Optimal control problems with time delays: Two case studies in biomedicine

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doi:10.3934/mbe.2018051

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2018, Volume 15, Issue 5: 1137-1154. Doi: 10.3934/mbe.2018051

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Optimal control problems with time delays: Two case studies in biomedicine

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1.
Münster University of Applied Sciences, Department of Mechanical Engineering, Stegerwaldstr. 39, 48565 Steinfurt, Germany
2.
University of Münster, Applied Mathematics: Institute of Analysis and Numerics, Einsteinstr. 62, 49143 Münster, Germany

* Corresponding author: H. Maurer, maurer@math.uni-muenster.de
* Corresponding author: H. Maurer, maurer@math.uni-muenster.de

Received: April 29, 2017

Accepted: March 17, 2018

Published: September 2018

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Abstract

There exists an extensive literature on delay differential models in biology and biomedicine, but only a few papers study such models in the framework of optimal control theory. In this paper, we consider optimal control problems with multiple time delays in state and control variables and present two applications in biomedicine. After discussing the necessary optimality conditions for delayed optimal control problems with control-state constraints, we propose discretization methods by which the delayed optimal control problem is transformed into a large-scale nonlinear programming problem. The first case study is concerned with the delay differential model in [21] describing the tumour-immune response to a chemo-immuno-therapy. Assuming $ L^1$-type objectives, which are linear in control, we obtain optimal controls of bang-bang type. In the second case study, we introduce a control variable in the delay differential model of Hepatitis B virus infection developed in [7]. For $ L^1$-type objectives we obtain extremal controls of bang-bang type.

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Mathematics Subject Classification: Primary: 49K15, 49M37; Secondary: 92C50.

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Figure 1. Optimal solution of the non-delayed control problem with $\tau_1 = \tau_2 = 0$ and weights $B_1 = 5, B_2 = 10$. Top row: (a) dose control $u_1(t)$ of chemotherapy, (b) effector cells $E(t)$, (c) tumour cells $T(t)$. Bottom row: (a) dose control $u_2(t)$ of immune therapy, (b) healthy cells $N(t)$, (c) cytostatic agent $U(t)$.

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Figure 2. Optimal controls $u_k(t)$ and switching functions $\phi_k(t), \,(k = 1,2)$ in a neighborhood of the switching times $t_k$ illustrating the control-law (35) and the strict bang-bang property (37).

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Figure 3. Optimal controls $u_1(t)$ and $u_2(t)$ for functionals $J_p(p,u), p = 1,2,$ with weights $B_1 = 5, B_2 = 10$.

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Figure 4. Optimal solution of the delayed control problem with state delay $\tau_1 = 1.5$, control delay $\tau_2 = 3.0$ and weights $B_1 = 5, B_2 = 10$. Top row: (a) dose control $u_1(t)$ of chemotherapy, (b) effector cells $E(t)$, (c) tumour cells $T(t)$. Bottom row: (a) dose control $u_2(t)$ of immune therapy, (b) healthy cells $N(t)$, (c) cytostatic agent $U(t)$.

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Figure 5. Delayed solution with $\tau_1 = 1.5$ and $\tau_2 = 3.0$: controls $u_k(t)$ and switching functions $\phi_k(t), \,(k = 1,2)$ in a neighborhood of the switching times $t_k$ illustrating the control-law (35) and the strict bang-bang property (37).

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Figure 6. Optimal controls $u_1(t)$ and $u_2(t)$ for functionals $J_1(x,u)$ and $J_2(x,u)$ with delays $\tau_1 = 1.5, \tau_2 = 3.0$ and weights $B_1 = 5, B_2 = 10$.

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Figure 7. Optimal solution of the delayed control problem with state delay $\tau_1 = 1.5$, control delay $\tau_2 = 3.0$ and mixed control-state constraint $U(t) + u_2(t) \leq 3$. Top row: (a) dose control $u_1(t)$ of chemotherapy, (b) function $U(t)+u_2(t)$, (c) effector cells $E(t)$. Bottom row: (a) dose control $u_2(t)$ of immune therapy, (b) multiplier $\mu(t)$ for mixed constraint, (c) tumour cells $T(t)$.

