Systemic analysis of molecular networks

分子网络的系统分析

• 批准号: 6685832
• 负责人: ILYA A MAZO
• 金额: $ 17万
• 依托单位: ARIADNE GENOMICS, INC.
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-06-15 至 2007-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6685832
• 关键词: Caenorhabditis elegans; Drosophilidae; bioengineering /biomedical engineering; biological signal transduction; biophysics; cell biology; computer program /software; computer system design /evaluation; gene expression; genetic regulation; interdisciplinary collaboration; laboratory mouse; mathematics; molecular biology; molecular dynamics; protein protein interaction; protein structure function; statistics /biometry

DESCRIPTION (provided by applicant): Information about protein function and cellular pathways is central to the system-level understanding of living organisms. The current proposal discusses the development strategy of a set of algorithms aimed at the analysis of cellular pathways, gene regulation, and protein interaction maps. The proposed algorithms analyze different aspects of molecular networks and will be applied to a comprehensive database of human pathways that has been specifically compiled for these purposes. This study will serve two goals: 1) to develop, improve and validate the algorithms, and 2) to identify general principles and key regulators of cell signaling. The novelty of the proposed research stems from the combination of the state of the art statistical approaches and a unique database of cell signaling networks compiled for these purposes. Different topological features of biological networks will be studied: correlation profiles - to better understand the underlying complex system and to find general principles that distinguish the system in different states (e.g., normal vs. diseased cell, different organisms), and network motifs - to identify key regulators in cell signaling. Network information will be combined with gene expression and sequence data to build models aimed at the prediction of new regulatory links. This will include the application of knowledge networks, Bayesian nets, and other statistical tools. Information about existing pathways from the database will be used as a seed for such modeling algorithms and is instrumental to producing high quality predictions. The present project is proposed as a collaboration between a small business and the national laboratory. It is one of the project goals to foster and build on this collaboration to establish coherent information and idea exchange between molecular biologists and theoretical physicists. The longer-term plans include commercialization of developed tools and giving academic researchers access to validated databases. Finally, it is expected that developed approaches will have a substantial impact on in silico biology by accelerating validation of pharmaceutical drug targets.

描述（由申请人提供）：有关蛋白质功能和细胞途径的信息对于系统级对生物体的理解至关重要。当前的建议讨论了一组旨在分析细胞途径，基因调节和蛋白质相互作用图的发展策略。所提出的算法分析了分子网络的不同方面，并将应用于为这些目的而专门编制的人类途径的综合数据库。这项研究将实现两个目标：1）开发，改进和验证算法，以及2）确定细胞信号传导的一般原理和关键调节剂。拟议研究的新颖性源于艺术统计方法的结合以及为这些目的编译的细胞信号网络的独特数据库。 将研究生物网络的不同拓扑特征：相关曲线 - 更好地理解潜在的复杂系统，并找到区分不同状态中该系统（例如，正常细胞，不同的生物体，不同生物体）和网络基序的一般原理，以识别细胞信号中的关键调节剂。网络信息将与基因表达和序列数据相结合，以构建旨在预测新调节链接的模型。这将包括知识网络，贝叶斯网和其他统计工具的应用。有关数据库现有途径的信息将用作这种建模算法的种子，并有助于产生高质量的预测。 本项目被认为是小型企业与国家实验室之间的合作。它是促进和建立这种合作的项目目标之一，以建立分子生物学家和理论物理学家之间建立连贯的信息和思想交流。长期计划包括对开发工具的商业化以及使学术研究人员访问经过验证的数据库。最后，预计开发的方法将通过加速对药物靶标的验证来对硅生物学产生重大影响。