Competing constraints shape the nonequilibrium 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 noncognate 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 noncognate factor interference is sufficiently large.
Additional Information
© 2023 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY). We are grateful to Jane Kondev, Sara Mahdavi, and Vahe Galstyan for substantial comments and discussion on the manuscript. Thanks also go to Rob Phillips, Muir Morrison, and Ben Kuznets-Speck for helpful discussion at various stages of this project. N.C.L. was supported by NIH Genomics and Computational Biology training grant 5T32HG000047-18, the HHMI, and DARPA award number N66001-20-2-4033. A.I.F. was supported 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 a Postdoctoral Fellowship from the Jane Coffin Childs Memorial Fund for Medical Research. H.G.G. was supported by NIH Director's New Innovator Award (DP2 OD024541-01) and NSF CAREER Award (1652236), NIH R01 Award (R01GM139913), and the Koret-UC Berkeley-Tel Aviv University Initiative in Computational Biology and Bioinformatics. H.G.G. is also a Chan Zuckerberg Biohub Investigator. Author Contributions. N.C.L., A.I.F., and H.G.G. designed research; N.C.L. performed research; and N.C.L., A.I.F., and H.G.G. wrote the paper. The authors declare no competing interest.Attached Files
Published - pnas.2211203120.pdf
Supplemental Material - pnas.2211203120.sapp.pdf
Files
pnas.2211203120.pdf
Additional details
Identifiers
- PMCID
- PMC10013869
- Eprint ID
- 121071
- Resolver ID
- CaltechAUTHORS:20230420-711199500.16
Funding
- NIH Predoctoral Fellowship
- 5T32HG000047-18
- Howard Hughes Medical Institute (HHMI)
- Defense Advanced Research Projects Agency (DARPA)
- N66001-20-2-4033
- NSF
- PHY-1748958
- Gordon and Betty Moore Foundation
- 2919.02
- Kavli Foundation
- Jane Coffin Childs Memorial Fund for Medical Research
- NIH
- DP2 1089 OD024541-0
- NSF
- PHY-1652236
- NIH
- R01GM139913
- Koret-UC Berkeley-Tel Aviv University Initiative in Computational Biology and Bioinformatics
- Chan Zuckerberg Initiative
Dates
- Created
-
2023-05-05Created from EPrint's datestamp field
- Updated
-
2023-05-05Created from EPrint's last_modified field