Computational assessment of lipid production in Rhodosporidium toruloides in two-stage and one-stage batch bioprocesses.

Oleaginous yeasts are promising platforms for microbial lipids production as a renewable and sustainable alternative to vegetable oils in biodiesel production. In this paper, a thorough in silico assessment of lipid production in batch cultivation by Rhodosporidium toruloides was developed. By means of dynamic flux balance analysis, the traditional two-stage bioprocess (TSB) performed by the native strain was contrasted with one-stage bioprocess (OSB) using four designed strains obtained by gene knockout strategies. Lipid titer, yield, content, and productivity were analyzed at different initial C/N ratios as relevant performance indicators used in bioprocesses. By weighting these indicators, a global lipid efficiency metric (GLEM) was defined to consider different scenarios. Under simulated conditions, designed strains for lipid overproduction in OSB outperformed the TSB in terms of lipid title (up to threefold), lipid yield (up to 2.4-fold), lipid content (up to 2.8-fold, with a maximum of 76%), and productivity (up to 1.3-fold), depending on C/N ratios. Using these efficiency parameters and the proposed GLEM, the process of selecting the most suitable candidates for lipid production could be carried out before experimental assays. This methodology holds the potential to be extended to other oleaginous microorganisms and diverse strain design techniques.

Modeling and estimation of production rate for the production phase of non-growth-associated high cell density processes.

This paper addresses the estimation of the specific production rate of intracellular products and the modeling of the bioreactor volume dynamics in high cell density fed-batch reactors. In particular, a new model for the bioreactor volume is proposed, suitable to be used in high cell density cultures where large amounts of intracellular products are stored. Based on the proposed volume model, two forms of a high-order sliding mode observer are proposed. Each form corresponds to the cases with residual biomass concentration or volume measurement, respectively. The observers achieve finite time convergence and robustness to process uncertainties as the kinetic model is not required. Stability proofs for the proposed observer are given. The observer algorithm is assessed numerically and experimentally.

Specific growth rate observer for the growing phase of a Polyhydroxybutyrate production process.

This paper focuses on the specific growth rate estimation problem in a Polyhydroxybutyrate bioplastic production process by industrial fermentation. The kinetics of the process are unknown and there are uncertainties in the model parameters and inputs. During the first hours of the growth phase of the process, biomass concentration can be measured online by an optical density sensor, but as cell density increases this method becomes ineffective and biomass measurement is lost. An asymptotic observer is developed to estimate the growth rate for the case without biomass measurement based on corrections made by a pH control loop. Furthermore, an exponential observer based on the biomass measurement is developed to estimate the growth rate during the first hours, which gives the initial condition to the asymptotic observer. Error bounds and robustness to uncertainties in the models and in the inputs are found. The estimation is independent of the kinetic models of the microorganism. The characteristic features of the observer are illustrated by numerical simulations and validated by experimental results.