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Figure 8. Controls and switching functions (51) for delays $\tau = 0$, $\tau = 10$ and $\tau = 15$. For all delays the control law (52) is satisfied and the strict bang-bang property holds.

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Figure 9. Comparison of state variables for delays $\tau = 0, 10, 15$. Top row: (a) healthy cells $x$, (b) exposed cells $p$. Bottom row: (a) infected cells $y$, (b) free virions $v$.

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Table 1. Parameters in the control problem of chemo-immunotherapy [21].

Parameter	Description	Value
$t_f$	final time	$30$ d (days)
$\tau_1$	state delay	$1.5$ d
$\tau_2$	control delay	$3.0$ d
$(u_{k,\min},u_{k,\max})$	control bounds	$(0, 1)$ for $\,k=1,2$
$(a_1,\,a_2,\,a_3)$	cell kill rate response	$(0.2,\,0.4,\,0.1)$
$(\beta,\, \beta_2)$	reciprocal carrying capacities of tumour and host cells	$(0.002,\,1.0)$
$(c_1,\, c_2)$	scaling parameters	$(3\times 10^{-5},\,3\times 10^{-8})$
$d_1$	drug decay rate	$0.01$
$\delta$	immune cell death rate	$0.2$
$\eta$	steepness of immune response	$0.3$
$\mu_e$	uninfected effector cell decrease rate	$0.003611$
$(\sigma,\,\rho)$	immune cell influx and decay rate resp.	$(0.2,\,0.2)$
$(s_1,\, r_2,\, r_3)$	cell growth rates	$(0.3,\,1.03,\,1.0)$
$n_T$	immune effector cell decrease rate	$1.0$
$(B_1,B_2) $	weights	$ (5,\,10)$

