A computational algebra approach to the reverse engineering of gene regulatory networks.
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. An analysis of the complexity of the algorithm is performed. The algorithm is validated on a recently published Boolean network model of segment polarity development in Drosophila melanogaster.
Laubenbacher
Reinhard
R
Virginia Bioinformatics Institute at Virginia Tech, 1880 Pratt Drive, Building XV, Blacksburg, VA 24061, USA. reinhard@vbi.vt.edu
Stigler
Brandilyn
B
D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods; D004331:Drosophila melanogaster / Q000235:genetics; D005786:Gene Expression Regulation; D017344:Genes, Insect; D008957:Models, Genetic