Prediction of transcriptional profiles of Synechocystis PCC6803 by dynamic autoregressive modeling of DNA microarray data.

Time-series profiles of gene expression generated by DNA microarrays possess sufficient information for building dynamic models of transcriptional behavior. This, however, requires properly designed experiments and sufficient independent data to validate such models. Here we report the use of AutoRegressive with eXogenous input (ARX) models to fit dynamic gene expression data obtained by subjecting cultures of the photosynthetic bacterium Synechocystis PCC6803 to consecutive light-to-dark transitions. Autoregressive with exogenous input models of appropriate complexity were selected by applying Akaike's information criterion (AIC) such as to maximize agreement between model predictions with experimental data without overfitting. These models were subsequently used to design the experimental profile of an optimal validating data set. Predictions from these models were tested in a second experiment and were found to match well with the validation data. Additionally, the models with the least error in predicting the expression profiles of the validation data set exactly match the model complexity predicted by AIC. Such models offer insights into cellular responses to environmental conditions and form the basis for hypothesizing and quantifying relationships that are presently poorly understood at the level of fundamental mechanisms.