Submit Manuscript  

Article Details

A Systems Biology Study of Two Distinct Growth Phases of Saccharomyces cerevisiae Cultures

[ Vol. 5 , Issue. 8 ]


A. M. Martins, D. Camacho, J. Shuman, W. Sha, P. Mendes and V. Shulaev   Pages 649 - 663 ( 15 )


Saccharomyces cerevisiae cultures growing exponentially and after starvation are distinctly different, as shown by several studies at the physiological, biochemical, and morphological levels. One group of studies attempted to be mechanistic, characterizing a few molecules and interactions, while another focused on global observations but remained descriptive or at best phenomenological. Recent advances in large-scale molecular profiling technologies, theoretical, and computational biology, are making possible integrative studies of biological systems, where global observations are explained through computational models with solid theoretical bases. A case study of the systems biology approach applied to the characterization of bakers yeast cultures in exponential growth and post-diauxic phases is presented. Twenty cell cultures of S. cerevisiae were grown under similar environmental conditions. Samples from ten of these cultures were collected 11 hours after inoculation, while samples from the other ten were collected 4 days after inoculation. These samples were analyzed for their RNA and metabolite composition using Affymetrix chips and gas chromatography-mass spectrometry (GC-MS). The data were interpreted with statistical analyses and through the use of computer simulations of a kinetic model that was built by merging two independent models of glycolysis and glycerol biosynthesis. The simulation results agree with the exponential growth phase data, while no model is available for the post-diauxic phase. We discuss the need for expanding the number of kinetic models of S. cerevisiae, combining metabolism and genetic regulation, and covering all of its biochemistry.


saccharomyces cerevisiae, exponential phase, post-diauxic phase, transcriptomics, metabolomics, systems biology, metabolite correlation, mathematical modeling


Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, BioinformaticsFacility, Washington St., Blacksburg, Virginia 24061, USA.

Read Full-Text article