Flux balance analysis in the production of clavulanic acid byStreptomyces clavuligerus

Abstract

In this work, in silico flux balance analysis is used for predicting the metabolic behavior of Streptomyces clavuligerus during clavulanic acid production. To choose the best objective function for use in the analysis, three different optimization problems are evaluated inside the flux balance analysis formulation: (i) maximization of the specific growth rate, (ii) maximization of the ATP yield, and (iii) maximization of clavulanic acid production. Maximization of ATP yield showed the best predictions for the cellular behavior. Therefore, flux balance analysis using ATP as objective function was used for analyzing different scenarios of nutrient limitations toward establishing the effect of limiting the carbon, nitrogen, phosphorous, and oxygen sources on the growth and clavulanic acid production rates. Obtained results showed that ammonia and phosphate limitations are the ones most strongly affecting clavulanic acid biosynthesis. Furthermore, it was possible to identify the ornithine flux from the urea cycle and the α-ketoglutarate flux from the TCA cycle as the most determinant internal fluxes for promoting clavulanic acid production.

ATP regulation in bioproduction

“…Intracellular ATP levels are normally regulated and maintained at a constant level by a robust cellular system. Indeed, in silico flux balance analysis of Streptomyces clavuligerus as a model organism indicates that the maximization of ATP yield is the best predictor of cellular behavior [ 76 ].…”

The Evolution of Cell Free Biomanufacturing

“…FBA and metabolic flux analysis (MFA) [113], as well as convex network decomposition approaches such as elementary modes [114] and extreme pathways [115], model intracellular metabolism using the biochemical stoichiometry and other constraints such as thermodynamic feasibility [116,117] under pseudo steady state conditions. Constraint based approaches use linear programming [118] to predict productivity [111,119], yield [111], mutant behavior [120], and growth phenotypes [121] for biochemical networks of varying complexity, including genome scale networks. Constraint based models have also been used to identify strategies for the overproduction of desired compounds.…”

An enhanced genome-scale metabolic reconstruction of Streptomyces clavuligerus identifies novel strain improvement strategies

“…FBA studies have been previously used as a valuable tool for process design in antibiotic production, using maximization of cell growth as obfuncs [6,7,5]. Sánchez et al, [37] used CA production and ATP yield as obfuncs, as alternative functions to cell growth maximization [37]. In these simulations, using the experimental dataset from Bushell et al [19] as constraints, the maximization of ATP yield showed to be the best predictor of cellular behaviour [19].…”