dc.description.abstract
Bioprocesses have evolved into an important platform for the production of various products, from biopharmaceuticals to industrial chemicals. The multifaceted catalytic abilities of microorganisms, their capacity to thrive under harsh environmental conditions, and their ability to utilize diverse raw materials, contribute to the increasingly wide-spread applications of biological organisms as production hosts. However, despite the vast potential, bio-based production technologies are facing numerous challenges, for instance, of regulatory or economical nature. These challenges have led to a need for higher productivities and increased process robustness, which are important aims of current bioprocess development activities. Due to the inherent complexities of biological organisms, achievement of these objectives necessitates an in-depth understanding of the production mechanisms, i.e. the biological host and its interactions with the environment. Yet, traditional development strategies often suffer from inadequate evaluation of experimental data and subsequently limited generation of relevant physiological information and knowledge. Hence, for satisfying the requirements of modern production systems, an enhanced process development paradigm, centered on knowledge-oriented methodologies, is needed. Therefore, the main aim of this thesis is to facilitate knowledge-oriented bioprocess development by establishing methodologies that aid the comprehensive evaluation of experimental data and its conversion to information and knowledge, subsequent codification of knowledge in the form of mathematical models, and utilization of gained knowledge for achieving tangible performance improvements. In this work, using a novel meta-analysis workflow that combines large-scale digitization and organization of previously-published cell culture bioprocessing datasets with state-of-the-art statistical analysis and signal processing methodologies, a strategy for the efficient conversion of data to information is presented that can be used for large-scale mining of historical bioprocess datasets. Furthermore, a detailed review and assessment of existing Knowledge Management (KM) technologies in the context of Quality of Design (QbD) principles was conducted, leading to the identification of promising technologies that ultimately shape the future of knowledge-oriented development practices. Generated insights can be codified into mathematical models using first-principle relationships. Such -mechanistic- approaches provide an excellent strategy for storage and utilization of knowledge and can potentially lead to advantages, such as better characterization of optimal process conditions and a reduction of the number of experiments needed for successful process development. In addition to presenting strategies for establishing mechanistic models for the penicillin production process by filamentous fungi, several applications of mechanistic models in bioprocess development are established. Observability analysis is presented as a valuable tool for bioprocess development because it can lead to an increase of information content of bioprocess measurements; thus, reducing the total number of required experiments. Furthermore, for real-time estimation of physiological variables, model-based soft-sensing (estimation) strategies were devised that can lead to a better characterization and identification of optimal production conditions. In addition, a model-predictive control strategy allowed the utilization of knowledge for steering processes toward desired physiological traits, which can ultimately lead to improved bioprocess performance. Together, these applications represent strategies for effective utilization of process understanding toward achievement of tangible improvements in today-s bioprocess development, ultimately increasing the applicability of bioprocesses as production platforms of the future. In short, for achievement of the main aim of the thesis, that is facilitation of knowledge-oriented bioprocess development, the following objectives are defined. Together, these objectives, enable a bioprocess development strategy that is more efficient with respect to time and cost, in addition to leading to improved production performance. I) Development of methods for improved extraction of information and knowledge from experimental data, leading to more efficient utilization of experimental results during bioprocess development. II) Establishment of a basis for Knowledge Management (KM) such that generated insights can be stored and applied effectively. III) Application of knowledge in the form of mathematical models for achievement of improved process monitoring and control, resulting in faster identification of optimal conditions during bioprocess development.
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