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EN
Recently, data on multiple gene expression at sequential time points were analyzed, using Singular Value Decomposition (SVD) as a means to capture dominant trends, called characteristic modes, followed by fitting of a linear discrete-time dynamical model in which the expression values at a given time point are linear combinations of the values at a previous time point. We attempt to address several aspects of the method. To obtain the model we formulate a nonlinear optimization problem and present how to solve it numerically using standard MATLAB procedures. We use publicly available data to test the approach. Then, we investigate the sensitivity of the method to missing measurements and its possibilities to reconstruct missing data. Summarizing we point out that approximation of multiple gene expression data preceded by SVD provides some insight into the dynamics but may also lead to unexpected difficulties.
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