权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Graduate Training in Computational and Systems Biology

计算和系统生物学研究生培训

基本信息

批准号：
10681214
负责人：
CHRISTOPHER B BURGE
金额：
$ 53.05万
依托单位：
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10681214
关键词：
Computational Biology Systems Biology Training

项目摘要

Project Summary The objective is to support a Computational Systems Biology training program (CSBTP) to train students to become leaders in biomedical research at the interface of biology, computation and engineering. The effort is centered in MIT's interdisciplinary predoctoral training program in Computational and Systems Biology (CSB), which is the primary training program at MIT for students interested in computational and systems biology and is the only program that emphasizes interdisciplinary training and research in the field. Program faculty are concentrated in the three founding departments – Biological Engineering (BE), Biology, and Electrical Engineering & Computer Science (EECS) – with additional involvement of faculty from other departments. Research interests of training faculty span a wide range of CSB-related areas, including Regulatory Genomics and Proteomics, Precision Medicine and Medical Genomics, Molecular Biophysics and Structural Biology, Biological Networks and Machine Learning, and Cancer Systems Biology. This proposal seeks to expand the pool of training faculty significantly, including 7 faculty newly hired in the past 5 years who have active research programs in the field. Students apply directly to the CSB Ph.D. program from their undergraduate or Master’s institution and receive multi- and inter-disciplinary training in the field of computational and systems biology. The proposal seeks funding for 10 predoctoral traineeships per year, enabling extended research rotations and participation in special program activities. Unique aspects of the program include: (a) unusually diverse collection of research areas across science and engineering, with highly collaborative interdisciplinary faculty; (b) a unique core of interdisciplinary classroom subjects that combine biology, engineering, statistics and computation; (c) intensive advising and multi-disciplinary thesis committees to optimize the training experience for students from diverse academic backgrounds; (d) an annual retreat with participation of students and faculty focusing on research, careers, and challenges to interdisciplinary research.

项目摘要其目标是支持计算系统生物学培训计划(CSBTP)，以培训学生成为生物、计算和工程领域生物医学研究的领先者。我们的努力是以麻省理工学院计算和系统生物学(CSB)跨学科博士前培训项目为中心，这是麻省理工学院为对计算和系统生物学感兴趣的学生提供的主要培训计划，是唯一一个强调该领域的跨学科培训和研究的项目。项目教职员工是集中在三个创始部门-生物工程(BE)、生物和电气工程与计算机科学(EECS)-其他系的教员也参与进来。培训教师的研究兴趣涵盖了与CSB相关的广泛领域，包括监管基因组学和蛋白质组学，精密医学和医学基因组学，分子生物物理学和结构生物学，生物网络和机器学习，以及癌症系统生物学。这项建议旨在扩大大量培训教员，包括过去5年新招聘的7名从事积极研究的教员该领域的计划。学生从本科生或硕士直接申请CSB博士项目并接受计算和系统生物学领域的多学科和跨学科培训。该提案寻求为每年10个博士前实习生提供资金，从而能够延长研究轮换和参与特殊计划活动。该方案的独特方面包括：(A)异乎寻常的多样性汇集了科学和工程领域的研究领域，拥有高度协作的跨学科教员； (B)结合了生物学、工程学、统计学和计算；(C)密集的咨询和多学科论文委员会，以优化培训经验为不同学术背景的学生举办的年度务虚会；(D)有学生和专注于研究、职业和对跨学科研究的挑战的教师。

项目成果

期刊论文数量（100）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Computational counterselection identifies nonspecific therapeutic biologic candidates.

DOI：
10.1016/j.crmeth.2022.100254
发表时间：
2022-07-18
期刊：
Cell reports methods
影响因子：
0
作者：
通讯作者：

Antagonistic regulation of mRNA expression and splicing by CELF and MBNL proteins.

DOI：
10.1101/gr.184390.114
发表时间：
2015-06
期刊：
Genome research
影响因子：
7
作者：
Wang ET;Ward AJ;Cherone JM;Giudice J;Wang TT;Treacy DJ;Lambert NJ;Freese P;Saxena T;Cooper TA;Burge CB
通讯作者：
Burge CB

Machine learning based CRISPR gRNA design for therapeutic exon skipping.