Computational Biology of Transcriptional Networks in Aging

衰老转录网络的计算生物学

• 批准号: 7652367
• 负责人: NICOLA NERETTI
• 金额: $ 12.48万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7652367
• 关键词: Address; Affect; Age; Aging; Algorithms; Animal Model; Biochemical Pathway; Biochemistry; Bioinformatics; Biological; Biological Models; Biology; Caenorhabditis elegans; Cells; Cellular biology; Collaborations; Complex; Computational Biology; Computational Molecular Biology; Data; Databases; Dependency; Disease; Doctor of Philosophy; Drosophila genus; Ecology; Educational workshop; Evaluation; Five-Year Plans; Funding Applicant; Gene Cluster; Gene Expression; Gene Mutation; Genes; Genetic; Genetic Transcription; Genotype; Goals; Graph; Growth; Hand; Homologous Gene; Insulin; Insulin Receptor; Laboratories; Longevity; Machine Learning; Mammals; Mathematics; Mentors; Metabolic; Metabolism; Methods; Molecular; Molecular Analysis; Molecular Biology; Mutation; Nematoda; Nutrient; Organism; Paper; Pathway interactions; Physics; Play; Process; Publishing; Regulatory Element; Reproduction; Research; Research Personnel; Research Training; Resolution; Response Elements; Role; Rotation; Sampling; Series; Signal Transduction; Structure; System; Systems Biology; Techniques; Testing; Time; Training; Universities; Work; base; c-myc Genes; computerized data processing; design; detection of nutrient; dietary restriction; experience; fly; functional genomics; improved; insulin signaling; member; novel; nutrition; receptor; research study; response; senescence

DESCRIPTION (provided by applicant): Funding is sought for a five year mentored training period at Brown University for Dr. Nicola Neretti to transition from physics to independent investigator in computational biology. The candidate has a Physics PhD from Brown University, and has been working in the field of signal processing and machine learning. During the past year he has been part of a collaborative project with Dr. John Sedivy (Department of Molecular and Cell Biology and Biochemistry) which resulted in a published paper about the analysis of gene expression array data to target c-Myc-activated genes with a correlation-based method. He has established other productive collaborations with members of the Center for Computational Molecular Biology (CCMB) at Brown University. The principal mentor will be Dr. Marc Tatar (Department of Ecology and Evolutionary Biology). The secondary mentors will be Dr. Charles Lawrence (Dep. Applied Mathematics) and Dr. John Sedivy. The work plan for the five years is to split the training/research effort evenly between computation and biology. For the training requirements the candidate plans to attend courses and workshops in genetics, biochemistry, molecular biology, bioinformatics, and related fields. Dr. Neretti also plans to complete lab rotations in the laboratory of Dr. Marc Tatar and Dr. John Sedivy, to acquire first hand experience in generating the biological data he will later analyze. The main focus of the research effort will be to use microarray data in time course experiments with a high temporal resolution to elucidate the complex interactions among genes and develop novel analytic techniques in functional genomic. In particular, the candidate proposes to integrate the results of gene clustering/graph analysis (e.g. correlation and tagged correlation based clustering) obtained from the time course data with the information available in genetic/pathway databases relevant to the process of senescence. This will allow the evaluation of given hypotheses about functional relationships among genes and the identification of novel dependencies, which can then be directly tested via experiments in model systems of aging. By using this approach the candidate proposes to address key questions in aging research such as what transcriptional changes are under the control of a nutrient sensing system, the temporal and hierarchical relationship of these changes, the magnitude of change that is biologically relevant, and whether genes within a functional metabolic network are co-regulated at the transcriptional level.

描述（由申请人提供）： 在布朗大学为Nicola Neretti博士寻求为期五年的指导培训期，以便从物理学过渡到计算生物学的独立研究者。该候选人拥有布朗大学的物理学博士学位，并一直从事信号处理和机器学习领域的工作。在过去的一年中，他一直与John Sedivy博士（分子和细胞生物学与生物化学系）合作，发表了一篇关于基因表达阵列数据分析的论文，以基于相关性的方法靶向c-Myc激活基因。他还与布朗大学计算分子生物学中心（CCMB）的成员建立了其他富有成效的合作关系。主要导师将是Marc Tatar博士（生态学和进化生物学系）。二级导师将是查尔斯劳伦斯博士（副。应用数学）和博士约翰Sedivy。五年的工作计划是在计算和生物学之间平均分配培训/研究工作。对于培训要求，候选人计划参加遗传学，生物化学，分子生物学，生物信息学和相关领域的课程和研讨会。Neretti博士还计划在Marc Tatar博士和John Sedivy博士的实验室完成实验室轮换，以获得生成他稍后将分析的生物数据的第一手经验。研究工作的主要重点将是使用微阵列数据在时间过程中的实验与高时间分辨率，以阐明基因之间的复杂的相互作用，并在功能基因组学开发新的分析技术。特别是，候选人建议将从时间过程数据中获得的基因聚类/图形分析（例如相关性和基于标记相关性的聚类）的结果与遗传/途径数据库中与衰老过程相关的信息相结合。这将允许评估给定的假设基因之间的功能关系和识别新的依赖关系，然后可以直接通过实验在模型系统的老化。通过使用这种方法，候选人提出解决衰老研究中的关键问题，例如营养传感系统控制下的转录变化，这些变化的时间和层次关系，生物学相关的变化幅度，以及功能代谢网络中的基因是否在转录水平上共同调节。