Modeling molecular evolution

1. Modeling molecular evolution Jodi Schwarz and Marc Smith Vassar College Biol/CS353 Bioinformatics

2. Biol / CS 353 Bioinformatics • Team taught Biol and CompSci course • 7 students: • CS experience: 3 yes, 4 no • Bio experience: 5 yes, 2 no • Project-based course; no exams • Worked in Biol/CS pairs on projects • I3U near end of course; last project before independent research projects

3. Common approach for all projects • Biological question • Algorithm design • Step-by-step approach to complete a task or solve the problem • Implementation • The actual programming “script” that will carry out the steps of the algorithm • Evaluation of implementation and algorithm • Revision or augmentation

4. I3U: added an experimental component to our basic approach • Previous projects focused on pattern finding, mining whole genome data Goal of I3U: • Model a biological/evolutionary process • Test the model with empirical data • Perform computational experiments

5. Model molecular evolution • Step 1: model the effect of random vs targeted nucleotide substitutions on a protein sequence • What do we mean by random? • determine the similarity of the original protein sequence to the “evolved” sequence • Step 2: Assess the real nt diversity at positions 1, 2, 3 of codons in real homologs (HSP70) • Construct alignment of homologs and determine nt diversity at each position • Evaluate the models using the empirical data

6. Learning goals • CS students: To apply their knowledge of data structures and algorithms to a biological domain • Biology students: To apply their knowledge of the biology to design algorithms • For the collaboration: • To become familiar with modeling a biological process: a simple model must be constructed and tested first • To test the model using empirical data

7. Assessment • Assignments • Alignment assignment • 2 Perl scripts • Model random vs targeted substitution pattern • Determine the codon nt diversity in HSP70 genes • Output from the 2 Perl scripts • Raw output • Graphs summarizing data • Observation • Collaboration • Critical thinking

8. Example student results Effect of random vs targeted substitutions on a protein sequence (compared the “ancestral” sequence to the “evolved” sequence) 100 runs Random substitutions substitutions targeted to 3rd psn

9. Example student results of empirical data Average diversity by nucleotide position within codons: Codon position 1: 1.50 Codon position 2: 1.29 Codon position 3: 2.32 Most variation occurs in position 3

10. Collaboration across disciplines • How we tried to teach collaboration: • We defined the meaning of collaboration • CS students do not need to become biologists and vice versa • Each person contributes a different set of expertise • Learning how to speak each other’s language • Communication • We modeled it • Overt reliance on each other’s expertise • Spontaneous discussions • Giving students lots of experience collaborating: several shifts in pairs over the semester

11. Assessment of collaboration Attitude: reluctant vs eager At beginning (self) vs. during project (experience)

12. Likert Scale (1-5) Most improvement: questions that are explicitly bioinformatic Least: questions that are more broadly about genomics (CS)

13. What worked well • Overall approach was great: question, algorithm, implementation, analysis, iteration • Use of starter code allowed students to • Undertake much more sophisticated projects • see examples of more advanced algorithm/code • Encountering unanticipated results and problems • Gaps in alignments not in groups of 3 • Spontaneous discussions leading to AHA moments • Students enjoyed the modeling process • One student’s final project focused on modeling molecular evolution

14. What didn’t work as well • Some collaborations are not successful • Ran out of time: insufficient analysis and reflection • For the I3U: Assessment strategy not well developed • Can we retroactively extract more informative assessment?

16. Assessing biology knowledge • Algorithm development • Ability to help partner understand different mutation vs selection • Ability to recognize assumptions of model • Ability to use the empirical data to evaluate model

17. Assessing the CS • Variables • Abstraction: representing information as data • Types of data: predefined, atomic, aggregate • Scope: declaration, initialization, mutation • Algorithms • Control flow: unconditional, conditional, repetition • Input/Output and regex (pattern matching) • Top-down design: subroutines • To reuse or not to reuse (code)? • Incremental development / experimentation • Elegance: readability and maintainability

18. Biological question • What pattern of nucleotide substitution occurs in protein-coding genes? • Algorithm • What does we know about mutation, nt/AA sequences? • Assumptions • Implementation • Instructors provided “starter code” • Students read and ran the code to see what it did • Pairs discussed how to add and refine it, and did so • Evaluation • Analyze the CS: Did it run and did it do the job we asked? • Analyze the biology: Did it accurately represent the biological process? • Testing the models against empirical evidence • Aligned HSP70 genes and evaluated the pattern of substitution • Which model most closely matched the biology?