Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis

Abstract

Thermodynamic metabolic flux analysis (TMFA) can narrow down the space of steady-state flux distributions, but requires knowledge of the standard Gibbs free energy for the modelled reactions. The latter are often not available due to unknown Gibbs free energy change of formation $$, {\Delta }_{f} G^{0}$$ , Δ f G 0 , of metabolites. To optimize the usage of data on thermodynamics in constraining a model, reaction lumping has been proposed to eliminate metabolites with unknown $${\Delta }_{f} G^{0}$$ Δ f G 0 . However, the lumping procedure has not been formalized nor implemented for systematic identification of lumped reactions. Here, we propose, implement, and test a combined procedure for reaction lumping, applicable to genome-scale metabolic models. It is based on identification of groups of metabolites with unknown $${\Delta }_{f} G^{0}$$ Δ f G 0 whose elimination can be conducted independently of the others via: (1) group implementation, aiming to eliminate an entire such group, and, if this is infeasible, (2) a sequential implementation to ensure that a maximal number of metabolites with unknown $${\Delta }_{f} G^{0}$$ Δ f G 0 are eliminated. Our comparative analysis with genome-scale metabolic models of Escherichia coli, Bacillus subtilis, and Homo sapiens shows that the combined procedure provides an efficient means for systematic identification of lumped reactions. We also demonstrate that TMFA applied to models with reactions lumped according to the proposed procedure lead to more precise predictions in comparison to the original models. The provided implementation thus ensures the reproducibility of the findings and their application with standard TMFA.

Functional universality in slow-growing microbial communities arises from thermodynamic constraints

“…However, in many natural and industrial environments, microbial communities grow under energy-limited conditions and are forced to use low free energy reactions to drive their growth. [3][4][5][6][7][8][9][10][11][12][13] . Here, by explicitly modeling the thermodynamic inhibition of microbial catabolism and energy-limitation of growth, we have developed a minimal model of microbial communities in low-energy environments.…”

“…The conversion of the substrate to the product could proceed over multiple steps; this will not make a difference to our results [2]. One can also think of this reaction as a coarse-grained or lumped description of the full reaction [3,4,5].…”

“…Over half of earth's prokaryotic biomass resides in ocean sediments and deep soil 1,2 . In many of these natural environments and anaerobic bioreactors, communities grow under energy-limited conditions due to the absence of strong electron acceptors, like oxygen or nitrate, and strong electron donors, like glucose [3][4][5][6][7][8] . Hence species in these communities are forced to harvest energy required for biomass growth from low free energy reactions [8][9][10][11][12][13] .…”

“…Hence species in these communities are forced to harvest energy required for biomass growth from low free energy reactions [8][9][10][11][12][13] . Growth in these environments can be very slow-division times are measured in weeks and years-instead of minutes and hours used for growth in the lab 4,[13][14][15][16] . These long timescales and difficulty in culturing these species in a lab environment make experimental investigation of these communities challenging.…”