Yang Gang, Gómez Tejeda Zañudo Jorge, Albert Réka
Department of Physics, Pennsylvania State University, University Park, PA, United States.
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States.
Front Physiol. 2018 May 8;9:454. doi: 10.3389/fphys.2018.00454. eCollection 2018.
Dynamical models of biomolecular networks are successfully used to understand the mechanisms underlying complex diseases and to design therapeutic strategies. Network control and its special case of target control, is a promising avenue toward developing disease therapies. In target control it is assumed that a small subset of nodes is most relevant to the system's state and the goal is to drive the target nodes into their desired states. An example of target control would be driving a cell to commit to apoptosis (programmed cell death). From the experimental perspective, gene knockout, pharmacological inhibition of proteins, and providing sustained external signals are among practical intervention techniques. We identify methodologies to use the stabilizing effect of sustained interventions for target control in Boolean network models of biomolecular networks. Specifically, we define the domain of influence (DOI) of a node (in a certain state) to be the nodes (and their corresponding states) that will be ultimately stabilized by the sustained state of this node regardless of the initial state of the system. We also define the related concept of the logical domain of influence (LDOI) of a node, and develop an algorithm for its identification using an auxiliary network that incorporates the regulatory logic. This way a solution to the target control problem is a set of nodes whose DOI can cover the desired target node states. We perform greedy randomized adaptive search in node state space to find such solutions. We apply our strategy to biological network models of real systems to demonstrate its effectiveness.
生物分子网络的动力学模型已成功用于理解复杂疾病背后的机制，并设计治疗策略。网络控制及其特殊情况——目标控制，是开发疾病治疗方法的一条有前景的途径。在目标控制中，假定一小部分节点与系统状态最为相关，目标是将目标节点驱动到其期望状态。目标控制的一个例子是促使细胞发生凋亡（程序性细胞死亡）。从实验角度来看，基因敲除、蛋白质的药理学抑制以及提供持续外部信号都是实际的干预技术。我们确定了在生物分子网络的布尔网络模型中利用持续干预的稳定作用进行目标控制的方法。具体而言，我们将节点（处于特定状态）的影响域（DOI）定义为无论系统初始状态如何，都会被该节点的持续状态最终稳定的节点（及其相应状态）。我们还定义了节点的逻辑影响域（LDOI）这一相关概念，并开发了一种使用包含调控逻辑的辅助网络来识别它的算法。这样，目标控制问题的一个解决方案就是一组其DOI能够覆盖期望目标节点状态的节点。我们在节点状态空间中执行贪婪随机自适应搜索以找到此类解决方案。我们将我们的策略应用于实际系统的生物网络模型以证明其有效性。
