Anne-Lise Hantson

Strategies for the production of high-content fructo-oligosaccharides through the removal of small saccharides by co-culture or successive fermentation with yeast

Fructo-oligosaccharides (FOS) obtained by fermentation of sucrose may be purified at large-scale by continuous chromatography (Simulated Moving Bed: SMB). In order to improve the efficiency of the subsequent SMB purification, the optimization of the fermentative broth composition in salts and sugars was investigated. Fermentations conducted at reduced amount of salts, using Aureobasidium pullulans whole cells, yielded 0.63 ± 0.03 g of FOS per gram of initial sucrose. Additionally, a microbial treatment was proposed to reduce the amount of small saccharides in the mixture. Two approaches were evaluated, namely a co-culture of A. pullulans with Saccharomyces cerevisiae; and a two-step fermentation in which FOS were first synthesized by A. pullulans and then the small saccharides were metabolized by S. cerevisiae. Assays were performed in 100mL shaken flasks and further scaled-up to a 3 L working volume bioreactor. Fermentations in two-step were found to be more efficient than the co-culture ones. FOS were obtained with a purity of 81.6 ± 0.8% (w/w), on a dry weight basis, after the second-step fermentation with S. cerevisiae. The sucrose amount was reduced from 13.5 to 5.4% in total sugars, which suggests that FOS from this culture broth will be more efficiently separated by SMB.

Design and test of a low-cost RGB sensor for online measurement of microalgae concentration within a photo-bioreactor.

In this study, a low-cost RGB sensor is developed to measure online the microalgae concentration within a photo-bioreactor. Two commercially available devices, i.e., a spectrophotometer for offline measurements and an immersed probe for online measurements, are used for calibration and comparison purposes. Furthermore, the potential of such a sensor for estimating other variables is illustrated with the design of an extended Luenberger observer.

Highly efficient, long life, reusable and robust photosynthetic hybrid core-shell beads for the sustainable production of high value compounds.

An efficient one-step process to synthesize highly porous (Ca-alginate-SiO2-polycation) shell: (Na-alginate-SiO2) core hybrid beads for cell encapsulation, yielding a reusable long-life photosynthetically active material for a sustainable manufacture of high-value metabolites is presented. Bead formation is based on crosslinking of an alginate biopolymer and mineralisation of silicic acid in combination with a coacervation process between a polycation and the silica sol, forming a semi-permeable external membrane. The excellent mechanical strength and durability of the monodispersed beads and the control of their porosity and textural properties is achieved by tailoring the silica and alginate loading, polycation concentration and incubation time during coacervation. This process has led to the formation of a remarkably robust hybrid material that confers exceptional protection to live cells against sheer stresses and contamination in a diverse range of applications. Dunaliella tertiolecta encapsulated within this hybrid core-shell system display high photosynthetic activity over a long duration (>1 year). This sustainable biotechnology could find use in high value chemical harvests and biofuel cells to photosynthetic solar cells (energy transformation, electricity production, water splitting technologies). Furthermore the material can be engineered into various forms from spheres to variable thickness films, broadening its potential applications.

Green and sustainable production of high value compounds via a microalgae encapsulation technology that relies on CO2 as a principle reactant

