The metabolic modelling community has established the gold standard for bottom-up systems biology with reconstruction, validation and simulation of mechanistic genome-scale models. Similar methods have not been established for signal transduction networks, where the representation of complexes and internal states leads to scalability issues in both model formulation and execution. While rule- and agent-based methods allow efficient model definition and execution, respectively, model parametrisation introduces an additional layer of uncertainty due to the sparsity of reliably measured parameters. Here, we present a scalable method for parameter-free simulation of mechanistic signal transduction networks. It is based on rxncon and uses a bipartite Boolean logic with separate update rules for reactions and states. Using two generic update rules, we enable translation of any rxncon model into a unique Boolean model, which can be used for network validation and simulation-allowing the prediction of system-level function directly from molecular mechanistic data. Through scalable model definition and simulation, and the independence of quantitative parameters, it opens up for simulation and validation of mechanistic genome-scale models of signal transduction networks.

中文翻译：

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一种用于机械细胞信号转导网络模型的无参数模拟和验证的可扩展方法。

代谢建模界通过重建、验证和模拟机械基因组规模模型，建立了自下而上系统生物学的黄金标准。尚未为信号转导网络建立类似的方法，其中复合体和内部状态的表示会导致模型制定和执行中的可扩展性问题。虽然基于规则和基于代理的方法分别允许有效的模型定义和执行，但由于可靠测量的参数的稀疏性，模型参数化引入了额外的不确定性。在这里，我们提出了一种用于机械信号传导网络的无参数模拟的可扩展方法。它基于 rxncon 并使用二分布尔逻辑，具有单独的反应和状态更新规则。使用两个通用更新规则，我们可以将任何 rxncon 模型转换为独特的布尔模型，该模型可用于网络验证和模拟 - 允许直接从分子机制数据预测系统级功能。通过可扩展的模型定义和模拟以及定量参数的独立性，它为信号转导网络的机械基因组规模模型的模拟和验证开辟了道路。