Genotype Components as Predictors of Phenotype in Model Gene Regulatory Networks
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Genotype Components as Predictors of Phenotype in Model Gene Regulatory Networks S. Garte1 · A. Albert2 Received: 12 November 2018 / Accepted: 4 July 2019 © Springer Nature B.V. 2019
Abstract Models of gene regulatory networks (GRN) have proven useful for understanding many aspects of the highly complex behavior of biological control networks. Randomly generated non-Boolean networks were used in experimental simulations to generate data on dynamic phenotypes as a function of several genotypic parameters. We found that predictive relationships between some phenotypes and quantitative genotypic parameters such as number of network genes, interaction density, and initial condition could be derived depending on the strength of the topological (positional) genotype on specific phenotypes. We quantitated the strength of the topological genotype effect (TGE) on a number of phenotypes in multi-gene networks. For phenotypes with a low influence of topological genotype, derived and empirical relationships using quantitative genotype parameters were accurate in phenotypic outcomes. We found a number of dynamic network properties, including oscillation behaviors, that were largely dependent on genotype topology, and for which no such general quantitative relationships were determinable. It remains to be determined if these results are applicable to biological gene regulatory networks. Keywords Gene networks · Gene density · Network topology · Predictive equation · Dynamic network properties
1 Introduction Gene regulatory network (GRN) architecture is believed to play a crucial role in biological function (Luscombe et al. 2004; Neph et al. 2012) and innovative evolutionary transitions (Prud’homme et al. 2007; Halfon 2017). Research into the relationship between innovation (necessary for evolution) and network robustness * S. Garte [email protected] 1
Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, 160 Frelinghuysen Road, Piscataway, NJ 08854‑8020, USA
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Natural Philosophy Institute, 516 Calvin Lane, Rockville, MD, USA
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has found important clues to the mechanisms of evolution in both biological GRNs (Sears et al. 2015; Peter et al. 2012; Arda et al. 2010; Hinman et al. 2009; Ettensohn 2009; Peter and Davidson 2017) and model GRNs (Aldana et al. 2007; Garfield et al. 2013; Wagner 2008, 2011; Jiménez et al. 2015; Steiner 2012). Non-Boolean, iterative model network systems have been used as generators of experimental data (Lim et al. 2013; Burda et al. 2010; Kauffman et al. 2007) to define critical modular topologies within networks (Ma et al. 2009; Prill et al. 2005; Solé and Valverde 2006), for the theoretical determination of dynamical network properties (Jiménez et al. 2015; Kappler et al. 2003; Bornholdt 2001), as well as to investigate the evolution of evolvability (Draghi and Wagner 2009) and other properties of complex networks. Given the highly complex nature of the behavior of even very simple models of a few
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