CAREER: Computational Models and Algorithms for Differential Network Analysis in Systems Biology

职业：系统生物学中微分网络分析的计算模型和算法

• 批准号: 0953195
• 负责人: Mehmet Koyuturk
• 金额: $ 42.03万
• 依托单位: Case Western Reserve University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-02-01 至 2016-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0953195&HistoricalAwards=false
• 关键词: CAREER; Computational; Models; Algorithms; Differential

CAREER: Computational Models and Algorithmsfor Differential Network Analysis in Systems BiologyPI: Mehmet Koyutürk, Case Western Reserve UniversityAn important challenge in life sciences, emerging from various successful genome sequencing efforts, is the characterization of molecular mechanisms that underlie phenotypic differences (variation in observable characteristics of biological systems). These variations include developmental stages, disease, and evolution. Molecular sequence and expression datasets have been used effectively to identify frequently mutated genes and the relative abundance/lack of their products in cells carrying the phenotype of interest. While the role of contextual information, manifested in networks of biomolecular interactions is widely acknowledged, lack of high-quality data has impeded the development and use of network-based analyses. Recent high-throughput methods for network inference hold tremendous potential for network-based phenotype analysis. However, existing computational methods are in relative infancy. The primary objective of this project is the development of computational models and algorithms that enable integration of disparate datasets (gene sequences, gene expression, protein expression, protein interactions) for phenotypic characterization.Network-based modeling of phenotypic differences gives rise to deep intellectual questions relating to computational abstraction, algorithm design, and biological validation. These challenges are exacerbated by the incomplete, static, and noisy nature of available network data. The proposed research aims to overcome these difficulties through innovative use of combinatorial, probabilistic, and algebraic methods, including the following: (i) combinatorial modeling of coordinate changes in the expression of multiple interacting genes, (ii) probabilistic modeling of the crosstalk in biomolecular networks, for statistically sound evaluation of functional association between multiple genes and proteins, and (iii) algebraic modeling of information flow in the cell, to make indirect inferences on the effects of multiple genetic perturbations. The resulting computational tools will be integrated into frequently accessed application frameworks, including Cytoscape, R, and Matlab, used extensively by domain scientists at Case Center for Proteomics and Bioinformatics, for testing, validation, and calibration, and subsequently disseminated to the broader scientific community.The proposed career plan also incorporates major educational and outreach initiatives that build on the interdisciplinary nature of proposed research. These initiatives include (i) novel instructional techniques based on active learning, (ii) continued development of a Computer Science education platform, TELESCOPE, for the purpose of illustrating basic algorithmic and problem solving principles, (iii) incorporation of TELESCOPE into outreach efforts, targeting K-12 schools, CWRUs TRIO programs for low income pre-college students, and CWRUs Equinox summer program, (iv) programs for student involvement (particularly minority and female) in outreach efforts, (v) development of new interdisciplinary courses and degree programs at undergraduate and graduate levels, and (vi) continued involvement of undergraduate students in research projects.

职业：系统生物学中差分网络分析的计算模型和算法i: Mehmet koyutrk，凯斯西储大学生命科学中的一个重要挑战，从各种成功的基因组测序工作中出现，是表征表型差异（生物系统可观察特征的变化）的分子机制。这些变化包括发育阶段、疾病和进化。分子序列和表达数据集已被有效地用于鉴定频繁突变的基因及其产物在携带感兴趣表型的细胞中的相对丰度/缺乏。虽然上下文信息在生物分子相互作用网络中的作用得到了广泛认可，但缺乏高质量的数据阻碍了基于网络的分析的发展和使用。最近用于网络推断的高通量方法对基于网络的表型分析具有巨大的潜力。然而，现有的计算方法还处于相对初级阶段。该项目的主要目标是开发计算模型和算法，使不同的数据集（基因序列、基因表达、蛋白质表达、蛋白质相互作用）能够集成，以进行表型表征。基于网络的表型差异建模引起了与计算抽象、算法设计和生物验证相关的深刻的智力问题。可用网络数据的不完整、静态和嘈杂特性加剧了这些挑战。提出的研究旨在通过创新地使用组合、概率和代数方法来克服这些困难，包括：(i)对多个相互作用基因表达坐标变化的组合建模，（ii）对生物分子网络中串扰的概率建模，以便对多个基因和蛋白质之间的功能关联进行统计上的合理评估，以及（iii）对细胞内信息流的代数建模，以间接推断多重遗传扰动的影响。由此产生的计算工具将集成到经常访问的应用程序框架中，包括Cytoscape、R和Matlab，由蛋白质组学和生物信息学案例中心的领域科学家广泛使用，用于测试、验证和校准，并随后传播到更广泛的科学界。拟议的职业规划还包括以拟议研究的跨学科性质为基础的主要教育和外联倡议。这些举措包括(i)基于主动学习的新颖教学技术，（ii）继续开发计算机科学教育平台，望远镜，以说明基本算法和解决问题的原则，（iii）将望远镜纳入推广工作，针对K-12学校，CWRUs TRIO项目，针对低收入大学预科学生，以及CWRUs Equinox暑期项目。（iv）鼓励学生（特别是少数族裔和女性）参与外展工作；(v)在本科和研究生阶段开发新的跨学科课程和学位课程；（vi）继续鼓励本科生参与研究项目。