Evolution of gene regulatory networks by means of selection and random genetic drift

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Bioinformatics and Genomics

Main article text

 

Introduction

Methods

The model

Regulatory regions define interactions

where pc is the popcount function, which counts the number of set bits (i.e., 1’s) that are common in the two vectors. The occurrence of interaction, as well as, the type (suppression or activation) is defined by the last bit of the Ri,c and Rj,t vectors as:

Interaction matrix and expression levels

Inheritance of regulation and recombination

Mutations

Selection

where EnEopt is a norm of the difference between En and Eopt expression vectors (here the Euclidean distance is used). σ2 is identical to the parameter s of Wagner (1996). This parameter models the ‘strength of selection’, i.e., how pronounced is the effect of the differences in expression vectors to individuals’ fitness. Parents are chosen proportionally to their fitness value F(Ein).

Maturation and equilibria

Results

Comparisons between neutral evolution and selection scenarios

Simulations setup

Optimum is gradually reached in a ladder-like fashion

Size of the regulatory space in neutrality and selection

Robustness of gene regulatory network

Effect of neutral genes

Mutational buffering

Discussion

Conclusions

Supplemental Information

Supplemental Information.

DOI: 10.7717/peerj.17918/supp-1

Additional Information and Declarations

Competing Interests

The authors declare that they have no competing interests.

Author Contributions

Stefanos Papadadonakis performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Antonios Kioukis conceived and designed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Charikleia Karageorgiou performed the experiments, analyzed the data, authored or reviewed drafts of the article, and approved the final draft.

Pavlos Pavlidis conceived and designed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Data Availability

The following information was supplied regarding data availability:

Data and code are available at Zenodo:

evolabics, & Stefanos_Papadantonakis. (2024). evolabics/evonet: EvoNET v1.0.2 (1.0.2). Zenodo. https://doi.org/10.5281/zenodo.11215048.

Funding

This work was supported by an internal grant of ICS-FORTH to Pavlos Pavlidis (Grant: ESO00121, EVONMDA). There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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