Department of Biosystems Science and Engineering, ETH Zürich, Zürich, Switzerland; Swiss Institute of Bioinformatics, Basel, Switzerland.
Biophys J. 2018 Feb 6;114(3):723-736. doi: 10.1016/j.bpj.2017.11.3780.
In pharmacology and systems biology, it is a fundamental problem to determine how biological systems change their dose-response behavior upon perturbations. In particular, it is unclear how topologies, reactions, and parameters (differentially) affect the dose response. Because parameters are often unknown, systematic approaches should directly relate network structure and function. Here, we outline a procedure to compare general non-monotone dose-response curves and subsequently develop a comprehensive theory for differential dose responses of biochemical networks captured by non-equilibrium steady-state linear framework models. Although these models are amenable to analytical derivations of non-equilibrium steady states in principle, their size frequently increases (super) exponentially with model size. We extract general principles of differential responses based on a model's graph structure and thereby alleviate the combinatorial explosion. This allows us, for example, to determine reactions that affect differential responses, to identify classes of networks with equivalent differential, and to reject hypothetical models reliably without needing to know parameter values. We exemplify such applications for models of insulin signaling.
在药理学和系统生物学中，确定生物系统在受到干扰时如何改变其剂量反应行为是一个基本问题。特别是，拓扑结构、反应和参数（差异）如何影响剂量反应尚不清楚。由于参数通常是未知的，因此系统的方法应该直接将网络结构和功能联系起来。在这里，我们概述了一种比较一般的非单调剂量反应曲线的方法，并随后为通过非平衡稳态线性框架模型捕获的生化网络的微分剂量反应开发了一个全面的理论。尽管这些模型原则上可以进行非平衡稳态的解析推导，但它们的大小通常会随着模型的大小呈（超）指数增长。我们基于模型的图结构提取微分响应的一般原理，从而缓解组合爆炸。例如，这使我们能够确定影响微分响应的反应，识别具有等效微分的网络类别，并在不需要知道参数值的情况下可靠地拒绝假设模型。我们以胰岛素信号模型为例说明了这种应用。
