Model-based analysis as a tool for intensification of a biocatalytic process in a microreactor

Chiral amines are highly valuable functionalised molecules which play an important role in the pharmaceutical, agrochemical and chemical industry. To produce these interesting compounds, chemical synthesis pathways are typically used. However, these chemical methods are operated under high temperatures and pressures, are air- and water-sensitive, and need highly flammable metal-organic reagents or heavy metals. The chemical approach thus requires specialised (and expensive) equipment and has a large environmental impact. To overcome these drawbacks, enzymatic processes have recently received increased attention to produce these chiral amines. However, the low productivity of enzymatic processes hampers the widespread industrial implementation of such enzymatic processes. In this dissertation, the aim is to make the enzymatic production of chiral amines more productive by using model-based analyses which allow to build process knowledge. First, the kinetic behaviour of the enzyme (i.e. ω-transaminase) is identified using an optimal experimental design approach, allowing a more accurate estimation of the kinetic parameters from the experimental data. Second, a generic methodology is developed to identify mass transfer limitations in microreactors. These mass transfer limitations reduce the reactor performance and should therefore be minimised. The use of the generic methodology allows to estimate productivity losses due to these mass transfer limitations. Finally, the estimation of kinetic parameters under mass transfer limited conditions is investigated. It is shown that accurate kinetic parameter estimates can be obtained under mass transfer limited conditions, but this is highly dependent on the experimental design. The results of this dissertation allow to speed up kinetic characterisation of enzymes and to improve overall productivity by reducing mass transfer limitations

A novel high-throughput method for kinetic characterisation of anaerobic bioproduction strains, applied to Clostridium kluyveri

Hexanoic acid (HA), also called caproic acid, can be used as an antimicrobial agent and as a precursor to various chemicals, such as fuels, solvents and fragrances. HA can be produced from ethanol and acetate by the mesophilic anaerobic bacterium Clostridium kluyveri, via two successive elongation steps over butyrate. A high-throughput anaerobic growth curve technique was coupled to a data analysis framework to assess growth kinetics for a range of substrate and product concentrations. Using this method, growth rates and several kinetic parameters were determined for C. kluyveri. A maximum growth rate (mu(max)) of 0.24 +/- 0.01 h(-1) was found, with a half-saturation index for acetic acid (K-S,K-AA) of 3.8 +/- 0.9 mM. Inhibition by butyric acid occurred at of 124.7 +/- 5.7 mM (K-I,K-BA), while the final product, HA, linearly inhibited growth with complete inhibition above 91.3 +/- 10.8 mM (K-HA of 10.9*10(-3) +/- 1.3*10(-3) mM(-1)) at pH = 7, indicating that the hexanoate anion also exerts toxicity. These parameters were used to create a dynamic mass-balance model for bioproduction of HA. By coupling data collection and analysis to this modelling framework, we have produced a powerful tool to assess the kinetics of anaerobic micro-organisms, demonstrated here with C. kluyveri, in order further explore the potential of micro-organisms for chemicals production

Application of Iterative Robust Model-based Optimal Experimental Design for the Calibration of Biocatalytic Models

The aim of model calibration is to estimate unique parameter values from available experimental data, here applied to a biocatalytic process. The traditional approach of first gathering data followed by performing a model calibration is inefficient, since the information gathered during experimentation is not actively used to optimize the experimental design. By applying an iterative robust model-based optimal experimental design, the limited amount of data collected is used to design additional informative experiments. The algorithm is used here to calibrate the initial reaction rate of an ω-transaminase catalyzed reaction in a more accurate way. The parameter confidence region estimated from the Fisher Information Matrix is compared with the likelihood confidence region, which is not only more accurate but also a computationally more expensive method. As a result, an important deviation between both approaches is found, confirming that linearization methods should be applied with care for nonlinear models

An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. RESULTS: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. CONCLUSIONS: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups