Reconstructing genetic networks from time ordered gene expression data using Bayesian method with global search algorithm.
Different genes of an organism are expressed to different levels at different times during the life cycle and in response to various environmental stresses. Elucidating the network of gene-gene interactions responsible for the expression helps understand living processes. Microarray technology allows concurrent genomic scale measurement of an organism's mRNA levels. We describe a power-law formalism to model the combinatorial effect of regulators on gene transcription. The dynamic model allows delayed transcription. We employ a principled network reconstruction approach that accounts for the high noise and low replicate characteristics of present day microarray data. An important feature of our approach is that the detail of the reconstructed network is limited to the noise level of the data. We apply the methodology to a microarray dataset of yeast cells grown in glucose and experiencing a diauxic transition upon glucose depletion. The reconstructed transcriptional regulations of yeast glycolytic genes are consistent with published findings.
Wang
Sun-Chong
SC
Institute of Statistical Science, Academia Sinica, Nankang, Taipei, 11529 Taiwan, ROC. wangs@gate.sinica.edu.tw
D000465:Algorithms; D001499:Bayes Theorem; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D005947:Glucose / Q000378:metabolism*; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010865:Pilot Projects; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
D029701:Saccharomyces cerevisiae Proteins; D005947:Glucose