CAREER: Robustness in Genetic Regulatory Network Modeling and Control

职业：基因调控网络建模和控制的稳健性Ranadip Palp德克萨斯理工大学电气与计算机工程系目前一刀切的癌症治疗方法选择针对整个人群或某一大部分人群的药物。概述的研究基于这样一个愿景，即考虑患者-S的个体基因构成将有助于选择产生最佳个性化预后的药物。在过去的十年里，设计用于干扰特定分子靶点的新一代抗癌药物已经开发出来。这些靶向药物的选择和给药的时间和顺序都是基于经验原则，没有数学模型来设计和评估干预策略的效率，因此这些靶向药物的成功受到了限制。该提案的目标是提供一个理论和计算框架，以估计基因调控网络建模中的不确定性，并生成针对癌症等遗传性疾病的强有力的治疗策略。基因调控网络建模的目标之一是设计和分析旨在将网络从不良状态(如与疾病相关的状态)转移到理想状态的治疗干预策略。然而，有限的实验数据阻碍了对遗传调控网络数学模型的准确推断。为了成功地设计数学设计的遗传病干预策略，关键是(A)研究建模误差对推断的网络模型的预测能力和干预结果的影响，以及(B)设计对建模不确定性具有一定程度稳健性的控制策略。该项目是及时和适当的，因为在临床环境中增加癌症治疗的有效性将需要对建模过程中的不确定性具有一定程度的稳健性的干预策略。这项拟议的研究有望为数学设计的干预策略的表现提供界限。将开发设计稳健控制策略的算法，以避免极不理想的结果(极小极大设计)或提高预期的成功机会(贝叶斯方法)。开发的策略将与翻译基因组研究所(TGEN)和德克萨斯大学圣安东尼奥健康科学中心(UTHSCSA)的医学合作者一起，应用于干预人类癌细胞系和小鼠模型的靶向治疗的问题。这项提议的跨学科性质有望通过研究和教育促进电子工程和系统生物学之间的思想交流。教育和外联计划的一些突出特点是：(I)开设关于遗传调控网络建模与控制的跨学科研究生课程和生物学工程应用本科课程；(Ii)在德克萨斯理工大学建立研究实验室，培养具备基因组信号处理研究技能的学生；(Iii)通过项目实验室课程和德克萨斯州立大学霍华德·休斯医学院本科生科学教育计划让本科生参与基因组信号处理研究；(Iv)通过克拉克学者计划和在有高中生参加的活动中发表演讲，提高K-12岁至12岁学生对基因信号处理的认识；以及(V)与医学研究界进一步互动。研究成果将通过同行评议出版物、会议报告和研讨会向广大受众传播。

CAREER: Robustness in Genetic Regulatory Network Modeling and ControlRanadip PalDepartment of Electrical and Computer Engineering, Texas Tech UniversityCurrent one-size-fits-all methods of cancer treatment select drugs that target the whole population, or some large segment of the population. The outlined research is based on the vision that considering a patient?s individual genetic make-up will help in selecting drugs that yields the best personalized prognosis. New generation of cancer drugs designed to interfere with specific molecular targets have been developed in the last decade. The success of these targeted drugs have been limited since the selection of the drugs and the time and sequence of drug administration are based on empirical principles without mathematical models to design and estimate the efficiency of intervention strategies. The goal of this proposal is to provide a theoretical and computational framework to estimate the uncertainties in genetic regulatory network modeling and generate robust therapeutic strategies for genetic diseases such as cancer. One of the objectives of genetic regulatory network modeling is to design and analyze therapeutic intervention strategies aimed at moving the network out of undesirable states, such as those associated with disease, and into desirable ones. However, limited experimental data prevent accurate inference of the mathematical model of the genetic regulatory network. For the success of a mathematically designed intervention strategy for genetic diseases, it is critical to (a) study the effect of modeling errors on the predictive power of the inferred network model and on the intervention outcome and (b) design control strategies that posses some degree of robustness to the modeling uncertainties. The project is timely and appropriate as increasing the effectiveness of cancer therapies in the clinical setting will require intervention strategies that possess some degree of robustness to the uncertainties in the modeling process. The proposed research is expected to provide bounds on the performance of mathematically designed intervention strategies. Algorithms to design robust control strategies with the objectives of avoiding extremely undesirable results (minimax design) or improving the expected chances of success (Bayesian approach) will be developed. The developed strategies will be applied to the problem of intervention in human cancer cell lines and targeted therapy in mice models, in collaboration with the PI?s medical collaborators at Translational Genomics Research Institute (TGen) and University of Texas Health Sciences Center at San Antonio (UTHSCSA).The interdisciplinary nature of this proposal promises to foster cross-fertilization of ideas between electrical engineering and systems biology through research and education. Some of the salient features of the education and outreach plan are to (i) develop an interdisciplinary graduate course on Genetic Regulatory Network Modeling and Control and an undergraduate course on Engineering Applications in Biology, (ii) establish a research laboratory at Texas Tech University to prepare students with skills in Genomic Signal Processing (GSP) research, (iii) involve undergraduates in GSP research through Project Laboratory Courses and the TTU Howard Hughes Medical Institute Undergraduate Science Education Program, (iv) increase awareness of GSP among K-12 students through the Clark Scholars program and presentations at events involving high school students, and (v) further interactions with the Medical Research Community. The research results will be disseminated to the broad audience via peer reviewed publications, conference presentations and seminars.