modeling and simulation of biological processes in the context of genomics (2003) 41-48.
  • Ballet P., Zemirline A. and Marcé L. The BioDyn Language and Simulator. Application to an immune response and E. Coli and Phage interaction. Journal of Biological Physics and Chemistry 4 (2004) 93-101.
  • Durett R. and Levin S. The importance of being discrete (and spatial). Theoretical population biology 46 (1994) 363-394.
  • Gonzalez, P., et al. Cellulat: an agent-based intracellular signalling model. BioSystems 68 (2003) 171-185.
  • Kerdelo S., Abgrall J. and Tisseau J. Multi-agent systems: a useful tool for the modelization and simulation of the blood coagulation cascade. In Proc. Bioinformatics and Multi-agent systems (2002) pp. 33-36.
  • Khan S., Makkena R., McGeary F., Decker K., Gillis W. and Schmidt C. A multi-agent system for the quantitative simulation of biological networks. In Proc. Autonomous Agents and Multi-Agent Systems'03 (2003) pp. 385-392.
  • Kier L.B., Cheng C.K., Testa B. and Carrupt P.A. A cellular automata model of enzyme kinetics. J Mol Graph 14(4) (1996) 227-231.
  • Kreft J.U., Booth G. et al. BacSim, a simulator for individual-based modeling of bacterial colony growth (1998) http://www.eeb.yale.edu/ginger/bacillus/node1.html
  • Le Page C. and Cury P. How spatial heterogeneity influences population dynamics: simulations in SeaLab. Adaptive Behavior vol. 4, n° 3/4 (1996) 255-281.
  • Le Sceller L., Ripoll C., Demarty M., Cabin-Flaman A., Nyström T., Saier Jnr. M. and Norris V. Modeling bacterial hyperstructures with cellular automata. Interjournal paper 366, http://www.interjournal.org (2000).
  • Pouchard L., Ward R. and Leuze M. An agent modeling approach to complex biological pathways. In Proc. Bioinformatics and Multi-agent systems (2002) pp. 37-39.
  • Reynolds C. Flocks, herds and schools: a distributed behavioral model. Computer graphics vol. 21, n° 4 (1987) 25-34.
  • Shnerb N. M., Louzoun Y., Bettelheim E. and Solomon S. The importance of being discrete: Life always wins on a surface. PNAS vol. 97, n°19 (2000) 10322-10324.
  • Van Dyke Parunak H., Savit R. et al. Agent based modeling vs equation based modeling: a case study and user's guide. In Multi-agent systems and agent based simulation LNAI n°1534 (ed. J. Sichman, R. Conte and N. Gilbert) Berlin: Springer-Verlag (1998) 10-25.
  • Webb K. and White T. Cell modeling using agent-based formalisms. In Proc. Autonomous Agents and Multi-Agent Systems'04 (2004) pp. 1188-1194.
  • Wilensky_99} Wilensky U. NetLogo. http://ccl.northwestern.edu/netlogo. (1999) Center for Connected Learning and Computer-Based Modeling. Northwestern University, Evanston, IL.

Last modified on September 17th 2010