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

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

SESSION 1: OVERVIEW

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

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

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)

SESSION 2: GENERAL PRINCIPLES

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

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

Algorithm-STELLA implementation

Back to AIDS model

STELLA implementation

SESSION 3: EPIDEMIOLOGY

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

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

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

SESSION 4: PHARMACOKINETICS

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

Basic Model

Graphical Results

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

SESSION 5: PHYSIOLOGY

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

Sample model Baroreceptor dynamics Describes control of blood pressure

Pacemaker section

Hormonal control section

Blood Flow section

Web-based model AIDA: diabetes simulator http://www.shodor.org/aida