Jump to content

De Pillis chemotherapy model: Difference between revisions

From mintOC
mNo edit summary
Line 15: Line 15:
\begin{array}{llcl}
\begin{array}{llcl}
  \displaystyle \min_{x, u} & p_0 x_0(t_f) & + & \int_{t_0}^{t_f} p_1 x_0(t)^2 \mbox{d}t + \sum_{i=0}^{3} \int_{t_0}^{t_f} p_{i+2} u_i(t)^2 \mbox{d}t  \\[1.5ex]
  \displaystyle \min_{x, u} & p_0 x_0(t_f) & + & \int_{t_0}^{t_f} p_1 x_0(t)^2 \mbox{d}t + \sum_{i=0}^{3} \int_{t_0}^{t_f} p_{i+2} u_i(t)^2 \mbox{d}t  \\[1.5ex]
  \mbox{s.t.} & \dot{x}_0(t) & = & a x_0 (1-b x_0) -c x_1 x_0 - D x_0 - K_T (1- \mbox{e}^{- x_4}) x_0 ,\\
  \mbox{s.t.} & \dot{x}_0 & = & a x_0 (1-b x_0) -c x_1 x_0 - D x_0 - K_T (1- \mbox{e}^{- x_4}) x_0 ,\\
             & \dot{x}_1(t) & = & e x_3 - f x_1 + g \frac{x_0^2}{h+x_0^2}-p x_1 x_0 - K_N (1- \mbox{e}^{- x_4}) x_1,  \\
             & \dot{x}_1 & = & e x_3 - f x_1 + g \frac{x_0^2}{h+x_0^2}-p x_1 x_0 - K_N (1- \mbox{e}^{- x_4}) x_1,  \\
             & \dot{x}_2(t) & = & -m x_2 + j \frac{D^2 x_0^2}{k+ D^2 x_0^2} x_2 - q x_1 x_2 + (r_1 x_1 + r_2 x_3) x_0 \\
             & \dot{x}_2 & = & -m x_2 + j \frac{D^2 x_0^2}{k+ D^2 x_0^2} x_2 - q x_1 x_2 + (r_1 x_1 + r_2 x_3) x_0 \\
& & & - v x_1 x_2^2 - K_L (1- \mbox{e}^{- x_4}) x_2 + \frac{p_I x_2 x_5}{g_I + x_5} + u_2,  \\
& & & - v x_1 x_2^2 - K_L (1- \mbox{e}^{- x_4}) x_2 + \frac{p_I x_2 x_5}{g_I + x_5} + u_2,  \\
             & \dot{x}_3(t) & = & \alpha - \beta x_3 - K_C (1- \mbox{e}^{- x_4}) x_3,\\
             & \dot{x}_3 & = & \alpha - \beta x_3 - K_C (1- \mbox{e}^{- x_4}) x_3,\\
             & \dot{x}_4(t) & = & - \gamma x_4 + u_0,\\
             & \dot{x}_4 & = & - \gamma x_4 + u_0,\\
             & \dot{x}_5(t) & = & - \mu_I x_5 + u_1.
             & \dot{x}_5 & = & - \mu_I x_5 + u_1.
\end{array}  
\end{array}  
</math>
</math>

Revision as of 09:28, 10 April 2016

De Pillis chemotherapy model
State dimension: 1
Differential states: 6
Continuous control functions: 3


The model by de Pillis combines chemotherapy with immunotherapy.

Mathematical formulation

For t[t0,tf] and D=d(x2/x0)ls+(x2/x0)l the optimal control problem is given by

minx,up0x0(tf)+t0tfp1x0(t)2dt+i=03t0tfpi+2ui(t)2dts.t.x˙0=ax0(1bx0)cx1x0Dx0KT(1ex4)x0,x˙1=ex3fx1+gx02h+x02px1x0KN(1ex4)x1,x˙2=mx2+jD2x02k+D2x02x2qx1x2+(r1x1+r2x3)x0vx1x22KL(1ex4)x2+pIx2x5gI+x5+u2,x˙3=αβx3KC(1ex4)x3,x˙4=γx4+u0,x˙5=μIx5+u1.

The states x0 to x3 are measured in absolute cell counts, where x0 describes the number of tumor cells, x1 of unspecific immune cells, x2 of tumor-specific cytotoxic T-cells (CD8+ T) and x3 of circulating lymphocytes. The chemotherapeutic drug concentration is given by x4 and the immunotherapeutic by x5 (Interleukin-2) respectively.

Parameters

This set of parameters can be found as “patient 9” in [].

a=4.31×101b=1.02×109c=6.41×1011d=2.34e=2.08×107f=4.12×102g=1.25×102h=2.02×107j=2.49×102k=3.66×107l=2.09m=2.04×101q=1.42×106p=3.42×106s=8.39×102r1=1.01×107r2=6.50×1011u=3.00×1010α=7.50×108β=1.20×102γ=9.00×101pI=1.25×101gI=2.00×107μI=1.00×101KT=9.00×101KN=KL=KC=6×101

Reference Solutions

The problem can be solved with the [multiple shooting method]. For the following solutions the control functions and states are discretized on the same grid, with 100 nodes. For the objective function parameters have been chosen from the following sets to grand the results shown in the graphics below.

Objective function 1 p0=1,p2=1,pi=0,otherwise.

Objective function 2 p0=1,p2=1,pi=0,otherwise.

In both objective function the amount of chemotherapeutic drugs is penalized. The objective function 2 describes the worst case scenario of the tumor growth at end time.

Source Code

References