Department of Physics and Astronomy, Purdue University, West Lafayette, IN, 47907, USA.
School of Mechanical Engineering, Purdue University, West Lafayette, IN, 47907, USA.
NPJ Syst Biol Appl. 2022 Nov 30;8(1):48. doi: 10.1038/s41540-022-00262-5.
Cell signaling networks are complex and often incompletely characterized, making it difficult to obtain a comprehensive picture of the mechanisms they encode. Mathematical modeling of these networks provides important clues, but the models themselves are often complex, and it is not always clear how to extract falsifiable predictions. Here we take an inverse approach, using experimental data at the cell level to deduce the minimal signaling network. We focus on cells' response to multiple cues, specifically on the surprising case in which the response is antagonistic: the response to multiple cues is weaker than the response to the individual cues. We systematically build candidate signaling networks one node at a time, using the ubiquitous ingredients of (i) up- or down-regulation, (ii) molecular conversion, or (iii) reversible binding. In each case, our method reveals a minimal, interpretable signaling mechanism that explains the antagonistic response. Our work provides a systematic way to deduce molecular mechanisms from cell-level data.
细胞信号网络非常复杂,且通常无法完全描述,因此很难全面了解它们所编码的机制。对这些网络进行数学建模提供了重要线索,但这些模型本身通常很复杂,而且并不总是清楚如何从中提取可验证的预测。在这里,我们采用一种相反的方法,使用细胞水平的实验数据来推断最小的信号网络。我们专注于细胞对多种信号的反应,特别是当反应是拮抗的情况:对多种信号的反应比单个信号的反应更弱。我们系统地一次构建一个候选信号网络节点,使用(i)上调或下调、(ii)分子转换或(iii)可逆结合这些普遍存在的成分。在每种情况下,我们的方法都揭示了一种最小的、可解释的信号机制,可以解释拮抗反应。我们的工作为从细胞水平数据推断分子机制提供了一种系统的方法。