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Analyses of Multi-dimensional Single Cell Trajectories Quantify Transition Paths Between Nonequilibrium Steady States

Weikang Wang, Jianhua Xing
doi: https://doi.org/10.1101/2020.01.27.920371
Weikang Wang
1Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15232, USA
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Jianhua Xing
1Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15232, USA
2Department of Physics, University of Pittsburgh, Pittsburgh, PA 15232, USA
3UPMC-Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
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  • For correspondence: xing1@pitt.edu
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ABSTRACT

A problem ubiquitous in almost all scientific areas is escape from a metastable state, or relaxation from one stationary distribution to a new one1. More than a century of studies lead to celebrated theoretical and computational developments such as the transition state theory and reactive flux formulation. Modern transition path sampling and transition path theory focus on an ensemble of trajectories that connect the initial and final states in a state space2, 3. However, it is generally unfeasible to experimentally observe these trajectories in multiple dimensions and compare to theoretical results. Here we report and analyze single cell trajectories of human A549 cells undergoing TGF-β induced epithelial-to-mesenchymal transition (EMT) in a combined morphology and protein texture space obtained through time lapse imaging. From the trajectories we identify parallel reaction paths with corresponding reaction coordinates and quasi-potentials. Studying cell phenotypic transition dynamics will provide testing grounds for nonequilibrium reaction rate theories.

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Posted January 28, 2020.
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Analyses of Multi-dimensional Single Cell Trajectories Quantify Transition Paths Between Nonequilibrium Steady States
Weikang Wang, Jianhua Xing
bioRxiv 2020.01.27.920371; doi: https://doi.org/10.1101/2020.01.27.920371
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Analyses of Multi-dimensional Single Cell Trajectories Quantify Transition Paths Between Nonequilibrium Steady States
Weikang Wang, Jianhua Xing
bioRxiv 2020.01.27.920371; doi: https://doi.org/10.1101/2020.01.27.920371

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