Dynamical analysis of circadian gene expression

C. LAYANA; L. DIAMBRA

Santa Clara, Cuba

Congreso; II International Workshop on Bioinformatic '08; 2008

Central University of Las Villas and Flemish Inter-University Council

Microarrays technique allows the simultaneous measurementsof the expression levels of thousands of mRNAs. By mining this data onecan identify the dynamics of the gene expression time series. By recourseof principal component analysis, we uncover the circadian rhythmic pat-terns underlying the gene expression profiles from Cyanobacterium Syne-chocystis. We applied PCA to reduce the dimensionality of the data set.Examination of the components also provides insight into the underly-ing factors measured in the experiments. Our results suggest that allrhythmic content of data can be reduced to three main components.