A systems biology approach for the study of biomolecules and bionetworks.

摘要

We propose a systems biology approach to integrate non-kinetic data from interacting biological reactions into informative Bionetwork-Boolean models. We developed a descriptive and predictive bionetwork model for phospholipase C-coupled calcium signaling pathways, built with non-kinetic experimental information. Boolean models generated from these data yield oscillatory activity patterns for both the endoplasmic reticulum resident inositol-1,4,5-trisphosphate receptor (IP3R) and the plasma-membrane resident canonical transient receptor potential channel 3 (TRPC3). Furthermore, knock-out simulations of the IP3R, TRPC3, and multiple other proteins recapitulate experimentally derived results. The potential of this approach can be observed by its ability to predict previously undescribed cellular phenotypes. Indeed, our cellular analysis of DANGER1a confirms the counter-intuitive predictions from our Boolean models in two highly relevant cellular models. Based on these results, we theorize that with sufficient legacy knowledge, Boolean networks provide a robust method for predictive-modeling of any biological system.;A limiting factor to this bionetwork-boolean approach is the lack of information regarding structural, functional and evolutionary characteristics of individual network components. In most cases, this lack of information arises from inability of conventional homology detection programs to measure homology in highly divergent datasets. Further, Inability to resolve deep node relationships is a major factor that stymies evolutionary studies of highly divergent/rapidly evolving protein families. To resolve the shortcomings of conventional homology detection programs, we propose a computational approach towards resolving homology between highly divergent familial proteins using phylogenetic profiles. Indeed, phylogenetic profiles have been demonstrated as a method for simultaneous measurements of structure, function, and evolution.;Herein, we describe a MSA independent method to infer evolutionary relationships, and use this method to study rapidly evolving (Mab21-containing DANGER superfamily), highly divergent (Retroelements) and convergent (Haloacid Dehalogenase) benchmark superfamilies. We also compare the results obtained from our method (PHYRN) with other MSA dependent methods and show that PHYRN provides better evolutionary history recapitulation, and provides more robust measurements at deep nodes. Further, PHYRN also provides quantitative measures that can aid in identifying outgroups and convergent evolutionary events. Using Retroelements (RT) as a benchmark superfamily, we show that this approach can be scaled up efficiently to study mega-phylogenies with thousands of sequences. Taken together with PHYRN's adaptability to any protein family, this method can serve as a good tool in resolving ambiguities in evolutionary studies of rapidly evolving/highly divergent protein families.