A Caltech Library Service

Reconciling kinetic and thermodynamic models of bacterial transcription

Morrison, Muir and Razo-Mejia, Manuel and Phillips, Rob (2021) Reconciling kinetic and thermodynamic models of bacterial transcription. PLOS Computational Biology, 17 (1). Art. No. e1008572. ISSN 1553-7358. PMCID PMC7845990. doi:10.1371/journal.pcbi.1008572.

[img] PDF - Published Version
Creative Commons Attribution.

[img] PDF - Submitted Version
Creative Commons Attribution Non-commercial.

[img] PDF (S1 Supporting Information. Detailed mathematical derivations and extended analysis) - Supplemental Material
See Usage Policy.


Use this Persistent URL to link to this item:


The study of transcription remains one of the centerpieces of modern biology with implications in settings from development to metabolism to evolution to disease. Precision measurements using a host of different techniques including fluorescence and sequencing readouts have raised the bar for what it means to quantitatively understand transcriptional regulation. In particular our understanding of the simplest genetic circuit is sufficiently refined both experimentally and theoretically that it has become possible to carefully discriminate between different conceptual pictures of how this regulatory system works. This regulatory motif, originally posited by Jacob and Monod in the 1960s, consists of a single transcriptional repressor binding to a promoter site and inhibiting transcription. In this paper, we show how seven distinct models of this so-called simple-repression motif, based both on thermodynamic and kinetic thinking, can be used to derive the predicted levels of gene expression and shed light on the often surprising past success of the thermodynamic models. These different models are then invoked to confront a variety of different data on mean, variance and full gene expression distributions, illustrating the extent to which such models can and cannot be distinguished, and suggesting a two-state model with a distribution of burst sizes as the most potent of the seven for describing the simple-repression motif.

Item Type:Article
Related URLs:
URLURL TypeDescription CentralArticle ItemCode for Bayesian inference and figure generation Paper
Morrison, Muir0000-0002-0768-7234
Razo-Mejia, Manuel0000-0002-9510-0527
Phillips, Rob0000-0003-3082-2809
Alternate Title:Reconciling Kinetic and Equilibrium Models of Bacterial Transcription
Additional Information:© 2021 Morrison et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received: June 14, 2020; Accepted: November 28, 2020; Published: January 19, 2021. We thank Rob Brewster for providing the raw single-molecule mRNA FISH data. We thank Justin Bois for his key support with the Bayesian inference section. We would also like to thank Griffin Chure for invaluable feedback on the manuscript. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1745301 (to M.J.M.). This work was also supported by La Fondation Pierre-Gilles de Gennes, the Rosen Center at Caltech, and the NIH 5R35GM118043-05 (MIRA) to R.P. M.R.M. was supported by the Caldwell CEMI fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author Contributions: Conceptualization: Muir Morrison, Manuel Razo-Mejia, Rob Phillips. Formal analysis: Muir Morrison. Funding acquisition: Rob Phillips. Investigation: Muir Morrison. Methodology: Muir Morrison, Rob Phillips. Project administration: Muir Morrison, Manuel Razo-Mejia, Rob Phillips. Software: Muir Morrison, Manuel Razo-Mejia. Supervision: Muir Morrison, Manuel Razo-Mejia, Rob Phillips. Validation: Muir Morrison, Manuel Razo-Mejia, Rob Phillips. Visualization: Muir Morrison, Manuel Razo-Mejia. Writing – original draft: Muir Morrison, Manuel Razo-Mejia, Rob Phillips. Writing – review & editing: Muir Morrison, Manuel Razo-Mejia, Rob Phillips. The authors have declared that no competing interests exist. Data Availability Statement: All data and custom scripts were collected and stored using Git version control. Code for Bayesian inference and figure generation is available on the GitHub repository (
Group:Caltech Center for Environmental Microbial Interactions (CEMI), Rosen Bioengineering Center
Funding AgencyGrant Number
NSF Graduate Research FellowshipDGE-1745301
Fondation Pierre Gilles de GennesUNSPECIFIED
Donna and Benjamin M. Rosen Bioengineering CenterUNSPECIFIED
Caltech Center for Environmental Microbial Interactions (CEMI)UNSPECIFIED
Issue or Number:1
PubMed Central ID:PMC7845990
Record Number:CaltechAUTHORS:20200615-130249775
Persistent URL:
Official Citation:Morrison M, Razo-Mejia M, Phillips R (2021) Reconciling kinetic and thermodynamic models of bacterial transcription. PLoS Comput Biol 17(1): e1008572.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:103922
Deposited By: Tony Diaz
Deposited On:15 Jun 2020 21:00
Last Modified:05 Oct 2021 18:26

Repository Staff Only: item control page