Ben Boina Nadine, Mossé Brigitte, Baudot Anaïs, Remy Elisabeth
Aix Marseille Univ, CNRS, I2M (UMR 7373), Turing Center for Living systems, Marseille, France.
Aix Marseille Univ, INSERM, MMG, Marseille, France.
Bioinformatics. 2025 Mar 29;41(4). doi: 10.1093/bioinformatics/btaf123.
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.
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.
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).
在系统生物学中,建模策略旨在解读分子成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,但是仅用两个值的离散变量所产生的动态描述可能过于有限,无法捕捉某些动态特性。多值逻辑模型可以通过允许每个成分有两个以上的水平来克服这一限制。然而,对布尔模型进行多值化具有挑战性。
我们提出了MRBM,这是一种有效识别布尔模型中需要进行多值化的成分的方法,以便在异步动态中捕捉特定的定点可达性。为了实现这一目标,我们定义了一种新的更新方案,在最宽松的动态中定位可达性属性。MRBM得到了数学论证的支持,并在一个玩具模型和两个干细胞分化模型上进行了说明。
本文中使用的MRBM方法和布尔模型可在GitHub上获取:https://github.com/NdnBnBn/MRBM,并已存档于Zenodo(doi: 10.5281/ZENODO.14979798)。