Probabilistic Graphical Modeling in Systems Biology: A Framework for Integrative Approaches

  • Christine SinoquetEmail author


Systems biology may be defined as a discipline aiming at integrating various sources of heterogeneous data, with the objective to describe and predict the function of biological systems. The purpose is to cross many (possibly weak) evidences from several data types that describe different biological features of genes or proteins. Probabilistic graphical models offer an appealing framework for this objective. Through the thorough review of five selected examples, this chapter highlights how probabilistic graphical models can contribute to build the bridge between biology and computational modeling. In this methodological framework, the five cases illustrate three features of these models, which we discuss: flexibility, scalability and ability to combine heterogeneous sources of data. The applications covered address genetic association studies, identification of protein–protein interactions, identification of the target genes of transcription factors, inference of causal phenotype networks and protein function prediction.


Systems biology Integrative approach Integration of omics data Heterogeneous sources of data Computational modeling Machine learning Probabilistic framework Probabilistic graphical model Bayesian network Markov random field 

List of Acronyms


Bayesian network


Chromatin immunoprecipitation on chip


Chromatin immunoprecipitation followed by sequencing


Causal phenotype network


Domain-domain interaction


Deoxyribonucleic acid


Genetic architecture


Gene ontology


GO sub-ontology


Genome wide association study


Monte Carlo Markov chain


Markov random field


MRF mixture joint model


Probabilistic graphical model


Protein–protein interaction


Quantitative trait loci


Ribonucleic acid


RNA interference

ROC curve

Receiver operating characteristic curve


Standard mixture model


Transcription factor



The author wishes to thank the anonymous reviewer for constructive comments on her manuscript, and feedback most helpful to produce the final version.


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.LINA, UMR CNRS 6241Université de NantesNantes CedexFrance

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