An information theoretic treatment of sequence-to-expression modeling

PLoS Computational Biology
Farzaneh Khajouei, Saurabh Sinha

Abstract

Studying a gene's regulatory mechanisms is a tedious process that involves identification of candidate regulators by transcription factor (TF) knockout or over-expression experiments, delineation of enhancers by reporter assays, and demonstration of direct TF influence by site mutagenesis, among other approaches. Such experiments are often chosen based on the biologist's intuition, from several testable hypotheses. We pursue the goal of making this process systematic by using ideas from information theory to reason about experiments in gene regulation, in the hope of ultimately enabling rigorous experiment design strategies. For this, we make use of a state-of-the-art mathematical model of gene expression, which provides a way to formalize our current knowledge of cis- as well as trans- regulatory mechanisms of a gene. Ambiguities in such knowledge can be expressed as uncertainties in the model, which we capture formally by building an ensemble of plausible models that fit the existing data and defining a probability distribution over the ensemble. We then characterize the impact of a new experiment on our understanding of the gene's regulation based on how the ensemble of plausible models and its probability distribution chang...Continue Reading

References

Jan 5, 2020·Proceedings of the National Academy of Sciences of the United States of America·Neta RahimiBen-Zion Shilo

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Related Concepts

In Silico
Study
Genes
Genus Cis
Mutagenesis, Site-Directed
Knock-out
Gene Expression
Evaluation
Normal Statistical Distribution
Genes, Regulator

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