Dynamic metagenome‐scale metabolic modeling of a yogurt bacterial community

Abstract

Genome-scale metabolic models and flux balance analysis (FBA) have been extensively used for modeling and designing bacterial fermentation. However, FBA-based metabolic models that accurately simulate the dynamics of coculture are still rare, especially for lactic acid bacteria used in yogurt fermentation. To investigate metabolic interactions in yogurt starter culture of Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus, this study built a dynamic metagenome-scale metabolic model which integrated constrained proteome allocation. The accuracy of the model was evaluated by comparing predicted bacterial growth, consumption of lactose and production of lactic acid with reference experimental data. The model was then used to predict the impact of different initial bacterial inoculation ratios on acidification. The dynamic simulation demonstrated the mutual dependence of S. thermophilus and L. d. bulgaricus during the yogurt fermentation process. As the first dynamic metabolic model of the yogurt bacterial community, it provided a foundation for the computer-aided process design and control of the production of fermented dairy products.

Proteome trade-off between primary and secondary metabolism shapes acid stress induced bacterial exopolysaccharide production

“…Most importantly, the RPCFBA model cannot yet account for the inhibition of undissociated lactic acid on cellular growth of LP, thus is incapable of simulating the decrease of growth rate in time when the carbon source is abundant. The experimental data of this study reflected that the empirical function used in Özcan et al, 202142 and Qiu et al, 202343 to model the inhibition of undissociated lactic acid is not universally applicable. The growth rates at constant pH 6.5 and pH 5.5 dropped fast in time (Figure2C), though the theoretical concentrations of undissociated lactic acid should be small based on Henderson-Hasselbalch equation.…”

“…The upper bound of the summation of the 4 flexible sector fractions ( ϕ c + ϕ A + ϕ T + ϕ U ) was assumed to be 50% of the total proteome (Eq. 5 and 6) 43,67,68 . Φ represented the mass fraction of the sector 1 for 1 = A, c, T, u. pTOT was the total mass of the proteome normalized to 1 gDW of biomass and set as 0.299 for LP.…”

“…The upper bound of the summation of the 4 flexible sector fractions (߶ ߶ ߶ ் ߶ ) was assumed to be 50% of the total proteome (Eq. 5 and 6) 43,67,68 . ߶ represented the mass fraction of the sector ݆ for ݆ ൌ ‫,ܣ‬ ‫,ܥ‬ ܶ, ܷ.…”

Flux balance analysis-based metabolic modeling of microbial secondary metabolism: Current status and outlook

“…[ E i ], kcat i are the concentration and turnover rate of the enzyme i , respectively, and [ P TOT ] is the concentration of total cellular proteins. The proteome allocation of secondary metabolism ( Eq 5 ) can materialize in different forms in actual implementation, e.g., modeling the consequence of differing proteomic costs caused for different stress factors, such as temperature, inhibitors, or nutrient limitation [ 102 , 110 , 111 ].…”

Global sensitivity analysis for investigation of bacterial physiology in complex media with FBA-PRCC

“…But that type of model identification impose some limitations to the interpretation of the model results in nutrient-rich set up. The rich environment is very common as in biotechnological application, for example in the food industry (Mendoza Farías 2023;Qiu et al 2023) and when bacteria participate in the community and interact with its members by production and consumption of nutrients (Kost et al 2023). The interaction between pathways utilizing different substrates make solution space more complex, and increase number of non-unique solutions to the optimization FBA problem.…”