Avoiding the Enumeration of Infeasible Elementary Flux Modes by Including Transcriptional Regulatory Rules in the Enumeration Process Saves Computational Costs.

Christian Jungreuthmayer,Michael Hanscho,David E Ruckerbauer,Jürgen Zanghellini,Matthias P Gerstl,Jérémie Bourdon

doi:10.1371/journal.pone.0129840

Abstract

Despite the significant progress made in recent years, the computation of the complete set of elementary flux modes of large or even genome-scale metabolic networks is still impossible. We introduce a novel approach to speed up the calculation of elementary flux modes by including transcriptional regulatory information into the analysis of metabolic networks. Taking into account gene regulation dramatically reduces the solution space and allows the presented algorithm to constantly eliminate biologically infeasible modes at an early stage of the computation procedure. Thereby, computational costs, such as runtime, memory usage, and disk space, are extremely reduced. Moreover, we show that the application of transcriptional rules identifies non-trivial system-wide effects on metabolism. Using the presented algorithm pushes the size of metabolic networks that can be studied by elementary flux modes to new and much higher limits without the loss of predictive quality. This makes unbiased, system-wide predictions in large scale metabolic networks possible without resorting to any optimization principle.

Highlights

Elementary flux modes (EFMs) are indivisible sets of reactions that represent biologically meaningful pathways [1, 2] under steady state condition
The rules consider four genes (ACH1, ICL1, MDH1 and MDH2) which are glucose repressed in S. cerevisiae[26]
Growing scientific effort is put into the investigation of transcriptional regulatory mechanisms

Summary

Introduction

Elementary flux modes (EFMs) are indivisible sets of reactions that represent biologically meaningful pathways [1, 2] under steady state condition. Removing only a single reaction of an EFM results in the extinction of the entire pathway. EFMs can be used to mathematically decompose metabolic networks into minimal functional building blocks and investigate them unbiasedly. For that reason EFMs have gained increasing attention in the field of metabolic engineering in recent years [3]. The computational costs for calculating EFMs increase sharply with the size of the analyzed network [4]. The calculation of all EFMs of small networks

Objectives

Methods

Results

Conclusion