Literature DB >> 26086389

Using noise to control heterogeneity of isogenic populations in homogenous environments.

Paulina Szymańska1, Nicola Gritti, Johannes M Keegstra, Mohammad Soltani, Brian Munsky.   

Abstract

We explore the extent to which the phenotypes of individual, genetically identical cells can be controlled independently from each other using only a single homogeneous environmental input. We show that such control is theoretically impossible if restricted to a deterministic setting, but it can be achieved readily if one exploits heterogeneities introduced at the single-cell level due to stochastic fluctuations in gene regulation. Using stochastic analyses of a bistable genetic toggle switch, we develop a control strategy that maximizes the chances that a chosen cell will express one phenotype, while the rest express another. The control mechanism uses UV radiation to enhance identically protein degradation in all cells. Control of individual cells is made possible only by monitoring stochastic protein fluctuations and applying UV control at favorable times and levels. For two identical cells, our stochastic control law can drive protein expression of a chosen cell above its neighbor with a better than 99% success rate. In a population of 30 identical cells, we can drive a given cell to remain consistently within the top 20%. Although cellular noise typically impairs predictability for biological responses, our results show that it can also simultaneously improve controllability for those same responses.

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Year:  2015        PMID: 26086389      PMCID: PMC4497367          DOI: 10.1088/1478-3975/12/4/045003

Source DB:  PubMed          Journal:  Phys Biol        ISSN: 1478-3967            Impact factor:   2.583


  30 in total

1.  Bacterial persistence as a phenotypic switch.

Authors:  Nathalie Q Balaban; Jack Merrin; Remy Chait; Lukasz Kowalik; Stanislas Leibler
Journal:  Science       Date:  2004-08-12       Impact factor: 47.728

2.  Identification from stochastic cell-to-cell variation: a genetic switch case study.

Authors:  B Munsky; M Khammash
Journal:  IET Syst Biol       Date:  2010-11       Impact factor: 1.615

Review 3.  Stochasticity in gene expression: from theories to phenotypes.

Authors:  Mads Kaern; Timothy C Elston; William J Blake; James J Collins
Journal:  Nat Rev Genet       Date:  2005-06       Impact factor: 53.242

4.  Chemical models of genetic toggle switches.

Authors:  Patrick B Warren; Pieter Rein ten Wolde
Journal:  J Phys Chem B       Date:  2005-04-14       Impact factor: 2.991

5.  Long-term model predictive control of gene expression at the population and single-cell levels.

Authors:  Jannis Uhlendorf; Agnès Miermont; Thierry Delaveau; Gilles Charvin; François Fages; Samuel Bottani; Gregory Batt; Pascal Hersen
Journal:  Proc Natl Acad Sci U S A       Date:  2012-08-14       Impact factor: 11.205

6.  Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells.

Authors:  A Arkin; J Ross; H H McAdams
Journal:  Genetics       Date:  1998-08       Impact factor: 4.562

Review 7.  Measuring and modeling apoptosis in single cells.

Authors:  Sabrina L Spencer; Peter K Sorger
Journal:  Cell       Date:  2011-03-18       Impact factor: 41.582

8.  In silico feedback for in vivo regulation of a gene expression circuit.

Authors:  Andreas Milias-Argeitis; Sean Summers; Jacob Stewart-Ornstein; Ignacio Zuleta; David Pincus; Hana El-Samad; Mustafa Khammash; John Lygeros
Journal:  Nat Biotechnol       Date:  2011-11-06       Impact factor: 54.908

Review 9.  Using gene expression noise to understand gene regulation.

Authors:  Brian Munsky; Gregor Neuert; Alexander van Oudenaarden
Journal:  Science       Date:  2012-04-13       Impact factor: 47.728

10.  Quantifying intrinsic and extrinsic variability in stochastic gene expression models.

Authors:  Abhyudai Singh; Mohammad Soltani
Journal:  PLoS One       Date:  2013-12-31       Impact factor: 3.240

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