Szedlak Anthony, Paternostro Giovanni, Piermarocchi Carlo
Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan, United States of America.
Sanford-Burnham Medical Research Institute, La Jolla, California, United States of America; Salgomed Inc., Del Mar, California, United States of America.
PLoS One. 2014 Aug 29;9(8):e105842. doi: 10.1371/journal.pone.0105842. eCollection 2014.
The asymmetric Hopfield model is used to simulate signaling dynamics in gene regulatory networks. The model allows for a direct mapping of a gene expression pattern into attractor states. We analyze different control strategies aimed at disrupting attractor patterns using selective local fields representing therapeutic interventions. The control strategies are based on the identification of signaling bottlenecks, which are single nodes or strongly connected clusters of nodes that have a large impact on the signaling. We provide a theorem with bounds on the minimum number of nodes that guarantee control of bottlenecks consisting of strongly connected components. The control strategies are applied to the identification of sets of proteins that, when inhibited, selectively disrupt the signaling of cancer cells while preserving the signaling of normal cells. We use an experimentally validated non-specific and an algorithmically-assembled specific B cell gene regulatory network reconstructed from gene expression data to model cancer signaling in lung and B cells, respectively. Among the potential targets identified here are TP53, FOXM1, BCL6 and SRC. This model could help in the rational design of novel robust therapeutic interventions based on our increasing knowledge of complex gene signaling networks.
非对称霍普菲尔德模型用于模拟基因调控网络中的信号动力学。该模型允许将基因表达模式直接映射到吸引子状态。我们分析了不同的控制策略，这些策略旨在使用代表治疗干预的选择性局部场来破坏吸引子模式。控制策略基于信号瓶颈的识别，信号瓶颈是对信号有重大影响的单个节点或强连接的节点簇。我们给出了一个定理，该定理给出了保证对由强连接组件组成的瓶颈进行控制的最小节点数的界限。控制策略被应用于识别这样一组蛋白质：当这些蛋白质被抑制时，能选择性地破坏癌细胞的信号传导，同时保留正常细胞的信号传导。我们分别使用一个经过实验验证的非特异性和一个从基因表达数据重建的算法组装的特异性B细胞基因调控网络，来模拟肺癌和B细胞中的癌症信号传导。在此确定的潜在靶点包括TP53、FOXM1、BCL6和SRC。基于我们对复杂基因信号网络不断增加的了解，该模型有助于合理设计新型的稳健治疗干预措施。
