On the origin and features of an evolved boolean model for subcellular signal transduction systems

Abstract

In this paper we deal with the evolved Boolean model of the subcellular network for a hypothetical subcellular task that performs some of the basic cellular functions. The Boolean network is trained with a genetic algorithm and the obtained results are analyzed. We show that the size of the evolved Boolean network relates strongly to the task, that the number of output combinations is decreased, which is in concordance with the biological (measured) networks, and that the number of non-canalyzing inputs is increased, which indicates its specialization to the task. We conclude that the structure of the evolved network is biologically relevant, since it incorporates properties of evolved biological systems. © 2011 Springer-Verlag.