This talk describes the interdisciplinary undergraduate mathematical biology program at Truman State University, its history and development, and the minor degree it offers.

1. Highs and Lows of the
Interdepartmental MathBio
Program
Jason Miller, Ph.D.
Department of Mathematics
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2. Outline
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3. Outline
• Truman’s Program (Development and Current
State)
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4. Outline
• Truman’s Program (Development and Current
State)
• Successes
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5. Outline
• Truman’s Program (Development and Current
State)
• Successes
• Failures ... Challenges
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6. Outline
• Truman’s Program (Development and Current
State)
• Successes
• Failures ... Challenges
• Lessons Learned & Conversation
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7. Truman is...
• ... public liberal arts & sciences
• ... medium sized (≈6000 students, 320
faculty)
• ... geographically isolated, rural
• ... highly selective (ave. ACT of first-year
student≥27)
• ... lean, affordability Mission
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8. Other Truman Factoids
• T&P Scholarly expectation varies between
departments
• Biology: medium
• Math & CS: (very) low
• No formalized definition for faculty
workload beyond ‘credit load’ or ‘contact
hours’
• Deep commitment to undergraduate
research
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9. Photo by w4nd3rl0st (InspiredinDesMoines) - http://flic.kr/p/aPw9Xe
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10. Mathematical Biology
Program
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11. pre-2003
Biology
Math
(Silos not to scale)
Photo by keeva999 - http://flic.kr/p/bXchED SMB 2012

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13. 2003
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14. 2003 2005
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15. 2003 2005
2010
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18. UBM Program
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19. UBM Program
• a small group of faculty from math, CS, and
biology leveraged Truman strengths and
Hopper’s Law of Retroaction:
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20. UBM Program
• a small group of faculty from math, CS, and
biology leveraged Truman strengths and
Hopper’s Law of Retroaction:
“It is easier to seek forgiveness than
permission.”
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21. UBM Program
• a small group of faculty from math, CS, and
biology leveraged Truman strengths and
Hopper’s Law of Retroaction:
“It is easier to seek forgiveness than
permission.”
• NSF UBM grants in 2003, 2004, and 2009
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22. UBM Program
• a small group of faculty from math, CS, and
biology leveraged Truman strengths and
Hopper’s Law of Retroaction:
“It is easier to seek forgiveness than
permission.”
• NSF UBM grants in 2003, 2004, and 2009
• Established research-focused
interdisciplinary training program in mathbio.
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23. Research-focus
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24. Research-focus
• Multidisciplinary teams
• two students, two faculty
• students (resp. faculty) from each discipline
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25. Research-focus
• Multidisciplinary teams
• two students, two faculty
• students (resp. faculty) from each discipline
• focus on a question arising from biologist’s
lab, research programme
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26. Research-focus
• Multidisciplinary teams
• two students, two faculty
• students (resp. faculty) from each discipline
• focus on a question arising from biologist’s
lab, research programme
• start 1 Jan to prepare & plan; execute during
residential & coordinated Summer; complete
arc in Fall, end by 31 Dec.
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27. UBM Accomplishments
• about 60 student participants
• 20+ faculty participants
• 80%+ students to graduate school
• 10%+ students to industry
• 20 papers in peer-reviewed scientific
journals
• scores of presentations at regional, national,
and international meetings
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28. UBM Accomplishments
From more than one ‘mathphobic’ biology
faculty member, research mentor:
“This program has changed the way
I think about doing research.”
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29. UBM Accomplishments
From more than one ‘mathphobic’ biology
faculty member, research mentor:
“This program has changed the way
I think about doing research.”
If it’s changing the way they think in the lab,
then it’s changing the way they talk with
students about mathematics
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30. “We are not trying to turn mathematics
majors into biology majors, nor are we
trying to turn biology majors into
mathematics majors.
Rather, we are trying to bring both together
at the intersection of the life and
mathematical sciences to train them to work
across disciplinary boundaries.”
