Physical Science Oncology Center Training Program

描述（由申请人提供）：我们正在建立一个博士后培训计划，与我们的NIH资助的物理科学肿瘤中心（PSOC），将培训年轻的研究人员在癌症生物学和计算生物学之间的接口。PSOC是一个独特的NCI计划，将工程，物理和数学建模与癌症的遗传和分子研究相结合。PSOC倡议由全国12个中心组成的网络组成，这些中心高度互动，并开展多个合作项目。我们的PSOC结合了癌细胞进化动力学的数学建模与体外和体内建模，以验证和迭代修改的数学框架。PSOC和我们提议的PSOC培训计划（PSOC-TP）包括纪念斯隆-凯特琳癌症中心和达纳-法伯癌症研究所。PSOC-TP受训人员将完全进入PSOC网络，用于教育和专业网络目的。有了这个PSOC-TP，我们建议培训2个博士后队列，每个队列为期2年。在2年期结束时，学员将继续参加其他支持计划，下一批学员将进入我们的计划。我们将接受两种类型的学员：主要关注计算生物学的学员，他们将接受癌症生物学的教学和实践培训;以及对癌症生物学感兴趣的学员，他们将接受计算生物学的培训。我们计划的教学部分将包括每年两门课程，一门是水平学习（第1年的第1部分，第2年的第2部分），融合了计算生物学和癌症生物学;第二门课程是癌症的数学模型。癌症生物学和计算生物学导师将组成3对，每对都有一个跨越两个学科的独特项目。一旦被接受进入该计划，两名学员将选择其中一个项目共同工作。主要的导师将是那些与所选项目相关的人。PSOC-TP的研究部分将建立在三个PSOC研究项目之一的基础上。在第一个项目中，进化数学模型将用于预测肿瘤发展过程中突变累积的顺序。在第二个项目中，学员将使用数学和计算方法来预测最有可能的肿瘤起源细胞。在第三个项目中，学员将使用应用数学技术来预测在特定治疗策略中出现耐药性的风险，并确定防止耐药性出现的最佳策略。这些项目将构成学员的PSOC-TP独立研究项目的基础，这些项目可能远远超出PSOC中建议的研究。通过让学员学习这两个学科的互补观点，我们希望真正将这两个领域融合在一起，并建立一种超越该计划的伙伴关系。 公共卫生相关性：在这个PSOC培训计划中提出的工作将产生在计算和癌症生物学方面都受过培训的研究人员，以解决癌症研究中的问题，采用新颖的跨学科技术。学员将被嵌入到一个已经存在的，高度互联的物理科学肿瘤学网络，以帮助回答肿瘤学中的几个关键问题。通过建立物理科学肿瘤学培训计划，我们将有效地开始融合两个领域，并推动癌症的跨学科研究，并将癌症的数学建模建立为一个独立的学科。

DESCRIPTION (provided by applicant): We are establishing a postdoctoral training program to be associated with our NIH-funded Physical Science Oncology Center (PSOC) that will train young investigators to be at the interface between cancer biology and computational biology. The PSOC is a unique NCI program that blends engineering, physics and mathematical modeling with genetic and molecular studies of cancer. The PSOC initiative comprises a network of 12 centers around the country that are highly interactive and carry out multiple collaborative projects. Our PSOC combines mathematical modeling of the evolutionary dynamics of cancer cells with in vitro and in vivo modeling to validate and iteratively revise the mathematical frameworks. The PSOC and our proposed PSOC Training Program (PSOC-TP) includes Memorial Sloan-Kettering Cancer Center and Dana-Farber Cancer Institute. The PSOC-TP trainees will have full access to the PSOC network both for educational and professional networking purposes. With this PSOC-TP, we propose to train cohorts of 2 postdocs each for a 2- year period. At the end of the 2-year period, the trainees will move on to other support programs and the next cohort will enter our program. We will accept two types of trainees: trainees with a primary focus on computational biology, who will be provided with didactic and hands-on training in cancer biology; and trainees with a primary interest in cancer biology, who will receive training in computational biology. The instruction component of our program will be comprised of two courses per year, one in horizontal learning (part #1 in year 1, part #2 in year 2), blending both computational biology and cancer biology; and a second course on mathematical models of cancer. The cancer biology and computational biology mentors will form 3 pairs, each with a distinct project which spans across both disciplines. Once accepted into the program, the two trainees will choose one of the projects to work on jointly. The primary mentors will be those associated with the chosen project. The research component of the PSOC-TP will build upon one of the three PSOC research projects. In the first project, evolutionary mathematical modeling will be used to predict the order in which mutations are accumulated during tumor development. In the second project, trainees will use mathematical and computational methods to predict the most likely cell of origin for tumors. In the third projec, trainees will use applied mathematics techniques to predict the risk of resistance arising during specific treatment strategies, and identify the optimum strategy to prevent the emergence of resistance. These projects will form the basis of the PSOC-TP independent research projects of the trainees, which may go well beyond the research proposed in the PSOC. By having a trainee learning the complimentary view of both disciplines, we hope to truly merge the two fields together and create a partnership that will transcend the program. PUBLIC HEALTH RELEVANCE: The work proposed in this PSOC Training Program will produce researchers that are trained in both computational and cancer biology to address questions in cancer research with novel, interdisciplinary techniques. Trainees will be embedded into an already existing, highly interconnected physical science oncology network to help answer several key questions in oncology. By establishing a physical science oncology training program, we will effectively start to merge the two fields and drive forward the interdisciplinary study of cancer and establish mathematical modeling of cancer as an independent discipline.