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Bioinformatics Education at Harvey Mudd College

Learn about the integrated courses in bioinformatics at Harvey Mudd College, combining computer science and biology to provide students with computational tools and in-depth understanding of genetics and molecular biology.

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Bioinformatics Education at Harvey Mudd College

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  1. Bioinformatics Education at Harvey Mudd College Ran Libeskind-Hadas, Department of Computer Science Thanks to Eliot Bush (Biology) and Zach Dodds (Computer Science)

  2. Our name is Mudd… • Undergraduate only; 700 students • Sciences, mathematics, and engineering

  3. Our name is Mudd… • Undergraduate only; 700 students • Sciences, mathematics, and engineering

  4. Our name is Mudd… • Undergraduate only; 700 students • Sciences, mathematics, and engineering

  5. The HMC Curriculum Includes one semester of CS and one of Biology Electives Core Humanities Major

  6. Experiments in the Core Semester 1 Semester 2 Introduction to Biology 200 students per year Introduction to CS The “regular” path Integrated Introduction to CS and Biology 20 students in 2009-2010 An integrated full year course Introduction to Biology … or a second Biology course Computation and Biology Introduction to Biology A one semester integrated course Introduction to Biology 40 students in 2010-2011 Satisfies CS core requirement but not the Biology requirement

  7. Computation and Biology Core Course Objectives: • Cover the content of the “regular” CS intro course • Demonstrate the relationship between computing and biology • Use computation to teach biology fundamentals and use biology to motivate computing fundamentals • Provide students with computational tools to perform their own “dry lab” experiments

  8. Computation and Biology Core Course Objectives: • Cover the content of the “regular” CS intro course • Demonstrate the relationship between computing and biology • Use computation to teach biology fundamentals and use biology to motivate computing fundamentals • Provide students with computational tools to perform their own “dry lab” experiments

  9. Computation and Biology Core Course Objectives: • Cover the content of the “regular” CS intro course • Demonstrate the relationship between computing and biology • Use computation to teach biology fundamentals and use biology to motivate computing fundamentals • Provide students with computational tools to perform their own “dry lab” experiments

  10. Computation and Biology Core Course Objectives: • Cover the content of the “regular” CS intro course • Demonstrate the relationship between computing and biology • Use computation to teach biology fundamentals and use biology to motivate computing fundamentals • Provide students with computational tools to perform their own “dry lab” experiments

  11. Course Structure Assignment Biologist Lab! Tuesday C.S.ist Friday Weekend CSist Thursday

  12. CS Biology Subset of student HW Introduction to Python: Data, functions, and basic constructs DNA, RNA, central dogma, genes: Case study of lactose intolerance Gene finding, gene expression, lactase expression wks 1-3 Mitochondrial Eve, diploid populations with selection, molecular evolution simulations Designing a larger program, randomness, simulation Population genetics, molecular evolution wks 4-5 Implement alignment and extend to deal with substitutions Sequence alignment Recursion Wks 6-7 Recursion on trees and phylogenetic tree algorithms Implementing a phylogenetic tree algorithm and making inferences from the results Phylogenetics Wks 8-9

  13. CS Biology Subset of student HW RNA folding algorithm, efficiency, and memoization Folding: RNA to Proteins Implement RNA folding and visualize results wks 10-11 Systems biology and modeling: Chemotaxis Wks 11-12 Chemotaxis simulations and evaluation of models Computation and modeling Wks 13-14 Topics Limitations of computation Capstone Projects

  14. Using computation to teach biology fundamentals Population genetic model Explore effects of drift and selection, Hardy-Weinberg equilibrium

  15. Using biology to motivate computation: RNA Folding Recursion and memoization

  16. Above and Beyond…

  17. Above and Beyond…

  18. Final project example: What makes cholera pathogenic? • Pathogenic vs. non-pathogenic strains

  19. Final project example: What makes cholera pathogenic? • Compare all genes in one strain with all in other to find orthologs (use fast global alignment)

  20. Final project example: What makes cholera pathogenic? Programmatically Blast unique proteins to see what they are • Read about these unique genes and explain what they do

