CAREER: Novel Algorithms for Dynamic Network Analysis in Computational Biology

职业：计算生物学动态网络分析的新算法

• 批准号: 1452795
• 负责人: Tijana Milenkovic
• 金额: $ 54万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-03-01 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1452795&HistoricalAwards=false
• 关键词: CAREER; Novel; Algorithms; Dynamic; Network

Broader significance and importance. Proteins are major macromolecules of life. Thus, understanding how proteins function in the cell is critical. Genomic sequence research has revolutionized understanding of cellular functioning. However, as recognized in the post-genomic era, genes (proteins) do not function in isolation. Instead, they carry out cellular processes by interacting with each other. This is exactly what biological networks model. Unlike genomic sequence data, biological network data enable the study of complex cellular processes that emerge from the collective behavior of the proteins. Thus, biological network research is promising to give new insights into principles of life, evolution, disease, and therapeutics. However, current network research deals with static representations of biological data, even though cellular functioning is dynamic. This is in part due to unavailability of experimentally-derived dynamic biological network data, owing to limitations of biotechnologies for data collection. Efficient computational strategies for both inference and analysis of dynamic biological networks are needed to advance understanding of cellular functioning compared to static biological network research. This is exactly the focus of this project. Dynamic biological network research has biological applications of societal importance, such as studying cellular changes with disease progression, drug treatment, or age, which will be explored as a part of this project. Thus, the project could contribute to global health. It may impact other domains as well, e.g., social networks. Also, this project will result in educational activities that are intertwined with its research, such as forming interdisciplinary scientists via novel curriculum development activities, or strengthening the computer science population via research supervision, career mentoring, and community outreach to K-12 and (under)graduate students, focusing on women.Technical description. This proposal will result in new computational directions for dynamic biological network research. New algorithms will be developed for inference of systems-level biological networks underlying a dynamic biological process, by combining the static network topology with other data types, such as measurements of gene expression or protein abundance at different times. Then, novel methods for analyzing the dynamic network data will be developed to gain insights into the underlying cellular changes. For example, the idea of graphlets (small subgraphs), which has been well established in static biological network research, will be taken to the next level to allow for graphlet-based analyses of dynamic biological networks. Also, novel computational strategies will be designed to allow for dynamic network clustering. The proposed methods will be used in collaborative applications that encompass representative dynamic biological processes: early cancer detection and chemotherapy resistance, both in the context of pancreatic cancer, as well as studying human aging. These interdisciplinary applications will be used as concrete model systems to innovate fundamental computational research. Because network research spans many domains, open-source software implementing the new methods will be offered to researchers from diverse disciplines. The software will also serve as an educational tool. Integration of research and education will be promoted even further. Interdisciplinary student training will be offered via novel courses on network research. A literate approach to education will aim to advance students' communication skills. Proven pedagogical strategies will be used to improve student learning. Research supervision and career mentoring will be offered to K-12 and (under)graduate students, with focus on minorities and women, thus integrating diversity into the project. Interdisciplinary research and educational collaborations will allow for wide distribution of the proposed ideas and results. The results will also be disseminated through tutorial and workshop organization at renowned international conferences.

更广泛的意义和重要性。蛋白质是生命的主要大分子。因此，了解蛋白质在细胞中的功能至关重要。基因组序列研究已经彻底改变了对细胞功能的理解。然而，正如后基因组时代所认识到的那样，基因（蛋白质）并不是孤立地发挥作用的。相反，它们通过相互作用来进行细胞过程。这正是生物网络的模式。与基因组序列数据不同，生物网络数据能够研究蛋白质集体行为产生的复杂细胞过程。因此，生物网络研究有望为生命、进化、疾病和治疗学的原理提供新的见解。然而，目前的网络研究涉及生物数据的静态表示，即使细胞功能是动态的。部分原因是由于生物技术在数据收集方面的局限性，无法获得实验得出的动态生物网络数据。与静态生物网络研究相比，需要用于动态生物网络的推理和分析的有效计算策略来促进对细胞功能的理解。这正是这个项目的重点。动态生物网络研究具有社会重要性的生物学应用，例如研究疾病进展，药物治疗或年龄的细胞变化，这将作为本项目的一部分进行探索。因此，该项目可以促进全球健康。它也可能影响其他领域，例如，社交网络。此外，该项目还将开展与研究相关的教育活动，例如通过新课程开发活动培养跨学科科学家，或通过研究监督，职业指导和社区推广来加强计算机科学人口，以K-12和（下）研究生为重点。技术说明这一建议将导致新的计算方向的动态生物网络的研究。将开发新的算法，通过将静态网络拓扑结构与其他数据类型（如不同时间的基因表达或蛋白质丰度测量）相结合，推断动态生物过程中系统级生物网络。然后，将开发用于分析动态网络数据的新方法，以深入了解潜在的细胞变化。例如，在静态生物网络研究中已经建立的小图（小的子图）的想法将被带到下一个层次，以允许基于小图的动态生物网络分析。此外，新的计算策略将被设计为允许动态网络聚类。所提出的方法将用于协作应用，包括代表性的动态生物过程：早期癌症检测和化疗耐药性，无论是在胰腺癌的背景下，以及研究人类衰老。这些跨学科的应用将被用作具体的模型系统，以创新基础计算研究。由于网络研究跨越许多领域，因此将向来自不同学科的研究人员提供实现新方法的开源软件。该软件还将作为一种教育工具。将进一步促进研究与教育的结合。跨学科的学生培训将通过网络研究的新课程提供。识字教育的目的是提高学生的沟通技能。将采用可持续的教学策略来改善学生的学习。研究监督和职业指导将提供给K-12和（下）研究生，重点是少数民族和妇女，从而将多样性纳入项目。跨学科研究和教育合作将允许广泛传播所提出的想法和成果。还将通过在著名的国际会议上组织辅导和讲习班来传播成果。