Tryptophan Production Maximization in a Fed-Batch Bioreactor with Modified E. coli Cells, by Optimizing Its Operating Policy Based on an Extended Structured Cell Kinetic Model

A novel kinetic model for a cocoa waste fermentation to ethanol reaction and its experimental validation

A non-segregated kinetic model is proposed to describe a fermentation process of agro-industrial residues derived via cocoa (mucilage juice) by Pichia kudriavzevii. The novel proposed hybrid model is based on a multiple coupling reaction mechanisms (structured) to describe the kinetics of substrate consumption, biomass, carbon dioxide, and ethanol, coupled to an unstructured model for the activity enzyme. The parameters of the kinetic model are estimated by non-linear least-squares curve fitting using the Marquardt-Levenberg algorithm. In addition, numerical simulations were compared with the experimental data via residual graphs. The effectiveness of the model was statistically evaluated using dimensionless efficiency coefficients under different initial conditions. A global sensitivity analysis was applied (Fisher's information matrix). The experimental results of the batch reactor showed a maximum ethanol concentration of 29 g/L, with a yield of 0.48 g-ethanol/g-glucose and a productivity of 0.30 g/L h. The method determined that the cell formation coefficient and the specific substrate consumption rate (θ1 and θ2) directly influence most of the states of our system. The proposed scheme is particularly suitable to assist in the rational design of cell factory properties or fermentation processes because it can represent the complex biochemistry in more detail and under different initial experimental conditions; the above reveals that the generated model is robust and can be considered for control and optimization purposes.

A Molecular Dynamic Model of Tryptophan Overproduction in Escherichia coli

Several deterministic models simulate the main molecular biology interactions among the numerous mechanisms controlling the dynamics of the tryptophan operon in native strains. However, no models exist to investigate bacterial tryptophan production from a biotechnological point of view. Here, we modified tryptophan models for native production to propose a biotechnological working model that incorporates the activity of tryptophan secretion systems and genetic modifications made in two reported E. coli strains. The resultant deterministic model could emulate the production of tryptophan in the same order of magnitude as those quantified experimentally by the genetically engineered E. coli strains GPT1001 and GPT1002 in shake flasks. We hope this work may contribute to the rational development of biological models that define and include the main parameters and molecular components for designing and engineering efficient biotechnological chassis to produce valuable chemicals.