Michael Henson
Michael Henson, Professor and Director of the Center for Process Design and Control
259A Goessmann Lab
Chemical Engineering Department
University of Massachusetts Amherst
686 N. Pleasant Street
Amherst, MA 01003-3110
413-545-3481 (office)
413-545-1647 (fax)
henson@ecs.umass.edu
ChE 361: http://www.ecs.umass.edu/che/che361/
Education
B.S., Chemical Engineering, University of Colorado, Boulder, 1985
M.S., Chemical Engineering, University of Texas, Austin, 1988
Ph.D., Chemical Engineering, University of California, Santa Barbara, 1992
Recognitions
Career Development Award, National Science Foundation, 1995
Research Fellowship, Alexander von Humboldt Foundation, 2001
Associate Editor, Journal of Process Control
Associate Editor, Automatica
Academic Trustee, CACHE Corporation
Institute Affiliations
Center for Process Design and Control
Institute for Cellular Engineering
Massachusetts Center for Renewable Science and Technology
Process Systems Engineering Consortium
Group Page
Henson Group
Interests
Nonlinear system dynamics and control, systems biology, yeast metabolic modeling, particulate systems
Current Focus of Research
Dynamic Modeling of Tumor Metabolism and Growth
The objective of this collaborative project with Prof. Neil Forbes (UMass Chemical Engineering) is to develop model-based analysis techniques for the dependence of tumor growth on the metabolism of individual cells under normal conditions and chemotherapeutic treatment. Our initial work involved the development and analysis of a dynamic model of multicellular spheroid growth that includes the diffusion of multiple nutrients and their effects on cellular metabolism in different microenvironments. Dynamic simulation and parametric sensitivity studies were used to evaluate model behavior, including the spatial distribution of proliferating, quiescent, and dead cells for different cellular characteristics. The critical cell survival parameters that have the greatest impact on overall spheroid physiology were determined, demonstrating that oxygen transport has a greater effect than glucose transport on the distribution of quiescent cells. Our current work is focused on extending the spheroid growth model to account for the effects of cell cycle progression and chemotherapeutic drugs. The model produces some counterintuitive predictions, such as drugs with intermediate diffusion coefficients are most effective at reducing spheroid volume. Our long-term goal is to develop in vivo tumor models that have the potential to predict therapeutic efficiency and can be used to design effective chemotherapeutic strategies.
Dynamic Modeling and Genome-Scale Analysis of Yeast Metabolism
The objective of this project supported by the UMass Process Design and Control Center is to develop yeast metabolic models for the dynamic analysis of gene knockouts and insertions on metabolite production in batch and fed-batch culture. We have developed dynamic flux balance models that combine stoichiometric equations for intracellular metabolism with dynamic mass balances on key extracellular nutrients and products. A small-scale stoichiometric model of yeast primary metabolism has been used to develop a dynamic flux balance model for determining fed-batch operating policies that optimize ethanol production. Optimal solutions generated to analyze the tradeoff between maximal productivity and yield objectives show that the prediction of a microaerobic region is significant. We have developed a more comprehensive dynamic model based on a genome-scale reconstruction of yeast metabolism to investigate metabolic engineering strategies for ethanol production in glucose and glucose/xylose media. Our initial results show that optimal cellular engineering strategies can be strongly dependent on the fermenter operating mode, such that conventional steady-state flux balance analysis can produce misleading results for batch and fed-batch cultures. Our long-term goal is to develop an integrated optimization framework that simultaneously identifies promising genetic manipulations and favorable dynamic operating policies.
Integrated Product and Process Design for Emulsified Products
The objective of this project is to develop design and operating strategies that allow the manufacturing of emulsified products with targeted end-use properties. Our academic collaborators are Surita Bhatia (UMass Chemical Engineering), Julian McClements (UMass Food Science), and Mike Malone (UMass Chemical Engineering). The research focuses on three types of products manufactured using high pressure homogenization: (1) emulsion systems for the delivery of nutraceuticals such as w-3 fatty acids and lycopene, which are increasingly important for improving human health and performance (supported by NSF and Unilever); and (2) pharmaceutical emulsions that contain hydrophobic drugs such as doxorubicin and melphalan, which are used as injectable anti-cancer agents (supported by the UMass Process Design and Control Center); and (3) oil-in-water emulsions used to improve the flow characteristics of heavy oils for pipeline transportation (supported by the ACS Petroleum Research Fund). Our initial work has focused on the development of population balance equation (PBE) models for the prediction of drop size distributions. Simplified PBE and physical property models will be developed to allow the prediction of feasible end-use properties as a function of formulation and processing variables. The hierarchical application of design heuristics will be used to efficiently reduce the space of feasible designs and to generate a few promising candidates for more detailed experimental and computational analysis. Modeling errors will be addressed through the development of run-to-run control strategies that use drop size distribution and rheological measurements available after each pass of the homogenizer to update the processing variables for the next pass. Proof-of-concept will be demonstrated by designing and producing functional emulsions using the high pressure homogenization facility in our laboratory.
