Genetic Regulatory Networks: Comp. & Ext. Investigations

遗传调控网络：比较。

• 批准号: 6949608
• 负责人: ILYA SHMULEVICH
• 金额: $ 21.21万
• 依托单位: INSTITUTE FOR SYSTEMS BIOLOGY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-16 至 2010-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6949608
• 关键词: bioinformatics; cell line; clinical research; computational biology; functional /structural genomics; gene expression; gene interaction; genetic models; genetic regulation; glioma; human data; microarray technology; molecular biology information system; polymerase chain reaction; transfection

DESCRIPTION (provided by applicant): Biology and related fields are currently witnessing a revolution. Quantitative approaches and methods are now becoming increasingly common, largely due to remarkable achievements in sequencing technologies and high-throughput genomic screening methods. One of the major challenges is to use such approaches for understanding the underlying mechanisms and functions of genes. The approach that will be taken in this project is to model the genetic regulatory system and infer the model structure and parameters from gene expression data. The goal of the project is to further develop and refine the mathematical and computational methods and models and to apply them to a concrete biological model system. This will not only serve as a validation of the modeling approach, but will also lead to an improved understanding of the genetic regulatory mechanisms underlying cellular function and dysfunction. In particular, the specific aims are 1) to compare different network inference algorithms and validate them with experimental methods; 2) to generate and evaluate sub-networks using time-course microarray data; 3) to predict and validate the effects of gene perturbations on steady-state expressions of genes in the sub-networks. The research program will closely integrate both computational and experimental approaches to achieve our goals.

描述（由申请人提供）：生物学和相关领域目前正在见证一场革命。定量方法和手段现在变得越来越普遍，这主要是由于测序技术和高通量基因组筛选方法的显著成就。主要挑战之一是使用这种方法来理解基因的潜在机制和功能。本计画所采用的方法是建立基因调控系统的模型，并从基因表现资料中推论出模型的结构与参数。该项目的目标是进一步开发和完善数学和计算方法和模型，并将其应用于具体的生物模型系统。这不仅将作为建模方法的验证，而且还将导致对细胞功能和功能障碍的遗传调控机制的更好理解。具体目标是：1）比较不同的网络推理算法，并用实验方法验证它们; 2）使用时间过程微阵列数据生成和评估子网络; 3）预测和验证基因扰动对子网络中基因稳态表达的影响。该研究计划将紧密结合计算和实验方法，以实现我们的目标。