A perturbation approach for refining Boolean models of cell cycle regulation

TL;DR

This paper introduces a new method to improve Boolean models of cell cycle regulation using perturbation analysis.

Contribution

A novel perturbation approach is introduced to refine Boolean models by optimizing their dynamical behavior.

Findings

The perturbation method successfully refines Boolean models of cell cycle regulation in budding yeast.

The approach improves model reliability by increasing the frequency of correct cell cycle paths in dynamical trajectories.

The method is also applied to mammalian cell cycle models, demonstrating its broader applicability.

Abstract

Considerable effort is required to build mathematical models of large protein regulatory networks. Utilizing computational algorithms that guide model development can significantly streamline the process and enhance the reliability of the resulting models. In this article, we present a perturbation approach for developing data-centric Boolean models of cell cycle regulation. To evaluate networks, we assign a score based on their steady states and the dynamical trajectories corresponding to the initial conditions. Then, perturbation analysis is used to find new networks with lower scores, in which dynamical trajectories traverse through the correct cell cycle path with high frequency. We apply this method to refine Boolean models of cell cycle regulation in budding yeast and mammalian cells.

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