Damage Mitigation in Signal Transduction Networks

信号传导网络中的损伤减轻

• 批准号: 1205840
• 负责人: Reka Albert
• 金额: $ 65.35万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1205840&HistoricalAwards=false
• 关键词: Damage; Mitigation; Signal; Transduction; Networks

In this project the PI will design a comprehensive network-based framework that will allow the study of the attractors corresponding to signal transduction networks, and biological networks in general, even in the absence of quantitative information. A set of parsimonious methodologies will be developed that iteratively bridge qualitative and quantitative modeling, specifically graph theoretical analysis as well as Boolean, Boolean-continuous hybrid, and Hill-type continuous dynamic modeling methods. The proposed models allow for a direct interpretation of their results as they represent behaviors such as apoptosis, proliferation, migration, pathogen clearance, as output nodes of the network. The main hypothesis of this work is that the damage due to the cascading effects of a perturbation can be mitigated even if the original perturbation can no longer be reversed. The framework will be used to determine how node perturbations (changes in state or in interactions) change the output nodes dynamic attractors and furthermore to identify those interventions - such as externally supplying or depleting a node, or placing a node under the control of an upstream element - that can restore the normal dynamics of output nodes even in the presence of node perturbations. Two specific aims will be pursued: (i) to develop a suite of network modeling methods to identify, filter and rank candidate restorative interventions, and (ii) to make these network modeling methods easily usable by a large community. All analysis methods will be implemented in the open-source library BooleanNet, which will feature a web-based interface, a network analysis layer and a visualization layer. The damage mitigation methodologies developed in this project have implications in addressing major societal needs such as sustainable food production, ecosystem restoration, and human health. This project will provide funding and cross-disciplinary training for a graduate student and a postdoctoral scholar, both co-advised by the PI and co-PI. Both investigators have extensive experience in co-advising and in mentoring of female and minority scientists at all levels. The project will provide research opportunities for Penn State undergraduates recruited by various means such as the Physics REU program and the Women in Science and Engineering program. This research will be incorporated into the courses taught by the PIs, including the undergraduate course Systems Biology and Networks and the graduate course Practical Data Analysis for Life Scientists, both attended by students from diverse departments. Groups of under-represented middle school students will be hosted during summer visits to Penn State as part of the Higher Achievement Program, a non-profit, academic support program that prepares students to complete high school and be college-ready. In addition, one member of the team will yearly attend the SACNAS (Society for the Advancement of Chicano and Native American Scientists) conference and participate in conversations with scientists and other activities designed to facilitate entry of minorities into careers in science. This project is being jointly supported by the Physics of Living Systems program in the Division of Physics and the Physiological and Networks and Regulation Program in the Division of Molecular and Cellular Biosciences.

在这个项目中，PI将设计一个全面的基于网络的框架，即使在缺乏定量信息的情况下，也可以研究与信号转导网络和一般生物网络相对应的吸引子。将开发一套简洁的方法，迭代地连接定性和定量建模，特别是图理论分析以及布尔，布尔-连续混合和希尔型连续动态建模方法。所提出的模型允许对其结果进行直接解释，因为它们代表了诸如细胞凋亡、增殖、迁移、病原体清除等行为，作为网络的输出节点。这项工作的主要假设是，即使原始扰动不能再逆转，扰动的级联效应所造成的损害也可以减轻。该框架将用于确定节点扰动（状态或交互中的变化）如何改变输出节点的动态吸引子，并进一步识别那些干预——例如外部供应或耗尽节点，或将节点置于上游元素的控制之下——即使在节点扰动存在的情况下，也可以恢复输出节点的正常动态。将追求两个具体目标：(i)开发一套网络建模方法来识别，过滤和排序候选恢复性干预措施，以及（ii）使这些网络建模方法易于被大型社区使用。所有的分析方法都将在开源库BooleanNet中实现，该库将具有基于web的界面、网络分析层和可视化层。本项目制定的减轻损害方法对解决诸如可持续粮食生产、生态系统恢复和人类健康等主要社会需求具有影响。该项目将为一名研究生和一名博士后提供资助和跨学科培训，这两名研究生和博士后都由PI和共同PI共同指导。两位研究人员在为各级女性和少数民族科学家提供联合咨询和指导方面都有丰富的经验。该项目将为宾夕法尼亚州立大学通过各种方式招募的本科生提供研究机会，如物理REU项目和科学与工程领域的女性项目。这项研究将被纳入pi教授的课程中，包括本科课程系统生物学和网络以及研究生课程生命科学家实用数据分析，这两门课程都有来自不同系的学生参加。作为“更高成就计划”（Higher Achievement Program）的一部分，一些未被充分代表的中学生将在夏季访问宾夕法尼亚州立大学。“更高成就计划”是一个非营利的学术支持计划，旨在帮助学生完成高中学业并为上大学做好准备。此外，该小组的一名成员每年将参加SACNAS（促进墨西哥裔美国人和美国土著科学家进步协会）会议，并参加与科学家的对话和其他旨在促进少数民族进入科学事业的活动。该项目由物理系的生命系统物理学项目和分子与细胞生物科学系的生理和网络与调控项目共同支持。