Control and Optimization of Biomanufacturing Systems

该奖项的目标是发展生物制造系统控制和优化的理论和方法。该研究将解决与上游生物制造过程（细胞培养通过发酵分泌蛋白质）和下游生物制造过程（包括纯化步骤，如层析和过滤）相关的问题。由于细胞系的时变性质、潜在生物反应的复杂性以及工艺不确定性导致的随机失效，在每一步中，单个生产运行的产量、吞吐量和质量都存在显著的可变性。本研究的目标是将潜在的化学和生物过程理论与不确定性理论和优化原理相结合，并开发一个知识库，以提高生物制造操作的产量，质量和吞吐量。如果成功，这项研究将提供决策支持，帮助生物制造商提高效率，缩短时间和成本。对于上游生物反应器操作，将开发新的随机优化模型，以捕获与操作政策相关的权衡及其对质量、产量和总成本的影响。对于下游操作，将开发新的性能评估模型来分析与下游参数相关的权衡，例如批次质量、树脂结合能力、净化周期时间和效率。利用从上游和下游操作的专业模型中获得的功能关系，研究将开发优化系统设计和生产计划的数学模型。所有模型都将与行业合作伙伴进行验证，并将从本研究中获得的见解纳入实践。本研究开发的方法在生物燃料和纳米制造等其他新兴先进制造领域具有潜在的应用前景。这项研究还将培训一些学生，并影响相关领域的大学课程。

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.