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Stochastic Biomodelling

Stochastic biomodelling

Lecturer: Andrzej Mizera

 

Content


 

  • Introduction

    • Stochasticity in biological processes
  • Prerequisite

    • Crash course on probability theory
  • Stochastic modelling of chemical kinetics

    • The chemical master equation (CME)
  • Stochastic simulation of the CME

    • Gillespie’s direct method algorithm
  • Practicals

    • Implementing the Gillespie’s algorithm in Matlab and demonstrating its characteristics on various biochemical systems; comparing the obtained simulation results with the solutions in the deterministic formulation

 

 

  • Credits: 2

  • Date and time:

    • 07.02.2016-19.02.2016,
    • 3 2h lectures first week,
    • 2 2h lectures the second week
  • Lecture hall:

    • Agora, XX

 


 

[google-calendar-events id=”1202″ type=”list”]


 

Matlab files:

ABC.m

gene_expression.m

Additional reading

  • Michael B. Elowitz, Arnold J. Levine, Eric D. Siggia, Peter S. Swain, “Stochastic Gene Expression in a Single Cell”, Science, 297, pp.1183, 2002, Link
  • Lipniacki T, Kimmel M., “Deterministic and stochastic models of NFkappaB pathway”, Cardiovasc Toxicol., 7(4):215-34. 2007, Link
  • Lipniacki T, Hat B, Faeder JR, Hlavacek WS, “Stochastic effects and bistability in T cell receptor signaling”, Journal of Theoretical Biology, 254, pp. 110– 122, 2008. Link
  • McAdams HH, Arkin A., “Stochastic mechanisms in gene expression”, PNAS, 94(3), pp.814-9, 1997. Link
  • Raj A, van Oudenaarden A., “Nature, nurture, or chance: stochastic gene expression and its consequences”, Cell., 135(2), pp. 216-26, 2008. Link
  • Daniel T Gillespie, “A general method for numerically simulating the stochastic time evolution of coupled chemical reactions”, Journal of Computational Physics, 22(4), pp. 403-434, 1976 Link