Predicting rates of cell state change due to stochastic fluctuations using a data-driven landscape model

Published

November 20, 2012

Author(s)

Daniel R. Sisan, Michael Halter, Joseph B. Hubbard, Anne L. Plant

Abstract

In this paper, we develop a potential landscape approach to quantitatively describe experimental data from a fibroblast cell line that expresses GFP 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 kinetics of relaxation of isolated subpopulations of cells from a relatively narrow distribution of GFP expression back to the original broad distribution of responses. This broad stationary, or steady state, distribution is the basis for determining the landscape, which is connected to a probability distribution using a stochastic differential equation (Langevin/Fokker-Planck), with a defined force that is proportional to the gradient of the potential, and a kinetic constant for movement within the landscape. Analysis of the mean square displacement in GFP intensity in cells indicates that a single diffusion constant can describe the rate of fluctuation in log GFP space. This enables an unambiguous mathematical expression that describes the steady state distribution, allows application of the Kramers' model to calculate rates of switching between two attractor states, and enables an accurate simulation of the dynamics of relaxation back to the steady state with no adjustable parameters. With this approach, it is possible to use the steady state distribution of phenotypes and a quantitative description of the short term fluctuations in individual cells to accurately predict the rates at which different epigenetic phenotypes will arise from an isolated subpopulation of cells.

Citation

Proceedings of the National Academy of Sciences of the United States of America

Volume

109

Pub Type

Journals

Download Paper

https://doi.org/10.1073/pnas.1207544109

Local Download

Keywords

cell biology, stochastic process, dynamical system, Langevin, gene expression, GFP, phenotypic distribution, tenascin-C

Citation

Sisan, D. , Halter, M. , Hubbard, J. and Plant, A. (2012), Predicting rates of cell state change due to stochastic fluctuations using a data-driven landscape model, Proceedings of the National Academy of Sciences of the United States of America, [online], https://doi.org/10.1073/pnas.1207544109, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=910975 (Accessed April 23, 2024)