Identification of Qualitative Genetic Regulatory Network Models by a Mathematical Programming Approach

We propose a method of Genetic Regulatory Network (GRN) model identification using mathematical programming and global optimization techniques. The problem consists in the estimation of the unknown parameters of a GRN model such that the asymptotic dynamics of the model closely match a set of experimental observations. This problem can be naturally cast as an optimization problem that minimizes a given distance between a set of observed expression patterns and estimated values of the parameters, subject to constraints derived from the algebraic equations that describe the dynamics of the biological system. We apply this approach to the inference of GRNs controlling early flower organ development in the model plant Arabidopsis thaliana.