Integrative investigation of metabolic and transcriptomic data.
BACKGROUND
New analysis methods are being developed to integrate data from transcriptome, proteome, interactome, metabolome, and other investigative approaches. At the same time, existing methods are being modified to serve the objectives of systems biology and permit the interpretation of the huge datasets currently being generated by high-throughput methods.


RESULTS
Transcriptomic and metabolic data from chemostat fermentors were collected with the aim of investigating the relationship between these two data sets. The variation in transcriptome data in response to three physiological or genetic perturbations (medium composition, growth rate, and specific gene deletions) was investigated using linear modelling, and open reading-frames (ORFs) whose expression changed significantly in response to these perturbations were identified. Assuming that the metabolic profile is a function of the transcriptome profile, expression levels of the different ORFs were used to model the metabolic variables via Partial Least Squares (Projection to Latent Structures--PLS) using PLS toolbox in Matlab.


CONCLUSIONS
The experimental design allowed the analyses to discriminate between the effects which the growth medium, dilution rate, and the deletion of specific genes had on the transcriptome and metabolite profiles. Metabolite data were modelled as a function of the transcriptome to determine their congruence. The genes that are involved in central carbon metabolism of yeast cells were found to be the ORFs with the most significant contribution to the model.
Pir
Pinar
P
Department of Chemical Engineering, Boğaziçi University, Bebek 34342, Istanbul, Turkey. pinarpir@boun.edu.tr
Kirdar
Betül
B
Hayes
Andrew
A
Onsan
Z Ylsen
ZY
Ulgen
Kutlu O
KO
Oliver
Stephen G
SG
D000465:Algorithms; D003198:Computer Simulation; D030562:Databases, Protein; D016247:Information Storage and Retrieval / Q000379:methods; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D012441:Saccharomyces cerevisiae / Q000502:physiology*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D018511:Systems Integration; D014157:Transcription Factors / Q000502:physiology*
D020543:Proteome; D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors