Feedback Memetic Algorithms for Modeling Gene Regulatory Networks

Abstract

In this paper we address the problem of finding gene regulatory networks from experimental DNA microarray data. We focus on the evaluation of the performance of memetic algorithms on the inference problem. These algorithms are used to evolve an underlying quantitative mathematical model. The dynamics of the regulatory system are modeled with two commonly used approaches, namely linear weight matrices and S-systems. Due to the complexity of the inference problem, some researchers suggested evolutionary algorithms for this purpose. We introduce memetic enhancements to this optimization process to infer the parameters of sparsely connected nonlinear systems from the observed data. Due to the limited number of available data, the inferring problem is underdetermined and ambiguous. Further on, the problem often is multimodal and therefore appropriate optimization strategies become necessary. We propose a memetic method, which separates the overall inference problem into two subproblems to find the correct network: first, the search for a valid topology, and secondly, the optimization of the parameters of the mathematical model. The performance and the properties of the proposed methods are evaluated and compared to standard algorithms found in the literature.