Inferring genetic networks from DNA microarray data by multiple regression analysis.
Inferring gene regulatory networks by differential equations from the time series data of a DNA microarray is one of the most challenging tasks in the post-genomic era. However, there have been no studies actually inferring gene regulatory networks by differential equations from genome-level data. The reason for this is that the number of parameters in the equations exceeds the number of measured time points. We here succeeded in executing the inference, not by directly determining parameters but by applying multiple regression analysis to our equations. We derived our differential equations and steady state equations from the rate equations of transcriptional reactions in an organism. Verification with a number of genes related to respiration indicated the validity and effectiveness of our method. Moreover, the steady state equations were more appropriate than the differential equations for the microarray data used.
Kato
M
M
Department of Physics, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyou-ku, Tokyo 113-0033, Japan. mkato@ims.u-tokyo.ac.jp
Tsunoda
T
T
Takagi
T
T
D019295:Computational Biology; D003574:Cytochrome c Group / Q000235:genetics; D045304:Cytochromes c; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D005800:Genes, Fungal; D023281:Genomics; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D012331:RNA, Fungal / Q000235:genetics / Q000378:metabolism; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism; D012044:Regression Analysis; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors / Q000378:metabolism
C014059:CYC1 protein, S cerevisiae; D003574:Cytochrome c Group; D012331:RNA, Fungal; D012333:RNA, Messenger; D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors; D045304:Cytochromes c