Links between biology and math at Haverford College

1
Links between biology and math at Haverford
College

• Phil Meneely (Biology)
• Rob Manning (Math)

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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

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Quantitative AFM image analysis of unusual
filaments formed by the Acanthamoeba castellanii
Myosin II rod domain.
Collaborative Research from Anal. Bioch.(2005)
346189-200

• 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

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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

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

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Faculty Development (cont)

• 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)

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Faculty Development (cont)

• 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.

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Faculty Development Statistics Group
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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

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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

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Computational Genomics SyllabusSpring 2007
Week Dates Lecture
1 March 20 Genome projects Demo accessing databases
March 22 Workshop organism databases, NCBI
2 March 27 Reports Alignment basics Demo BLAST
March 29 Alignments. Workshop dot plots, BLAST
3 April 3 Reports Scoring Matrices, statistics
April 5 Workshop other BLAST tools, PSI-BLAST
4 April 10 Multiple alignments Demo CLUSTALW
April 12 Workshop Multiple Alignments
5 April 17 presentations on multiple alignments
April 19 Gene finding Demo EST assembly
6 April 24 Comparative genomics Workshop comparative genomics
April 26 Comparative genomics and predicting regulatory regions
7 May 1 Presentations
May 3 Presentations
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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

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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, were considering this model as a
  half-credit consulting course attach
  statistician and a few students to a different
  senior seminar each year?

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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

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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

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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

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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
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Outreach

• Haverford Summer Science Institute
• For incoming science/premed students from high
  schools with no AP courses (this audience often
  struggles in 1st 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 1st year
• Develops peer group, relationships with science
  faculty

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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