(430g) Thermodynamically Constrained Flux Analysis of Actinobacillus Succinogenes

The efficient production of platform chemicals from biofuel byproducts is important for the sustainability of biorefineries as well as for the successful deviation from a fossil fuel-based economy.  Biocatalysis is an attractive solution for such conversions to platform chemicals. We have extensively studied the bioconversion of crude glycerol from biodiesel production into succinic acid with the use of Actinobacillus succinogenes [1,2].

Metabolic analysis of organisms such as A. succinogenes [3] and Escherichia coli[4] provides target information for manipulating these systems at the genomic and environmental level in order to enhance yields, giving higher production rates, lower byproduct formation and lower costs when they are used for industrial fermentation.

Different stoichiometry-based methodologies have been proposed for the metabolic analysis of a microorganism [5,6], commonly Flux Balance Analysis (FBA) is used for the prediction of theoretical maximum fluxes through the reactions under certain environmental conditions. However, the predicted flux distribution will normally contain a number of thermodynamically infeasible fluxes. A new methodology called Thermodynamic Flux Analysis of Metabolic Systems (TFAMS) for the optimization of fluxes and metabolites’ concentrations subject to mass balance and thermodynamic constraints is implemented, which allows the estimation of thermodynamically feasible ranges of fluxes narrowing the solution space greatly compared with classical FBA, with the remaining uncertainty being due to the unknown mechanism of regulation, diffusion effects, experimental error, etc.

As it is known, a reaction is thermodynamically feasible in a certain direction when the Gibbs free energy change (Δ_rG) is negative. The value of Δ_rG is affected by environmental conditions such as temperature (T), ionic strength (I), pH, and the reactant’s concentrations [7,8]. Here we have used experimental fluxes [1] and applied TFAMS to evaluate the impact of T, I, pH, and even the volume exclusion effects on the thermodynamic constraints and hence their effect on the prediction of flux distribution for succinic acid production from glycerol using A. succinogenes.

A flux through a reaction may be zero if it is either 1) blocked, when the enzyme that catalyzes it is absent and therefore the value of the Gibbs free energy of the reaction (Δ_rG) is not taken in account, or 2) at equilibrium, when Δ_rGis equal to zero. Unlike the existing Thermodynamic-based Metabolic Flux Analysis (TMFA) [9], TFAMS considers both cases: blocked and thermodynamic equilibrium for reactions with zero flux. Hence our new method can compute tighter and therefore more accurate flux ranges than previous methods, also it can be easily applied to large systems. 

Our more accurate flux calculations are used to clarify the results of metabolic control analysis in previous studies [3] which relied on the classical FBA. This gives a better picture of how to enhance the organisms performance through specific genetic enhancements.

References

1. Vlysidis, A., Binns, M., Webb, C., and Theodoropoulos, C. 2011. Glycerol utilisation for the production of chemicals: conversion to succinic acid, a combined experimental and computational study. Biochemical Engineering Journal, 58-59: 1-11.
2. Vlysidis A., Binns M., Webb C., Theodoropoulos C. 2011. A techno-economic analysis of biodiesel biorefineries: Assessment of integrated designs for the co-production of fuels and chemicals. Energy, 36: 4671-4683.
3. Binns, M., Vlysidis, A., Webb, C., Theodoropoulos, C., de Atauri, P., and Cascante, M. 2011. Glycerol metabolic conversion to succinic acid using Actinobacillus succinogenes: a metabolic network-based analysis. Computer Aided Chemical Engineering 29:1421-1425
4. Angeles-Martinez, L., Binns, M., Theodoropoulos, C., de Atauri, P., and Cascante, M. 2012. Thermodynamically constrained Flux and Control analysis of Escherichia. Coli. Manuscript in preparation.
5. Llaneras, F., and Pico, J. 2007. A procedure for the estimation over time of metabolic fluxes in scenarios where measurements are uncertain and/or insufficient. BMC Bioinformatics 8:421
6. Klamt, S., and Stelling, J. 2003. Two approaches for metabolic pathway analysis? Trends in Biotechnology 21:64-69.
7. Alberty, R.A. 2003. Thermodynamics of Biochemical Reactions. Wiley-Interscience. The USA. pp 1-17, 35-88.
8. Stephanopoulos, G.N., Aristidou, A.A., and Nielsen, J. 1998. Metabolic Engineering. Principles and Methodologies. Academic Press. The USA. pp 629-694.
9. Henry, C.S., Broadbelt, L.J., and Hatzimanikatis, V. 2007. Thermodynamics-Based Metabolic Flux Analysis. Biophysical Journal 92:1792-1805.