A multidimensional matrix for systems biology research and its application to interaction networks

Chi Nam Ignatius Pang, Apurv Goel, Simone S. Li, Marc R. Wilkins

Research output: Contribution to journalArticleResearchpeer-review

7 Citations (Scopus)

Abstract

A multidimensional matrix containing 76 parameters from 21 transcriptomics, proteomics, interactomics, phenotypic and sequence-based data sets, in which each data set covered most of the Saccharomyces cerevisiae proteome, was compiled for systems biology research. The maximal information coefficient (MIC) was used to measure correlations between every pair of parameters. Out of 2850 possible comparisons, 340 pairs of variables (12%) showed statistically significant MIC scores. There were 321 relationships that were expected; these included relationships within physicochemical parameters of proteins, between abundance levels of genes/proteins and expression noise, and between different types of intracellular networks. We found 19 potentially novel relationships between different types of "-omics" data. The strongest of these involved genetic interaction networks, which were correlated with pleiotropy and cell-to-cell variability in protein expression. Protein disorder also showed a number of significant relationships with protein abundance, signaling and regulatory networks. Significant cross-talk was seen between the signaling and kinase interaction networks. Investigation of this revealed densely connected kinase clusters and significant signaling between them, along with signaling centers that act as integrators or broadcasters of intracellular information. These centers may allow for redundancy and a means of dampening noise in networks under a variety of genetic or environmental perturbations.

Original languageEnglish
Pages (from-to)5204-5220
Number of pages17
JournalJournal of Proteome Research
Volume11
Issue number11
DOIs
Publication statusPublished - 2 Nov 2012
Externally publishedYes

Keywords

  • maximal information coefficient
  • protein interaction networks
  • signaling
  • systems biology

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