A very promising and facile one-pot synthesis pathway is presented for the microencapsulation of live cells in a very porous core–shell system based upon a robust matrix. (Alginate–SiO2–polycation) shell@(alginate–SiO2) core hybrid beads, on the millimeter scale, containing live cells are obtained through cross-linking chemistry and the polycondensation of silicic acid in conjunction with the use of a polycation to negate the surface charge on silica. Very interestingly it is revealed that the polycation used (PDADMAC) plays a very important role in the formation of highly robust core–shell beads. PDADMAC acts as a catalyst in the polycondensation of silicic acid, leading to the formation of a resistant double layer shell comprising of an interior layer of alginate–SiO2 with a very homogeneous distribution of porous SiO2 and an external layer of porous PDADMAC that confines SiO2 within the bead. The photosynthetic chlorophyta Dunaliella tertiolecta, which produces high value metabolites (such as anti-oxidants, pharmacologically active compounds, neutraceuticals etc.) via photosynthesis, has been encapsulated within this core–shell system. Oximetry and fluorescence measurements highlight how this algal culture can remain photosynthetically active over an extraordinarily long period of 13 months for high value compound production, whilst entrapped within a highly porous, mechanically and chemically stable, optically transparent matrix, with no observable leaching of the cells from the core of the beads. HPLC has been employed to highlight the presence of excreted metabolites, based on neutral sugar building blocks such as arabinose, galactose and xylose, in the surrounding media. These results reveal how this kind of high performance, low-cost, and easily scaleable core–shell living material could be employed in large scale photobioreactors (PBRs), to potentially facilitate metabolite harvesting whilst protecting the culture from external contamination and for green energy production and environmental (CO2) remediation.

Parameter identification of Droop model: an experimental case study

Mathematical modeling and the development of predictive dynamic models are of paramount importance for the optimization, state estimation, and control of bioprocesses. This study is dedicated to the identification of a simple model of microalgae growth under substrate limitation, i.e., Droop model, and describes the design and instrumentation of a lab-scale flat-plate photobioreactor, the associated on-line and off-line instrumentation, the collection of experimental data, and the parameter identification procedure. In particular, a dedicated methodology for parameter identification is discussed, including the determination of an initial parameter set using an analytical procedure, the selection of a cost function, the evaluation of confidence intervals as well as direct and cross-validation tests.

Parameter identification of a dynamic model of CHO cell cultures: an experimental case study

In this paper, we address the problem of parameter identification in dynamic models of animal cultures, and we propose a step-by-step procedure, which gradually considers more detailed models. This procedure allows subsets of parameters to be estimated at each step, which can be used in the initialization of the next identification step. Finally, the full parameter set can be re-estimated starting from the results of the last step. The efficiency of the procedure is illustrated with a simulation case study and with the identification of a dynamic model from experimental data collected in CHO cell culture.

Hybridoma cell culture optimization using nonlinear model predictive control

Abstract This work addresses the application of control systems to the optimization of a monoclonal antibodies (MAb) production chain. The attention is focused on the maximization of hybridoma fed-batch culture productivity. The proposed model presents kinetics showing strong nonlinearities through min-max functions expressing overflow metabolism. A nonlinear model predictive control (NMPC) algorithm, choosing the best trajectory over a moving finite horizon among different sequences of inputs, is suggested in order to optimize productivity. Sensitivities of selected objective functions are considered in a minimax robust version of the NMPC in order to choose the best configuration with respect to practical operating conditions.

Nonlinear model predictive control of fed-batch cultures of micro-organisms exhibiting overflow metabolism

Overflow metabolism characterizes cells strains that are likely to produce inhibiting metabolites 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. This occurs for instance in Escherichia coli cultures with aerobic acetate formation. This paper considers the control problem of a lab-scale E. coli biomass production. A preliminary study is presented to access the application of a multivariable nonlinear model predictive control approach to maximize the biomass production. This strategy is tested by simulation and its performance to control the bioreactor system is evaluated with various objective cost functions, and in the presence of noise and dead-time on the acetate concentration measurement.

Macroscopic Dynamic Modeling of Sequential Batch Cultures of Hybridoma Cells: An Experimental Validation

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.

Parameter identification of the fermentative production of Fructo-oligosaccharides by Aureobasidium pullulans

In this study, a mathematical model for the production of Fructo-oligosaccharides (FOS) by Aureobasidium pullulans is developed. This model contains a relatively large set of unknown parameters, and the identification problem is analyzed using simulation data, as well as experimental data. Batch experiments were not sufficiently informative to uniquely estimate all the unknown parameters, thus, additional experiments have to be achieved in fed-batch mode to supplement the missing information.