Dataset Information

Quantifying cell transitions in C. elegans with data-fitted landscape models.

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.

SUBMITTER: Camacho-Aguilar E

PROVIDER: S-EPMC8195438 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Quantifying cell transitions in C. elegans with data-fitted landscape models.

Camacho-Aguilar Elena E Warmflash Aryeh A Rand David A DA

PLoS computational biology 20210601 6

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, ...[more]

PMID: 34061834

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Dataset Information

Quantifying cell transitions in C. elegans with data-fitted landscape models.

Publications

Quantifying cell transitions in C. elegans with data-fitted landscape models.

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