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Authors: Chen M. Chen, Rosemary Yu
Plasticity is the potential for cells or cell populations to change their phenotypes and behaviours in response to internal or external cues. Plasticity is fundamental to many complex biological processes, yet to date there remains a lack of mathematical models that can elucidate and predict molecular behaviours in a plasticity programme. Here we report a new mathematical framework that models cell plasticity as a multi-step completion process, where the system moves from the initial state along a path guided by multiple intermediate attractors until the final state (i.e. a new homeostasis) is reached. Using omics time-series data as model input, we show that our method fits data well, and identifies attractor states by their timing and molecular markers which are well-aligned with domain knowledge. Importantly, our model can make non-trivial predictions such as the molecular outcomes of blocking a plasticity programme from reaching completion, in a quantitative and time-resolved manner.
