A multi-step completion process model of cell plasticity

Plasticity is the potential for cells or cell populations to change their phenotypes and behaviours in response to internal or external cues. A common way to study a plasticity programme is to track the underlying molecular changes over time, by collecting omics time-series data. However, there remains a lack of mathematical models to elucidate and predict molecular behaviours in a plasticity programme using omics time-series data. 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. In benchmarking tests, we show that our developed Control Point (CP) model fits omics time-series data and identifies attractor states that are well-aligned with prior knowledge. Importantly, we show that the CP 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. Significance statementCell plasticity is fundamental to a wide range of complex biological processes, for example in embryonic development, immunology, regenerative medicine, aging, cancer, and others. We developed a new mathematical model that leverages omics time-series data to understand and make accurate predictions about cell plasticity. This can lead to new discoveries and open up new avenues of research in medicine and bioengineering.