A Computational Algebra Approach to the Reverse Engineering of Gene Regulatory Networks

Details A Computational Algebra Approach to the Reverse Engineering of Gene Regulatory Networks A Computational Algebra Approach to the Reverse Engineering of Gene Regulatory Networks

2003-12-17

arxiv.org/abs/q-bio/0312026v1

Journal of Theoretical Biology 229 (2004) 523-537

Biology

Quantitative Biology

Quantitative Methods

28 pages, 5 EPS figures, uses elsart.cls

Scientific paper

10.1016/j.jtbi.2004.04.037

This paper proposes a new method to reverse engineer gene regulatory networks from experimental data. The modeling framework used is time-discrete deterministic dynamical systems, with a finite set of states for each of the variables. The simplest examples of such models are Boolean networks, in which variables have only two possible states. The use of a larger number of possible states allows a finer discretization of experimental data and more than one possible mode of action for the variables, depending on threshold values. Furthermore, with a suitable choice of state set, one can employ powerful tools from computational algebra, that underlie the reverse-engineering algorithm, avoiding costly enumeration strategies. To perform well, the algorithm requires wildtype together with perturbation time courses. This makes it suitable for small to meso-scale networks rather than networks on a genome-wide scale. The complexity of the algorithm is quadratic in the number of variables and cubic in the number of time points. The algorithm is validated on a recently published Boolean network model of segment polarity development in Drosophila melanogaster.

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