John Nardini and Maria-Veronica Ciocanel
The era of big data has challenged researchers to develop novel methods to succinctly summarize and analyze complex datasets. Such methods include network and topological data analysis (TDA), which use methods from graph theory, dynamical systems, and topology to describe the patterns underlying a given data set over different scales. These areas of data science have proven successful for analyzing many biological phenomena, including disease transmission, ecological swarming, medical diagnostics, and within-cell protein interactions. In this minisymposium, as part of the SMB subgroup on Methods for Biological Modeling, the speakers will highlight the topological analysis of biological data. Topological techniques such as persistent homology can help characterize networks in genomic, tumour vessel, and physiological data. Challenges in this area include developing frameworks for incorporating TDA with network analysis, the statistical analysis of topological computations, and interpretation of biological data through such topological methods.