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Unpacking “ESTEEM”

Unpacking “ESTEEM”. • Excel : ubiquitous, easy, flexible, non-intimidating • Exploratory: apply to real-world data; extend & improve • Experiential: students engage directly with the math. The ESTEEEM Project Homepage. http://bioquest.org/esteem. • 55 modules: Broad range of topics

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Unpacking “ESTEEM”

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  1. Unpacking “ESTEEM” • Excel: ubiquitous, easy, flexible, non-intimidating • Exploratory: apply to real-world data; extend & improve • Experiential: students engage directly with the math

  2. The ESTEEEM Project Homepage http://bioquest.org/esteem • 55 modules: Broad range of topics and data sets • Go to Island Biogeography

  3. Module Main Page • Screenshots & brief description • Mathematical expression • Research articles & primary data • User manual & curriculum materials (in progress) • Downloadable Excel sheet

  4. Population growth Bioinformatics Operon function Population genetics

  5. Epidemiology Phylogenetics Enzyme kinetics Protein structure

  6. y = axb y = axb ? Black box: Hide the model Glass box: Study the model No box: Build the model! Three Boxes How do students interact with the mathematical model underlying the biology?

  7. the software, w/proper attribution Users may freely Copyleft • download • use • modify • share More info available at Free Software Foundation website

  8. Continuous Growth Models Biological Goals: • Explore the growth of microbial populations • Discriminate among alternate hypotheses Quantitative Goals: • Distinguish between absolute & relative, limited & unlimited growth • Gain hands-on experience with selecting & fitting models

  9. Continuous Growth Models 1. Wet lab portion • Inoculate bread • Take digital pictures at 1-day intervals • Calculate mold area using ImageJ (Mac) or Scion Image (PC) — or have students estimate area! Prediction: What pattern of growth will you see?

  10. Continuous Growth Models 2. Computational portion • Open "Continuous Growth Models" workbook • Go to "Data" tab • Enter observed data • Go to "Plots—Size" tab to view three diff. models of mold population growth

  11. Linear growth: Constant absolute growth rate (e.g., 20 mm2 / day) Exponential growth: Constant relative growth rate (e.g., 20% / day) Logistic growth: Relative growth rate  as pop. size (e.g., 0%/ day at 700 mm2) Which model, & what parameter values, can best explain the observed data?

  12. • Go to “Plots—Growth” tab • These graph the same models & data as before: How do they differ from the previous graphs?

  13. Epidemiological Model Goal: Introduce students to the process of modeling a biological system Hook: Predict the outbreak and course of a specific epidemic; model the efficacy of different intervention strategies

  14. Epidemiological Model Five-step process: Building formulas in Excel Setting parameters and variables Translating biology into math Implementing a mathematical model in Excel Visualizing & interpreting results

  15. Epidemiological Model • Open “SIR Model” • Simple model: 3 variables, 2 parameters • From this information, how would you calculate the initial # Susceptible, Infected, Recovered? • Go to View menu, choose “Formula Bar” • In Cells B2-D2, enter formulas to carry out those calculations

  16. Biological Knowledge  Mathematical Equations S I R • How do individuals move from one category to another? • Write “word equations”; for example: The # of Susceptibles at Time 0 The # of newly infected Susceptibles The # of Susceptibles at Time 1 ? ? – = ± • Write formulas for: # newly infected Susceptibles # newly recovered Infecteds

  17. Future Directions • More thorough documentation on biology, math, Excel • Pre-built curricular resources: “I need something NOW!” • More modules & improvements to current ones: share resources among community of ESTEEM users

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