Mechanistic models for the evaluation of biocatalytic reaction conditions and biosensor design optimization

Abstract

Nowadays, a great variety of industrial products, including pharmaceuticals, fuels, bulk and fine chemicals, etc., is produced via bio-based manufacturing. Handling of renewable plant, animal and/or microbial-based biological resources as raw materials is in general a more environmentally friendly approach compared to the use of fossil fuels, and does not require the frequent use of aggressive solvents, heavy metals and other toxic chemicals. However, the identification, development and further industrial integration of novel bio-based pathways are very resource and time consuming processes. Therefore, various scaling-down approaches within industrial biotechnology have gained significant popularity in the last decades. It has resulted in the development and implementation of small scale reactors such as microbioreactors (µBRs) that potentially could serve as tools for the identification of interesting and valuable reaction or production strain candidates for further scaling-up of bioprocesses. The design and development of µBR technologies with integrated sensors is an adequate solution for rapid, high-throughput, and cost-effective screening, with considerably reduced reagent usage and waste generation. One of the significant challenges in the successful application of µBR technology remains the lack of the appropriate software and automated data interpretation of the µBR experiments. The µBR supporting software and data interpretation tools should allow maximizing the exploitation of the flexibility and the capabilities of the microfluidic platforms to deliver information-rich experiments on the one hand, and on extracting as much information as possible from the obtained experimental data on the other hand. Therefore, the main goal of this work is the development of mathematical models that can provide qualitative and quantitative information on the biological variables of interest. The capabilities of the presented mechanistic models are demonstrated by applying them for the evaluation of the biocatalytic reaction conditions inside µBRs and in amperometric biosensor design optimization. In the first case study a mechanistic model was developed to describe the enzymatic reaction of glucose oxidase and glucose in the presence of catalase inside a commercial microfluidic platform with integrated oxygen sensor spots. The simplicity of the proposed model allowed an easy calibration of the reaction mechanism structure and estimation of the kinetic rate constants. Moreover, the obtained simulation results were independently confirmed for µL- and mL- scale experiments. Thereby, the developed model recommended itself as a helpful tool in achieving better understanding of the reaction mechanism inside the microfluidic device. In the second case study the flexible microfluidic platform with integrated amperometric glucose biosensors was developed for continuous monitoring of glucose consumption rates. The integration of the mixing chamber inside the platform allowed performing sample dilutions which subsequently adjusted the concentration of analytes of interest to the sensor’s detection range. The platform was developed using a simple design, standard connectors and low-cost materials, which allows further exploiting of its multi-functional capabilities in a “plug-and-play” approach connection to other µBRs. In the third case study the mechanistic model of the cyclic voltammetry response of the first generation glucose biosensors was developed and applied for the biosensor design optimization. Furthermore the obtained qualitative and quantitative dependencies between the model output and experimental results were independently confirmed by thorough electrochemical and morphological studies. In the fourth case study the novel analytical procedure for simultaneous multiple-substrate monitoring in a droplet was developed. Moreover, the specific protocols were developed for detection of oxygen conversion, iron and Nafion elution rates inside the biosensor system. The presented analytical methods were evaluated for their optimal operating conditions and glucose biosensor designs in order to provide the most stable electrochemical response. Thereby, the novel tools, approaches and workflow schemes associated with supporting experimental data are presented throughout the thesis. Throughout the thesis, the role of performing a substantial number of experiments supported by multi-analytical analysis and validation of the obtained results was emphasized, in order to guarantee the reliability and accuracy of the proposed mathematical models.