Mimimal-Perturbation Dynamic Control of the Melanoma Gene Regulatory Network

描述(由申请人提供)：了解和干预管理网络的动态行为是现代癌症治疗发展中新出现的努力的核心。在癌症网络分析和控制方面，许多先前工作的严重局限性是假设网络拓扑是遍历的，即假设基因调控网络的结构是无循环的，并且网络的所有状态相互通信。尽管遍历性假设与一小类基因调控网络有关，但这个假设对大多数调控网络是不正确的。事实上，如果一个人接受被广泛接受的假设，即在基因调控网络中，细胞类型的特征是吸引子，那么多种细胞类型的存在意味着该网络一定有多个遍历集。 当应用于非遍历基因调控网络时，假设遍历的动态分析和控制策略的影响可能是误导的。该项目将开发最小扰动干预策略，以控制基因调控网络，这些网络不一定是遍历的，达到与正常或良性吸引子相对应的所需细胞状态。引入最小扰动控制策略的目的是为了在网络结构中引起很少的变化，从而将干预策略的结果的潜在不利影响降至最低。为了实现这一雄心勃勃的目标，拟议的项目将开发一个新的数学框架来解决遍历和非遍历马尔可夫链的逆摄动问题；即，给定一个概率转移矩阵和期望的稳态分布，确定将收敛到期望分布的转移矩阵的最小扰动。理论分析将得到黑色素瘤细胞系彻底的实验验证的补充：研究人员将设计RNAi和质粒分子来调节黑色素瘤网络中特定基因的表达水平，以测试预测模型的结果。

DESCRIPTION (provided by applicant): Understanding and intervention in the dynamic behavior of regulatory networks is at the heart of emerging efforts in the development of modern treatment of cancer. A serious limitation of much of the previous work in cancer network analysis and control is the assumption of an ergodic network topology; that is, the underlying assumption that the structure of gene regulatory networks is cycle-free and all states of the network communicate with each other. Although the ergodicity assumption pertains to a small class of gene regulatory networks, this assumption is incorrect for most regulatory networks. Indeed, if one adopts the widely-accepted hypothesis that cell types are characterized by attractors in gene regulatory networks, then the presence of multiple cell types implies that the networks must have multiple ergodic sets. The impact of dynamic analysis and control strategies that assume ergodicity when applied to nonergodic gene regulatory networks can be misleading. This project will develop minimal-perturbation intervention strategies to control gene regulatory networks, that are not necessarily ergodic, to desired cellular states corresponding to normal or benign attractors. The aim to introduce a minimal-perturbation control policy is designed to induce few changes in the network structure and thus minimize potential adverse effects as a consequence of the intervention strategy. In order to achieve this ambitious goal, the proposed project will develop a new mathematical framework for the solution of the inverse perturbation problem for ergodic and non-ergodic Markov chains; i.e. given a probability transition matrix and desired steady-state distribution, determine the minimal perturbation of the transition matrix that will converge to the desired distribution. The theoretical analysis will be complemented by a thorough experimental verification in the melanoma cell line: the investigators will design RNAi and plasmid molecules for regulation of the expression levels of specific genes of the melanoma network to test the results of the predicted model.