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31. “We are not trying to turn mathematics
majors into biology majors, nor are we
trying to turn biology majors into
mathematics majors.
Rather, we are trying to bring both together
at the intersection of the life and
mathematical sciences to train them to work
across disciplinary boundaries.”
We work to bridge an epistemological gap
between the mathematical and life sciences.
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40. ‘Convergent’ teamwork is going to be a defining
characteristic of 21st century science and
mathematics.
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41. ‘Convergent’ teamwork is going to be a defining
characteristic of 21st century science and
mathematics.
It can’t be taught through a series of lectures.
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42. ‘Convergent’ teamwork is going to be a defining
characteristic of 21st century science and
mathematics.
It can’t be taught through a series of lectures.
It can’t be taught from a textbook or by reading a
journal paper.
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43. ‘Convergent’ teamwork is going to be a defining
characteristic of 21st century science and
mathematics.
It can’t be taught through a series of lectures.
It can’t be taught from a textbook or by reading a
journal paper.
It can’t be taught in a course for a (single) major.
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44. ‘Convergent’ teamwork is going to be a defining
characteristic of 21st century science and
mathematics.
It can’t be taught through a series of lectures.
It can’t be taught from a textbook or by reading a
journal paper.
It can’t be taught in a course for a (single) major.
The above activities can motivate students and
prepare them to learn to be ‘convergent’
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45. ‘Convergence’ can be taught...
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46. ‘Convergence’ can be taught...
• Our experience provides strong evidence that
proper hands-on undergraduate research (or
research-like) projects can train undergraduates to
be ‘convergent’
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47. ‘Convergence’ can be taught...
• Our experience provides strong evidence that
proper hands-on undergraduate research (or
research-like) projects can train undergraduates to
be ‘convergent’
• Essential characteristics:
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48. ‘Convergence’ can be taught...
• Our experience provides strong evidence that
proper hands-on undergraduate research (or
research-like) projects can train undergraduates to
be ‘convergent’
• Essential characteristics:
• it’s a real research project to the mentors
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49. ‘Convergence’ can be taught...
• Our experience provides strong evidence that
proper hands-on undergraduate research (or
research-like) projects can train undergraduates to
be ‘convergent’
• Essential characteristics:
• it’s a real research project to the mentors
• mentors from different disciplines
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50. ‘Convergence’ can be taught...
• Our experience provides strong evidence that
proper hands-on undergraduate research (or
research-like) projects can train undergraduates to
be ‘convergent’
• Essential characteristics:
• it’s a real research project to the mentors
• mentors from different disciplines
• undergraduates from different disciplines
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51. ‘Convergence’ can be taught...
• Our experience provides strong evidence that
proper hands-on undergraduate research (or
research-like) projects can train undergraduates to
be ‘convergent’
• Essential characteristics:
• it’s a real research project to the mentors
• mentors from different disciplines
• undergraduates from different disciplines
• long-term immersion
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52. ‘Convergence’ can be taught...
• Our experience provides strong evidence that
proper hands-on undergraduate research (or
research-like) projects can train undergraduates to
be ‘convergent’
• Essential characteristics:
• it’s a real research project to the mentors
• mentors from different disciplines
• undergraduates from different disciplines
• long-term immersion
• students have sense of significant ownership
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53. We Wanted More...
