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Properties of a singular value decomposition based dynamical model of gene expression data

100%

Simek K.

International Journal of Applied Mathematics and Computer Science

2003

tom 13

nr 3

337-345

Recently, data on multiple gene expression at sequential time points were analyzed using the Singular Value Decomposition (SVD) as a means to capture dominant trends, called characteristic modes, followed by the fitting of a linear discrete-time dynamical system 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 the standard MATLAB procedures. We use freely available data to test the approach. We discuss the possible consequences of data regularization, called sometimes ``polishing'', on the outcome of the analysis, especially when the model is to be used for prediction purposes. Then, we investigate the sensitivity of the method to missing measurements and its abilities to reconstruct the 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, like overfitting problems.

The strategy of fusion genes construction determines efficient expression of introduced transcription factors

88%

Adamus T. , Konieczny P. , Sekuła M. , Sułkowski M. , Majka M.

Acta Biochimica Polonica

2014

tom 61

nr 4

773-778

The main goal in gene therapy and biomedical research is an efficient transcription factors (TFs) delivery system. SNAIL, a zinc finger transcription factor, is strongly involved in tumor, what makes its signaling pathways an interesting research subject. The necessity of tracking activation of intracellular pathways has prompted fluorescent proteins usage as localization markers. Advanced molecular cloning techniques allow to generate fusion proteins from fluorescent markers and transcription factors. Depending on fusion strategy, the protein expression levels and nuclear transport ability are significantly different. The P2A self-cleavage motif through its cleavage ability allows two single proteins to be simultaneously expressed. The aim of this study was to compare two strategies for introducing a pair of genes using expression vector system. We have examined GFP and SNAI1 gene fusions by comprising common nucleotide polylinker (multiple cloning site) or P2A motif in between them, resulting in one fusion or two independent protein expressions respectively. In each case transgene expression levels and translation efficiency as well as nuclear localization of expressed protein have been analyzed. Our data showed that usage of P2A motif provides more effective nuclear transport of SNAIL transcription factor than conventional genes linker. At the same time the fluorescent marker spreads evenly in subcellular space.

Analysis of dynamics of gene exspression using singular value decomposition

75%

Simek K. , Kimmel M.

Journal of Medical Informatics & Technologies

2002

tom Vol. 3

MI31--40

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.

Properties of a Singular Value Decomposition Based Dynamical Model of Gene Expression Data

75%

Simek K.

International Journal of Applied Mathematics and Computer Science

2003

tom Vol. 13, no 3

337-345

Recently, data on multiple gene expression at sequential time points were analyzed using the Singular Value Decomposition (SVD) as a means to capture dominant trends, called characteristic modes, followed by the fitting of a linear discrete-time dynamical system 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 the standard MATLAB procedures. We use freely available data to test the approach. We discuss the possible consequences of data regularization, called sometimes "polishing", on the outcome of the analysis, especially when the model is to be used for prediction purposes. Then, we investigate the sensitivity of the method to missing measurements and its abilities to reconstruct the 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, like overfitting problems.