title: New components of the Dictyostelium PKA pathway revealed by Bayesian analysis of expression data.
abstract: BACKGROUND
  Identifying 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.
  
  
  RESULTS
  Here 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.
  
  
  CONCLUSIONS
  The 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.
authors.affiliation: Graduate Program in Structural Computational Biology and Molecular Biophysics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.
authors.forename: Anup
authors.initials: A
authors.lastname: Parikh
authors.forename: Eryong
authors.initials: E
authors.lastname: Huang
authors.forename: Christopher
authors.initials: C
authors.lastname: Dinh
authors.forename: Blaz
authors.initials: B
authors.lastname: Zupan
authors.forename: Adam
authors.initials: A
authors.lastname: Kuspa
authors.forename: Devika
authors.initials: D
authors.lastname: Subramanian
authors.forename: Gad
authors.initials: G
authors.lastname: Shaulsky
mesh_terms: D001499:Bayes Theorem; D017868:Cyclic AMP-Dependent Protein Kinases / Q000378:metabolism*; D004023:Dictyostelium / Q000201:enzymology* / Q000235:genetics / Q000254:growth & development; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods; D023281:Genomics / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D015398:Signal Transduction / Q000235:genetics
chemical_list: D017868:Cyclic AMP-Dependent Protein Kinases