Reconstructing data-driven governing equations for cell phenotypic transitions: integration of data science and
Jianhua Xing1,2,3
1Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15232, United States of America.
Physical Biology
|August 23, 2022
Summary
Cellular phenotypes can change due to stimuli. New methods reconstruct cell dynamics from single-cell data, advancing biological modeling beyond statistical approaches.
Area of Science:
- Cell Biology
- Systems Biology
- Computational Biology
Background:
- Cells with identical genomes exhibit diverse phenotypes.
- Phenotypic transitions are crucial in development, disease (cancer, fibrosis), and reprogramming.
- Understanding cell state dynamics is a fundamental biological challenge.
Purpose of the Study:
- To review recent advancements in modeling cell phenotypic conversion.
- To highlight methods for reconstructing cellular dynamical systems from quantitative single-cell data.
- To provide perspectives on future directions in mechanism-driven biological modeling.
Main Methods:
- Review of recent studies utilizing live- and fixed-cell data.
- Application of quantitative experimental approaches, including high-throughput single-cell techniques.
- Reconstruction of dynamical equations governing cellular systems from single-cell data.
Main Results:
- Emergence of new directions for mechanism-driven modeling beyond statistical approaches.
- Demonstration of reconstructing cellular dynamics from quantitative single-cell data.
- Identification of challenges in acquiring sufficient quantitative data for model parameter constraints.
Conclusions:
- Quantitative single-cell data enables novel approaches to model cell phenotypic conversion.
- Future research should focus on integrating advanced experimental data with dynamical systems modeling.
- Improved data acquisition and modeling techniques are essential for understanding complex cellular dynamics.
Keywords:
Fokker–Planck equationLangevin equationMarkov modelequation of motionlive-cell imagingnonequilibriumsingle cell genomicsMore Related Videos
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