Physical Science Oncology Center Training Program

描述（由申请人提供）：我们正在与美国国立卫生研究院资助的物理科学肿瘤中心（PSOC）建立博士后培训计划，该计划将培养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倡议包括一个由全国12个中心组成的网络，这些中心高度互动，并开展多个合作项目。我们的PSOC将癌细胞进化动力学的数学建模与体外和体内建模相结合，以验证和迭代修改数学框架。PSOC和我们提议的PSOC培训计划（PSOC- tp）包括纪念斯隆-凯特琳癌症中心和丹娜-法伯癌症研究所。PSOC- tp学员将可以完全访问PSOC网络，用于教育和专业网络目的。在这个PSOC-TP项目中，我们建议培养2名博士后，每组2年。在为期两年的培训结束后，学员将转到其他支持项目，下一批学员将进入我们的项目。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；对癌症生物学有主要兴趣的学员，将接受计算生物学的培训。我们项目的教学部分每年将包括两门课程，一门是横向学习（第一部分在第一年，第二部分在第二年），融合了计算生物学和癌症生物学；第二门课是关于癌症的数学模型。癌症生物学和计算生物学导师将组成3对，每对导师都有一个跨越这两个学科的独特项目。一旦被录取，两位学员将选择一个项目共同工作。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积累的顺序。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预测在特定治疗策略中产生耐药性的风险，并确定防止耐药性出现的最佳策略。这些项目将构成学员PSOC- tp独立研究项目的基础，这些项目可能远远超出PSOC中提出的研究。通过让学员学习这两个学科的互补观点，我们希望将这两个领域真正融合在一起，并建立一种超越项目的伙伴关系。

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.