1 / 17

Lung Cancer

Lung Cancer. By: Phillip Pulley David Shaw Paul Farag. Lung Cancer.

laura-myers
Download Presentation

Lung Cancer

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

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. Causes • Smoking • Radon Gas • Asbestos • Genetics • Other: Production and Manufacturing

  4. Cell Growth Over Time

  5. BAC

  6. Method 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.

  7. Method 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

  8. Method 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.

  9. Method 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.

  10. Method 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.

  11. Method 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

  12. Results A=0.41418153 B=0.1262651 a=0.41418153 b=0.30485449

  13. Results

  14. Results

  15. The End Thank You for Listening

More Related