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Maximal entropy inference of oncogenicity from phosphorylation signaling
Contributed by Michael E. Phelps, January 29, 2010 (sent for review December 7, 2009)

Abstract
Point mutations in the phosphorylation domain of the Bcr-Abl fusion oncogene give rise to drug resistance in chronic myelogenous leukemia patients. These mutations alter kinase-mediated signaling function and phenotypic outcome. An information theoretic analysis of the correlation of phosphoproteomic profiling and transformation potency of the oncogene in different mutants is presented. The theory seeks to predict the leukemic transformation potency from the observed signaling by constructing a distribution of maximal entropy of site-specific phosphorylation events. The theory is developed with special reference to systems biology where high throughput measurements are typical. We seek sets of phosphorylation events most contributory to predicting the phenotype by determining the constraints on the signaling system. The relevance of a constraint is measured by how much it reduces the value of the entropy from its global maximum, where all events are equally likely. Application to experimental phospho-proteomics data for kinase inhibitor-resistant mutants shows that there is one dominant constraint and that other constraints are not relevant to a similar extent. This single constraint accounts for much of the correlation of phosphorylation events with the oncogenic potency and thereby usefully predicts the trends in the phenotypic output. An additional constraint possibly accounts for biological fine structure.
- high-throughput measurements
- information theory
- phospho proteomics
- signal transduction networks
- systems biology
Footnotes
- 1To whom correspondence should be addressed. E-mail: mphelps{at}mednet.ucla.edu.
Author contributions: T.G.G., F.R., and R.D.L. designed research; T.G.G., J.R.H., B.J.S., M.E.P., F.R., and R.D.L. analyzed data; F.R. and R.D.L. performed research; and T.G.G., J.R.H., M.E.P., F.R., and R.D.L. wrote the paper.
↵2Permanent address: Département de Chimie, Université de Liège, B4000 Liège, Belgium.
The authors declare no conflict of interest.
This article contains supporting information online at www.pnas.org/cgi/content/full/1001149107/DCSupplemental.
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- Abstract
- II. Declaration Regarding Maximal Entropy of Biological Signaling States
- III. The Distribution of Maximal Entropy
- IV. Example: Predicting the Potencies from the Site-Specific Phosphorylation Data
- V. Relation to Other Methods
- VI. The Information That the Data X Conveys About the Phenotypic Output Y
- VII. Concluding Remarks
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