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References

	B. Buonomo and M. Cerasuolo , The effect of time delay in plant-pathogen interactions with host demography, Math. Biosciences and Engineering, 12 (2015) , 473-490. doi: 10.3934/mbe.2015.12.473.
	C. Büskens, Optimierungsmethoden und Sensitivitätsanalyse für optimale Steuerprozesse mit Steuer- und Zustands-Beschränkungen, PhD thesis, Institut für Numerische Mathematik, Westfälische Wilhelms-Universität Münster, Germany, 1998.
	C. Büskens and H. Maurer , SQP methods for solving optimal control problems with control and state constraints: adjoint variables, sensitivity analysis and real-time control, J. Comput. Appl. Math., 120 (2000) , 85-108. doi: 10.1016/S0377-0427(00)00305-8.
	C. Büskens and M. Gerdts, WORHP: Large-Scale Sparse Nonlinear Optimization Solver, http://www.worhp.de.
	Q. Chai , R. Loxton , K. L. Teo and C. Yang , A class of optimal state-delay control Problems, Nonlinear Analysis: Real World Applications, 14 (2013) , 1536-1550. doi: 10.1016/j.nonrwa.2012.10.017.
	R. V. Culshaw and S. Ruan , A delay-differential equation model of HIV infection of CD4⁺ T-cells, Mathematical Biosciences, 165 (2000) , 27-39. doi: 10.1016/S0025-5564(00)00006-7.
	S. Eikenberry , S. Hews , J. D. Nagy and Y. Kuang , The dynamics of a delay model of Hepatitis B virus infection with logistic hepatocyte growth, Mathematical Biosciences, 6 (2009) , 283-299. doi: 10.3934/mbe.2009.6.283.
	R. Fourer, D. M. Gay and B. W. Kernighan, AMPL: A Modeling Language for MathematicalProgramming, The Scientific Press, South San Francisco, California, 1993.
	L. Göllmann , D. Kern and H. Maurer , Optimal control problems with delays in state and control and mixed control-state constraints, Optimal Control Applications and Methods, 30 (2009) , 341-365. doi: 10.1002/oca.843.
	L. Göllmann and H. Maurer , Theory and applications of optimal control problems with multiple time-delays, Journal of Industrial and Management Optimization, 10 (2014) , 413-441.
	T. Guinn , Reduction of delayed optimal control problems to nondelayed problems, Journal of Optimization Theory and Applications, 18 (1976) , 371-377. doi: 10.1007/BF00933818.
	R. F. Hartl , S. P. Sethi and R. G. Vickson , A survey of the maximum principles for optimal control problems with state constraints, SIAM Review, 37 (1995) , 181-218. doi: 10.1137/1037043.
	M. R. Hestenes, Calculus of Variations and Optimal Control Theory, John Wiley, New York, 1966.
	S. C. Huang , Optimal Control problems with retardations and restricted phase coordinates, Journal of Optimization Theory and Applications, 3 (1969) , 316-360. doi: 10.1007/BF00931371.
	J. Klamka , H. Maurer and A. Swierniak , Local controllability and optimal control for a model of combined anticancer therapy with control delays, Mathematical Biosciences and Engineering, 14 (2017) , 195-216. doi: 10.3934/mbe.2017013.
	Y. Kuang, Delay Differential Equations with Applications in Population Dynamics, Academic Press, San Diego, 1993.
	H. Maurer , C. Büskens , J.-H. R. Kim and Y. Kaya , Optimization methods for the verification of second-order sufficient conditions for bang-bang controls, Optimal Control Methods and Applications, 26 (2005) , 129-156. doi: 10.1002/oca.756.
	R. M. May , Time-delay versus stability in population models with two and three tropic levels, Ecology, 54 (1973) , 315-325.
	N. P. Osmolovskii and H. Maurer, Applications to Regular and Bang-Bang Control: Second-Order Necessary and Sufficient Optimality Conditions in Calculus of Variations and Optimal Control, SIAM Advances in Design and Control, Vol. DC 24, SIAM Publications, Philadelphia, 2012. doi: 10.1137/1.9781611972368.
	L. S. Pontryagin, V. G. Boltyanskii, R. V. Gamkrelidze and E. F. Mishchenko, The Mathematical Theory of Optimal Processes, Translation by K. N. Trirogoff, Wiley, New York, 1962.
	F. Rihan, D. H. Abdelrahman, F. Al-Maskari, F. Ibrahim and M. A. Abdeen, Delay differential model for tumour-immune-response with chemoimmunotherapy and optimal control. Computational and Mathematical Methods in Medicine, Hindawi Publishing Corporation, Vol. 2014, Article ID 982978, (2014). doi: 10.1155/2014/982978.
	H. Schättler , U. Ledzewicz and H. Maurer , Sufficient conditions for strong local optimality in optimal control problems with L²-type objectives and control constraints, Discrete Contin. Dyn. Syst. Ser. B, 19 (2014) , 2657-2679. doi: 10.3934/dcdsb.2014.19.2657.
	C. Silva , H. Maurer and D.F.M. Torres , Optimal control of a tuberculosis model with state and control delays, Mathematical Biosciences and Engineering, 14 (2017) , 321-337. doi: 10.3934/mbe.2017021.
	C. T. Sreeramareddy , K. V. Panduru , J. Menten and J. V. den Ende , Time delays in diagnosis of pulmonary tuberculosis: A systematic review of literature, BMC Infectious Diseases, 9 (2009) , 91-100. doi: 10.1186/1471-2334-9-91.
	J. Stoer and R. Bulirsch, Introduction ot Numerical Analysis, Third Edition, Texts in Applied Mathematics, Springer-Verlag, Berlin, 1990. doi: 10.1007/978-3-662-22250-8.
	D. G. Storla , S. Yimer and G. A. Bjune , A systematic review in delay in the diagnosis and treatment of tuberculosis, BMC Public Health, 8 (2008) , p15. doi: 10.1186/1471-2458-8-15.
	P. van den Driessche, Some Epidemiological Models with Delays, Report DMS-679-IR, University of Victoria, Department of Mathematics, 1994.
	A. Wächter and L. T. Biegler , On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming, Mathematical Programming, 106 (2006) , 25-57. doi: 10.1007/s10107-004-0559-y.
	H. Yang and J. Wei , Global behaviour of a delayed viral kinetic model with general incidence rate, Discrete Contin. Dyn. Syst. Ser. B, 20 (2015) , 1573-1582. doi: 10.3934/dcdsb.2015.20.1573.