Comprehensive analysis of gene regulatory dynamics, fitness landscape, and population evolution during sexual reproduction
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Abstract
The fitness landscape is a critical concept in biophysics, evolutionary biology, and genetics that depicts fitness in the genotype space and visualizes the relationship between genotype and fitness. However, the fitness landscape is challenging to characterize because the quantitative relationships between genotype and phenotype and their association to fitness has not been comprehensively well described. To address this challenge, we adopted gene regulatory networks to determine gene expression dynamics. We analyzed how phenotype and fitness are shaped by the genotype in two-gene networks. A two-by-two matrix provided the two-gene regulatory network in which a vector with two angle values (Θ) was introduced to characterize the genotype. Mapping from this angle vector to phenotypes allowed for the classification of steady-state expression patterns of genes into seven types. We then studied all possible fitness functions given by the Boolean output from the on/off expression of the two genes. The possible fitness landscapes were obtained as a function of the genetic parameters Θ. Finally, the evolution of the population distribution under sexual reproduction was investigated in the obtained landscape. We found that the distribution was restricted to a convex region within the landscape, resulting in the branching of population distribution, including the speciation process.
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This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/10204346.
The paper provides a thoroughly developed technique to relate gene expression dynamics to fitness landscapes and comment on the survivability of a population reproducing either by sexual or asexual mode. The authors use Gene Regulatory Networks (GRNs) to define continuous and measurable genetic parameters. The steady state expression of these genes lead to seven distinct phenotypes. Genetic expression level at steady state was converted into a boolean value and the fitness was assumed to take the maximal value at either 0 or 1, i.e the fitness was assumed to depend monotonically at the combination of each expression level. The fitness value was mapped from this boolean input to a …
This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/10204346.
The paper provides a thoroughly developed technique to relate gene expression dynamics to fitness landscapes and comment on the survivability of a population reproducing either by sexual or asexual mode. The authors use Gene Regulatory Networks (GRNs) to define continuous and measurable genetic parameters. The steady state expression of these genes lead to seven distinct phenotypes. Genetic expression level at steady state was converted into a boolean value and the fitness was assumed to take the maximal value at either 0 or 1, i.e the fitness was assumed to depend monotonically at the combination of each expression level. The fitness value was mapped from this boolean input to a continuous function outputting fitness values. By evolving populations on these different fitness landscapes, via two modes of reproduction, they obtained distributions of populations on the fitness landscapes, which were regionalised into convex polygons when the mode of evolution was sexual. This convex regionalisation suggests speciation in the populations.
Major comments
The model provides a comprehensive theoretical insight however, fails to support with experimental results
Figure 6 seems quite unclear in explaining the sexual reproduction mode adopted in the simulations. How exactly are the transfer of genetic parameters is carried out is quite unclear.
Minor comments
There are some typographical errors in the manuscript.
Competing interests
The author declares that they have no competing interests.
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