Efficient parameter generation for constrained models using MCMC

Author:

Kravtsova Natalia,Chamberlin Helen M.,Dawes Adriana T.

Abstract

AbstractMathematical models of complex systems rely on parameter values to produce a desired behavior. As mathematical and computational models increase in complexity, it becomes correspondingly difficult to find parameter values that satisfy system constraints. We propose a Markov Chain Monte Carlo (MCMC) approach for the problem of constrained model parameter generation by designing a Markov chain that efficiently explores a model’s parameter space. We demonstrate the use of our proposed methodology to analyze responses of a newly constructed bistability-constrained model of protein phosphorylation to perturbations in the underlying protein network. Our results suggest that parameter generation for constrained models using MCMC provides powerful tools for modeling-aided analysis of complex natural processes.

Funder

Directorate for Mathematical and Physical Sciences

National Institute of General Medical Sciences

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

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