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。基因组序列研究已彻底改变了对细胞功能的理解。但是，正如在基因组时代所识别的那样，基因（蛋白质）不能孤立起作用。相反，他们通过相互作用进行蜂窝过程。这正是生物网络模型。与基因组序列数据不同，生物网络数据可以研究从蛋白质的集体行为中产生的复杂细胞过程。因此，生物网络研究有望对生命，进化，疾病和治疗学原理的新见解。但是，当前的网络研究涉及生物学数据的静态表示，即使细胞功能是动态的。这部分是由于实验衍生的动态生物网络数据不可用，这是由于数据收集的生物技术的局限性。与静态生物网络研究相比，需要进行动态生物网络的推理和分析的有效计算策略，以提高对细胞功能的了解。这正是该项目的重点。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，这将作为该项目的一部分进行探讨。因此，该项目可能有助于全球健康。它也可能影响其他领域，例如社交网络。此外，该项目将导致与研究相互交织的教育活动，例如通过新的课程开发活动形成跨学科的科学家，或通过研究监督，职业指导以及对K-12和（下）研究生的社区宣传来加强计算机科学人群，专注于女性。技术描述。该建议将为动态生物网络研究提供新的计算方向。通过将静态网络拓扑与其他数据类型相结合，例如在不同时间的基因表达或蛋白质丰度的测量，将开发新算法来推断动态生物学过程的系统级生物网络。然后，将开发用于分析动态网络数据的新方法，以洞悉潜在的细胞变化。例如，在静态生物网络研究中已建立的图形（小子图）的概念将被带到一个新的水平，以允许基于图形的动态生物网络分析。此外，新型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涵盖代表性动态生物学过程的协作应用中：在胰腺癌的背景下以及研究人类衰老的情况下，早期的癌症检测和抗化疗耐药性。这些跨学科应用将用作创新基本计算研究的具体模型系统。由于网络研究涵盖了许多领域，因此将向来自不同学科的研究人员提供实施新方法的开源软件。该软件还将用作教育工具。研究和教育的整合将进一步促进。跨学科的学生培训将通过网络研究的新颖课程提供。识字教育方法将旨在提高学生的沟通能力。经过验证的教学策略将用于改善学生学习。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，从而将多样性融入该项目。跨学科的研究和教育合作将允许大量分发拟议的思想和结果。结果还将通过著名国际会议的教程和研讨会组织进行传播。