Unstructured kinetic models to simulate an arabinose switch that decouples cell growth from metabolite production

Abstract

Modeling synthetic gene circuits to implement dynamic flux balancing is crucial in teaching and exploring metabolic engineering strategies to repartition metabolic precursors and construct efficient microbial cell factories. Microbial fitness and production rates are often complex phenotypes that are governed by highly non-linear, multivariable functions which are intrinsically linked through carbon metabolism. The solution of such dynamic system can be difficult for synthetic biologists to visualize or conceptualize. Recently, researchers (Santala et al., Metab. Eng. Comm., 2018) have implemented an arabinose based genetic switch to dynamically partition the central carbon flux between cell growth and product formation. The autonomous switch allowed dynamic shift from arabinose-associated cell growth to acetate-associated product (wax ester) formation. This system clearly demonstrates the effectiveness of using a genetic switch to decouple cell growth from product formation in a one-pot bioreactor to minimize operational cost. Coupled with Michaelis-Menten kinetics, and Luedeking-Piret equations, we were able to reconstruct and analyze this metabolic switch in silica and achieved graphical solutions that qualitatively match with the experimental data. By assessing physiologically-accessible parameter space, we observed a wide range of dynamic behavior and examined the different limiting cases. Graphical solutions for this dynamic system can be viewed simultaneously and resolved in real time via buttons on the graphical user interface (GUI). Metabolic bottlenecks in the system can be accurately predicted by varying the respective rate constants. The GUI serves as a diagnosis toolkit to troubleshoot genetic circuits design constraints and as an interactive workflow of using this arabinose based genetic switch to dynamically control carbon flux, which may provide a valuable computational toolbox for metabolic engineers and synthetic biologists to simulate and understand complex genetic-metabolic system.

Engineering and finetuning expression of SerC for balanced metabolic flux in vitamin B6 production

“…These fitness costs may have affected the overall performance and productivity of the system, resulting in different product concentrations [ 47 , 48 ]. Microbial fitness and production rates are complex that are determined by multivariable factors, such as carbon metabolism and enzyme catalyzing efficiency [ [49] , [50] , [51] ]. The new fitness state could result from an alleviation of production load, or even site mutations in the enzyme from the match between the specialty and selection.…”

Dynamics of microbial competition, commensalism, and cooperation and its implications for coculture and microbiome engineering

“…Some representative eigenvalues for specific parameter conditions are compiled to the Supporting Information table. Alternatively, stability analysis could also be numerically analyzed (Edwards & Xu, 2020; Xu, 2020). Here is the generalized Jacobian Matrix that corresponds to systems ODE equations (3)–(5).…”

“…For the generalized systems ODE equations listed in Equations (3)-(5) ( Table 1) (Edwards & Xu, 2020;Xu, 2020). Here is the generalized Jacobian Matrix that corresponds to systems…”

Editorial for special issue on green biomanufacturing

“…The depletion of the inducer arabinose enabled the automatic switch from cell growth to product formation. A corresponding GUI platform was also constructed to analyze the dynamic properties of different parameters [ 2 ].…”