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An integrative network inference approach to predict mechanisms of cancer chemoresistance.
MedLine Citation:
PMID:  23340828     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
We present an integrative general network inference methodology to infer genetic and metabolic pathways associated with oncological drug chemoresistance. This methodology is general because it can infer different kinds of networks from different kinds of data. It is integrative because it integrates model simulation in its framework and it assembles into a larger integrated network all the inferred networks. The inference model is a variational approximation of Bayesian inference for stochastic processes. We used the Bayesian framework due to its ability to incorporate prior knowledge and constraints in the inference procedure and to treat both partial data and a large amount of data whose dynamics laws are mostly unknown. We show the performance of this approach using a case study of the gemcitabine chemoresistance in pancreatic cancer cells. Our method, inferred from time series data of gene expressions and metabolites, concentrates first the network of interactions of genes responsible for the sensitivity and resistance to gemcitabine, then the metabolic network, and finally it merges the two networks into a larger network predicting the correlations between genes and metabolizing enzymes.
Authors:
Paola Lecca
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2013-1-23
Journal Detail:
Title:  Integrative biology : quantitative biosciences from nano to macro     Volume:  -     ISSN:  1757-9708     ISO Abbreviation:  Integr Biol (Camb)     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2013-1-23     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101478378     Medline TA:  Integr Biol (Camb)     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto, Italy.
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