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First-principles prediction of the information processing capacity of a simple genetic circuit

Razo-Mejia, Manuel and Marzen, Sarah and Chure, Griffin and Taubman, Rachel and Morrison, Muir and Phillips, Rob (2020) First-principles prediction of the information processing capacity of a simple genetic circuit. Physical Review E, 102 (2). Art. No. 022404. ISSN 2470-0045.

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Given the stochastic nature of gene expression, genetically identical cells exposed to the same environmental inputs will produce different outputs. This heterogeneity has been hypothesized to have consequences for how cells are able to survive in changing environments. Recent work has explored the use of information theory as a framework to understand the accuracy with which cells can ascertain the state of their surroundings. Yet the predictive power of these approaches is limited and has not been rigorously tested using precision measurements. To that end, we generate a minimal model for a simple genetic circuit in which all parameter values for the model come from independently published data sets. We then predict the information processing capacity of the genetic circuit for a suite of biophysical parameters such as protein copy number and protein-DNA affinity. We compare these parameter-free predictions with an experimental determination of protein expression distributions and the resulting information processing capacity of E. coli cells. We find that our minimal model captures the scaling of the cell-to-cell variability in the data and the inferred information processing capacity of our simple genetic circuit up to a systematic deviation.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Razo-Mejia, Manuel0000-0002-9510-0527
Marzen, Sarah0000-0001-5386-1101
Chure, Griffin0000-0002-2216-2057
Taubman, Rachel0000-0002-7779-5185
Morrison, Muir0000-0002-0768-7234
Phillips, Rob0000-0003-3082-2809
Additional Information:© 2020 American Physical Society. Received 6 May 2020; revised 29 June 2020; accepted 2 July 2020; published 13 August 2020. We would like to thank Nathan Belliveau, Michael Betancourt, William Bialek, Justin Bois, Emanuel Flores, Hernan Garcia, Alejandro Granados, Porfirio Quintero, Catherine Triandafillou, and Ned Wingreen for useful advice and discussion. We would especially like to thank Alvaro Sanchez, Gašper Tkačik, and Jane Kondev for critical observations on the manuscript. We thank Rob Brewster for providing the raw mRNA FISH data for inferences, and David Drabold for advice on the maximum entropy inferences. We are grateful to Heun Jin Lee for his key support with the quantitative microscopy. This work was supported by La Fondation Pierre–Gilles de Gennes, the Rosen Center at Caltech, and the NIH 5R35GM118043-05 (MIRA). M.R.M. was supported by the Caldwell CEMI fellowship.
Group:Rosen Bioengineering Center, Caltech Center for Environmental Microbial Interactions (CEMI)
Funding AgencyGrant Number
La Fondation Pierre-Gilles de GennesUNSPECIFIED
Donna and Benjamin M. Rosen Bioengineering CenterUNSPECIFIED
Caltech Center for Environmental Microbial Interactions (CEMI)UNSPECIFIED
Issue or Number:2
Record Number:CaltechAUTHORS:20190402-080512452
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94350
Deposited By: Tony Diaz
Deposited On:02 Apr 2019 15:33
Last Modified:16 Nov 2020 17:54

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