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Predicting rates of cell state change caused by stochastic fluctuations using a data-driven landscape model
Daniel R Sisan1, Michael Halter, Joseph B Hubbard
1National Institute of Standards and Technology, Gaithersburg, MD 20899, USA.
Summary
We developed a potential landscape model to predict cell behavior. This approach uses gene expression data to simulate how cell populations change over time, offering insights into cellular dynamics.
Area of Science:
- Cellular dynamics and systems biology
- Quantitative biology
- Biophysics
Background:
- Fibroblast cell lines exhibit a broad range of GFP expression under tenascin-C promoter control.
- Short-term promoter activity fluctuations and long-term kinetics are observed.
- Cell subpopulations relax from narrow to broad expression distributions.
Purpose of the Study:
- To quantitatively describe experimental data using a potential landscape approach.
- To connect steady-state distributions to a potential-like function via stochastic differential equations.
- To predict the rates of phenotype emergence in isolated cell subpopulations.
Main Methods:
- Utilized time-lapse live-cell microscopy for short-term promoter activity.
- Employed flow cytometry for long-term kinetic measurements.
- Applied a potential landscape model derived from steady-state distributions and stochastic differential equations (Langevin/Fokker-Planck).
Main Results:
- Biochemical noise drives cell movement within the potential landscape.
- Analysis of GFP intensity fluctuations revealed a single diffusion constant in log GFP space.
- The Kramers' model accurately predicted switching rates between attractor states and relaxation dynamics without adjustable parameters.
Conclusions:
- The potential landscape approach accurately describes fibroblast cell behavior.
- This model quantitatively links steady-state phenotypes to short-term cellular fluctuations.
- Predictive modeling of phenotype emergence rates is achievable using this framework.
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