Clustering-based approach to identify solutions for the inference of regulatory networks

• Published in: 2005 IEEE Congress on Evolutionary Computation Vol. 1; pp. 660 - 667 Vol.1
• Main Authors: Spieth, C., Streichert, F., Speer, N., Zell, A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Bioinformatics; Biological system modeling; Differential equations; Evolutionary computation; Gene expression; Genetics; Genomics; Mathematical model; Proteins; Stochastic processes
• ISBN: 0780393635; 9780780393639
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/CEC.2005.1554746

In this paper we address the problem of finding valid solutions for the problem of inferring gene regulatory networks. Different approaches to directly infer the dependencies of gene regulatory networks by identifying parameters of mathematical models can be found in literature. The problem of reconstructing regulatory systems from experimental data is often multimodal and thus appropriate optimization strategies become necessary. Thus, we propose to use a clustering based niching evolutionary algorithm to maintain diversity in the optimization population to prevent premature convergence and to raise the probability of finding the global optimum by identifying multiple alternative networks. With this set of alternatives, the identification of the true solution has then to be addressed in a second post-processing step