This article is part of the supplement: Eleventh International Conference on Bioinformatics (InCoB2012): Bioinformatics
Temporal dynamics of protein complexes in PPI Networks: a case study using yeast cell cycle dynamics
1 Department of Computer Science, National University of Singapore, Singapore 117590
2 Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
BMC Bioinformatics 2012, 13(Suppl 17):S16 doi:10.1186/1471-2105-13-S17-S16Published: 13 December 2012
Complexes of physically interacting proteins are one of the fundamental functional units responsible for driving key biological mechanisms within the cell. With the advent of high-throughput techniques, significant amount of protein interaction (PPI) data has been catalogued for organisms such as yeast, which has in turn fueled computational methods for systematic identification and study of protein complexes. However, many complexes are dynamic entities - their subunits are known to assemble at a particular cellular space and time to perform a particular function and disassemble after that - and while current computational analyses have concentrated on studying the dynamics of individual or pairs of proteins in PPI networks, a crucial aspect overlooked is the dynamics of whole complex formations. In this work, using yeast as our model, we incorporate 'time' in the form of cell-cycle phases into the prediction of complexes from PPI networks and study the temporal phenomena of complex assembly and disassembly across phases. We hypothesize that 'staticness' (constitutive expression) of proteins might be related to their temporal "reusability" across complexes, and test this hypothesis using complexes predicted from large-scale PPI networks across the yeast cell cycle phases. Our results hint towards a biological design principle underlying cellular mechanisms - cells maintain generic proteins as 'static' to enable their "reusability" across multiple temporal complexes. We also demonstrate that these findings provide additional support and alternative explanations to findings from existing works on the dynamics in PPI networks.