Refining Boolean models with the partial most permissive scheme.

IF 5.4 Bioinformatics (Oxford, England) Pub Date : 2025-03-29 DOI:10.1093/bioinformatics/btaf123

Nadine Ben Boina, Brigitte Mossé, Anaïs Baudot, Elisabeth Remy

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Abstract

Motivation: In systems biology, modeling strategies aim to decode how molecular components interact to generate dynamical behavior. Boolean modeling is more and more used, but the description of the dynamics generated by discrete variables with only two values may be too limited to capture certain dynamical properties. Multivalued logical models can overcome this limitation by allowing more than two levels for each component. However, multivaluing a Boolean model is challenging.

Results: We present MRBM, a method for efficiently identifying the components of a Boolean model to be multivalued in order to capture specific fixed-point reachabilities in the asynchronous dynamics. To this goal, we defined a new updating scheme locating reachability properties in the most permissive dynamics. MRBM is supported by mathematical demonstrations and illustrated on a toy model and on two models of stem cell differentiation.

Availability and implementation: The MRBM method and the BMs used in this article are available on GitHub at: https://github.com/NdnBnBn/MRBM, and archived in Zenodo (doi: 10.5281/ZENODO.14979798).

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用部分最允许方案改进布尔模型。

动机：在系统生物学中，建模策略旨在解码分子组分如何相互作用以产生动态行为。布尔建模的应用越来越广泛，但对只有两个值的离散变量产生的动力学的描述可能过于有限，无法捕捉某些动力学特性。多值逻辑模型可以通过允许每个组件有两个以上的级别来克服这一限制。然而，对布尔模型进行多值处理是一项挑战。结果：我们提出了MRBM，一种有效识别布尔模型的多值组件的方法，以便在异步动态中捕获特定的定点可达性。为了实现这个目标，我们定义了一个新的更新方案，在最允许的动态中定位可达性属性。MRBM由数学演示支持，并在一个玩具模型和两个干细胞分化模型上进行说明。可用性和实现：本文中使用的MRBM方法和bm可在GitHub上获得：https://github.com/NdnBnBn/MRBM，并在Zenodo存档（doi: 10.5281/ Zenodo .14979798）。补充信息：补充数据可在生物信息学在线获取。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Bioinformatics (Oxford, England)

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