Abstract
This study describes a method for controlling the production of protein in individual cells using stochastic models of gene expression. By combining modern microscopy platforms with optogenetic gene expression, experimentalists are able to accurately apply light to individual cells, which can induce protein production. Here we use a finite state projection based stochastic model of gene expression, along with Bayesian state estimation to control protein copy numbers within individual cells. We compare this method to previous methods that use population based approaches. We also demonstrate the ability of this control strategy to ameliorate discrepancies between the predictions of a deterministic model and stochastic switching system.
| Original language | English |
|---|---|
| Article number | 055003 |
| Journal | Physical Biology |
| Volume | 20 |
| Issue number | 5 |
| DOIs | |
| State | Published - Sep 1 2023 |
Funding
This study was supported in part by the Center for Nonlinear Studies at Los Alamos National Laboratory, which is operated by Triad National Security, LLC under the auspices of the National Nuclear Security Administration of U.S. Department of Energy under Contract No. 89233218CNA000001. This work was also supported in part by ANR Grants CyberCircuits (ANR-18-CE91-0002), MEMIP (ANR-16-CE33-0018), and Cogex (ANR-16-CE12-0025), by the H2020 Fet-Open COSY-BIO Grant (Grant Agreement No. 766840) and by the Inria IPL Grant COSY. Z R F thanks Huy Vo and Anatoly Zlotnik for helpful discussions regarding the presentation of this work. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). This study was supported in part by the Center for Nonlinear Studies at Los Alamos National Laboratory, which is operated by Triad National Security, LLC under the auspices of the National Nuclear Security Administration of U.S. Department of Energy under Contract No. 89233218CNA000001. This work was also supported in part by ANR Grants CyberCircuits (ANR-18-CE91-0002), MEMIP (ANR-16-CE33-0018), and Cogex (ANR-16-CE12-0025), by the H2020 Fet-Open COSY-BIO Grant (Grant Agreement No. 766840) and by the Inria IPL Grant COSY. Z R F thanks Huy Vo and Anatoly Zlotnik for helpful discussions regarding the presentation of this work. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
Keywords
- Bayesian filter
- chemical master equation
- model predictive control
- optogenetics
- stochastic model
- systems biology