Increasingly, high throughput studies, coupled with microscale techniques, are being employed as an integral element of bioprocess development to generate valuable knowledge from the early stages of the development train. This has led to high throughput bioprocess development as a time- and cost-efficient approach to developing bioprocesses. High throughput studies aim to identify influential process parameter,and the operating ranges leading to favorable process performance. These are refined as development progresses to establish eventually the process design space. At the early stages of bioprocess development a greater number of parameters with wider ranges are investigated and these lead to the assessment of an increased number of samples. This can frequently result in a bottleneck which is a persisting challenge in implementing high throughput bioprocess development. The goal of this thesis is to alleviate this bottleneck and it seeks to achieve it with three systematic methodologies (1) strategic assay selection; (2) strategic assay deployment; and (3) a hybrid experimental simplex algorithm for identifying process 'sweet spots'. The first two attack the bottleneck from the perspective of analytical methods and they aim to select methods which are fit for high throughput applications and then deploy them in a fashion that reduces the analytical burden and concurrently ensures the derivation of correct conclusions upon the completion of a study. The third methodology is an algorithmic approach for iteratively stepping through an experimental space to identify and define favorable process operating regions while reducing the expenditure of resources for investigating sub-optimal regions. These methodologies are described and then demonstrated by applying them to industrially relevant case studies. The obtained results are analyzed and used to support the argument that they have the potential to facilitate high throughput activities and consequently high throughput bioprocess development.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:626300 |
| Date | January 2013 |
| Creators | Konstantinidis, S. |
| Publisher | University College London (University of London) |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://discovery.ucl.ac.uk/1418837/ |
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