Multiscale Modeling and Analysis of Circadian Rhythm Generation and Synchronization
The objective of this NIH supported project is to develop an integrated experimental, modeling, and computational program to decipher the molecular mechanisms responsible for mammalian circadian rhythm generation and synchronization. Our collaborators are Frank Doyle (UCSB Chemical Engineering), Erik Herzog (Washington University Biology), Linda Petzold (UCSB Computer Science), and Guillaume Bonnet (UCSB Mathematics). Our initial computational work involves the construction of a multicellular molecular model that employs the neurotransmitter vasoactive intestinal polypeptide (VIP) as the synchronizing biochemical species. The release of VIP from individual cells is hypothesized to be rhythmic and light dependent. A heterogeneous cell ensemble including both intrinsically rhythmic pacemakers and damped oscillators exhibits experimentally observed behavior such as self-synchronization, entrainment to ambient light-dark cycles, and desynchronization in constant bright light. These simulations suggest that intercellular coupling allows coherent timekeeping with large heterogeneous populations of relatively imprecise pacemaker cells. Our current work is focused on refinement of the multicellular model using neurophysiological data on cellular coupling and population synchronization.
Molecular Modeling of Bacterial Quorum Sensing Systems
The objective of this joint project with Prof. Lianhong Sun (UMass Chemical Engineering) is to develop an integrated experimental and modeling program for the synthetic design of bacterial quorum sensing systems. We envision that these engineered systems will have a wide range of potential applications including microbial fermentation, chemical detection, and ecosystem analysis. Our initial work has focused on the conceptual design of a two-species artificial bacterial cooperative ecosystem (symbiosis) in which the survival of one species is dependent on the cell density of the other species. A simple dynamic model of this synthetic ecosystem has been used to investigate the stability of competitive cell strains in continuous culture with a single growth limiting substrate. Bifurcation analysis shows the existence of multiple steady-state solutions, including two non-trivial solutions for the coexistence of the two species. One coexistence solution is stable over a wide range of dilution rates and produces large variations in the fractions of the two species, suggesting that the proposed design has potential applications in competitive mixed culture fermentations by allowing stable production of two microbial populations with different growth rates. Our current work is focused on experimental realization of this synthetic ecosystem design and more fundamental modeling at the gene/protein level.
Controlling Heterogeneity in Plant Cell Culture and Secondary Product Accumulation through Metabolic Analysis and Modeling of Population Behavior
The objective of this NSF supported project with Prof. Susan Roberts (UMass Chemical Engineering) is to study the effects of plant cell heterogeneity on the production of valuable secondary metabolites. The research is focused on the anticancer agent paclitaxel, which can be produced from Taxus suspension cell cultures that are highly heterogeneous in terms of both metabolic and subpopulation variability. Baseline Taxus population information is being collected using multiparameter flow cytometry to characterize metabolic heterogeneity and to develop population balance equation models to correlate metabolic data with process scale information. Subpopulations based on paclitaxel accumulation (i.e., high, medium and low) will be collected using novel Fluorescence Activated Cell Sorting (FACS) techniques, and metabolic heterogeneity in the subpopulations will be characterized. The long-term stability of the sorted population will be analyzed in terms of growth and paclitaxel accumulation, and population balance equation models will be developed to predict population behavior. The results from this research will provide a detailed characterization of Taxus heterogeneity, which can then be used to design effective strategies to stabilize and optimize paclitaxel accumulation.
TEACHING
ChE 361 - Mathematical Modeling
ChE 401 - Chemical Engineering Laboratory I
ChE 402 - Chemical Engineering Laboratory II
ChE 446 - Process Control
ChE 697A - Molecular and Systems Biotechnology
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