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A computational framework for harnessing data and knowledge for bioprocess design

Zhang, J; (2013) A computational framework for harnessing data and knowledge for bioprocess design. Doctoral thesis , UCL (University College London). Green open access

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Abstract

Bioprocess design requires substantial resources for the required experimental investigation of the options for each bioprocess step. With the aim of reducing the amount of experimentation needed for bioprocess development, a new computational framework called Bioprocess Data and Knowledge Framework (BDKF) has been developed to explore the data and knowledge systematically. In BDKF, the representation of four types of data and knowledge i.e. experimental data, ontologies, theoretical knowledge and empirical knowledge, have been established. The experimental data is the data that comes from previous experiments. The ontologies are the systematic description of the bioprocess terminologies used in the experimental data and knowledge. It can organize the terminologies of a domain as a hierarchy that allows the experimental data to be searched. The theoretical knowledge is the knowledge represented by formal definitions in the bioprocess, such as fundamental equations. The empirical knowledge is the knowledge obtained from practical studies, e.g. the relationships between different scales established through ultra scale-down experimentation. Three reasoning functionalities, search, prediction and suggestion, have been established to imitate human reasoning on using data and knowledge. The search functionality finds relevant experimental data to the bioprocess design problems. With this data, the prediction functionality analyses the data and estimates the possible performance of the bioprocess step. The suggestion functionality produces solutions for further experiments that either confirm the solutions or narrow down the design space. A prototype applying the BDKF approach to illustrate how to capture data and knowledge and how reasoning functionalities work for the operating conditions identification was developed for a case study on centrifugation. Design queries that represented relevant process material information and separation requirements were generated to initiate the BDKF approach. The prototype demonstrated that data from strain variants and data from different scales can be utilized through ontologies, theoretical knowledge and empirical knowledge. A more complicated prototype was developed for the chromatography case study. The prototype introduced a hierarchical heuristic approach to solve the chromatographic process design problems, such as column selection, buffer composition identification and operating conditions determination. This prototype demonstrated that BDKF can be used for both screening and optimisation to propose several potential bioprocess solutions. Evaluation results of each prototype showed that the BDKF approach can make good performance predictions and suggestions for further experiments. It is very promising as an early stage process development tool. Finally, a method for finding a design solution for a giving sequence by using mass balance calculations has been developed. A case study including centrifugation, filtration and chromatography has been examined. This demonstrated that BDKE method had the potential to allow all of the data and knowledge to be used for the whole bioprocess design. Therefore, the BDKF approach can provide a systematic way to harness bioprocess data and knowledge to enhance the efficiency of bioprocess development.

Type: Thesis (Doctoral)
Title: A computational framework for harnessing data and knowledge for bioprocess design
Open access status: An open access version is available from UCL Discovery
Language: English
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Biochemical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/1381868
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