Tijana Milenkovic
Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, USA
tmilenko [at] nd.edu
Abstract
Network biology, an interdisciplinary field bridging computational and biological sciences, has revolutionized understanding of cellular functions and diseases. The field, which has existed for two decades, has witnessed rapid evolution, accompanied by emerging challenges. These challenges stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different scales of biological organization.
This talk will discuss some of key research areas in network biology and highlight recent breakthroughs in these areas; offer a perspective on the future directions of network biology; and touch on scientific communities, educational initiatives, and the importance of fostering diversity within the field.
Two specific research directions will be discussed. The first is on our network-of-networks analyses of multi-scale biological systems with application to protein function prediction. The function of a protein is determined by the protein’s 3D structure, which also affects which other proteins the protein interacts with. Because of this, and because nodes in a protein-protein interaction (PPI) network can be represented as protein structure networks (PSNs), we modeled the integrated PPI and PSN data as a network-of-networks. We found that the multi-scale network-of-network analysis often resulted in more accurate protein function prediction than traditional single-scale analysis of PPI data alone or PSN data alone.
Second, the talk will discuss our network-based analyses of protein folding. We had proposed several approaches for modeling protein 3D structures as PSNs. Static PSNs model the whole, final 3D structure of a protein. Because the folding of a protein is a dynamic process, where some parts (3D sub-structures) of a protein fold before others, most recently, we modeled a protein as a dynamic PSN that captures these sub-structures. We evaluated our PSN models in the task of protein structural classification. We found that our PSN models outperformed state-of-the-art approaches for the same task, with dynamic PSNs being superior to static PSNs.
Keywords: network biology, protein-protein interaction networks, protein structure networks, protein function prediction, protein folding
Acknowledgement: A part of the talk will be based on our collaborative paper titled “Current and future directions in network biology” (arXiv:2309.08478 [q-bio.MN], 2023) that is co-authored by numerous experts in network biology, who initialized the discussion from the paper at the Workshop on Future Directions in Network Biology held at the University of Notre Dame during June 12-14, 2022. The workshop was supported by the U.S. National Science Foundation [grant number CCF-1941447].