Can ODE gene regulatory models neglect time lag or measurement scaling?

MOTIVATION Many ordinary differential equation (ODE) models have been introduced to replace linear regression models for inferring gene regulatory relationships from time-course gene expression data. But since the observed data are usually not direct measurements of the gene products or there is an unknown time lag in gene regulation, it is problematic to directly apply traditional ODE models or linear regression models. RESULTS We introduce a lagged ODE model to infer lagged gene regulatory relationships from time-course measurements, which are modelled as linear transformation of the gene products. A time-course microarray dataset from a yeast cell-cycle study is used for simulation assessment of the methods and real data analysis. The results show that our method, by considering both time lag and measurement scaling, performs much better than other linear and ODE models. It indicates the necessity of explicitly modelling the time lag and measurement scaling in ODE gene regulatory models.

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