CAREER: Novel Algorithms for Dynamic Network Analysis in Computational Biology

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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.