1. Computational Science for Medicine and Biosciences
PRESENTER:
Robert R. Gotwals, Jr.
The Shodor Education Foundation, Inc.

2. Presentation Overview
Session 1: Overview
Areas of interest, student involvement, Shodor’s experiences
Session 2: General Principles
Application, algorithm, architecture with example
Session 3: Epidemiology
Basic algorithm, sample model with variations, example models
Session 4: Pharmacokinetics
Basic principles, sample model with variations
Session 5: Physiology
basic STELLA model, Web-based diabetes simulator

3. SESSION 1: OVERVIEW

4. Areas of Interest Epidemiology: Study of diseases (epidemics)
Pharmacokinetics
Study of the bodily absorption, distribution, metabolism, and excretion of drugs
Physiology
Study of the systems of the body and their individual and collective interactions
(Biochemistry)
Study of chemical systems found in living organisms

5. Computational Science in Medicine and Biosciences
Benefits
Topics are highly interdisciplinary
Topics tend to attract those students underrepresented in computational sciences
Mathematical algorithms “reachable” by most students
Personal connections very engaging

6. Shodor’s experience
Explorations in Computational Science: Medicine and Biosciences
Week-long workshop
Duke University Center for Emerging Cardiovascular Technologies (CECT)
Research Experience for Undergraduates (REU) students in cardiovascular modeling
Duke School for Children Middle School Sub-Sahara Epidemiology Project
Integrated unit in science, mathematics, computing, social and political sciences
Work with medical schools such as Mt. Sinai School of Medicine (NYC)

7. SESSION 2: GENERAL PRINCIPLES

8. General Principles Application
Three target areas: epidemiology, pharmacokinetics, physiology, (biochemistry)
Algorithm
Algorithms tend to be differential equations
dX/dt: change in some property X as a function of time (t)
Architecture
Most computing tools well-suited for biomedical modeling
STELLA
Spreadsheets
Mathematica
Viz tools

9. Example: AIDS epidemic
Application: Determining spread of AIDS epidemic
Source: Mathematical Biology
Study conducted in 198x?
Algorithm: a system of five ordinary differential equations (ODEs), by Anderson
Architecture: STELLA

10. Algorithm-STELLA implementation

11. Back to AIDS model

12. STELLA implementation

13. SESSION 3: EPIDEMIOLOGY

14. Epidemiology Basic algorithm: “SIR” algorithm S: susceptibles
I: infecteds
R: recovereds

15. Variations Include population dynamics (births, deaths, etc.)
Include effect of medical intervention
prevention
vaccines
treatments
handwashing, hygiene
Look for “driving variable”

16. Sample models Basic epidemiology model (SIR) Full-blown AIDS
Trypanosomiasis (African sleeping sickness)
Malaria
Yellow fever
Measles
Guinea worm disease
Bubonic plague (“Black Death”)

17. SESSION 4: PHARMACOKINETICS

18. Pharmacokinetics
Study of the bodily absorption, distribution, metabolism, and excretion of drugs
Basic algorithm
Mass balance mathematics

19. Basic Model

20. Graphical Results

21. Variations Dosing schemes Oral (PO) Intravenous (IV)
Intramuscular (IM)
Single
Multiple
Maintenance
Physiological influences
Multiple systems

22. SESSION 5: PHYSIOLOGY

23. Physiology
study of the systems of the body and their individual and collective interactions
Example model: Windkessel cardiac output
Looks at effects of compliance and resistance in veins and arteries

24. Sample model
Baroreceptor dynamics
Describes control of blood pressure

25. Pacemaker section

26. Hormonal control section

27. Blood Flow section

28. Web-based model
AIDA: diabetes simulator