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Cilium height difference between strokes is more effective in driving fluid transport in mucociliary clearance: A numerical study
Combining robust state estimation with nonlinear model predictive control to regulate the acute inflammatory response to pathogen
1. | Department of Mathematics, University of California, Irvine, 340 Rowland Hall, Bldg #400, Irvine, CA 92697-3875, United States |
2. | Electrical Engineering and Computer Science Department, Masdar Institute of Science and Technology, Masdar City, Abu Dhabi, United Arab Emirates |
3. | Department of Mathematics, University of Tennessee, 1403 Circle Dr, Ayres Hall 227, Knoxville, TN, 37996-2250, United States |
References:
[1] |
D. C. Angus and T. van der Poll, Severe sepsis and septic shock, New Eng J Med, 369 (2013), 840-851. |
[2] |
O. Bara, J. Day and S. Djouadi, Nonlinear state estimation for complex immune responses, Proceedings of the $52^{nd}$ IEEE Conference on Decision and Control, Florence, Italy, December 10-13 (2013), 3373-3378. |
[3] |
G. Conte, C. H. Moog and A. M. Perdon, Nonlinear Control Systems: An Algebraic Setting, Springer-Verlag London, Ltd., London, 1999. |
[4] |
J. M. Coron, Control and Nonlinearity, American Mathematical Society, Providence, RI, 2007. |
[5] |
J. Day, J. Rubin and G. Clermont, Using nonlinear model predictive control to find optimal therapeutic strategies to modulate inflammation, Math Biosci Eng, 7 (2010), 739-763.
doi: 10.3934/mbe.2010.7.739. |
[6] |
M. de Waal, J. Abrams, C. Bennett, B. Figdor and J. de Vries, Interleukin 10(il-10) inhibits cytokine synthesis by human monocytes: An autoregulatory role of il-10 produced by monocytes, J Exp Med, 174 (1991), 1209-1220. |
[7] |
J. A. Florian Jr., J. L. Eiseman and R. S. Parker, Nonlinear model predictive control for dosing daily anticancer agents using a novel saturating-rate cell-cycle model, Comput. Biol. Med., 38 (2008), 339-347. |
[8] |
J. Hogg, G. Clermont and R. S. Parker, Acute inflammation treatment via particle filter state estimation and mpc, 9th International Symposium on Dynamics and Control of Process Systems, 9 (2010), 272-277. |
[9] |
H. Nijmeijer and A. van der Schaft, Nonlinear Dynamical Control Systems, Springer-Verlag, New York, 1990.
doi: 10.1007/978-1-4757-2101-0. |
[10] |
A. Reynolds, J. Rubin, G. Clermont, J. Day, Y. Vodovotz and G. B. Ermentrout, A reduced mathematical model of the acute inflammatory response. i. derivation of model and analysis of anti-inflammation, J Theor Bio, 242 (2006), 220-236.
doi: 10.1016/j.jtbi.2006.02.016. |
[11] |
D. Simon, Optimal State Estimation: Kalman, H-infinity and Nonlinear Approaches, Wiley-Interscience, Hoboken, NJ, 2006.
doi: 10.1002/0470045345. |
[12] |
J. Xiong, An Introduction to Stochastic Filtering Theory, Oxford University Press, Oxford, 2008. |
[13] |
J. Zabczyk, Mathematical Control Theory: An Introduction, Birkhäuser Boston, Inc, Boston, MA, 1992. |
show all references
References:
[1] |
D. C. Angus and T. van der Poll, Severe sepsis and septic shock, New Eng J Med, 369 (2013), 840-851. |
[2] |
O. Bara, J. Day and S. Djouadi, Nonlinear state estimation for complex immune responses, Proceedings of the $52^{nd}$ IEEE Conference on Decision and Control, Florence, Italy, December 10-13 (2013), 3373-3378. |
[3] |
G. Conte, C. H. Moog and A. M. Perdon, Nonlinear Control Systems: An Algebraic Setting, Springer-Verlag London, Ltd., London, 1999. |
[4] |
J. M. Coron, Control and Nonlinearity, American Mathematical Society, Providence, RI, 2007. |
[5] |
J. Day, J. Rubin and G. Clermont, Using nonlinear model predictive control to find optimal therapeutic strategies to modulate inflammation, Math Biosci Eng, 7 (2010), 739-763.
doi: 10.3934/mbe.2010.7.739. |
[6] |
M. de Waal, J. Abrams, C. Bennett, B. Figdor and J. de Vries, Interleukin 10(il-10) inhibits cytokine synthesis by human monocytes: An autoregulatory role of il-10 produced by monocytes, J Exp Med, 174 (1991), 1209-1220. |
[7] |
J. A. Florian Jr., J. L. Eiseman and R. S. Parker, Nonlinear model predictive control for dosing daily anticancer agents using a novel saturating-rate cell-cycle model, Comput. Biol. Med., 38 (2008), 339-347. |
[8] |
J. Hogg, G. Clermont and R. S. Parker, Acute inflammation treatment via particle filter state estimation and mpc, 9th International Symposium on Dynamics and Control of Process Systems, 9 (2010), 272-277. |
[9] |
H. Nijmeijer and A. van der Schaft, Nonlinear Dynamical Control Systems, Springer-Verlag, New York, 1990.
doi: 10.1007/978-1-4757-2101-0. |
[10] |
A. Reynolds, J. Rubin, G. Clermont, J. Day, Y. Vodovotz and G. B. Ermentrout, A reduced mathematical model of the acute inflammatory response. i. derivation of model and analysis of anti-inflammation, J Theor Bio, 242 (2006), 220-236.
doi: 10.1016/j.jtbi.2006.02.016. |
[11] |
D. Simon, Optimal State Estimation: Kalman, H-infinity and Nonlinear Approaches, Wiley-Interscience, Hoboken, NJ, 2006.
doi: 10.1002/0470045345. |
[12] |
J. Xiong, An Introduction to Stochastic Filtering Theory, Oxford University Press, Oxford, 2008. |
[13] |
J. Zabczyk, Mathematical Control Theory: An Introduction, Birkhäuser Boston, Inc, Boston, MA, 1992. |
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