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Genotype Components as Predictors of Phenotype in Model Gene Regulatory Networks

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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.

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Acknowledgements

This work was supported by a grant (#57657) from the John Templeton Foundation.

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Authors and Affiliations

  1. Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, 160 Frelinghuysen Road, Piscataway, NJ, 08854-8020, USA

    S. Garte

  2. Natural Philosophy Institute, 516 Calvin Lane, Rockville, MD, USA

    A. Albert

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Garte, S., Albert, A. Genotype Components as Predictors of Phenotype in Model Gene Regulatory Networks. Acta Biotheor 67, 299–320 (2019). https://doi.org/10.1007/s10441-019-09350-2

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