Abstract
BackgroundThe statistical thermodynamics based approach provides a promising framework for construction of the genotype-phenotype map in many biological systems. Among important aspects of a good model connecting the DNA sequence information with that of a molecular phenotype (gene expression) is the selection of regulatory interactions and relevant transcription factor bindings sites. As the model may predict different levels of the functional importance of specific binding sites in different genomic and regulatory contexts, it is essential to formulate and study such models under different modeling assumptions.ResultsWe elaborate a two-layer model for the Drosophila gap gene network and include in the model a combined set of transcription factor binding sites and concentration dependent regulatory interaction between gap genes hunchback and Kruppel. We show that the new variants of the model are more consistent in terms of gene expression predictions for various genetic constructs in comparison to previous work. We quantify the functional importance of binding sites by calculating their impact on gene expression in the model and calculate how these impacts correlate across all sites under different modeling assumptions.ConclusionsThe assumption about the dual interaction between hb and Kr leads to the most consistent modeling results, but, on the other hand, may obscure existence of indirect interactions between binding sites in regulatory regions of distinct genes. The analysis confirms the previously formulated regulation concept of many weak binding sites working in concert. The model predicts a more or less uniform distribution of functionally important binding sites over the sets of experimentally characterized regulatory modules and other open chromatin domains.
Highlights
The statistical thermodynamics based approach provides a promising framework for construction of the genotype-phenotype map in many biological systems
In the framework of the new model variants, we show that the transcription factor binding sites (TFBSs) with the strongest impact on gene expressions appear in all domains of the regulatory regions considered in the model
We show how TFBSs work in concert by elucidating the correlation between impacts on gene expression from TFBSs of different transcription factors (TFs) and from different regulatory regions
Summary
The statistical thermodynamics based approach provides a promising framework for construction of the genotype-phenotype map in many biological systems. Among important aspects of a good model connecting the DNA sequence information with that of a molecular phenotype (gene expression) is the selection of regulatory interactions and relevant transcription factor bindings sites. Mathematical models of gene regulatory networks explicitly connecting the DNA sequence level with that of gene expression or other molecular phenotypic traits represent a flexible framework for studying various aspects of genotype-phenotype mapping [1]. By using this approach, it is possible to assess the importance of various mechanisms involved in translation of genetic information into phenotype and their interplays. We aim to use this model in this study to analyze how functional important TFBSs are distributed in the regulatory regions and how their impacts on gene expression patterns correlate with each other
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