Table of Contents
International Scholarly Research Notices
Volume 2014 (2014), Article ID 817102, 9 pages
http://dx.doi.org/10.1155/2014/817102
Research Article

Relationship between Metabolic Fluxes and Sequence-Derived Properties of Enzymes

Institute of Microbiology and Biotechnology, University of Latvia, Kronvalda Boulevard 4, Riga LV-1010, Latvia

Received 16 April 2014; Accepted 24 August 2014; Published 29 October 2014

Academic Editor: Fernando Tadeo

Copyright © 2014 Peteris Zikmanis and Inara Kampenusa. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Supplementary Material

Supplementary information 1:

Table S1-1: The dataset used in multivariate analysis which comprises metabolic fluxes and numerical vectors representing the average amino acid (AA) property for each enzyme sequence (Teusink's kinetic model, VARIMAX scales).

Table S1-2: The dataset used in multivariate analysis which comprises metabolic fluxes and numerical vectors representing the average AA property for each enzyme sequence (Hynne's kinetic model, VARIMAX scales).

Table S1-3: The dataset used in multivariate analysis which comprises metabolic fluxes and numerical vectors representing the average AA property for each enzyme sequence (Teusink's kinetic model, specific AA indices).

Table S1-4: The dataset used in multivariate analysis which comprises metabolic fluxes and numerical vectors representing the average AA property for each enzyme sequence (Hynne's kinetic model, specific AA indices).

Supplementary information 2:

Figure S2: The linear plots for the metabolic fluxes of the yeast Saccharomyces cerevisiae glycolysis pathway estimated by kinetic models against those predicted by linear regression models I-IV and by the leave-one-out cross-validation (LOOCV) of the models.

Table S2-1: The variance analysis of the regression models.

Table S2-2: Standard errors and confidence intervals for the linear regression models.

Supplementary information 3:

Table S3: Elements and the statistical indices for multiple linear regression model which links the values of metabolic fluxes and the average AA properties of the yeast Saccharomyces cerevisiae enzyme sequences expressed according to the specific AA indices.

Figure S3: The linear plot for the metabolic fluxes of the yeast Saccharomyces cerevisiae glycolysis pathway estimated by kinetic models against those predicted by linear regression model.

  1. Supplementary Material