Using nonlinear model predictive control to find optimal therapeutic strategies to modulate inflammation
Modulation of the inflammatory response has become a key focal point in the treatment of critically ill patients. Much of the computational work in this emerging field has been carried out with the goal of unraveling the primary drivers, interconnections, and dynamics of systemic inflammation. To tr...
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| Format: | Article |
| Language: | English |
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AIMS Press
2010-09-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2010.7.739 |
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| author | Judy Day Jonathan Rubin Gilles Clermont |
| author_facet | Judy Day Jonathan Rubin Gilles Clermont |
| author_sort | Judy Day |
| collection | DOAJ |
| description | Modulation of the inflammatory response has become a key focal point in the treatment of critically ill patients. Much of the computational work in this emerging field has been carried out with the goal of unraveling the primary drivers, interconnections, and dynamics of systemic inflammation. To translate these theoretical efforts into clinical approaches, the proper biological targets and specific manipulations must be identified. In this work, we pursue this goal by implementing a nonlinear model predictive control (NMPC) algorithm in the context of a reduced computational model of the acute inflammatory response to severe infection. In our simulations, NMPC successfully identifies patient-specific therapeutic strategies, based on simulated observations of clinically accessible inflammatory mediators, which outperform standardized therapies, even when the latter are derived using a general optimization routine. These results imply that a combination of computational modeling and NMPC may be of practical use in suggesting novel immuno-modulatory strategies for the treatment of intensive care patients. |
| format | Article |
| id | doaj-art-95796f6fc29d403ebf415770f39d2dd2 |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2010-09-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-95796f6fc29d403ebf415770f39d2dd22025-01-24T02:00:58ZengAIMS PressMathematical Biosciences and Engineering1551-00182010-09-017473976310.3934/mbe.2010.7.739Using nonlinear model predictive control to find optimal therapeutic strategies to modulate inflammationJudy Day0Jonathan Rubin1Gilles Clermont2Mathematical Biosciences Institute, The Ohio State University, 1735 Neil Ave, 377 Jennings Hall, Columbus, OH 43210Mathematical Biosciences Institute, The Ohio State University, 1735 Neil Ave, 377 Jennings Hall, Columbus, OH 43210Mathematical Biosciences Institute, The Ohio State University, 1735 Neil Ave, 377 Jennings Hall, Columbus, OH 43210Modulation of the inflammatory response has become a key focal point in the treatment of critically ill patients. Much of the computational work in this emerging field has been carried out with the goal of unraveling the primary drivers, interconnections, and dynamics of systemic inflammation. To translate these theoretical efforts into clinical approaches, the proper biological targets and specific manipulations must be identified. In this work, we pursue this goal by implementing a nonlinear model predictive control (NMPC) algorithm in the context of a reduced computational model of the acute inflammatory response to severe infection. In our simulations, NMPC successfully identifies patient-specific therapeutic strategies, based on simulated observations of clinically accessible inflammatory mediators, which outperform standardized therapies, even when the latter are derived using a general optimization routine. These results imply that a combination of computational modeling and NMPC may be of practical use in suggesting novel immuno-modulatory strategies for the treatment of intensive care patients.https://www.aimspress.com/article/doi/10.3934/mbe.2010.7.739dosing control.inflammationimmuno-modulationnonlinear model predictive control |
| spellingShingle | Judy Day Jonathan Rubin Gilles Clermont Using nonlinear model predictive control to find optimal therapeutic strategies to modulate inflammation Mathematical Biosciences and Engineering dosing control. inflammation immuno-modulation nonlinear model predictive control |
| title | Using nonlinear model predictive control to find optimal therapeutic strategies to modulate inflammation |
| title_full | Using nonlinear model predictive control to find optimal therapeutic strategies to modulate inflammation |
| title_fullStr | Using nonlinear model predictive control to find optimal therapeutic strategies to modulate inflammation |
| title_full_unstemmed | Using nonlinear model predictive control to find optimal therapeutic strategies to modulate inflammation |
| title_short | Using nonlinear model predictive control to find optimal therapeutic strategies to modulate inflammation |
| title_sort | using nonlinear model predictive control to find optimal therapeutic strategies to modulate inflammation |
| topic | dosing control. inflammation immuno-modulation nonlinear model predictive control |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2010.7.739 |
| work_keys_str_mv | AT judyday usingnonlinearmodelpredictivecontroltofindoptimaltherapeuticstrategiestomodulateinflammation AT jonathanrubin usingnonlinearmodelpredictivecontroltofindoptimaltherapeuticstrategiestomodulateinflammation AT gillesclermont usingnonlinearmodelpredictivecontroltofindoptimaltherapeuticstrategiestomodulateinflammation |