title: Reconstructing dynamic regulatory maps.
abstract: Even simple organisms have the ability to respond to internal and external stimuli. This response is carried out by a dynamic network of protein-DNA interactions that allows the specific regulation of genes needed for the response. We have developed a novel computational method that uses an input-output hidden Markov model to model these regulatory networks while taking into account their dynamic nature. Our method works by identifying bifurcation points, places in the time series where the expression of a subset of genes diverges from the rest of the genes. These points are annotated with the transcription factors regulating these transitions resulting in a unified temporal map. Applying our method to study yeast response to stress, we derive dynamic models that are able to recover many of the known aspects of these responses. Predictions made by our method have been experimentally validated leading to new roles for Ino4 and Gcn4 in controlling yeast response to stress. The temporal cascade of factors reveals common pathways and highlights differences between master and secondary factors in the utilization of network motifs and in condition-specific regulation.
authors.affiliation: Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
authors.forename: Jason
authors.initials: J
authors.lastname: Ernst
authors.forename: Oded
authors.initials: O
authors.lastname: Vainas
authors.forename: Christopher T
authors.initials: CT
authors.lastname: Harbison
authors.forename: Itamar
authors.initials: I
authors.lastname: Simon
authors.forename: Ziv
authors.initials: Z
authors.lastname: Bar-Joseph
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000172:deficiency; D050976:Basic-Leucine Zipper Transcription Factors; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004268:DNA-Binding Proteins / Q000235:genetics / Q000378:metabolism; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D015966:Gene Expression Regulation, Fungal; D053263:Gene Regulatory Networks; D008390:Markov Chains; D008957:Models, Genetic; D016011:Normal Distribution; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000235:genetics* / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism; D013997:Time Factors; D015534:Trans-Activators / Q000235:genetics / Q000378:metabolism; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism
chemical_list: D000596:Amino Acids; D050976:Basic-Leucine Zipper Transcription Factors; D004268:DNA-Binding Proteins; C501391:GCN4 protein, S cerevisiae; C063311:INO4 protein, S cerevisiae; D029701:Saccharomyces cerevisiae Proteins; D015534:Trans-Activators; D014157:Transcription Factors