title: Using noise to control heterogeneity of isogenic populations in homogenous environments.
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.
authors.affiliation: Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5 b, 02-106 Warsaw, Poland.
authors.forename: Paulina
authors.initials: P
authors.lastname: Szymańska
authors.forename: Nicola
authors.initials: N
authors.lastname: Gritti
authors.forename: Johannes M
authors.initials: JM
authors.lastname: Keegstra
authors.forename: Mohammad
authors.initials: M
authors.lastname: Soltani
authors.forename: Brian
authors.initials: B
authors.lastname: Munsky
mesh_terms: D004777:Environment; D004926:Escherichia coli / Q000235:genetics* / Q000378:metabolism; D005786:Gene Expression Regulation; D008957:Models, Genetic; D010641:Phenotype; D013269:Stochastic Processes; D013997:Time Factors