Tuning transcriptional regulation through signaling: A predictive theory of allosteric induction
Abstract
Allosteric regulation is found across all domains of life, yet we still lack simple, predictive theories that directly link the experimentally tunable parameters of a system to its input-output response. To that end, we present a general theory of allosteric transcriptional regulation using the Monod-Wyman-Changeux model. We rigorously test this model using the ubiquitous simple repression motif in bacteria by first predicting the behavior of strains that span a large range of repressor copy numbers and DNA binding strengths and then constructing and measuring their response. Our model not only accurately captures the induction profiles of these strains, but also enables us to derive analytic expressions for key properties such as the dynamic range and [EC_(50)]. Finally, we derive an expression for the free energy of allosteric repressors that enables us to collapse our experimental data onto a single master curve that captures the diverse phenomenology of the induction profiles.
Additional Information
© 2018 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Open Access funded by National Institutes of Health. Received 26 October 2017, Revised 2 February 2018, Accepted 9 February 2018, Available online 21 March 2018. This work has been a wonderful exercise in scientific collaboration. We thank Hernan Garcia for information and advice for working with these bacterial strains, Pamela Björkman and Rachel Galimidi for access and training for use of the Miltenyi Biotec MACSQuant flow cytometer, and Colin deBakker of Milteny Biotec for useful advice and instruction in flow cytometry. The experimental front of this work began at the Physiology summer course at the Marine Biological Laboratory in Woods Hole, MA, operated by the University of Chicago. We thank Simon Alamos, Nalin Ratnayeke, and Shane McInally for their work on the project during the course. We also thank Suzannah Beeler, Justin Bois, Robert Brewster, Ido Golding, Soichi Hirokawa, Jané Kondev, Tom Kuhlman, Heun Jin Lee, Muir Morrison, Nigel Orme, Alvaro Sanchez, and Julie Theriot for useful advice and discussion. This work was supported by La Fondation Pierre-Gilles de Gennes, the Rosen Center at Caltech, and the NIHDP1 OD000217 (Director's Pioneer Award), R01 GM085286, and 1R35 GM118043 (MIRA). N.M.B. was supported by a Howard Hughes Medical Institute International Student Research fellowship. Data and Software Availability: All of the data used in this work as well as all relevant code can be found at this dedicated website. Data were collected, stored, and preserved using the Git version control software in combination with off-site storage and hosting website GitHub. Code used to generate all figures and complete all processing step as and analyses are available on the GitHub repository. Many analysis files are stored as instructive Jupyter Notebooks. The scientific community is invited to fork our repositories and open constructive issues on the GitHub repository (http://doi.org/10.5281/zenodo.1163620). Author Contributions: M.R.-M., S.L.B., N.M.B., G.C., and T.E. contributed equally to this work. M.R.-M., S.L.B., N.M.B., and G.C. performed experiments. T.E. and M.R.-M. laid groundwork for the model. M.R.-M., S.L.B., N.M.B., G.C., and T.E. performed the data analysis. M.R.-M., G.C., N.M.B., and S.L.B. wrote code used for all experimental analysis and parameter estimation. G.C. made the figures for the main text and G.C., M.R.-M., S.L.B., and N.M.B. made figures for the Supplemental Information. M.R.-M., S.L.B., N.M.B., G.C., T.E., and R.P. wrote the paper. M.L. and R.P. provided useful insight and advice in designing and executing the work. The authors declare no competing interests.Attached Files
Published - 1-s2.0-S2405471218300577-main.pdf
Submitted - 111013.full.pdf
Submitted - 1702.07460.pdf
Supplemental Material - 1-s2.0-S2405471218300577-mmc1.pdf
Supplemental Material - 111013-1.pdf
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Additional details
- PMCID
- PMC5991102
- Eprint ID
- 83656
- Resolver ID
- CaltechAUTHORS:20171204-090149985
- La Fondation Pierre-Gilles de Gennes
- Donna and Benjamin M. Rosen Bioengineering Center
- DP1 OD000217
- NIH
- R01 GM085286
- NIH
- 1R35 GM118043-01
- NIH
- Howard Hughes Medical Institute (HHMI)
- Created
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2017-12-13Created from EPrint's datestamp field
- Updated
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2022-03-11Created from EPrint's last_modified field