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Policy Optimization in Dynamic Bayesian Network Hybrid Models of Biomanufacturing Processes

Author

Listed:
  • Hua Zheng

    (Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115)

  • Wei Xie

    (Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115)

  • Ilya O. Ryzhov

    (Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742)

  • Dongming Xie

    (Department of Chemical Engineering, University of Massachusetts, Lowell, Massachusetts 01854)

Abstract

Biopharmaceutical manufacturing is a rapidly growing industry with impact in virtually all branches of medicine. Biomanufacturing processes require close monitoring and control, in the presence of complex bioprocess dynamics with many interdependent factors, as well as extremely limited data due to the high cost of experiments and the novelty of personalized bio-drugs. We develop a new model-based reinforcement learning framework that can achieve human-level control in low-data environments. A dynamic Bayesian network is used to capture causal interdependencies between factors and predict how the effects of different inputs propagate through the pathways of the bioprocess mechanisms. This model is interpretable and enables the design of process control policies that are robust against model risk. We present a computationally efficient, provably convergent stochastic gradient method for optimizing such policies. Validation is conducted on a realistic application with a multidimensional, continuous state variable.

Suggested Citation

  • Hua Zheng & Wei Xie & Ilya O. Ryzhov & Dongming Xie, 2023. "Policy Optimization in Dynamic Bayesian Network Hybrid Models of Biomanufacturing Processes," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 66-82, January.
  • Handle: RePEc:inm:orijoc:v:35:y:2023:i:1:p:66-82
    DOI: 10.1287/ijoc.2022.1232
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    References listed on IDEAS

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    1. Neythen J Treloar & Alex J H Fedorec & Brian Ingalls & Chris P Barnes, 2020. "Deep reinforcement learning for the control of microbial co-cultures in bioreactors," PLOS Computational Biology, Public Library of Science, vol. 16(4), pages 1-18, April.
    2. Tugce Martagan & Ananth Krishnamurthy & Peter A. Leland & Christos T. Maravelias, 2018. "Performance Guarantees and Optimal Purification Decisions for Engineered Proteins," Operations Research, INFORMS, vol. 66(1), pages 18-41, 1-2.
    3. Tugce Martagan & Ananth Krishnamurthy & Christos T. Maravelias, 2016. "Optimal condition-based harvesting policies for biomanufacturing operations with failure risks," IISE Transactions, Taylor & Francis Journals, vol. 48(5), pages 440-461, May.
    4. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
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    Cited by:

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