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White Box Function Estimation using Convenience Kinetics

White Box Function Estimation using Convenience Kinetics. COMP 150GA – Class Project Fall 2011 Tufts University YOUR NAME, Your.Name@tufts.edu. Goal.

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White Box Function Estimation using Convenience Kinetics

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  1. White Box Function Estimationusing Convenience Kinetics COMP 150GA – Class Project Fall 2011 Tufts University YOUR NAME, Your.Name@tufts.edu

  2. Goal • Develop approximate expressions for rate change of concentrations in metabolic networks, when given measurement data (data sets that correspond to various operating conditions). • Grading is as follows: • 25% - problem formulation • 25% - solution quality • 30% - results on each test case • 10% - summary of project contributions • 10% - Overall code quality/clarify/documentation/organization • Add as many slides as needed to get your points across

  3. 25% Problem Formulation • Explain HOW you formulated the problem as an optimization problem, and JUSTIFY your choices. Please make sure to include an intuitive explanation and a mathematical equation if appropriate. • Define your design space, and explain how you represent a solution in the design space • What is your objective function? • What is your evaluation function? How does it capture trading off accuracy for form simplicity? How are solutions compared? (see next slide)

  4. Design Space & representing a solution in the design space

  5. Objective Function

  6. Evaluation Function • Show and explain how two possible solutions (from any of the examples) were evaluated

  7. 25% Your Solution • One slide that provides Outline/Overview/Summary/FlowGraphof Heuristic • Additional slides with details on each key idea

  8. RESULTS • The results should show the following for EACH test case: • One or more plot that shows the accuracy of your best solution against the given data. • One or more plot (or explanation supported by tables/data) that shows how you performed tradeoffs for a particular example. For example, if you did multiple objective optimization, then show or explain a graph of your pareto front, making sure that your axis are clearly explained.

  9. Test Case #1 • D-Glucose + 2 ATP ↔ 2 ADP + FDP ATP ADP FDP

  10. 10% Results for Test Case #1

  11. Test Case #2 • S ↔ P

  12. 10% Results for Test Case #2

  13. GLUC6P GAP Test Case #3 • GLUC + 2NAD + NADP + ADP ↔ 2Ethanol + 2NADH + NADPH + ATP • GLUC6P + 2NAD + NADP + 2ADP ↔ 2Ethanol + 2NADH + NADPH + 2ATP • GAP + NAD + 2ADP ↔ 2Ethanol + NADH + 2ATP

  14. 10% Results for Test Case #3

  15. 10% Summary of Project Contributions • What was your project able to accomplish? • If someone else was going to use some of your ideas to solve this problem, which ones would you recommend?

  16. 5% BONUS points What Worked and Did not Work • List any clever or seemingly obvious ideas that you tried and did not work.

  17. 10% BONUS points Make a skype or in-person appointment with Prof Dec 20 or 21 to review your project

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