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

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 Major Core Humanities Electives Includes one semester of CS and one of Biology

6. Experiments in the Core The “regular” path Introduction to CS Semester 1Semester 2 Introduction to Biology An integrated full year course Integrated Introduction to CS and Biology A one semester integrated course 20 students in 2009-2010 200 students per year Computation and Biology Introduction to Biology … or a second Biology course 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 Tuesday Thursday Friday Biologist Lab! C.S.ist Weekend Assignment CSist

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

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

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…

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

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

23. 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: Assuming a Binary Input Matrix 10101110 0 1 01 111 0 conditions gene 1 gene 2 00 100001 00000101 gene 3 gene 4 1 3 2 4 1 3 2 4 OKNOT OK Courtesy of Prof. Russell Schwartz

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 “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 Likert scale (1 low, 7 high) survey: “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:4.98 hours (std. dev 2.42) Computation and Biology: 6.28 hours

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