A primer on thermodynamic-based models for deciphering transcriptional regulatory logic

Dresch, Jacqueline M.; Richards, Megan; Ay, Ahmet

doi:10.1016/j.bbagrm.2013.04.011

Cited by 6 publications

(1 citation statement)

References 81 publications

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“…Decades of genetic work have identified all of the important genes in this system, and provided a well-supported network topology, making the modeling more reliable [ 10 , 11 ]. While early models of gene expression often ignored information concerning the CRM sequence [ 24 – 26 ], more recently a thermodynamic approach has been developed in which sequence information regarding individual CRMs is incorporated [ 27 – 33 ]. An extension of this approach was undertaken and validated in our model for the gap gene regulatory network [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

Translating natural genetic variation to gene expression in a computational model of the Drosophila gap gene regulatory network

et al. 2017

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Annotating the genotype-phenotype relationship, and developing a proper quantitative description of the relationship, requires understanding the impact of natural genomic variation on gene expression. We apply a sequence-level model of gap gene expression in the early development of Drosophila to analyze single nucleotide polymorphisms (SNPs) in a panel of natural sequenced D. melanogaster lines. Using a thermodynamic modeling framework, we provide both analytical and computational descriptions of how single-nucleotide variants affect gene expression. The analysis reveals that the sequence variants increase (decrease) gene expression if located within binding sites of repressors (activators). We show that the sign of SNP influence (activation or repression) may change in time and space and elucidate the origin of this change in specific examples. The thermodynamic modeling approach predicts non-local and non-linear effects arising from SNPs, and combinations of SNPs, in individual fly genotypes. Simulation of individual fly genotypes using our model reveals that this non-linearity reduces to almost additive inputs from multiple SNPs. Further, we see signatures of the action of purifying selection in the gap gene regulatory regions. To infer the specific targets of purifying selection, we analyze the patterns of polymorphism in the data at two phenotypic levels: the strengths of binding and expression. We find that combinations of SNPs show evidence of being under selective pressure, while individual SNPs do not. The model predicts that SNPs appear to accumulate in the genotypes of the natural population in a way biased towards small increases in activating action on the expression pattern. Taken together, these results provide a systems-level view of how genetic variation translates to the level of gene regulatory networks via combinatorial SNP effects.

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Section: Introductionmentioning

confidence: 99%

Translating natural genetic variation to gene expression in a computational model of the Drosophila gap gene regulatory network

et al. 2017

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Fitting thermodynamic-based models: Incorporating parameter sensitivity improves the performance of an evolutionary algorithm

Gaiewski

Drewell

Dresch

2021

Mathematical Biosciences

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Two coacting shadow enhancers regulate twin of eyeless expression during early Drosophila development

Dresch,

Nourie,

Conrad

et al. 2024

Genetics

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The Drosophila PAX6 homolog twin of eyeless (toy) sits at the pinnacle of the genetic pathway controlling eye development, the retinal determination network. Expression of toy in the embryo is first detectable at cellular blastoderm stage 5 in an anterior–dorsal band in the presumptive procephalic neuroectoderm, which gives rise to the primordia of the visual system and brain. Although several maternal and gap transcription factors that generate positional information in the embryo have been implicated in controlling toy, the regulation of toy expression in the early embryo is currently not well characterized. In this study, we adopt an integrated experimental approach utilizing bioinformatics, molecular genetic testing of putative enhancers in transgenic reporter gene assays and quantitative analysis of expression patterns in the early embryo, to identify 2 novel coacting enhancers at the toy gene. In addition, we apply mathematical modeling to dissect the regulatory landscape for toy. We demonstrate that relatively simple thermodynamic-based models, incorporating only 5 TF binding sites, can accurately predict gene expression from the 2 coacting enhancers and that the HUNCHBACK TF plays a critical regulatory role through a dual-modality function as an activator and repressor. Our analysis also reveals that the molecular architecture of the 2 enhancers is very different, indicating that the underlying regulatory logic they employ is distinct.

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A primer on thermodynamic-based models for deciphering transcriptional regulatory logic

Cited by 6 publications

References 81 publications

Translating natural genetic variation to gene expression in a computational model of the Drosophila gap gene regulatory network

Translating natural genetic variation to gene expression in a computational model of the Drosophila gap gene regulatory network

Fitting thermodynamic-based models: Incorporating parameter sensitivity improves the performance of an evolutionary algorithm

Two coacting shadow enhancers regulate twin of eyeless expression during early Drosophila development

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