title: Inferring genetic regulatory logic from expression data.
abstract: BACKGROUND
  High-throughput molecular genetics methods allow the collection of data about the expression of genes at different time points and under different conditions. The challenge is to infer gene regulatory interactions from these data and to get an insight into the mechanisms of genetic regulation.
  
  
  RESULTS
  We propose a model for genetic regulatory interactions, which has a biologically motivated Boolean logic semantics, but is of a probabilistic nature, and is hence able to confront noisy biological processes and data. We propose a method for learning the model from data based on the Bayesian approach and utilizing Gibbs sampling. We tested our method with previously published data of the Saccharomyces cerevisiae cell cycle and found relations between genes consistent with biological knowledge.
authors.affiliation: Division Theoretical Bioinformatics, German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany. s.bulashevska@dkfz.de
authors.forename: Svetlana
authors.initials: S
authors.lastname: Bulashevska
authors.forename: Roland
authors.initials: R
authors.lastname: Eils
mesh_terms: D001499:Bayes Theorem; D018797:Cell Cycle Proteins / Q000378:metabolism*; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D016015:Logistic Models; D008954:Models, Biological; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000502:physiology; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D012984:Software
chemical_list: D018797:Cell Cycle Proteins; D029701:Saccharomyces cerevisiae Proteins