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Performance Guarantees and Optimal Purification Decisions for Engineered Proteins

Author

Listed:
  • Tugce Martagan

    (School of Industrial Engineering, Eindhoven University of Technology, Eindhoven, 5612 AZ Netherlands)

  • Ananth Krishnamurthy

    (Department of Industrial and Systems Engineering, University of Wisconsin Madison, Madison, Wisconsin 53706)

  • Peter A. Leland

    (Aldevron, Madison, Wisconsin 53719)

  • Christos T. Maravelias

    (Department of Chemical and Biological Engineering, University of Wisconsin Madison, Madison, Wisconsin 53706)

Abstract

We investigate protein purification operations conducted by biomanufacturers and pharmaceutical companies as part of their research and development efforts. Purification of these proteins involves unique challenges such as balancing the yield and purity trade-offs, dealing with uncertainty in the starting material, and estimating the impact of several interlinked decisions. We develop a Markov decision model and partition the state space into decision zones that provide managerial insights to optimize purification operations. We develop practical guidelines to quantify financial risks, and we characterize the optimal operating decisions based on specific production requirements. The optimization framework has been implemented at Aldevron, a contract biomanufacturer specializing in proteins, and has resulted in 25% reduction in the total lead times and 20% reduction in the costs of protein purification operations on average.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:oropre:v:66:y:2018:i:1:p:18-41
    DOI: 10.1287/opre.2017.1661
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. 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.
    2. Tugce Martagan & Ananth Krishnamurthy & Peter A. Leland, 2020. "Managing Trade-offs in Protein Manufacturing: How Much to Waste?," Manufacturing & Service Operations Management, INFORMS, vol. 22(2), pages 330-345, March.
    3. Tugce Martagan & Ivo Adan & Marc Baaijens & Coen Dirckx & Oscar Repping & Bram van Ravenstein & PK Yegneswaran, 2023. "Merck Animal Health Uses Operations Research Methods to Transform Biomanufacturing Productivity for Lifesaving Medicines," Interfaces, INFORMS, vol. 53(1), pages 85-95, January.
    4. Tugce Martagan & Yasemin Limon & Ananth Krishnamurthy & Tom Foti & Peter Leland, 2019. "Aldevron Accelerates Growth Using Operations Research in Biomanufacturing," Interfaces, INFORMS, vol. 49(2), pages 137-153, March.
    5. Boute, Robert N. & Gijsbrechts, Joren & van Jaarsveld, Willem & Vanvuchelen, Nathalie, 2022. "Deep reinforcement learning for inventory control: A roadmap," European Journal of Operational Research, Elsevier, vol. 298(2), pages 401-412.

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    1. Tugce Martagan & Ananth Krishnamurthy & Peter A. Leland, 2020. "Managing Trade-offs in Protein Manufacturing: How Much to Waste?," Manufacturing & Service Operations Management, INFORMS, vol. 22(2), pages 330-345, March.
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