- Publikační typ
- abstrakt z konference MeSH
Inference of gene expression networks has become one of the primary challenges in computational biology. Analysis of microarray experiments using appropriate mathematical models can reveal interactions among protein regulators and target genes. This paper presents a combined approach to the inference of gene expression networks from time series measurements, ChIP-on-chip experiments, analyses of promoter sequences, and protein-protein interaction data. A recursive model of gene expression allowing for identification of active gene expression control networks with up to two regulators of one target gene is presented. The model was used to inspect all possible regulator-target gene combinations and predict those that are active during the underlying biological process. The procedure was applied to the inference of part of a regulatory network of the S. cerevisiae cell cycle, formed by 37 target genes and 128 transcription factors. A set of the most probable networks was suggested and analyzed.
Microarray studies are capable of providing data for temporal gene expression patterns of thousands of genes simultaneously, comprising rich but cryptic information about transcriptional control. However available methods are still not adequate in extraction of useful information about transcriptional regulation from these data. This study presents a dynamic model of gene expression which allows for identification of transcriptional regulators using time series of gene expression. The algorithm was applied for identification of transcriptional regulators controlling 40 cell cycle regulated genes of Saccharomyces cerevisiae. The presented algorithm uses a dynamic model of time continuous gene expression with the assumption that the target gene expression profile results from the action of the upstream regulator. The goal is to apply the model to putative regulators to estimate the transcription pattern of a target gene using a least squares minimization procedure. The procedure iteratively tests all possible transcription factors and selects those that best approximate the target gene expression profile. Results were compared with independently published data and good agreement between the published and identified transcriptional regulators was found.
- MeSH
- algoritmy MeSH
- CDC geny MeSH
- financování organizované MeSH
- genetická transkripce MeSH
- genové regulační sítě MeSH
- lineární modely MeSH
- metoda nejmenších čtverců MeSH
- modely genetické MeSH
- regulace genové exprese u hub MeSH
- Saccharomyces cerevisiae genetika MeSH
- sekvenční analýza hybridizací s uspořádaným souborem oligonukleotidů MeSH
- stanovení celkové genové exprese MeSH
- transkripční faktory analýza MeSH
- výpočetní biologie MeSH
- Publikační typ
- hodnotící studie MeSH
- srovnávací studie MeSH
