1. Links between biology and math at Haverford College Phil Meneely (Biology) Rob Manning (Math)

2. Collaborative Research We have a relatively large student-faculty research program, especially in biology (typical biology professor runs a lab with 4-8 students during the academic year and summer). Some projects have linked math & bio, often co-advised, e.g., –Quantitative analysis of AFM images of myosin rod-domain filaments (Rigotti et al, Anal. Biochem. 346 (2005) 189.) –Multiple alignments of chromosomal proteins in C. elegans –Network analysis of the C. elegans germline proteins –Elastic rod models of DNA cyclization experiments –Epidemiological modeling of the effect of various testing procedures on the spread of Ebola –Statistical analysis of flexibility of helix-pairs in the PDB

3. Quantitative AFM image analysis of unusual filaments formed by the Acanthamoeba castellanii Myosin II rod domain. Daniel J. Rigotti,* Bashkim Kokona,* Theresa Horne, † Eric K. Acton, § Carl D. Lederman, † Karl A. Johnson,* Robert S. Manning, § Suzanne Amador Kane, † Walter F. Smith, † and Robert Fairman*,1 *Department of Biology, † Department of Physics and, § Department of Mathematics, Haverford College, 370 Lancaster Ave, Haverford, PA 19041 Collaborative Research: from Anal. Bioch.(2005) 346:189-200

4. Summer journal club Weekly summer journal club for entire science division (for faculty and 50+ summer research students) Many topics are interdisciplinary, often linking math, CS, & bio, e.g., –“Evidence for dynamically organized modularity in the yeast protein-protein interaction network” –“Superfamilies of evolved and designed networks” –“Coupling between catalytic site and collective dynamics: A Requirement for Mechanochemical Activity of Enzymes”

5. Milo et al. 2004, Science 303: 1538-1542 Does the frequency of “motifs” characterize a network? A Journal Club Talk from the Summer of 2005

6. Faculty Development Series of HHMI faculty seminars involving 6-12 faculty from multiple science departments (plus some social science and humanities): –Computing Across the Sciences (2000-01) –Bioinformatics (2001-02) –Science and Society (2002-03) –Statistics Across the Curriculum (2003-04) –Imaging (2007-08)

7. Faculty Development (con’t) Weekly presentations made by groups of 2-3 faculty from different departments, e.g.,: –Computational techniques in genomics (math, biology) –Numerical methods in molecular mechanics (math, chemistry, physics) –Hypothesis testing (biology, economics) –Analysis of Variance (psychology, chemistry, math) –Drug development and public health (chemistry, biology, economics) –What is modeling? (physics, CS, math)

8. Faculty Development (con’t) Concrete Goals –New course “Computing Across the Sciences”, and production of “course modules” in scientific computation by seminar participants –New course “Computational Genomics” and outside experts for specific technical training in bioinformatics Intangible Goals –For many, best part of seminar was chance to work with faculty from another department and division –Great way to see firsthand some differences between departments: terminology, level of mathematical formalism, what do students “need to know”, etc.

9. Faculty Development Statistics Group

10. Curriculum: Math in Biology Challenges A distinctive constraint: our biology department is entirely molecular/cellular, so some familiar applications of mathematical biology such as population dynamics are not in our curriculum (but others, like bioinformatics and network biology, fit naturally). Due to limited number of courses in liberal arts curriculum (32 in 4 years, including distribution requirements, and several chemistry prereqs for biology major), no math course required for biology major

11. Curriculum: Math in Biology (cont) Biology 354: Computational Genomics –Junior/senior level course –Mostly biology or biochemistry students, few math students –Lecture and workshop format –Open-ended student projects and presentations

12. Computational Genomics Syllabus Spring 2007 WeekDatesLecture 1March 20Genome projectsDemo: accessing databases March 22Workshop: organism databases, NCBI 2March 27Reports; Alignment basicsDemo: BLAST March 29Alignments. Workshop: dot plots, BLAST 3April 3Reports; Scoring Matrices, statistics April 5Workshop: other BLAST tools, PSI-BLAST 4April 10Multiple alignmentsDemo: CLUSTALW April 12Workshop: Multiple Alignments 5April 17presentations on multiple alignments April 19Gene findingDemo: EST assembly 6April 24Comparative genomicsWorkshop: comparative genomics April 26Comparative genomics and predicting regulatory regions 7May 1Presentations May 3Presentations

13. Curriculum: Math in Biology (cont) Lab module on bacterial growth in Bio 200 (Intro Bio) –Basic understanding of dN/dt = kN: sample mathematical derivation of solution; assigned reading: Neidhardt, “Bacterial Growth: Constant Obsession with dN/dt”, J. Bacteriology, 181 (1999) 7405. –Grow E. coli in Luria-Bertani medium –Quantify growth via optical density measurements (serial dilution added for improved accuracy) –Examine effect of antibiotics on growth curves, also situations in which dN/dt = kN model breaks down

14. Curriculum: Math in Biology (cont) Statistics modules/consulting in Bio 499 (senior seminar) –Statistician made a couple of presentations to biology seniors and faculty on basics of experimental design and data analysis –Throughout the year, served as statistical consultant for students as they developed their senior project –Future development: with a new tenure-line statistician, we’re considering this model as a half- credit “consulting course” – attach statistician and a few students to a different senior seminar each year?

15. Curriculum: Biology in CS/Math Challenges With small student body and faculty size, unlikely to regularly offer classes dedicated to biology students (though we have offered such a class every few years) Core of our major is in pure math; applied electives often not taken until junior year or later

16. Curriculum: Biology in CS/Math CS 185: Computing Across the Sciences  Co-taught by computer science and other faculty members (including biology)  Involves some programming with different scientific questions: the n-body problem, alignments, protein structure  BUT: enrollments have been small

17. Curriculum: Biology in CS/Math Math 222: Introduction to Scientific Computing –“Look under the hood” at fundamental algorithms: nonlinear equations, optimization, random simulation, discretizations of differential equations –Lab-based (Mathematica): each problem offers students a choice between application in natural or social science –Some biological applications: bioinformatics, molecular mechanics, polymer statistics, reaction- diffusion equations, genetic algorithms

18. Curriculum: Biology in CS/Math Math 222: Introduction to Scientific Computing –Some examples Genetic algorithm solving a knapsack problem Persistence length via simulated random polymers Best-fit drug decay curves: exponential, bi-exponential

19. Outreach Haverford Summer Science Institute –For incoming science/premed students from high schools with no AP courses (this audience often struggles in 1 st year chemistry and calculus, never making it to biology) –5-week “boot camp” in chemistry, pre-calculus at a level representative of our intro courses –Weekly labs in each science discipline –Mentoring during the 1st year –Research placement during summer after 1 st year –Develops peer group, relationships with science faculty

20. Future goals Develop core set of mathematical/computational skills in biology students, tailored to some degree to our molecular/cellular specialization Develop stronger curricular ties between math and biology at upper level Minor in computational science – more bio majors taking advanced math/CS courses, and vice versa