We developed model-based techniques for dynamic analysis and feedback control of continuous biochemical reactors. Numerical bifurcation analysis was proposed as a tool for obtaining efficient and complete characterization of bioreactor model behavior. Application of this methodology to three previously published bioreactor models revealed unexpected steady-state and transient behaviors which can facilitate model development and validation. We also developed a nonlinear control strategy that allows stabilization of a desired steady state for mixed culture fermentations where two microbial populations compete for a common rate limiting substrate. The proposed method was successfully applied to a simulation model describing the production of two strains of Saccharomyces cerevisiae.

More recent work focused on the use of yeast cell population models for model-based optimization and control. A simple nonlinear control scheme based on a population balance equation model and feedback linearization was proposed for attenuation of sustained oscillations resulting from cell cycle synchronization. We have developed a more sophisticated model predictive control strategy that allows attenuation of oscillations that adversely affect bioreactor stability and/or induction of oscillations that lead to increased production of important metabolites synthesized only during part of the cell cycle. Cell-cycle dependent metabolite synthesis was further explored using a simple population balance model for dynamic optimization of ethanol production in fed-batch culture.

Funding: National Science Foundation (BES-9522274, CTS-9501368) and UMass

Students: Michael J. Kurtz (Ph.D.), Guang-Yan Zhu (Ph.D.), Yongchun Zhang (Ph.D.), Pavan Kumar Reddy Kambam (Ph.D.) and Jared Hjersted (5th year Ph.D.)

Collaborators: Martin Hjortso (LSU),Matthias Reuss (U. Stuttgart) and Lianhong Sun (University of Science and Technology of China)

Publications:

1. Kurtz, M. J., M. A. Henson, and M. A. Hjortso, "Nonlinear Control of Competitive Mixed-Culture Bioreactors via Specific Cell Adhesion," Canadian Journal of Chemical Engineering, 78, 237-247 (2000). [PDF]
2. Zhu, G.-Y., A. M. Zamamiri, M. A. Henson and M. A. Hjortso, "Model Predictive Control of Continuous Yeast Bioreactors Using Cell Population Models," Chemical Engineering Science, 55, 6155-6167 (2000). [PDF]
3. Zhang, Y. and M. A. Henson, "Bifurcation Analysis of Continuous Biochemical Reactor Models," Biotechnology Progress, 17, 647-660 (2001). [PDF]
4. Zhang, Y., A. M. Zamamiri, M. A. Henson and M. A. Hjortso, "Cell Population Models for Bifurcation Analysis and Nonlinear Control of Continuous Yeast Bioreactors," Journal of Process Control, 12, 721-734 (2002). [PDF]
5. Henson, M. A., "Distribution Control of Particulate Systems Based on Population Balance Equation Models," Proc. American Control Conference, Denver, CO, 3967-3972 (2003). [PDF]
6. Henson, M. A., D. Muller and M. Reuss, "Combined Metabolic and Cell Population Modeling for Yeast Bioreactor Control," Proc. IFAC Symposium on Advanced Control of Chemical Processes, Hong Kong (2004). [PDF]
7. Hjersted, J. and M. A. Henson, "Population Modeling for Ethanol Productivity Optimization in Fed-Batch Yeast Fermenters," Proc. American Control Conference, Portland, OR (2005).
8. Henson, M. A., "Biochemical Reactor Modeling and Control: Exploiting Cellular Biology to Manufacture High Value Products," IEEE Control Systems Magazine, 26, 54-62, August (2006). [PDF]
9. Kambam, P. K. R., M. A. Henson and L. Sun, "Design and Mathematical Modeling of a Synthetic Symbiotic Ecosystem," IET Systems Biology, 2, 33-38 (2008). [PDF]
10. Henson, M. A., "Model-Based Control of Biochemical Reactors," in The Control Handbook, 2nd edition, William Levine (Ed.), Taylor and Francis, New York, NY, 2010.