The objective of this award is to develop theories and methods for the control and optimization of bio-manufacturing systems. The research will address issues related to upstream bio-manufacturing processes (where cell cultures secrete proteins through fermentation) and downstream bio-manufacturing processes (consisting of purification steps such as chromatography and filtration). At each step, the yield, throughput, and quality of individual production runs are subject to significant variability due to time varying nature of the cell lines, complex nature of the underlying biological reactions, and stochastic failures due to process uncertainty. The goal of this research is to integrate the theories of the underlying chemical and biological processes with the principles from uncertainty theory and optimization, and develop a knowledge base that will enable improvements yield, quality and throughput of bio-manufacturing operations.
If successful, this research will provide decision support to help bio-manufacturers improve efficiency, reduce timelines, and costs. For the upstream bioreactor operations, new stochastic optimization models will be developed to capture the trade-offs related to operating policies and their impact on quality, yield, and total costs. For the downstream operations, new performance evaluation models will be developed to analyze trade-offs related to downstream parameters, such as, the quality of the batch, the resin binding capability, purification cycle times, and efficiency. Leveraging the functional relationships obtained from specialized models for upstream and downstream operations, the research will develop mathematical models for optimizing system design and production schedules. All models will be validated with industry partners and insights obtained from this research will be incorporated into practice. The methodologies developed in this research have potential applications in other emerging areas of advanced manufacturing such as bio-fuels and nano-manufacturing. The research will also train several students and influence university curricula in related areas.
