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Overflow metabolism characterizes cells strains that are likely to produce inhibiting by-products resulting from an excess of substrate feeding and a saturated respiratory capacity. The critical substrate level separating the two different metabolic pathways is generally not well defined. Monitoring of this kind of cultures, going from model identification to state estimation, is first discussed. Then, a review of control techniques which all aim at maximizing the cell productivity of fed-batch fermentations is presented. Two main adaptive control strategies, one using an estimation of the critical substrate level as set-point and another regulating the by-product concentration, are proposed. Finally, experimental investigations of an adaptive RST control scheme using the observer polynomial for the regulation of the ethanol concentration in Saccharomyces cerevisiae fed-batch cultures ranging from laboratory to industrial scales, are also presented.

Polyhydroxyalkanoates (PHA) represent an environmentally friendly alternative to petroleum based plastics for a broad range of applications from packaging to biomedical devices. In the prospect of an industrial PHA production, it is highly valuable to accurately control the incorporation of different repeating units into the polymer, to produce a polyester with specific material characteristics. In this study, we develop macroscopic dynamic models predicting the polymer production and composition when mixtures containing up to four volatile fatty acids (VFA) are used as substrates. These models successfully reproduce the sequential (and preferential) substrate consumption and polymer production/reconsumption patterns, experimentally observed during biomass growth, thanks to simple kinetic structures based on Monod and inhibition factors. These models can serve as a basis for numerical simulation and process analysis, as well as process intensification through model-based optimization and control., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

In this study, two dynamic models of beer fermentation are proposed, and their parameters are estimated using experimental data collected during several batch experiments initiated with different sugar concentrations. Biomass, sugar, ethanol, and vicinal diketone concentrations are measured off-line with an analytical system while two on-line immersed probes deliver temperature, ethanol concentration, and carbon dioxide exhaust rate measurements. Before proceeding to the estimation of the unknown model parameters, a structural identifiability analysis is carried out to investigate the measurement configuration and the kinetic model structure. The model predictive capability is investigated in cross-validation, in view of opening up new perspectives for monitoring and control purposes. For instance, the dynamic model could be used as a predictor in receding-horizon observers and controllers.

Poly(hydroxybutyrate- co -hydroxyhexanoate) [P(HB- co -HHx)] and poly(hydroxybutyrate- co -hydroxyvalerate- co- hydroxyhexanoate) [P(HB- co -HV- co -HHx)] demonstrate interesting mechanical and thermal properties as well as excellent biocompatibility, making them suitable for multiple applications and notably biomedical purposes. The production of such polymers was described in Rhodospirillum rubrum, a purple nonsulfur bacteria in a nutrient-lacking environment where the HHx synthesis is triggered by the presence of hexanoate in the medium. However, the production of P(HB- co -HHx) under nutrient-balanced growth conditions in R. rubrum has not been described so far, and the assimilation of hexanoate is poorly documented. In this study, we used proteomic analysis and a mutant fitness assay to demonstrate that hexanoate assimilation involve β-oxidation and the ethylmalonyl-coenzyme A (CoA) (EMC) and methylbutanoyl-CoA (MBC) pathways, both being anaplerotic pathways already described in R. rubrum . Polyhydroxyalkanoate (PHA) production is likely to involve the de novo fatty acid synthesis pathway. Concerning the polymer composition, HB is the main component of the polymer, probably as acetyl-CoA and butyryl-CoA are intermediates of hexanoate assimilation pathways. When no essential nutrient is lacking in the medium, the synthesis of PHA seems to help maintain the redox balance of the cell. In this framework, we showed that the fixation of CO 2 is required to sustain the growth. An increase in the proportion of HHx in the polymer was observed when redox stress was engendered in the cell under bicarbonate-limiting growth conditions. The addition of isoleucine or valerate in the medium also increased the HHx content of the polymer and allowed the production of a terpolymer of P(HB- co -HV- co -HHx). IMPORTANCE The use of purple bacteria, which can assimilate volatile fatty acids, for biotechnological applications has increased, since they reduce the production costs of added-value compounds such as PHA. P(HB- co -HHx) and P(HB- co -HV- co -HHx) have demonstrated interesting properties, notably for biomedical applications. In a nutrient-lacking environment, R. rubrum is known to synthesize such polymers when hexanoate is used as the carbon source. However, their production in R. rubrum in non-nutrient-lacking growth conditions has not been described so far, and the assimilation of hexanoate is poorly documented. As the carbon source and its assimilation directly impact the polymer composition, we studied under non-nutrient-lacking growth conditions the assimilation pathway of hexanoate and PHA production in R. rubrum. Proteomic analysis and mutant fitness assays allowed us to explain PHA production and composition. An increase in HHx content of the polymer and production of P(HB- co -HV- co -HHx) was possible using the knowledge gained on metabolism under hexanoate growth conditions.

