Biological and Neural Computation Group, Science and Technology Research Institute, University of Hertfordshire, College Lane, Hatfield, United Kingdom.
Biophys Chem. 2012 Mar;162:22-34. doi: 10.1016/j.bpc.2011.12.003. Epub 2012 Jan 5.
Transforming growth factor β (TGF-β) ligands activate a signaling cascade with multiple cell context dependent outcomes. Disruption or disturbance leads to variant clinical disorders. To develop strategies for disease intervention, delineation of the pathway in further detail is required. Current theoretical models of this pathway describe production and degradation of signal mediating proteins and signal transduction from the cell surface into the nucleus, whereas feedback loops have not exhaustively been included. In this study we present a mathematical model to determine the relevance of feedback regulators (Arkadia, Smad7, Smurf1, Smurf2, SnoN and Ski) on TGF-β target gene expression and the potential to initiate stable oscillations within a realistic parameter space. We employed massive sampling of the parameters space to pinpoint crucial players for potential oscillations as well as transcriptional product levels. We identified Smad7 and Smurf2 with the highest impact on the dynamics. Based on these findings, we conducted preliminary time course experiments.
转化生长因子β（TGF-β）配体激活具有多种细胞上下文依赖性结果的信号级联。破坏或干扰会导致不同的临床疾病。为了制定疾病干预策略，需要更详细地描述该途径。该途径的当前理论模型描述了信号介导蛋白的产生和降解以及从细胞表面到细胞核的信号转导，而反馈环尚未详尽包括。在这项研究中，我们提出了一个数学模型，以确定反馈调节剂（Arkadia、Smad7、Smurf1、Smurf2、SnoN 和 Ski）对 TGF-β靶基因表达的相关性，以及在现实参数空间内引发稳定振荡的潜力。我们采用大量参数空间采样来确定潜在振荡的关键因素以及转录产物水平。我们确定 Smad7 和 Smurf2 对动力学有最大的影响。基于这些发现，我们进行了初步的时间过程实验。
