AI Article Synopsis
- New mathematical approaches are being explored to better understand complex differentiation processes through Waddington's landscape metaphor, but a solid method for creating these models is still a challenge.
- The study focuses on vulval development in C. elegans by applying Catastrophe Theory and approximate Bayesian computation to create data-fitted landscape models.
- Findings indicate a two-step decision-making mechanism influenced by EGF and Notch-Delta signals, leading to a transition between non-vulval/vulval states, and this model can explain a wide range of data while making new predictions.
Article Abstract
Increasing interest has emerged in new mathematical approaches that simplify the study of complex differentiation processes by formalizing Waddington's landscape metaphor. However, a rational method to build these landscape models remains an open problem. Here we study vulval development in C. elegans by developing a framework based on Catastrophe Theory (CT) and approximate Bayesian computation (ABC) to build data-fitted landscape models. We first identify the candidate qualitative landscapes, and then use CT to build the simplest model consistent with the data, which we quantitatively fit using ABC. The resulting model suggests that the underlying mechanism is a quantifiable two-step decision controlled by EGF and Notch-Delta signals, where a non-vulval/vulval decision is followed by a bistable transition to the two vulval states. This new model fits a broad set of data and makes several novel predictions.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8195438 | PMC |
| http://dx.doi.org/10.1371/journal.pcbi.1009034 | DOI Listing |
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