This chapter describes a mathematical approach based on the study of the 2-D autocovariance function (2-D ACVF) useful for decoding the complex signals resulting from the separation of protein mixtures. The method allows to obtain fundamental analytical information hidden in 2-D PAGE maps by spot overlapping, such as the number of proteins present in the sample and the mean standard deviation of the spots, describing the separation performance. In addition, it is possible to identify ordered patterns potentially present in spot positions, which can be related to the chemical composition of the protein mixture, such as post-translational modifications. The procedure was validated on computer-simulated maps and successfully applied to reference maps obtained from literature sources.
Decoding 2-d maps by autocovariance function
PIETROGRANDE, Maria Chiara;MARCHETTI, Nicola;DONDI, Francesco
2016
Abstract
This chapter describes a mathematical approach based on the study of the 2-D autocovariance function (2-D ACVF) useful for decoding the complex signals resulting from the separation of protein mixtures. The method allows to obtain fundamental analytical information hidden in 2-D PAGE maps by spot overlapping, such as the number of proteins present in the sample and the mean standard deviation of the spots, describing the separation performance. In addition, it is possible to identify ordered patterns potentially present in spot positions, which can be related to the chemical composition of the protein mixture, such as post-translational modifications. The procedure was validated on computer-simulated maps and successfully applied to reference maps obtained from literature sources.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
