Modeling the evolution of complex genetic systems: The gene network family tree

Abstract

In 1994 and 1996, Andreas Wagner introduced a novel model in two papers addressing the evolution of genetic regulatory networks. This work, and a suite of papers that followed using similar models, helped integrate network thinking into biology and motivate research focused on the evolution of genetic networks. The Wagner network has its mathematical roots in the Ising model, a statistical physics model describing the activity of atoms on a lattice, and in neural networks. These models have given rise to two branches of applications, one in physics and biology and one in artificial intelligence and machine learning. Here, we review development along these branches, outline similarities and differences between biological models of genetic regulatory circuits and neural circuits models used in machine learning, and identify ways in which these models can provide novel insights into biological systems.

Selection for distinct gene expression properties favours the evolution of mutational robustness in gene regulatory networks

“…The model of gene regulatory networks that I employ has allowed addressing important topics in evolutionary biology (Fierst & Phillips, ). Such topics include the relationship between sexual reproduction and epistasis (Azevedo et al ., ; MacCarthy & Bergman, ; Martin & Wagner, ; Lohaus et al ., ), how network structure affects evolution (Siegal et al ., ; Espinosa‐Soto & Wagner, ; Pinho et al ., ), the evolution of evolvability (Ciliberti et al ., ; Kimbrell & Holt, ; Draghi & Wagner, ; Kimbrell, ; Whitacre & Bender, ; Espinosa‐Soto et al ., ; Fierst, ; Steiner, ), the effects of phenotypic plasticity on evolution (Masel, ; Espinosa‐Soto et al ., , b; Fierst, ; Pinho et al ., ), how hybrid incompatibility evolves (Palmer & Feldman, ; Le Cunff & Pakdaman, ) and even the role of gene network dynamics in the evolution of organismal complexity (Lohaus et al ., ).…”

“…Computational models of transcriptional regulation have allowed addressing the evolution of gene networks through computer simulation (Fierst & Phillips, ). In particular, these models have been useful to study the evolution of the fraction of mutations that preserve a GAP, that is its mutational robustness.…”

“…To study the evolution of robustness in gene regulatory networks, I use a model, first proposed by Wagner (), that allows following the developmental dynamics of individual gene networks and also the evolutionary dynamics of network populations (Fierst & Phillips, ). As for the developmental dynamics, the model follows changes in gene activity from an initial gene activity pattern to the network's GAP (Huang, ).…”

Theoretical Study of the One Self-Regulating Gene in the Modified Wagner Model

“…More generally, [8] provides a complete review of biological systems inspired by network science. An interesting subset of gene network models does not aim at predicting the behavior of a specific group of identified genes in an organism, but are rather used as a general abstraction of a gene network, in order to study their evolutionary properties in individual-based simulations [9,10]. Although naive in terms of biological hypotheses, these models are particularly important because they could contribute to unifying systems biology and evolutionary genetics.…”

Theoretical Study of the 1 Self-Regulating Gene in the Modified Wagner Model

“…The literature proposes several modeling frameworks for each of them based on various biological hypotheses and different time scales [4][5][6]. An interesting subset of gene network models does not aim at predicting the behavior of a specific group of identified genes in an organism, but are rather used as a general abstraction of a gene network, in order to study their evolutionary properties in individual-based simulations [7,8]. Although naive in terms of biological hypotheses, these models are particularly important because they could contribute to unifying systems biology and evolutionary genetics.…”