Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.746076
Title: An intelligent automation platform for bioprocess development
Author: Wu, T.
ISNI:       0000 0004 7229 7317
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2016
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Abstract:
Bioprocess development is very labour intensive, requiring many experiments to characterize each unit operation in a process sequence to achieve product safety and process efficiency. Recent advances in microscale biochemical engineering have led to automated high throughput experimentations. The activities for bioprocess development are implemented sequentially in which 1) liquid handling system performs the wet lab experiments; 2) standalone analytical devices detect the data; and 3) specific software is used for data analysis and experiment design. The experiment design, data analysis and process understanding require substantial engineer’s time. It becomes one of the time consuming bottlenecks in bioprocess development particularly when a high number of experiments are needed to explore a large design space in order to discover high process performance. This thesis addresses this challenge by reducing the development time yet achieving high process efficiency. A closed-loop learning approach has been adopted in bioprocess design in which the design objectives are driven iteratively by intelligent data acquisition. A framework that brings all of the elements performed manually into an automated fashion has been established to deliver the closed-loop learning approach. Based on the framework, a novel prototype of Intelligent Automation Platform for Bioprocess Development (IAPBD) has been built using multi-agent architecture. A rational database and four agents including Coordinate Agent, Experiment Design Agent, Execution Agent and Assay Agent have been designed to perform individual tasks and worked as a team to deliver the bioprocess design objectives. The multi-agent architecture used a blackboard mechanism to connect the four agents so that they are able to communicate with each other during operations. Starting with a set of initial experiments from the user or database, IAPBD is able to drive the robotic arm to perform defined initial experiments, and drive the analytical devices to detect the data after the experiments are completed. Then it will pick up the data from the analytical devices, and carry out data processing and process evaluation based on the optimization objective to achieve the design solution. After evaluation of the design solution, it will decide to stop or continue to design the next round experiments for optimization. The prototype has been evaluated first by a lysozyme precipitation process design, which involves typical microscale experimental procedures such as mixing, shaking, sample preparation and high throughput data detection instruments. All of the devices used have programmable interface so agents can control them directly. An optimal design solution that maximizes the yield of lysozyme and maximizes the ammonium sulphate concentration was found within a few of iterations using simplex search algorithm or Artificial Neural Network (ANN) and the whole tasks were completed automatically without human intervention. The success of this case study proved the concept of IAPBD. The second case study was designed to further evaluate the prototype by the precipitation for monoclonal antibody purification. A new “watcher” algorithm in the Assay Agent has been further developed to communicate with instruments that do not have programmable interface e.g. HPLC. IAPBD carried out two sets of precipitation experiments automatically. The “watcher” algorithm has been proved robust and efficient to retrieve the data from HPLC. The third case study is designed to use the sequential Design of Experiment method to optimize the production of a whole cell biocatalyst in fermentation. An optimal solution for medium composition and operating conditions was found successfully. It was then confirmed by large scale fermentation experiment that the optimal solution has increased the biomass/product more than 5 folds. The benefit of this novel IAPBD is its automation that reduces the bioprocess design time significantly and frees engineer’s time for other intellectual tasks. Prove-of- concept of IAPBD has been achieved with limited real experimental evaluation. With further development and evaluation, its potential to significantly reduce the time and cost of the whole bioprocess development may be realized.
Supervisor: Zhou, Y. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.746076  DOI: Not available
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