ran libeskind-hadas, department of computer science thanks to eliot bush (biology) and zach dodds...
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Ran Libeskind-Hadas, Department of Computer Science
Thanks to Eliot Bush (Biology) and Zach Dodds (Computer Science)
Bioinformatics Education at Harvey Mudd College
Our name is Mudd…
• Undergraduate only; 700 students
• Sciences, mathematics, and engineering
Our name is Mudd…
• Undergraduate only; 700 students
• Sciences, mathematics, and engineering
Our name is Mudd…
• Undergraduate only; 700 students
• Sciences, mathematics, and engineering
The HMC Curriculum
Major
CoreHumanities
Electives
Includes one semester of CSand one of Biology
Experiments in the Core
The “regular” pathIntroduction to CSIntroduction to CS
Semester 1 Semester 2
Introduction to BiologyIntroduction to Biology
An integrated fullyear course
Integrated Introduction to CS and BiologyIntegrated Introduction to CS and Biology
A one semesterintegrated course
20 studentsin 2009-2010
200 studentsper year
Computation and BiologyComputation and Biology Introduction
to BiologyIntroduction to Biology
Introduction to BiologyIntroduction to Biology
Introduction to BiologyIntroduction to Biology
… or a second Biology course
40 studentsin 2010-2011
Satisfies CS core requirementbut not the Biology requirement
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
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
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
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
Course Structure
Tuesday
Thursday
Friday
Biologist
Lab!
C.S.ist
Weekend
Assignment
CSist
Biology CS Subset of student HWw
ks 1
-3w
ks 4
-5W
ks 6
-7W
ks 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
Biology CS Subset of student HWw
ks 1
0-1
1W
ks 1
1-12
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 modelingSystems biology and
modeling: Chemotaxis
Topics Limitations of computation
Folding: RNA to Proteins
Using computation to teach biology fundamentals
Population genetic model
Explore effects of drift and selection,
Hardy-Weinberg equilibrium
Using biology to motivate computation: RNA Folding
Recursion and memoization
Above and Beyond…
Above and Beyond…
Final project example: What makes cholera pathogenic?
Pathogenic vs. non-pathogenic strains
Final project example: What makes cholera pathogenic?
Compare all genes in one strain with all in other to find orthologs (use fast global alignment)
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
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
Courtesy of Prof. Russell Schwartz
Intuition Behind Network Inference
0
11
11
00
10
11
01
00
00
11
1
1
4
32
+ -
-
1
32
+
-
1
32
+ -
1
32 -
-
1
32
+
-
-
…
conditions
correlated expression implies common regulation that intuition still leaves a lot of ambiguity
Courtesy of Prof. Russell Schwartz
gene 1gene 2gene 3gene 4
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
1 01 0 1 1 1 00 1 0 1 1 1 1 0
conditions
gene 1gene 2
0 0 1 0 0 0 0 10 0 0 0 0 1 0 1
gene 3gene 4
1
32
4 1
32
4OK NOT OK
Courtesy of Prof. Russell Schwartz
The Project
Take binary microarray data as input Find the acyclic regulatory network with the
highest likelihood Display the network somehow
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
What did students choose to do the following term?
Students have one elective in the spring term
Took introductory biology: 0/40Took an elective other than CS or biology: 0/40Took an “upper division” biology course: 18/40Took the second CS course: 22/40 Outperformed
their peers
• 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
• 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
• 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
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
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
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
Questions, Comments, Heckles