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Lung Cancer By: Phillip Pulley David Shaw Paul Farag.

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Presentation on theme: "Lung Cancer By: Phillip Pulley David Shaw Paul Farag."— Presentation transcript:

1 Lung Cancer By: Phillip Pulley David Shaw Paul Farag

2 Lung Cancer Lung cancer is a disease characterized by uncontrolled cell growth in tissues of the lung. If left untreated, this growth can spread beyond the lung in a process called metastasis into nearby tissue or other parts of the body.

3 Smoking Radon Gas Asbestos Genetics Other: Production and Manufacturing Causes

4 Cell Growth Over Time

5 BAC

6 Days (x100)Volume (mm3) 1410 2375 3425 4395 5350 6315 7300 8305 9345 10355 11360 12485 13610 14925

7 The most general equation describing the dynamics of tumor growth can be written: x' = xf(x) x is the cell population size at time t and f(x) specifies the density dependents of in the proliferation and death of tumor cells. f(x) = p(x) - d(x) where p(x) is cell proliferation and d(x) is cell death. Method

8 The single equation can be properly used if it incorporates a time-dependent treatment term: x' = x(p(x) - d(x)) - a[phi](t)x a represents the strength of the chemotherapeutic agent and phi(t) represents the concentration of the agent during the treatment schedule Method

9 This solves a macro not micro system of cells. A two equation model is necessary to take into account effector cells and immune cells. Immune cells play the role of the predator, while the tumor cells are the prey. Method

10 x' = x(fx) - dx(x,y) y' = py(x,y) - dy(x,y) - ay(y) + phi(t) x represents size of the tumor cell population and y represents size of the effector cell population. py(x,y) is the growth term for the immune cells. dy(x,y) is the death term for the immune cells. ay(y) is the apoptosis term. Method

11 phi(t) is the time dependent treatment term. The result will depend on the interaction of the two equations on each other. Also, the functions can be reduced to form other functions. Method

12 Also, the functions can be reduced to form other functions. f(x) = a(1-[beta]x) dx(x,y) = nxy py(x,y) = (pxy)/ (g + x) dy(x,y) = mxy ay(y) = dy phi(t) = s Method

13 Results A=0.41418153 B=0.1262651 a=0.41418153 b=0.30485449

14 Results

15

16

17 The End Thank You for Listening


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