Combining robust state estimation with nonlinear model predictive control to regulate the acute inflammatory response to pathogen
The inflammatory responseaims to restore homeostasis by means of removing a biological stress, such as an invading bacterial pathogen.In cases of acute systemic inflammation, the possibility of collateral tissuedamage arises, which leads to a necessary down-regulation of the response.A reduced ordin...
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AIMS Press
2015-05-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2015.12.1127 |
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| author | Gregory Zitelli Seddik M. Djouadi Judy D. Day |
| author_facet | Gregory Zitelli Seddik M. Djouadi Judy D. Day |
| author_sort | Gregory Zitelli |
| collection | DOAJ |
| description | The inflammatory responseaims to restore homeostasis by means of removing a biological stress, such as an invading bacterial pathogen.In cases of acute systemic inflammation, the possibility of collateral tissuedamage arises, which leads to a necessary down-regulation of the response.A reduced ordinary differential equations (ODE) model of acute inflammation was presented and investigated in [10]. That system contains multiple positive and negative feedback loops and is a highly coupled and nonlinear ODE. The implementation of nonlinear model predictive control (NMPC) as a methodology for determining proper therapeutic intervention for in silico patients displaying complex inflammatory states was initially explored in [5]. Since direct measurements of the bacterial population and the magnitude of tissue damage/dysfunction are not readily available or biologically feasible, the need for robust state estimation was evident. In this present work, we present resultson the nonlinear reachability of the underlying model, and then focus our attention on improving the predictability of the underlying model by coupling the NMPC with a particle filter. The results, though comparable to the initial exploratory study, show that robust state estimation of this highly nonlinear model can provide an alternative to prior updating strategies used when only partial access to the unmeasurable states of the system are available. |
| format | Article |
| id | doaj-art-8eadfefad5d14e6c828c85500a20036b |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2015-05-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-8eadfefad5d14e6c828c85500a20036b2025-01-24T02:33:20ZengAIMS PressMathematical Biosciences and Engineering1551-00182015-05-011251127113910.3934/mbe.2015.12.1127Combining robust state estimation with nonlinear model predictive control to regulate the acute inflammatory response to pathogenGregory Zitelli0Seddik M. Djouadi1Judy D. Day2Department of Mathematics, University of California, Irvine, 340 Rowland Hall, Bldg #400, Irvine, CA 92697-3875Electrical Engineering and Computer Science Department, Masdar Institute of Science and Technology, Masdar City, Abu DhabiDepartment of Mathematics, University of Tennessee, 1403 Circle Dr, Ayres Hall 227, Knoxville, TN, 37996-2250The inflammatory responseaims to restore homeostasis by means of removing a biological stress, such as an invading bacterial pathogen.In cases of acute systemic inflammation, the possibility of collateral tissuedamage arises, which leads to a necessary down-regulation of the response.A reduced ordinary differential equations (ODE) model of acute inflammation was presented and investigated in [10]. That system contains multiple positive and negative feedback loops and is a highly coupled and nonlinear ODE. The implementation of nonlinear model predictive control (NMPC) as a methodology for determining proper therapeutic intervention for in silico patients displaying complex inflammatory states was initially explored in [5]. Since direct measurements of the bacterial population and the magnitude of tissue damage/dysfunction are not readily available or biologically feasible, the need for robust state estimation was evident. In this present work, we present resultson the nonlinear reachability of the underlying model, and then focus our attention on improving the predictability of the underlying model by coupling the NMPC with a particle filter. The results, though comparable to the initial exploratory study, show that robust state estimation of this highly nonlinear model can provide an alternative to prior updating strategies used when only partial access to the unmeasurable states of the system are available.https://www.aimspress.com/article/doi/10.3934/mbe.2015.12.1127nonlinear observability.particle filterimmuno-modulationnonlinear controllabilityinflammation |
| spellingShingle | Gregory Zitelli Seddik M. Djouadi Judy D. Day Combining robust state estimation with nonlinear model predictive control to regulate the acute inflammatory response to pathogen Mathematical Biosciences and Engineering nonlinear observability. particle filter immuno-modulation nonlinear controllability inflammation |
| title | Combining robust state estimation with nonlinear model predictive control to regulate the acute inflammatory response to pathogen |
| title_full | Combining robust state estimation with nonlinear model predictive control to regulate the acute inflammatory response to pathogen |
| title_fullStr | Combining robust state estimation with nonlinear model predictive control to regulate the acute inflammatory response to pathogen |
| title_full_unstemmed | Combining robust state estimation with nonlinear model predictive control to regulate the acute inflammatory response to pathogen |
| title_short | Combining robust state estimation with nonlinear model predictive control to regulate the acute inflammatory response to pathogen |
| title_sort | combining robust state estimation with nonlinear model predictive control to regulate the acute inflammatory response to pathogen |
| topic | nonlinear observability. particle filter immuno-modulation nonlinear controllability inflammation |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2015.12.1127 |
| work_keys_str_mv | AT gregoryzitelli combiningrobuststateestimationwithnonlinearmodelpredictivecontroltoregulatetheacuteinflammatoryresponsetopathogen AT seddikmdjouadi combiningrobuststateestimationwithnonlinearmodelpredictivecontroltoregulatetheacuteinflammatoryresponsetopathogen AT judydday combiningrobuststateestimationwithnonlinearmodelpredictivecontroltoregulatetheacuteinflammatoryresponsetopathogen |