Rhodospirillum rubrum has a versatile metabolism, and as such can assimilate a broad range of carbon sources, including volatile fatty acids. These carbon sources are gaining increasing interest for biotechnological processes, since they reduce the production costs for numerous value-added compounds and contribute to the development of a more circular economy. Usually, studies characterizing carbon metabolism are performed by supplying a single carbon source; however, in both environmental and engineered conditions, cells would rather grow on mixtures of volatile fatty acids (VFAs) generated via anaerobic fermentation. In this study, we show that the use of a mixture of VFAs as carbon source appears to have a synergy effect on growth phenotype. In addition, while propionate and butyrate assimilation in Rs. rubrum is known to require an excess of bicarbonate in the culture medium, mixing them reduces the requirement for bicarbonate supplementation. The fixation of CO 2 is one of the main electron sinks in purple bacteria; therefore, this observation suggests an adaptation of both metabolic pathways used for the assimilation of these VFAs and redox homeostasis mechanism. Based on proteomic data, modification of the propionate assimilation pathway seems to occur with a switch from a methylmalonyl-CoA intermediate to the methylcitrate cycle. Moreover, it seems that the presence of a mixture of VFAs switches electron sinking from CO 2 fixation to H 2 and isoleucine production.

In this work, the application of a model-free extremum seeking strategy is investigated to achieve the hypothetical control of the covid-19 pandemics by acting on social distancing. The advantage of this procedure is that it does not rely on the accurate knowledge of an epidemiological model and takes realistic constraints into account, such as hospital capacities. The simulation study reveals that the convergence has two time scales, with a fast catch of the transient optimum of the measurable cost function, followed by a slow tracking of this optimum following the original SIR dynamics. Several issues are discussed such as quantization of the sanitary measures., (© 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd.)

In this study, a dynamic model of a Vero cell culture-based dengue vaccine production process is developed. The approach consists in describing the process dynamics as functions of the whole living (uninfected and infected) biomass whereas previous works are based on population balance approaches. Based on the assumption that infected biomass evolves faster than other variable, the model can be simplified using a slow-fast approximation. The structural identifiability of the model is analysed using differential algebra as implemented in the software DAISY. The model parameters are inferred from experimental datasets collected from an actual vaccine production process and the model predictive capability is confirmed both in direct and cross-validation. The model prediction shows the impact of the metabolism on virus yield and confirms observations reported in previous studies. Multi-modality and sensitivity analysis complement the parameter estimation, and allow to obtain confidence intervals on both parameters and state estimates. Finally, the model is used to compute the maximum infectious virus yield that can be obtained for different combinations of multiplicity of infection (MOI) and time of infection (TOI). © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2687, 2019., (© 2018 American Institute of Chemical Engineers.)

Hybridoma cells are commonly grown for the production of monoclonal antibodies (MAb). For monitoring and control purposes of the bioreactors, dynamic models of the cultures are required. However these models are difficult to infer from the usually limited amount of available experimental data and do not focus on target protein production optimization. This paper explores an experimental case study where hybridoma cells are grown in a sequential batch reactor. The simplest macroscopic reaction scheme translating the data is first derived using a maximum likelihood principal component analysis. Subsequently, nonlinear least-squares estimation is used to determine the kinetic laws. The resulting dynamic model reproduces quite satisfactorily the experimental data, as evidenced in direct and cross-validation tests. Furthermore, model predictions can also be used to predict optimal medium renewal time and composition.