In silicoevolution of thehunchbackgene indicates redundancy in cis-regulatory organization and spatial gene expression

Abstract

Biological development depends on the coordinated expression of genes in time and space. Developmental genes have extensive cis-regulatory regions which control their expression. These regions are organized in a modular manner, with different modules controlling expression at different times and locations. Both how modularity evolved and what function it serves are open questions. We present a computational model for the cis-regulation of the hunchback (hb) gene in the fruit fly (Drosophila). We simulate evolution (using an evolutionary computation approach from computer science) to find the optimal cis-regulatory arrangements for fitting experimental hb expression patterns. We find that the cis-regulatory region tends to readily evolve modularity. These cis-regulatory modules (CRMs) do not tend to control single spatial domains, but show a multi-CRM/multi-domain correspondence. We find that the CRM-domain correspondence seen in Drosophila evolves with a high probability in our model, supporting the biological relevance of the approach. The partial redundancy resulting from multi-CRM control may confer some biological robustness against corruption of regulatory sequences. The technique developed on hb could readily be applied to other multi-CRM developmental genes.

Mid-Embryo Patterning and Precision in Drosophila Segmentation: Krüppel Dual Regulation of hunchback

“…For example, in preliminary work on a related project, we have used an evolutionary computations optimization approach (e.g. see [ 83 ]) to search numerous potential Hb-Kr interactions for producing the PS4 stripe, and corroborated (at a coarse-grained/non-BS level) the same dual regulation, co-factor, cross-inhibition dynamics of the current model (Tables 1 – 3 ).…”

Quantitative Analysis of the Dynamics of Maternal Gradients in the Early Drosophila Embryo

“…Fitting the model to the data by genetic algorithms. The model was fitted to real experimental data by the genetic algorithms (GA), as described in detail in Zagrijchuk et al (2014). Chromosomes were implemented as sequences of four real numbers.…”

Forced evolution in silico by artificial transposons and their genetic operators: The ant navigation problem

“…This could have prospects for real-life automatic navigation. Second, is in the area of in silico evolution (evolution on the computer) of gene regulatory networks, GRNs (see our publications [76, 83] and our review in this area [67]). A third direction would be extension of the MGE approach itself.…”

“…For GRN in silico evolution and evolutionary gene design by computer, we plan to apply the symbolic string representation of a gene [83]. This perfectly suits our transposon technique.…”