A hypothesis in the yeast example is how genes organize into groups to perform a specific concerted behavior. "However, these gene groupings are not permanent, but shift as the cell begins orchestrating its next step. These transitions correspond to significant 'regrouping' of genes, which is indicative of a change in cellular state," said Richard Helm, associate professor of biochemistry at Virginia Tech, and co-author. Tracking down these transitions in time-based experiments is difficult, especially with thousands of genes changing in levels simultaneously. "When confronted with datasets this large we tend to focus on our 'favorite' genes or processes, leading potentially to a biased viewpoint," said Helm.
"GOALIE blends techniques from mathematical optimization, computer science data mining, and computational biology," said Layne Watson, professor of computer science and mathematics at Virginia Tech, and co-author. "It automatically mines the data in an unsupervised manner, identifying temporal relationships between groups of genes in order to gain a more unbiased and holistic understanding of time-based cellular behavior."
Specific strains of S. cerevisiae have been shown to have two robust biological cycles occurring simultaneously, namely the metabolic and cell division cycles. While the yeast cell division cycle has been well studied, its relationship to and coordination with metabolism are only now being worked out. GOALIE was able to recover the underlying tempo
|Contact: Susan Trulove|