Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France.
Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France.
PLoS Comput Biol. 2022 Jun 13;18(6):e1010175. doi: 10.1371/journal.pcbi.1010175. eCollection 2022 Jun.
Most biological processes are orchestrated by large-scale molecular networks which are described in large-scale model repositories and whose dynamics are extremely complex. An observed phenotype is a state of this system that results from control mechanisms whose identification is key to its understanding. The Biological Pathway Exchange (BioPAX) format is widely used to standardize the biological information relative to regulatory processes. However, few modeling approaches developed so far enable for computing the events that control a phenotype in large-scale networks. Here we developed an integrated approach to build large-scale dynamic networks from BioPAX knowledge databases in order to analyse trajectories and to identify sets of biological entities that control a phenotype. The Cadbiom approach relies on the guarded transitions formalism, a discrete modeling approach which models a system dynamics by taking into account competition and cooperation events in chains of reactions. The method can be applied to every BioPAX (large-scale) model thanks to a specific package which automatically generates Cadbiom models from BioPAX files. The Cadbiom framework was applied to the BioPAX version of two resources (PID, KEGG) of the Pathway Commons database and to the Atlas of Cancer Signalling Network (ACSN). As a case-study, it was used to characterize sets of biological entities implicated in the epithelial-mesenchymal transition. Our results highlight the similarities between the PID and ACSN resources in terms of biological content, and underline the heterogeneity of usage of the BioPAX semantics limiting the fusion of models that require curation. Causality analyses demonstrate the smart complementarity of the databases in terms of combinatorics of controllers that explain a phenotype. From a biological perspective, our results show the specificity of controllers for epithelial and mesenchymal phenotypes that are consistent with the literature and identify a novel signature for intermediate states.
大多数生物过程都是由大规模分子网络协调的，这些网络被描述在大规模模型存储库中，其动态极其复杂。观察到的表型是系统的一种状态，是由控制机制产生的，而识别这些控制机制是理解系统的关键。生物途径交换（BioPAX）格式被广泛用于标准化与调控过程相关的生物学信息。然而，到目前为止，很少有建模方法能够计算大规模网络中控制表型的事件。在这里，我们开发了一种综合方法，从 BioPAX 知识库构建大规模动态网络，以便分析轨迹并确定控制表型的生物实体集。Cadbiom 方法依赖于受保护转换形式化方法，这是一种离散建模方法，通过考虑反应链中的竞争和合作事件来对系统动态进行建模。该方法可以应用于每个 BioPAX（大规模）模型，因为有一种特定的软件包可以自动从 BioPAX 文件生成 Cadbiom 模型。Cadbiom 框架应用于 Pathway Commons 数据库的 PID 和 KEGG 两个资源的 BioPAX 版本以及癌症信号网络图谱（ACSN）。作为一个案例研究，它用于描述参与上皮-间充质转化的生物实体集。我们的结果强调了 PID 和 ACSN 资源在生物学内容方面的相似性，并强调了限制需要编辑的模型融合的 BioPAX 语义的使用异质性。因果分析表明，从组合控制器的角度来看，这些数据库在解释表型方面具有智能互补性。从生物学的角度来看，我们的结果表明了控制器对于上皮和间充质表型的特异性，这些结果与文献一致，并确定了中间状态的一个新特征。
