A signal transduction pathway database/modeling system

信号转导通路数据库/建模系统

• 批准号: 6798470
• 负责人: ERIC D MJOLSNESS
• 金额: $ 57.21万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2007-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6798470
• 关键词: Internet; biological signal transduction; chemical reaction; computer data analysis; computer program /software; computer simulation; computer system design /evaluation; data management; mathematical model; mitogen activated protein kinase; model design /development; molecular biology information system; yeasts

DESCRIPTION (provided by applicant): Our goal is to develop a computational framework for cell signaling networks, and to demonstrate its effectiveness by developing high-quality models for several pathways of special biological interest. Its purpose is to enable biologists to use mathematical modeling to frame and constrain hypotheses before experimental testing. The need for the system and its design are driven by the hallmarks of cell signaling - complexity and dynamics. Web-like diagrams of cross-talking pathways and information flow currently convey the complexity. Dynamic properties include both transient and permanent changes in network architecture in which pathway components move from one subcellular location to another, components associate in different protein complexes, and the entire repertoire of receptors, transducers and targets and connectivity change as the cell state changes. To mathematically probe signaling processes, we are developing a "pathway modeling database" (SIGMOID) designed to: 1) Capture and organize diverse expert knowledge and data in a manner that computationally mirrors the underlying biochemistry. Pathways are therefore represented as their underlying chemical reactions and molecular associations. It also specifies signal-ontingent spatial compartmentalization of reactants and products, and provides for biologically appropriate dependence of reactions on the molecular state of reactants (phosphorylation, methylation, GDP binding, etc). 2) Provide automated generation of computational models based on the stored reaction network architecture that is coupled with an expandable suite of modeling programs. 3) Ingest and store results from computational simulations and provide tools for comparing, visualizing and mining simulation results.

描述（由申请人提供）：我们的目标是为细胞信号网络开发一个计算框架，并通过为多种特殊生物学兴趣途径开发高质量模型来证明其有效性。它的目的是使生物学家在实验测试之前使用数学建模来构架和限制假设。对系统及其设计的需求是由细胞信号传导的标志 - 复杂性和动力学驱动的。当前，跨言语途径和信息流的网络状图表传达了复杂性。动态属性包括网络体系结构的瞬态和永久变化，其中途径成分从一个亚细胞位置移动到另一个亚细胞位置，不同蛋白质复合物中的组件，以及整个受体，传感器和目标的曲目，随着细胞状态的变化而变化。为了在数学上探测信号传导过程，我们正在开发一个“途径建模数据库”（Sigmoid）设计用于：1）以计算方式捕获和组织多样的专家知识和数据，以反映基本的生物化学。因此，途径表示为其基本化学反应和分子关联。它还指定了反应物和产物的信号空间隔室化，并提供了反应物在生物学上适当的依赖性对反应剂分子状态的依赖性（磷酸化，甲基化，GDP结合等）。 2）基于存储的反应网络体系结构提供自动生成的计算模型，该架构与可扩展的建模程序相结合。 3）从计算模拟中摄入和存储结果，并提供了用于比较，可视化和采矿模拟结果的工具。