New components of the Dictyostelium PKA pathway revealed by Bayesian analysis of expression data

BMC Bioinformatics

Research Article

Christopher Dinh¹  Devika Subramanian²  Eryong Huang³  Adam Kuspa⁴  Blaz Zupan⁵  Anup Parikh⁶  Gad Shaulsky⁶ 

[1] Department of Biochemistry and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, 77030, Houston, TX, USA;Department of Computer Science, Rice University, MS 132, 6100 Main St, 77005, Houston, TX, USA;Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, 77030, Houston, TX, USA;Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, 77030, Houston, TX, USA;Department of Biochemistry and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, 77030, Houston, TX, USA;Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, 77030, Houston, TX, USA;Faculty of Computer and Information Science, University of Ljubljana, Trzaska cesta 25, SI-1001, Ljubljana, Slovenia;Graduate program in Structural Computational Biology and Molecular Biophysics, Baylor College of Medicine, One Baylor Plaza, 77030, Houston, TX, USA;Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, 77030, Houston, TX, USA;

关键词: Bayesian Network;    Electrophoretic Mobility Shift Assay;    Core Network;    Probabilistic Boolean Network;    Prior Biological Knowledge;   

DOI  :  10.1186/1471-2105-11-163

 received in 2009-11-16, accepted in 2010-03-31,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundIdentifying candidate genes in genetic networks is important for understanding regulation and biological function. Large gene expression datasets contain relevant information about genetic networks, but mining the data is not a trivial task. Algorithms that infer Bayesian networks from expression data are powerful tools for learning complex genetic networks, since they can incorporate prior knowledge and uncover higher-order dependencies among genes. However, these algorithms are computationally demanding, so novel techniques that allow targeted exploration for discovering new members of known pathways are essential.ResultsHere we describe a Bayesian network approach that addresses a specific network within a large dataset to discover new components. Our algorithm draws individual genes from a large gene-expression repository, and ranks them as potential members of a known pathway. We apply this method to discover new components of the cAMP-dependent protein kinase (PKA) pathway, a central regulator of Dictyostelium discoideum development. The PKA network is well studied in D. discoideum but the transcriptional networks that regulate PKA activity and the transcriptional outcomes of PKA function are largely unknown. Most of the genes highly ranked by our method encode either known components of the PKA pathway or are good candidates. We tested 5 uncharacterized highly ranked genes by creating mutant strains and identified a candidate cAMP-response element-binding protein, yet undiscovered in D. discoideum, and a histidine kinase, a candidate upstream regulator of PKA activity.ConclusionsThe single-gene expansion method is useful in identifying new components of known pathways. The method takes advantage of the Bayesian framework to incorporate prior biological knowledge and discovers higher-order dependencies among genes while greatly reducing the computational resources required to process high-throughput datasets.

【 授权许可】

Unknown   
© Parikh et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.