• impact a bigger group of students
• institutionalize the changes in culture,
activity
• courses
• Bioinformatics
• Introduction to Mathematical Biology
• Biostatistics/Biometry
• Introduction to Computational Science*
• (new, 2012) Calculus & Mathematical Methods for
the Life Sciences
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54. Competency-based
Minor
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55. Competency-based
Minor
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56. Data Competency-based
Minor
Modeling
Computational
Statistics
Interdisciplinary
Research
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57. Data Competency-based
Minor
Modeling
Computational
• Demonstrate proficiencies in each
category (though research,
courses)
Statistics
Interdisciplinary
Research
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58. Data Competency-based
Minor
Modeling
Computational
• Demonstrate proficiencies in each
category (though research,
courses)
Statistics • Earn 15+ credits doing so (must
take Intro to MathBio course)
Interdisciplinary
Research
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59. Data Competency-based
Minor
Modeling
Computational
• Demonstrate proficiencies in each
category (though research,
courses)
Statistics • Earn 15+ credits doing so (must
take Intro to MathBio course)
Interdisciplinary • Attend MathBio Seminar
Research
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60. Data Competency-based
Minor
Modeling
• competencies straddle disciplinary
boundaries
Computational
• create learning plan
• use experiences (incl. courses) to
Statistics show competencies
• faculty oversight committee
Interdisciplinary approves plan, notifies Registrar
Research when completed
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61. Other Courses
• Any that makes a connection between the
areas. Some example:
• Math Modeling • Developmental
• Ecology Biology
• ODEs • (Electron)
Microscopy
• Genetics of • Plant/Animal
Animal and Plant
Improvement Breeding
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62. Nota Bene
• The 15+ credit minor only adds 2-ish courses
to a student’s major
• At conferences...
“I wish we could do that...”
• Getting it through governance...
[ovation]
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63. Challenges
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68. Our program is costly
• research program:
• ≈$40k per team
• time and effort to recruit
• not easily sustainable
• minor: faculty time
• oversight
• recruiting, mentoring students
• courses
• departmental zero-sum, silo mentality
• team-teaching is seen as frivolous
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69. Funding Reality
1998 2015
From Tuition From State
How to sustain a program in this environment?
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70. Funding Reality
• Show program’s cost-benefit lean
‘benefit’ (e.g., credit generation, revenue)
• Show your program’s outcomes align with
University’s strategic plan
• Track student successes (e.g., subsequence
grades, post-graduation experiences) and
share
• Cultivate faculty buy-in (individual, group,
departmental, and school)
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71. Minor Production
• MathBio Minor: competency based
(requires planning, paperwork, and approval)
• Cognitive Science Minor: course-based
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72. Minor Production
Cognitive Science MathBio
5
4
3
2
1
0
2009 2010 2011 2012
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73. Minor Production
Cognitive Science MathBio Env Studies
12
9
6
3
0
2009 2010 2011 2012
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74. Minor Production
Cognitive Science MathBio Mathematics
20
16
12
8
4
0
2009 2010 2011 2012
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75. Minor Production
Cognitive Science MathBio Mathematics Biology
100
80
60
40
20
0
2009 2010 2011 2012
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76. Minor Production
Cognitive Science MathBio
5
4
3
2
1
0
2009 2010 2011 2012
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77. Minor Production
Cognitive Science MathBio
5
4
3
2
1
0
2009 2010 2011 2012
‘Cost’ of the minor exceed ‘benefit’.
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78. Program Dashboard
• Applications to the summer research
program are low
• Enrollment in interdisciplinary courses is
low-ish
• Our Intro to MathBio course was not team-
taught last semester
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79. Lessons Learned
• Starting a new interdepartmental program
requires guts and a theme that you (or
your team) can carry
• Sustaining a interdepartmental program
requires strong leadership and
administrative champion(s)
• Grant money opens a door, but membership
requires faculty buy-in
• Bringing math & bio together prepares you
to bring other disciplines into the game
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80. Lessons Learned
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81. Lessons Learned
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82. Lessons Learned
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83. http://www.vonnegutgroup.net
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84. Funding Opportunities
• The UBM program has been archived
• Alternative pathways:
• STEM Talent Expansion Program (STEP)
in DUE
• rumor: Experiences in Education (NSF-
wide)
• rumor: ‘why-der’ (phonetic) in DUE
• NIH mechanisms (???)
JMM New Orleans
9 January 2011

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86. millerj@truman.edu
This material is based upon work supported by the National Science Foundation under NSF
UBM #0337769, #0436348, and #0926737. Any opinions, findings, and conclusions or
recommendations expressed in this material are those of the author(s) and do not necessarily
reflect the views of the National Science Foundation.
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