Complex Assembly and Activity States as Multifaceted Protein Attributes Explaining Phenotypic Variability

Abstract Cell function studies primarily focus on measuring overall molecular abundances while often overlooking critical clues—including protein modifications and molecular interaction networks—that critically determine the functional properties of the cell. In prior work, we introduced a suite of methods to reveal context-specific transcription factor-gene regulatory networks, kinase-substrate networks, and protein interaction networks and leveraged them to gain deeper insights into transcriptional regulation and signal transduction. However, the complex interdependencies between these networks are still elusive. To address this challenge, we introduce a multi-omics framework, aimed at harnessing measured or inferred protein activity in context-specific networks, which yields deeper functional insights into mechanisms underlying molecular phenotypes, compared to protein abundance alone. As proof of concept, we utilized progressively differentiated instances of HeLa CCL2 and Kyoto cell lines to explore the role of protein complexes and interactions in cell doubling time and susceptibility to Salmonella Typhimurium infection. Notably, this analysis underscores the pivotal role of protein interaction networks in linking molecular profiles to phenotypic outcomes, thus providing a highly generalizable framework for multi-omics dataset analysis. Competing Interest Statement A.C. is founder, equity holder, and consultant of DarwinHealth Inc, a company that has licensed some of the algorithms used in this manuscript from Columbia University. Columbia University is also an equity holder in DarwinHealth Inc and assignee of patent US10,790,040, which covers some components of the algorithms used in this manuscript. The other authors declare no competing interests.