Abstract
BackgroundWith the advent of genomic technology, the size of metabolic networks that are subject to analysis is growing. A common task when analyzing metabolic networks is to find all possible steady state regimes. There are several technical issues that have to be addressed when analyzing large metabolic networks including accumulation of numerical errors and presentation of the solution to the researcher. One way to resolve those technical issues is to analyze the network using symbolic methods. The aim of this paper is to develop a routine that symbolically finds the steady state solutions of large metabolic networks.ResultsA symbolic Gauss-Jordan elimination routine was developed for analyzing large metabolic networks. This routine was tested by finding the steady state solutions for a number of curated stoichiometric matrices with the largest having about 4000 reactions. The routine was able to find the solution with a computational time similar to the time used by a numerical singular value decomposition routine. As an advantage of symbolic solution, a set of independent fluxes can be suggested by the researcher leading to the formation of a desired flux basis describing the steady state solution of the network. These independent fluxes can be constrained using experimental data. We demonstrate the application of constraints by calculating a flux distribution for the central metabolic and amino acid biosynthesis pathways of yeast.ConclusionsWe were able to find symbolic solutions for the steady state flux distribution of large metabolic networks. The ability to choose a flux basis was found to be useful in the constraint process and provides a strong argument for using symbolic Gauss-Jordan elimination in place of singular value decomposition.
Highlights
With the advent of genomic technology, the size of metabolic networks that are subject to analysis is growing
Comparison of Gauss-Jordan Elimination (GJE) and singular value decomposition (SVD) Five large metabolic networks of increasing complexity were selected to test the performance of symbolic GJE to that of numerical SVD
The computational expense of symbolic GJE was not found to be overly restrictive with SympyCore [11], the package we used for analyzing genome-scale metabolic networks
Summary
With the advent of genomic technology, the size of metabolic networks that are subject to analysis is growing. The Systems Biology Markup Language (SBML) Web site [1] lists over 200 packages that make use of their library This large number of tools reflects both the wide variety and abundance of biological data available to constrain biological models as well as the large variety of simplifying assumptions made to gain insight from this plethora of data. With the advent of genomic technology, the size of networks that are subject to conservation analysis is growing. This is true of the amount of data that constrains biological function, forcing the analysis procedure to become more involved. This is especially true when faced with the realities of compartmentation in large biological systems
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