Dnyane Pooja A, Puntambekar Shraddha S, Gadgil Chetan J
Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Dr. Homi Bhabha Road, Pune 411 008, India.
Academy of Scientific and Innovative Research (AcSIR), CSIR-National Chemical Laboratory Campus, Pune 411 008, India.
IET Syst Biol. 2018 Feb;12(1):1-6. doi: 10.1049/iet-syb.2017.0039.
Biological systems are often represented as Boolean networks and analysed to identify sensitive nodes which on perturbation disproportionately change a predefined output. There exist different kinds of perturbation methods: perturbation of function, perturbation of state and perturbation in update scheme. Nodes may have defects in interpretation of the inputs from other nodes and calculation of the node output. To simulate these defects and systematically assess their effect on the system output, two new function perturbations, referred to as 'not of function' and 'function of not', are introduced. In the former, the inputs are assumed to be correctly interpreted but the output of the update rule is perturbed; and in the latter, each input is perturbed but the correct update rule is applied. These and previously used perturbation methods were applied to two existing Boolean models, namely the human melanogenesis signalling network and the fly segment polarity network. Through mathematical simulations, it was found that these methods successfully identified nodes earlier found to be sensitive using other methods, and were also able to identify sensitive nodes which were previously unreported.
生物系统通常被表示为布尔网络，并进行分析以识别敏感节点，这些节点在受到扰动时会不成比例地改变预定义输出。存在不同类型的扰动方法：功能扰动、状态扰动和更新方案扰动。节点在解释来自其他节点的输入以及计算节点输出时可能存在缺陷。为了模拟这些缺陷并系统地评估它们对系统输出的影响，引入了两种新的功能扰动，称为“非功能”和“非的功能”。在前者中，假设输入被正确解释，但更新规则的输出受到扰动；而在后者中，每个输入受到扰动，但应用正确的更新规则。这些以及先前使用的扰动方法被应用于两个现有的布尔模型，即人类黑色素生成信号网络和果蝇体节极性网络。通过数学模拟发现，这些方法成功地识别出了先前使用其他方法发现的敏感节点，并且还能够识别出先前未报告的敏感节点。
