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Kuate, C. A. F.

Publications and source records attributed to Kuate, C. A. F..

2 recordsLinked to original sources

Growth Mechanics and the emergence of metabolic oscillations in growing cells

Unicellular organisms exhibit oscillations in metabolic activity and gene expression, even when growing in static media with no external dynamic stimuli. The cause and possible function of these endogenous oscillations are still not fully understood, with different hypotheses including its origin as a byproduct of misguided regulatory processes. To investigate whether such oscillations could be functional rather than incidental, we introduce Growth Mechanics (GM) as a general mathematical framework to study the dynamic resource allocation in models of whole self-replicating cells, built exclusively on the first principles of fitness maximization, mass conservation, nonlinear reaction kinetics, and constant cell density. Inspired by the physical theory of classical mechanics, we first find the simplest mathematical description the problem in terms of generalized coordinates, and then solve the optimal dynamical resource allocation for cells growing in any given medium using the Euler-Lagrange equation, resulting in analytical "equations of optimal motion" (EOM) that apply for growing cells in general. We then solve these equations numerically for simple growing cell models and show that, in general, oscillations are necessary to maximize cell fitness by getting more saturated reactions at the time they are most needed. We also show how the EOM predict the emergence of quasi-linear dependencies of the metabolic oscillation frequency and the ribosome protein allocation with the cell growth rate at different growth media, in line with the "growth laws" observed experimentally in microbes. This work contributes to the foundations of theoretical cell biology with general quantitative principles and a bridge to theoretical tools of physics.

biophysics↗

Interrogating the effect of enzyme kinetics on metabolism using differentiable constraint-based models

1Metabolic models are typically characterized by a large number of parameters. Traditionally, metabolic control analysis is applied to differential equation-based models to investigate the sensitivity of predictions to parameters. A corresponding theory for constraint-based models is lacking, due to their formulation as optimization problems. Here, we show that optimal solutions of optimization problems can be efficiently differentiated using constrained optimization duality and implicit differentiation. We use this to calculate the sensitivities of predicted reaction fluxes and enzyme concentrations to turnover numbers in an enzyme-constrained metabolic model of Escherichia coli. The sensitivities quantitatively identify rate limiting enzymes and are mathematically precise, unlike current finite difference based approaches used for sensitivity analysis. Further, efficient differentiation of constraint-based models unlocks the ability to use gradient information for parameter estimation. We demonstrate this by improving, genome-wide, the state-of-the-art turnover number estimates for E. coli. Finally, we show that this technique can be generalized to arbitrarily complex models. By differentiating the optimal solution of a model incorporating both thermodynamic and kinetic rate equations, the effect of metabolite concentrations on biomass growth can be elucidated. We benchmark these metabolite sensitivities against a large experimental gene knockdown study, and find good alignment between the predicted sensitivities and in vivo metabolome changes. In sum, we demonstrate several applications of differentiating optimal solutions of constraint-based metabolic models, and show how it connects to classic metabolic control analysis.

bioengineering↗