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Published July 8, 2022 | Submitted
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Competing constraints shape the non-equilibrium limits of cellular decision making

Abstract

Gene regulation is central to cellular function. Yet, despite decades of work, we lack quantitative models that can predict how transcriptional control emerges from molecular interactions at the gene locus. Thermodynamic models of transcription, which assume that gene circuits operate at equilibrium, have previously been employed with considerable success in the context of bacterial systems. However, the presence of ATP-dependent processes within the eukaryotic transcriptional cycle suggests that equilibrium models may be insufficient to capture how eukaryotic gene circuits sense and respond to input transcription factor concentrations. Here, we employ simple kinetic models of transcription to investigate how energy dissipation within the transcriptional cycle impacts the rate at which genes transmit information and drive cellular decisions. We find that biologically plausible levels of energy input can lead to significant gains in how rapidly gene loci transmit information, but discover that the regulatory mechanisms underlying these gains change depending on the level of interference from non-cognate activator binding. When interference is low, information is maximized by harnessing energy to push the sensitivity of the transcriptional response to input transcription factors beyond its equilibrium limits. Conversely, when interference is high, conditions favor genes that harness energy to increase transcriptional specificity by proofreading activator identity. Our analysis further reveals that equilibrium gene regulatory mechanisms break down as transcriptional interference increases, suggesting that energy dissipation may be indispensable in systems where non-cognate factor interference is sufficiently large.

Additional Information

The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license. We are grateful to Jane Kondev, Sara Mahdavi, and Vahe Galstyan for substantial comments and discussion on the manuscript. Thanks also to Rob Phillips, Muir Morrison, and Ben Kuznets-Speck for their helpful discussion and insights at various stages of this project's development. NCL was supported by NIH Genomics and Computational Biology training grant 5T32HG000047-18, the Howard Hughes Medical Institute, and by DARPA under award number N66001-20-2-4033. AIF was supported in part by an NSF Graduate Research Fellowship, NSF Grant No. PHY-1748958, the Gordon and Betty Moore Foundation Grant No. 2919.02, the Kavli Foundation, and by a Postdoctoral Fellowship from the Jane Coffin Childs Memorial Fund for Medical Research. HGG was supported by the Burroughs Wellcome Fund Career Award at the Scientific Interface, the Sloan Research Foundation, the Human Frontiers Science Program, the Searle Scholars Program, the Shurl and Kay Curci Foundation, the Hellman Foundation, the NIH Director's New Innovator Award (DP2 OD024541-01) and NSF CAREER Award (1652236), an NIH R01 Award (R01GM139913) and the Koret-UC Berkeley-Tel Aviv University Initiative in Computational Biology and Bioinformatics. HGG is also a Chan Zuckerberg Biohub Investigator. The authors have declared no competing interest.

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Created:
August 20, 2023
Modified:
October 24, 2023