Detecting and investigating substrate cycles in a genome‐scale human metabolic network

Abstract

Substrate cycles, also known as futile cycles, are cyclic metabolic routes that dissipate energy by hydrolysing cofactors such as ATP. They were first described to occur in the muscles of bumblebees and brown adipose tissue in the 1970s. A popular example is the conversion of fructose 6‐phosphate to fructose 1,6‐bisphosphate and back. In the present study, we analyze a large number of substrate cycles in human metabolism that consume ATP and discuss their statistics. For this purpose, we use two recently published methods (i.e. EFMEvolver and the K‐shortest EFM method) to calculate samples of 100 000 and 15 000 substrate cycles, respectively. We find an unexpectedly high number of substrate cycles in human metabolism, with up to 100 reactions per cycle, utilizing reactions from up to six different compartments. An analysis of tissue‐specific models of liver and brain metabolism shows that there is selective pressure that acts against the uncontrolled dissipation of energy by avoiding the coexpression of enzymes belonging to the same substrate cycle. This selective force is particularly strong against futile cycles that have a high flux as a result of thermodynamic principles.

Discovery of substrate cycles in large scale metabolic networks using hierarchical modularity

“…For module # 141,811 (boxed in Figure 3), which is the largest module analyzed for EFMs in this study, the distribution of cyclical EFM lengths is bi-modal (Additional file 1: Figure S1). Multi-model distribution of cyclical EFM lengths was also reported previously [8]. …”

“…The trade-off here is that we compromise on the possibility of finding longer cyclical EFMs, as shown by the relatively short cyclical EFM lengths (Figure 3). Even for the module with the largest number of cyclical EFMs, the lengths range between 2 and 13 reactions (Additional file 1: Figure S1), compared to lengths of up to 100 reactions that can be identified from a global analysis [8]. The benefit of a modular approach is that one can be exhaustive in searching for all cyclical EFMs in a given module at a particular hierarchical resolution, which can then facilitate the association of a cyclical EFM with a recognizable metabolic function.…”

“…Recently, Gebauer and coworkers showed that cyclical elementary flux modes (EFMs) are potential substrate cycles [8]. Using EFMEvolver [9], the authors found more than 200,000 cyclical EFMs, with a median length (defined as the number of reactions in an EFM) of 35.…”

Biochemical Characterization of Human Gluconokinase and the Proposed Metabolic Impact of Gluconic Acid as Determined by Constraint Based Metabolic Network Analysis

“…Although human genome scale cell and tissue specific metabolic models have been generated for a variety of context specific applications [28]–[32], this model was chosen to model metabolism for several reasons. Firstly the model is small with few components, uncluttered with minimum possibilities of false positives introduced by metabolic gaps, dead ends and flux loops [30], [33], [34] and the model has defined metabolite exchange constraints [15]. Secondly the Na + /K + pump is a realistic objective function, the maintenance of the Na + concentration gradient has been shown to account for up to 70% of cell ATP expenditure [35].…”

Computing the various pathways of penicillin synthesis and their molar yields

“…An important concept in metabolic pathway analysis is that of elementary flux modes (EFMs) (Schuster et al, 1999(Schuster et al, , 2000; for reviews, see Trinh et al, 2009;Zanghellini et al, 2013). EFM analysis has been used successfully in analyzing multitudinous biochemical networks in a variety of species (Gebauer et al, 2012;Kr€ omer et al, 2006;Sch€ auble et al, 2011;Schwender et al, 2004;Trinh et al, 2009;Wittmann and Becker, 2007). EFM analysis has been used successfully in analyzing multitudinous biochemical networks in a variety of species (Gebauer et al, 2012;Kr€ omer et al, 2006;Sch€ auble et al, 2011;Schwender et al, 2004;Trinh et al, 2009;Wittmann and Becker, 2007).…”