CAREER: Models and Algorithms for Comparative Analysis of Biological Networks

职业：生物网络比较分析的模型和算法

• 批准号: 1149544
• 负责人: Byung-Jun Yoon
• 金额: $ 40万
• 依托单位: Texas A&M Engineering Experiment Station
• 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: 2012-02-01 至 2018-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1149544&HistoricalAwards=false
• 关键词: CAREER; Models; Algorithms; Comparative; Analysis

Recent advent of high-throughput technologies for measuring molecular interactions has yielded large collections of biological networks, which enable systematic studies of complex biological organisms. Since biological pathways with critical functions are often conserved across different organisms, comparative analysis of these networks can provide an excellent way of investigating the organization of biological networks, as well as tracking down novel pathways and studying their functions. Intellectual Merit: This project aims to develop a solid mathematical framework for comparative network analysis and devise innovative techniques for comparing genome-scale biological networks. The main research objectives include: (1) develop a multi-state semi-Markov random walk (SMRW) model for probabilistic comparison of large-scale networks; (2) develop efficient algorithms for querying and aligning biological networks; (3) apply the developed algorithms to identify novel biological pathways and investigate their functions and their detailed mechanisms.Broader Impact: The research activities in this project are closely integrated with a comprehensive educational plan, whose overall goal lies in enhancing students? learning experience through the integration of research and education. This goal is translated into a three-part educational plan: (1) develop a concept inventory (CI) for genomic signal processing and computational biology (called the CIGSP); (2) shift a graduate-level course on ?Probabilistic Graphical Models? to a problem-based format; (3) design a new undergraduate course on Probabilistic Models for Network Biology based on proven and emerging pedagogical approaches. The new concept inventory CIGSP will provide a valuable diagnosis/assessment tool for enhancing education in genomic signal processing and computational biology, and it will be used in the PI?s courses, to design measurable educational objectives and evaluate the learning outcomes.

最近出现的用于测量分子相互作用的高通量技术已经产生了大量的生物网络，这使得对复杂生物有机体的系统研究成为可能。由于具有关键功能的生物通路通常在不同的生物体中是保守的，因此对这些网络的比较分析可以为研究生物网络的组织，以及追踪新的通路和研究它们的功能提供一个很好的方法。智力优势：本计划旨在发展比较网络分析的坚实数学框架，并设计比较基因组尺度生物网络的创新技术。主要研究目标包括：(1)建立用于大规模网络概率比较的多状态半马尔可夫随机漫步（SMRW）模型；(2)开发查询和对齐生物网络的高效算法；(3)应用开发的算法识别新的生物通路，并研究其功能和详细机制。更广泛的影响：该项目的研究活动与一个全面的教育计划紧密结合，其总体目标是提高学生的能力。通过研究和教育的结合来学习经验。这一目标被转化为一个由三部分组成的教育计划：(1)为基因组信号处理和计算生物学（称为CIGSP）开发一个概念清单（CI）；(2)将研究生水平的课程转移到？概率图形模型？以问题为基础的形式；(3)基于成熟的和新兴的教学方法，设计一门新的网络生物学概率模型本科课程。新概念目录CIGSP将为加强基因组信号处理和计算生物学的教育提供有价值的诊断/评估工具，并将用于PI？设计可衡量的教育目标，并评估学习成果。