Authors

R W Achjian²; K Olavarría³; L Marchese²; P Michels¹; A M Silber²;  ¹ University of Edinburgh, UK;  ² Universidade de São Paulo, Brazil;  ³ Delft University of Technology, Netherlands

Discussion

The Proline-Glutamate pathway plays a significant role in the biology of T. cruzi, with the steady-state concentration of the former amino acid varying widely in different stages of the parasite. In fact, proline has been implicated in several functions, such as sustaining energetically the parasite, by transferring electrons from NAD⁺ and FAD to the Electron Transport Chain. In order to better understand the behavior of this pathway, and the fine control exerted on its enzyme activities under varying conditions, such as NAD⁺/NADH pool ratios, pH and others, we are developing an in silico kinetic metabolic model. It is comprised of three compartments: extracellular medium, cytosol and mitochondrion matrix. Aside from proline and glutamate, we include all intermediaries and cofactors in the model. The goal of the work is to obtain a model that can simulate and make predictions about fluxes and metabolite concentrations at steady state, given a set of initial conditions. Such a model requires kinetic data about both, the forward and reverse reactions for each enzyme and transporter represented. However, obtaining a detailed Bi-Bi description is experimentally difficult and time-consuming. Moreover, many enzymes and transporters in this pathway have not been characterized yet, and the literature on those that were presents only michaelian constants, which are insufficient for our goals. As a trade-off between precision and practicality, we propose the use of a generic equation, that is able to model bidirectional reactions with fewer parameters and acceptable error. Our simulations provide insights into essential characteristics of the pathway, such as the necessity of substrate channelling in the oxidative section, and the relative importance of each component to maintain physiological levels of metabolites. The next steps will involve obtaining experimental data on fluxes and concentrations, use machine learning techniques to infer unknown variables, refining and validating the model.