Skip to content
Code and data for the channel capacity of a genetic circuit project.
Jupyter Notebook Python
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.

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

This repository serves as a record for the experimental and theoretical work described in the publication "First-principles prediction of the information processing capacity of a simple genetic circuit" by Manuel Razo-Mejia and Rob Phillips.


Given the stochastic nature of gene expression, genetically identical cells exposed to the same environmental inputs will produce different outputs. This heterogeneity has 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 the information processing capacity of E. coli cells, and find that our minimal model accurately captures the experimental data.

You can’t perform that action at this time.