Maximum entropy reconstructions of dynamic signaling networks from quantitative proteomics data.

ABSTRACT: Advances in mass spectrometry among other technologies have allowed for quantitative, reproducible, proteome-wide measurements of levels of phosphorylation as signals propagate through complex networks in response to external stimuli under different conditions. However, computational approaches to infer elements of the signaling network strictly from the quantitative aspects of proteomics data are not well established. We considered a method using the principle of maximum entropy to infer a network of interacting phosphotyrosine sites from pairwise correlations in a mass spectrometry data set and derive a phosphorylation-dependent interaction network solely from quantitative proteomics data. We first investigated the applicability of this approach by using a simulation of a model biochemic

SUBMITTER: Locasale JW 

PROVIDER: S-EPMC2728537 | biostudies-literature | 2009 Aug

REPOSITORIES: biostudies-literature