Evolving Robust Gene Regulatory Networks

Abstract

Design and implementation of robust network modules is essential for construction of complex biological systems through hierarchical assembly of ‘parts’ and ‘devices’. The robustness of gene regulatory networks (GRNs) is ascribed chiefly to the underlying topology. The automatic designing capability of GRN topology that can exhibit robust behavior can dramatically change the current practice in synthetic biology. A recent study shows that Darwinian evolution can gradually develop higher topological robustness. Subsequently, this work presents an evolutionary algorithm that simulates natural evolution in silico, for identifying network topologies that are robust to perturbations. We present a Monte Carlo based method for quantifying topological robustness and designed a fitness approximation approach for efficient calculation of topological robustness which is computationally very intensive. The proposed framework was verified using two classic GRN behaviors: oscillation and bistability, although the framework is generalized for evolving other types of responses. The algorithm identified robust GRN architectures which were verified using different analysis and comparison. Analysis of the results also shed light on the relationship among robustness, cooperativity and complexity. This study also shows that nature has already evolved very robust architectures for its crucial systems; hence simulation of this natural process can be very valuable for designing robust biological systems.

The Role of Genotype in the Predictability of Dynamical Behavior in Complex Model Gene Regulatory Networks

“…The importance of specific topology in determining phenotype outcome in GRNs has been observed by other investigators [31,43], although it has also been found that topology alone is not sufficient to make accurate evolutionary predictions [32]. This is consistent with results of Payne and Wagner [33], who proposed that form and function in GRNs appear to have no consistent relationship.…”

Gene duplication and the evolution of moonlighting proteins

“…Protein moonlighting exacerbates the problem of genetic redundancy: not only redundant genes are retained, but more than one molecular function is conserved in duplicate. It has been argued that functional redundancy between paralogs can be selected to confront environmental, genetic, or stochastic perturbations ( Gu et al, 2003 ; Keane et al, 2014 ; Noman et al, 2015 ). Even duplicates that have noticeably diverged in regulation or molecular function can provide some degree of genetic buffering ( Ihmels et al, 2007 ; DeLuna et al, 2008 ; VanderSluis et al, 2010 ; Diss et al, 2014 ).…”

Design principles for robust oscillatory behavior

“…Several studies have made contributions towards identifying some design principles for oscillatory behaviour. Network motifs-recurrent patterns of interactions believed to form the building blocks of any complex network (Milo et al 2002;Yeger-Lotem et al 2004;Barabasi and Oltvai 2004;Alon 2007;Kim et al 2010)-have also been highlighted to some extent for oscillators (Ferrell et al 2011;Wagner 2005;Novak and Tyson 2008;Tsai et al 2008;Burda et al 2011;Lomnitz and Savageau 2014;Noman et al 2015;Semenov et al 2015).…”