Agent-based optimization of biological response networks
One of the major challenges in systems biology today is to devise generally robust methods of interpreting data concerning the expression levels of the genes in an organism in a way that will shed light on the complex relationships between multiple genes and their products. The ability to better understand and predict the structures and actions of complex biological systems is of significant importance to modern drug discovery as well as our understanding of the mechanisms behind an organism’s ability to react to its environment. In this paper we present a study for robust biological pathway construction through genetic algorithms. The platform is based on the construction of biological networks given different sets of interaction information and the optimization of sub-networks constrained by the gene expression data. As an application, expression data of drug response in M. tuberculosis is used to build generic response subnetworks. Subnetworks are then compared to identify the essential key components that are common to different networks. We are thus able to identify essential nodes in specific drug response. We expect that this approach will provide robust prediction of response networks and accelerate target identification for drug development in the future.