title: Evolving modular genetic regulatory networks with a recursive, top-down approach.
abstract: Being able to design genetic regulatory networks (GRNs) to achieve a desired cellular function is one of the main goals of synthetic biology. However, determining minimal GRNs that produce desired time-series behaviors is non-trivial. In this paper, we propose a 'top-down' approach to evolving small GRNs and then use these to recursively boot-strap the identification of larger, more complex, modular GRNs. We start with relatively dense GRNs and then use differential evolution (DE) to evolve interaction coefficients. When the target dynamical behavior is found embedded in a dense GRN, we narrow the focus of the search and begin aggressively pruning out excess interactions at the end of each generation. We first show that the method can quickly rediscover known small GRNs for a toggle switch and an oscillatory circuit. Next we include these GRNs as non-evolvable subnetworks in the subsequent evolution of more complex, modular GRNs. Successful solutions found in canonical DE where we truncated small interactions to zero, with or without an interaction penalty term, invariably contained many excess interactions. In contrast, by incorporating aggressive pruning and the penalty term, the DE was able to find minimal or nearly minimal GRNs in all test problems.
authors.affiliation: Department of Computer Science, University of Vermont, Burlington, VT 05405 USA.
authors.forename: Javier
authors.identifier: 0000-0002-8886-0640
authors.initials: J
authors.lastname: Garcia-Bernardo
authors.affiliation: Department of Computer Science, University of Vermont, Burlington, VT 05405 USA.
authors.forename: Margaret J
authors.initials: MJ
authors.lastname: Eppstein
keywords: Differential evolution; Genetic network inference; Genetic regulatory networks; Synthetic biology