  21. Courtesy of Prof. Russell Schwartz Microarray data… genes conditions Some genes encode for transcription factors that promote or inhibit the expression of other genes Purple is highly expressed, green is not expressed

  22. Courtesy of Prof. Russell Schwartz Intuition Behind Network Inference gene 1 0 1 0 1 1 gene 2 0 1 0 1 1 gene 3 1 0 1 0 0 gene 4 0 0 1 1 1 conditions 1 1 + - 1 + - 2 3 2 3 - - … 2 3 - 1 1 + + - - 4 2 3 2 3 - correlated expression implies common regulation that intuition still leaves a lot of ambiguity

  23. Courtesy of Prof. Russell Schwartz Assuming a Binary Input Matrix conditions gene 1 1 1 0 0 1 1 1 0 gene 2 1 1 1 1 0 0 1 0 gene 3 1 0 0 0 0 1 0 0 gene 4 0 0 0 0 0 1 0 1 1 1 4 4 OK NOT OK We will assume that genes only have two possible states: 0 (off) or 1 (on) We will also assume that we want to find directionality but not strength of regulatory interactions We will exclude the possibility of regulatory cycles: 2 2 3 3

  24. The Project Take binary microarray data as input Find the acyclic regulatory network with the highest likelihood Display the network somehow

  25. Student Response Likert scale (1 low, 7 high) survey: “This course stimulated my interest in the subject matter” College mean: 5.53/7.0 (std. dev 0.80) Computation and Biology: 6.51/7.0 “I learned a great deal in this course” College mean: 5.76/7.0 (std. dev 0.72) Computation and Biology: 6.49/7.0 “Time spent outside of class (per week)” College mean: 4.98hours (std. dev 2.42) Computation and Biology: 6.28hours

  26. What did students choose to do the following term? Students have one elective in the spring term Took introductory biology: 0/40 Took an elective other than CS or biology: 0/40 Took an “upper division” biology course: 18/40 Took the second CS course: 22/40 Outperformed their peers

  27. Students learned the foundational content of • “Intro CS” and “Intro Biology” • Students’ programs provide rich “dry lab” experiments • and simulations that reinforce understanding of biology • Students develop general problem-solving and • programming skills (e.g. DP) and have confidence to • solve “new” problems on their own

  28. Students learned the foundational content of • “Intro CS” and “Intro Biology” • Students’ programs provide rich “dry lab” experiments • and simulations that reinforce understanding of biology • Students develop general problem-solving and • programming skills (e.g. DP) and have confidence to • solve “new” problems on their own

  29. Students learned the foundational content of • “Intro CS” and “Intro Biology” • Students’ programs provide rich “dry lab” experiments • and simulations that reinforce understanding of biology • Students develop general problem-solving and • programming skills (e.g. DP) and have confidence to • solve “new” problems on their own

  30. Next steps… • Increasing student demand for more courses and even a major in computational biology • “Mathematical Biology Major” redesigned in Spring 2011 to “Mathematical and Computational Biology (MCB)” major • Good news: 9 MCB majors in sophomore year (6 Biology majors and 2 Biochemistry majors) • Bad news: Few faculty in a position to contribute

  31. Beyond the core (intro CS, intro Biology, 3 semesters math, 2 chemistry, 1 physics, …) • Introductory Sequence • Discrete Math • Biology laboratory • Introduction to Mathematical and Computational Biology • Biology Foundations • Three of: Comparative physiology, ecology and environmental biology, evolutionary biology, molecular biology • One biology seminar • One biology laboratory • Mathematical and Computation Courses • Intermediate Mathematical Biology • Computational Biology • One upper-division math course • One upper-division CS course • Three more math and CS courses • Electives, Thesis, Colloquium • One related elective • Colloquium • Senior thesis

  32. Future Plans… • Refine and improve introductory course • Write a book for the introductory course • Collaborate with “sister” institutions to expand computational biology curriculum • New faculty • New courses

  33. Questions, Comments, Heckles

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