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Stochastic algebraic models SAMSI Transition Workshop June 18, 2009 Reinhard Laubenbacher Virginia Bioinformatics Institute and Mathematics Department.

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Presentation on theme: "Stochastic algebraic models SAMSI Transition Workshop June 18, 2009 Reinhard Laubenbacher Virginia Bioinformatics Institute and Mathematics Department."— Presentation transcript:

1 Stochastic algebraic models SAMSI Transition Workshop June 18, 2009 Reinhard Laubenbacher Virginia Bioinformatics Institute and Mathematics Department Virginia Tech

2 Systems biology working group activities 1.Algebraic models of biological networks 2.ODE models of biochemical reaction networks Foci: Structure dynamics Dynamics structure Experimental design

3 Polynomial dynamical systems Let k be a finite field and f 1, …, f n k [ x 1, …, x n ] f = ( f 1, …, f n ) : k n k n is an n-dimensional polynomial dynamical system over k. Natural generalization of Boolean networks. Fact: Every function k n k can be represented by a polynomial, so all finite dynamical systems k n k n are polynomial dynamical systems.

4 Parameter estimation Problem: Given experimental time course data and a partially specified model f = ( f 1, …, f n ) : k n k n, with/without information on the function structure, estimate the unspecified functions by fitting them to the data.

5 Parameter estimation Variables x 1, …, x n with values in k. (E.g., protein concentrations, mRNA concentrations, etc.) (s 1, t 1 ), …, (s r, t r ) state transition observations with s j k n, t j k (E.g., consecutive measurements in a time course experiment.) Network inference: Identify a function g: k n k such that f(s j )=t j.

6 The model space Let I be the ideal of the points s 1, …, s r, that is, I =. Let g be one particular feasible function/parameter. Then the space M of all feasible parameters is M = g + I.

7 Model selection In the absence of other information, choose a model which is reduced with respect to the ideal I. Laubenbacher, Stigler, J. Theor. Biol Several other methods. Contributors: E. Dimitrova, L. Garcia, A. Jarrah, M. Stillman, P. Vera-Licona

8 Dimitrova, Hinkelmann, Garcia, Jarrah, L., Stigler, Vera-Licona

9 Model selection Model selection in original method requires choice of term order Improvement: Construct a wiring diagram using information from all term orders. Dimitrova, Jarrah, L., Stigler, A Gröbner-fan based method for biochemical network modeling, ISSAC 2007

10 Dynamic model Dimitrova, Jarrah: Construct a probabilistic polynomial dynamical system by sampling the reduced models in all the Groebner cones, together with a probability distribution on the models derived from cone volumes. Alternative method constructed by B. Stigler.

11 Probabilistic Boolean networks For each variable there is a family of Boolean functions, together with a joint probability distribution. At each update, choose a random function out of this family. Shmulevich, E. Dougherty, et al. Dimitrova, Jarrah produce a probabilistic polynomial dynamical system

12 Update-stochastic Boolean networks Update variables sequentially, in one of two ways: At each update, choose at random a permutation, which specifies an update order. At each update, choose at random a variable that gets updated. Sequential update is more realistic biologically. See, e.g., Chaves, Albert, Sontag, J. Theor. Biol., 2005

13 Philosophy: Stochastic sequential update arises through random delays in the completion time of molecular processes. Consequence: Can approximate update-stochastic systems through systems with random delays, i.e., special function-stochastic systems. This approach is taken in Polynome.

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15 General problem Study function-stochastic polynomial dynamical systems. Note: Can be viewed as a special family of Markov chains.

16 Why polynomial dynamical systems?

17 Algebraic models Most common algebraic model types in systems biology: Boolean networks, including cellular automata Logical models Petri nets

18 A common modeling framework 1.Boolean networks are equivalent to PDS over the field with two elements. 2.(Jarrah, L., Veliz-Cuba) There are algorithms that translate logical models and Petri nets into PDS. LM PN PDS

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26 T cell differentiation

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32 Stochastic systems Instead of f = (f 1, …, f n ) : k n k n consider f = ({f 1 }, …, {f n }) : k n k n, together with probability distributions on the sets {f i }. At each update, choose the ith update function from the set {f i } at random. This is a function-stochastic polynomial dynamical system.

33 Special case: Delay systems Let f = (f 1, …, f n ) : k n k n be a deterministic system. Let F = ({f 1, id}, …, {f n, id}), together with a probability distribution on each set. Each time id is chosen for an update, a delay occurs in that variable. What is the effect of delays on network dynamics?

34 An example Theorem. (Hinkelmann, Jarrah, L.) Let f be a Boolean linear system with dependency graph D. Let F be the associated delay system. Then F has periodic points if and only if D contains directed cycles (feedback loops).

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36 Open problems Study in more generality the effects of stochastic delays on algebraic model dynamics. Can one use stochastic delay systems to efficiently simulate deterministic sequential systems? What are good simulation methods for this purpose?


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