Predicting rates of cell state change caused by stochastic fluctuations using a data-driven landscape model.

ABSTRACT: We develop a potential landscape approach to quantitatively describe experimental data from a fibroblast cell line that exhibits a wide range of GFP expression levels under the control of the promoter for tenascin-C. Time-lapse live-cell microscopy provides data about short-term fluctuations in promoter activity, and flow cytometry measurements provide data about the long-term kinetics, because isolated subpopulations of cells relax from a relatively narrow distribution of GFP expression back to the original broad distribution of responses. The landscape is obtained from the steady state distribution of GFP expression and connected to a potential-like function using a stochastic differential equation description (Langevin/Fokker-Planck). The range of cell states is constrained by a force t

SUBMITTER: Sisan DR 

PROVIDER: S-EPMC3511108 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature