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Modeling and optimization of extracellular polysaccharides production by Enterobacter A47
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Orientador(es)
Resumo(s)
Polysaccharides are gaining increasing attention as potential environmental friendly
and sustainable building blocks in many fields of the (bio)chemical industry. The
microbial production of polysaccharides is envisioned as a promising path, since
higher biomass growth rates are possible and therefore higher productivities may be
achieved compared to vegetable or animal polysaccharides sources. This Ph.D. thesis
focuses on the modeling and optimization of a particular microbial polysaccharide,
namely the production of extracellular polysaccharides (EPS) by the bacterial strain
Enterobacter A47. Enterobacter A47 was found to be a metabolically versatile
organism in terms of its adaptability to complex media, notably capable of achieving
high growth rates in media containing glycerol byproduct from the biodiesel industry.
However, the industrial implementation of this production process is still hampered
due to a largely unoptimized process. Kinetic rates from the bioreactor operation
are heavily dependent on operational parameters such as temperature, pH, stirring
and aeration rate. The increase of culture broth viscosity is a common feature of this
culture and has a major impact on the overall performance. This fact complicates the
mathematical modeling of the process, limiting the possibility to understand, control
and optimize productivity. In order to tackle this difficulty, data-driven mathematical
methodologies such as Artificial Neural Networks can be employed to incorporate
additional process data to complement the known mathematical description of
the fermentation kinetics. In this Ph.D. thesis, we have adopted such an hybrid modeling framework that enabled the incorporation of temperature, pH and viscosity
effects on the fermentation kinetics in order to improve the dynamical modeling and
optimization of the process. A model-based optimization method was implemented
that enabled to design bioreactor optimal control strategies in the sense of EPS
productivity maximization. It is also critical to understand EPS synthesis at the
level of the bacterial metabolism, since the production of EPS is a tightly regulated
process. Methods of pathway analysis provide a means to unravel the fundamental
pathways and their controls in bioprocesses. In the present Ph.D. thesis, a novel
methodology called Principal Elementary Mode Analysis (PEMA) was developed
and implemented that enabled to identify which cellular fluxes are activated under
different conditions of temperature and pH. It is shown that differences in these two
parameters affect the chemical composition of EPS, hence they are critical for the
regulation of the product synthesis. In future studies, the knowledge provided by
PEMA could foster the development of metabolically meaningful control strategies
that target the EPS sugar content and oder product quality parameters.
Descrição
Palavras-chave
Exopolysaccharides Enterobacter A47 Hybrid semi-parametric modeling Model-based optimization