Zañudo Jorge G T, Albert Réka
Department of Physics, The Pennsylvania State University, University Park, Pennsylvania, United States of America.
Department of Physics, The Pennsylvania State University, University Park, Pennsylvania, United States of America; Department of Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America.
PLoS Comput Biol. 2015 Apr 7;11(4):e1004193. doi: 10.1371/journal.pcbi.1004193. eCollection 2015 Apr.
Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell's fate, such as disease therapeutics and stem cell reprogramming. Here we develop a novel network control framework that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our approach drives any initial state to the target state with 100% effectiveness and needs to be applied only transiently for the network to reach and stay in the desired state. We illustrate our method's potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of helper T cells. We find that the predicted control targets are effective in a broad dynamic framework. Moreover, several of the predicted interventions are supported by experiments.
识别生物网络的控制策略对于涉及重编程细胞命运的实际应用至关重要，例如疾病治疗和干细胞重编程。在此，我们开发了一种新颖的网络控制框架，该框架整合了细胞内网络可用的结构和功能信息以预测控制靶点。我们的方法以逻辑动态方案制定，能以100%的有效性将任何初始状态驱动至目标状态，并且仅需短暂应用就能使网络达到并维持在期望状态。我们通过将该方法应用于白血病信号网络以及控制辅助性T细胞分化的网络，展示了其寻找癌症治疗和细胞分化干预靶点的潜力。我们发现预测的控制靶点在广泛的动态框架中是有效的。此外，一些预测的干预措施得到了实验的支持。
