CAREER: Robustness in Genetic Regulatory Network Modeling and Control

职业：遗传调控网络建模和控制的鲁棒性

• 批准号: 0953366
• 负责人: Ranadip Pal
• 金额: $ 40.42万
• 依托单位: Texas Tech University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-02-01 至 2017-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0953366&HistoricalAwards=false
• 关键词: CAREER; Robustness; Genetic; Regulatory; Network

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.

职业：遗传调控网络建模和控制的鲁棒性Ranadip PalTexas Tech University电气与计算机工程系当前的一刀切的癌症治疗方法选择针对整个人群或大部分人群的药物。概述的研究是基于这样的愿景，即考虑病人？个体的遗传组成将有助于选择产生最佳个性化预后的药物。在过去的十年中，已经开发出了新一代的癌症药物，旨在干扰特定的分子靶点。这些靶向药物的成功受到限制，因为药物的选择以及给药的时间和顺序是基于经验原则，而没有数学模型来设计和估计干预策略的效率。该提案的目标是提供一个理论和计算框架，以估计遗传调控网络建模中的不确定性，并为遗传疾病（如癌症）制定稳健的治疗策略。 基因调控网络建模的目标之一是设计和分析治疗干预策略，旨在将网络从不期望的状态（如与疾病相关的状态）转移到期望的状态。然而，有限的实验数据，防止精确的推理的数学模型的遗传调控网络。为了使数学设计的遗传病干预策略获得成功，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鲁棒性的控制策略。该项目是及时和适当的，因为增加癌症治疗在临床环境中的有效性将需要干预策略，具有一定程度的鲁棒性建模过程中的不确定性。拟议中的研究预计将提供数学设计的干预策略的性能上的界限。将开发设计鲁棒控制策略的算法，其目标是避免极端不良的结果（极大极小设计）或提高预期的成功机会（贝叶斯方法）。所开发的策略将被应用到人类癌细胞系的干预和小鼠模型的靶向治疗的问题，与PI？的医学合作者在翻译基因组学研究所（TGen）和德克萨斯大学健康科学中心在圣安东尼奥（UTHSCSA）。跨学科的性质，这一建议有望促进电子工程和系统生物学之间的思想，通过研究和教育的交叉施肥。教育和推广计划的一些突出特点是：（i）开发关于遗传调控网络建模和控制的跨学科研究生课程和关于生物学工程应用的本科课程，（ii）在得克萨斯理工大学建立一个研究实验室，为学生提供基因组信号处理研究技能，（iii）通过项目实验室课程和TTU霍华德休斯医学研究所本科生科学教育计划，让本科生参与普惠制研究，（iv）通过克拉克学者计划和在涉及高中生的活动中的演讲，提高K-12学生对普惠制的认识，及（v）与医学研究界的进一步互动。研究结果将通过同行评审的出版物、会议介绍和研讨会向广大受众传播。