Department of Bioengineering, University of Washington, Seattle 98195, WA, USA.
Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland 99352, WA, USA.
J Theor Biol. 2024 Oct 7;593:111901. doi: 10.1016/j.jtbi.2024.111901. Epub 2024 Jul 14.
Predictive models of signaling pathways have proven to be difficult to develop. Traditional approaches to developing mechanistic models rely on collecting experimental data and fitting a single model to that data. This approach works for simple systems but has proven unreliable for complex systems such as biological signaling networks. Thus, there is a need to develop new approaches to create predictive mechanistic models of complex systems. To meet this need, we developed a method for generating artificial signaling networks that were reasonably realistic and thus could be treated as ground truth models. These synthetic models could then be used to generate synthetic data for developing and testing algorithms designed to recover the underlying network topology and associated parameters. We defined the reaction degree and reaction distance to measure the topology of reaction networks, especially to consider enzymes. To determine whether our generated signaling networks displayed meaningful behavior, we compared them with signaling networks from the BioModels Database. This comparison indicated that our generated signaling networks had high topological similarities with BioModels signaling networks with respect to the reaction degree and distance distributions. In addition, our synthetic signaling networks had similar behavioral dynamics with respect to both steady states and oscillations, suggesting that our method generated synthetic signaling networks comparable with BioModels and thus could be useful for building network evaluation tools.
预测信号通路的模型一直很难开发。传统的开发机制模型的方法依赖于收集实验数据，并将单一模型拟合到该数据上。这种方法适用于简单的系统，但对于复杂的系统（如生物信号网络）来说已经被证明是不可靠的。因此，需要开发新的方法来创建复杂系统的预测性机制模型。为了满足这一需求，我们开发了一种生成人工信号网络的方法，这些网络具有相当的现实性，因此可以作为真实模型。然后可以使用这些合成模型生成用于开发和测试算法的数据，这些算法旨在恢复底层网络拓扑结构和相关参数。我们定义了反应度和反应距离来测量反应网络的拓扑结构，特别是要考虑酶。为了确定我们生成的信号网络是否表现出有意义的行为，我们将它们与 BioModels 数据库中的信号网络进行了比较。该比较表明，我们生成的信号网络在反应度和距离分布方面与 BioModels 信号网络具有高度的拓扑相似性。此外，我们的合成信号网络在稳态和振荡方面具有相似的动态行为，这表明我们的方法生成的合成信号网络与 BioModels 相当，因此可用于构建网络评估工具。
