Dynamic modeling of gene expression data.
We describe the time evolution of gene expression levels by using a time translational matrix to predict future expression levels of genes based on their expression levels at some initial time. We deduce the time translational matrix for previously published DNA microarray gene expression data sets by modeling them within a linear framework by using the characteristic modes obtained by singular value decomposition. The resulting time translation matrix provides a measure of the relationships among the modes and governs their time evolution. We show that a truncated matrix linking just a few modes is a good approximation of the full time translation matrix. This finding suggests that the number of essential connections among the genes is small.
Holter
N S
NS
Department of Physics and Center for Materials Physics, 104 Davey Laboratory, Pennsylvania State University, University Park, PA 16802, USA.
Maritan
A
A
Cieplak
M
M
Fedoroff
N V
NV
Banavar
J R
JR
D018797:Cell Cycle Proteins / Q000235:genetics; D019204:GTP-Binding Proteins / Q000235:genetics; D020869:Gene Expression Profiling; D006801:Humans; D008957:Models, Genetic
C082279:CDC15 protein; D018797:Cell Cycle Proteins; D019204:GTP-Binding Proteins