CAREER: A Data-Driven Network Inference Framework for Context-Conditioned Protein Interaction Graphs

University Of Virginia Main Campus, Charlottesville VA

Investigators

Abstract

Biological systems can be studied as graphs, where nodes represent entities (e.g., proteins) and edges represent interactions (e.g., physical binding, functional dependency). The identification of important protein interaction networks enables new insights into principles of life, evolution change, disease study, and drug development. The network wiring and function of a protein interaction graph is determined by context: genetics, environment, and small molecules such as drugs. However, almost all protein interaction networks, to date, have been examined under a single static condition, due to limitations of biotechnologies for graph data collection. Therefore, the research objective of this proposal is to design novel and efficient machine-learning algorithms to identify context-specific protein interaction graphs. Identifying context-specific protein networks has biomedical applications of social importance, such as studying cellular developments across multiple cell stages or investigating cellular changes with different drug treatments in the context of leukemia. Both applications will be explored as evaluation components of the project through collaborating with the Center for Public Health Genomics and the Emily Couric Cancer Center at UVA School of Medicine. The proposed research is expected to impact other domains as well, for instance, social-network discovery and condition-specific network inference for brain connectivity. The proposed career plan will result in educational and outreach initiatives that build on the interdisciplinary nature of the research. These plans include: (a) designing new course projects that work on real-life network-inference problems and data; (b) developing novel instructional techniques to train graduate students professional skills such as "how to teach'' or "how to do research" using state-of-the-art structural learning problems as sample projects; (c) involving undergraduates in network learning research through UVA undergraduate capstone projects; (d) increasing awareness of graph-learning research among K-12 students through presentations at the UVA Introduction to Engineering (ITE) Program involving high school students; and (e) enhancing interactions with the UVA Medical School Community, especially through public release and tutorials of computational tools created from this project. The past decade has seen a revolution in genomic technologies that enable the simultaneous measurement of thousands of molecular entities (e.g., genes or proteins). The flood of genome-wide data generated by next-generation sequencing technologies has provided an unprecedented coverage of large-scale, context-conditioned signatures of relevant gene products that have great potential to infer network connectivity and function in each context. The proposal will develop a suite of novel machine-learning methods for inference of context-specific networks from multi-context molecular signature datasets that are high dimensional, heterogeneous and noisy. Aiming to overcome these data challenges, the proposed research includes the following three related tasks: (i) develop new and scalable structural learning algorithms to estimate multiple different but related sparse Gaussian Graphical Models (sGGMs) from data samples aggregated across multiple distinct conditions, (ii) develop novel learning strategies for modeling and detecting modules (i.e., multi-protein groups) within the framework of multitasking sGGMs, (iii) extend the above structural learning models to non-Gaussian cases, semi-supervised settings considering partial-observed networks and supervised disease diagnosis settings. Additional information about the project, including the publications, open-source implementations of algorithms, data sets and educational materials will be shared through the project website: http://www.cs.virginia.edu/yanjun/context_graph/