Elementary mode analysis: a useful metabolic pathway analysis tool for characterizing cellular metabolism

Abstract

Elementary Mode Analysis is a useful Metabolic Pathway Analysis tool to identify the structure of a metabolic network that links the cellular phenotype to the corresponding genotype. The analysis can decompose the intricate metabolic network comprised of highly interconnected reactions into uniquely organized pathways. These pathways consisting of a minimal set of enzymes that can support steady state operation of cellular metabolism represent independent cellular physiological states. Such pathway definition provides a rigorous basis to systematically characterize cellular phenotypes, metabolic network regulation, robustness, and fragility that facilitate understanding of cell physiology and implementation of metabolic engineering strategies. This mini-review aims to overview the development and application of elementary mode analysis as a metabolic pathway analysis tool in studying cell physiology and as a basis of metabolic engineering.

Design of optimally constructed metabolic networks of minimal functionality

“…Currently only medium scale metabolic models can be calculated, as the number of EFMs explodes [16]. However, significant progress on the efficient calculation of EFMs has been made in recent years [17], which allows for analysis of realistic models, which have (partly) been tested experimentally [12,[19][20][21].…”

“…When the EFMs are known and classified into desirable and undesirable modes, not only one but all possible MCSs leading to a desired state can be calculated, which usually offers different options for biological implementation. Successful examples include [12,[19][20][21][22].…”

Composition of mixed culture PHA biopolymers and implications for downstream processing

“…for undetermined systems), Flux Balance Analysis (FBA) is also a tool that can be used to determine the rate vector ν of a cellular physiological state. In FBA, through the use of linear programing and the setting of an objective function ܼ, it is possible to calculate the intracellular flux distributions which meet the system constraints, such as substrate uptake rates, thermodynamic constraints, and metabolic regulation (Stephanopoulos 1998, Trinh et al 2009). FBA only estimates the optimal solution, while other solutions still exist.…”

“…It can identify all metabolic flux vectors that exist in a network without requiring any knowledge of any flux rates or defining any objective function (Trinh et al 2009). It is also a tool to correlate the cellular genotypes with phenotypes by the analysis of flux ratios.…”

“…It can only predict isolated states of cellular metabolism, and the introduction of dynamic expressions into the modelling offers the potential to understand the dynamics of cellular metabolism (Trinh et al 2009, Franz et al 2011. …”