Recombination Is Surprisingly Constructive for Artificial Gene Regulatory Networks in the Context of Selection for Developmental Stability

Previous studies on network topology of artificial gene regulatory networks created by whole genome duplication and divergence processes show subgraph distributions similar to gene regulatory networks found in nature. In particular, certain network motifs are prominent in both types of networks. In this contribution, we analyze how duplication and divergence processes influence network topology and preferential generation of network motifs. We show that in the artificial model such preference originates from a stronger preservation of protein than regulatory sites by duplication and divergence. If these results can be transferred to regulatory networks in nature, we can infer that after duplication the paralogous transcription factor binding site is less likely to be preserved than the corresponding paralogous protein.

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Evolutionary strategies applied to artificial gene regulatory networks

10.1101/2021.09.28.462218 ◽

2021 ◽

Author(s):

André Luiz de Lucena Moreira ◽

César Rennó-Costa

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Task Complexity ◽

Single Cells ◽

Single Gene ◽

Evolutionary Strategy ◽

Evolutionary Strategies ◽

Gene Regulatory ◽

Artificial Gene ◽

The Impact

Evolution optimizes cellular behavior throughout sequential generations by selecting the successful individual cells in a given context. As gene regulatory networks (GRNs) determine the behavior of single cells by ruling the activation of different processes - such as cell differentiation and death - how GRNs change from one generation to the other might have a relevant impact on the course of evolution. It is not clear, however, which mechanisms that affect GRNs effectively favor evolution and how. Here, we use a population of computational robotic models controlled by artificial gene regulatory networks (AGRNs) to evaluate the impact of different genetic modification strategies in the course of evolution. The virtual agent senses the ambient and acts on it as a bacteria in different phototaxis-like tasks - orientation to light, phototaxis, and phototaxis with obstacles. We studied how the strategies of gradual and abrupt changes on the AGRNs impact evolution considering multiple levels of task complexity. The results indicated that a gradual increase in the complexity of the performed tasks is beneficial for the evolution of the model. Furthermore, we have seen that larger gene regulatory networks are needed for more complex tasks, with single-gene duplication being an excellent evolutionary strategy for growing these networks, as opposed to full-genome duplication. Studying how GRNs evolved in a biological environment allows us to improve the computational models produced and provide insights into aspects and events that influenced the development of life on earth.

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Computing with Artificial Gene Regulatory Networks

Evolutionary Computation in Gene Regulatory Network Research ◽

10.1002/9781119079453.ch15 ◽

2016 ◽

pp. 398-424 ◽

Cited By ~ 4

Author(s):

Michael A. Lones

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Gene Regulatory ◽

Artificial Gene

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Dynamic Links and Evolutionary History in Simulated Gene Regulatory Networks

Handbook of Research on Computational Methodologies in Gene Regulatory Networks ◽

10.4018/978-1-60566-685-3.ch021 ◽

2010 ◽

pp. 498-522

Author(s):

T. Steiner ◽

Y. Jin ◽

L. Schramm ◽

B. Sendhoff

Keyword(s):

Gene Regulatory Networks ◽

Biological Networks ◽

Regulatory Networks ◽

Evolutionary Process ◽

Computational Method ◽

Cellular Growth ◽

Small Scale ◽

Mutational Robustness ◽

Gene Regulatory ◽

Artificial Gene

In this chapter, we describe the use of evolutionary methods for the in silico generation of artificial gene regulatory networks (GRNs). These usually serve as models for biological networks and can be used for enhancing analysis methods in biology. We clarify our motivation in adopting this strategy by showing the importance of detailed knowledge of all processes, especially the regulatory dynamics of interactions undertaken during gene expression. To illustrate how such a methodology works, two different approaches to the evolution of small-scale GRNs with specified functions, are briefly reviewed and discussed. Thereafter, we present an approach to evolve medium sized GRNs with the ability to produce stable multi-cellular growth. The computational method employed allows for a detailed analysis of the dynamics of the GRNs as well as their evolution. We have observed the emergence of negative feedback during the evolutionary process, and we suggest its implication to the mutational robustness of the regulatory network which is further supported by evidence observed in additional experiments.

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Artificial Gene Regulatory Networks—A Review

Artificial Life ◽

10.1162/artl_a_00267 ◽

2019 ◽

Vol 24 (4) ◽

pp. 296-328 ◽

Cited By ~ 8

Author(s):

Sylvain Cussat-Blanc ◽

Kyle Harrington ◽

Wolfgang Banzhaf

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Environmental Signals ◽

The Past ◽

Current State ◽

Behavioral Expression ◽

Gene Regulatory ◽

Living Organisms ◽

Artificial Gene ◽

Engineering Modeling

In nature, gene regulatory networks are a key mediator between the information stored in the DNA of living organisms (their genotype) and the structural and behavioral expression this finds in their bodies, surviving in the world (their phenotype). They integrate environmental signals, steer development, buffer stochasticity, and allow evolution to proceed. In engineering, modeling and implementations of artificial gene regulatory networks have been an expanding field of research and development over the past few decades. This review discusses the concept of gene regulation, describes the current state of the art in gene regulatory networks, including modeling and simulation, and reviews their use in artificial evolutionary settings. We provide evidence for the benefits of this concept in natural and the engineering domains.

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