UMMS Affiliation

Program in Systems Biology; Department of Microbiology and Physiological Systems

Date

4-17-2017

Document Type

Article

Disciplines

Biochemistry | Computational Biology | Genetics | Systems Biology

Abstract

Gene expression is intrinsically a stochastic (noisy) process with important implications for cellular functions. Deciphering the underlying mechanisms of gene expression noise remains one of the key challenges of regulatory biology. Theoretical models of transcription often incorporate the kinetics of how transcription factors (TFs) interact with a single promoter to impact gene expression noise. However, inside single cells multiple identical gene copies as well as additional binding sites can compete for a limiting pool of TFs. Here we develop a simple kinetic model of transcription, which explicitly incorporates this interplay between TF copy number and its binding sites. We show that TF sharing enhances noise in mRNA distribution across an isogenic population of cells. Moreover, when a single gene copy shares it's TFs with multiple competitor sites, the mRNA variance as a function of the mean remains unaltered by their presence. Hence, all the data for variance as a function of mean expression collapse onto a single master curve independent of the strength and number of competitor sites. However, this result does not hold true when the competition stems from multiple copies of the same gene. Therefore, although previous studies showed that the mean expression follows a universal master curve, our findings suggest that different scenarios of competition bear distinct signatures at the level of variance. Intriguingly, the introduction of competitor sites can transform a unimodal mRNA distribution into a multimodal distribution. These results demonstrate the impact of limited availability of TF resource on the regulation of noise in gene expression.

Rights and Permissions

Copyright: © 2017 Das et al. Citation: PLoS Comput Biol. 2017 Apr 17;13(4):e1005491. doi: 10.1371/journal.pcbi.1005491. eCollection 2017 Apr. Link to article on publisher's site

Related Resources

Link to Article in PubMed

Journal Title

PLoS computational biology

PubMed ID

28414750

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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