Nonlinear Numerical Optimization Technique Based on a Genetic Algorithm for Inverse Problems: Towards the Inference of Genetic Networks

Nonlinear systems, such as metabolic pathways and genetic networks have richly complex structures and the details of the mechanism at the molecular level that govern interactions among system components are generally not well known. Estimation of the interaction mechanisms among system components via experimentally observed dynamic responses (timecourses) of some of the system components is generally an inverse problem. The Ssystem, which belongs to powerlaw formalism, is one of the best representations to solve this type of inverse problem: the Ssystem is rich enough in structure to capture all relevant dynamics. In the present paper, for the purpose of solving the inverse problem, we apply the Genetic Algorithm and propose an efficient procedure for the estimation of large numbers of parameters in the Ssystem formalism. The proposed procedure enables genetic network architecture to be reconstructed based on experimentally observed timecourses in gene expression.