Automated abstraction methodology for genetic regulatory networks

Authors:
Hiroyuki Kuwahara;Chris J. Myers;Michael S. Samoilov;Nathan A. Barker;Adam P. Arkin
Affiliations:
University of Utah, Salt Lake City, UT;University of Utah, Salt Lake City, UT;Lawrence Berkeley National Laboratory, Howard Hughes Medical Institute, University of California, Berkeley, CA;University of Utah, Salt Lake City, UT;Lawrence Berkeley National Laboratory, Howard Hughes Medical Institute, University of California, Berkeley, CA
Venue:
Transactions on Computational Systems Biology VI
Year:
2006

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Abstract

In order to efficiently analyze the complicated regulatory systems often encountered in biological settings, abstraction is essential. This paper presents an automated abstraction methodology that systematically reduces the small-scale complexity found in genetic regulatory network models, while broadly preserving the large-scale system behavior. Our method first reduces the number of reactions by using rapid equilibrium and quasi-steady-state approximations as well as a number of other stoichiometry-simplifying techniques, which together result in substantially shortened simulation time. To further reduce analysis time, our method can represent the molecular state of the system by a set of scaled Boolean (or n-ary) discrete levels. This results in a chemical master equation that is approximated by a Markov chain with a much smaller state space providing significant analysis time acceleration and computability gains. The genetic regulatory network for the phage λ lysis/lysogeny decision switch is used as an example throughout the paper to help illustrate the practical applications of our methodology.