Abstract
Mathematical biology has made significant contributions and advancements in the biological sciences. Recruitment efforts focus on encouraging students, especially those who are underrepresented and underserved, to pursue the field of mathematical biology, regardless of their undergraduate institution type, and raise awareness about the countless professional and academic possibilities provided by this specialized training. This article examines the need to expand, expose, and educate others about mathematical biology. To support field expansion, we give several recommendations of ways to integrate mathematics applied curricula to attract broader student interest. With this exposure—whether it is led by an individual, a department, a university, or researchers in mathematical biology—each can help to promote a base knowledge and appreciation of the field. In order to encourage the next generation of researchers to consider mathematical biology, we highlight current interdisciplinary programs, share popular mathematical tools, and present some thoughts on ways to support a thriving and inclusive mathematical biology community for years to come.
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References
21st Century Biology (2010). https://science.house.gov/hearings/21st-century-biology?1
Allman ES, Rhodes JA (2003) Mathematical models in biology—an introduction. Cambridge University Press, Cambridge
Anderson A (1998) Continuous and discrete mathematical models of tumor-induced angiogenesis. Bull Math Biol 60:857–899. https://doi.org/10.1006/bulm.1998.0042
Andrews SE, Aikens ML (2018) Life science majors’ math-biology task values relate to student characteristics and predict the likelihood of taking quantitative biology courses. J Microbiol Biol Educ. https://doi.org/10.1128/jmbe.v19i2.1589
Austin A (2018) Vision and change in undergraduate biology education: unpacking a movement and sharing lessons learned. American Association for the Advancement of Sciences. https://live-visionandchange.pantheonsite.io/wp-content/uploads/2018/09/VandC-2018-finrr.pdf
Baker B (2010) Have BIO2010 goals been achieved? Bioscience 60:496–497. https://doi.org/10.17226/10497
Beisser D, Klau GW, Dandekar T, Muller T, Dittrich MT (2010) BioNet: an R-package for the functional analysis of biological networks. Bioinformatics 26:1129–1130. https://doi.org/10.1093/bioinformatics/btq089
Berlin DF, Lee H (2005) Integrating science and mathematics education: historical analysis. School Sci Math 105:15–24. https://doi.org/10.1111/j.1949-8594.2005.tb18032.x
Bodine EN, Lenhart S, Gross LJ (2014) Mathematics for the life sciences. Princeton University Press, Princeton
Castillo-Chávez C, Castillo-Garsow C (2006) Increasing minority representation in the mathematical sciences: good models but no will to scale up their impact CHERI conference. Doctoral Education and the Faculty of the Future
Chiacchio F, Pennisi M, Russo G, Motta S, Pappalardo F (2014) Agent-based modeling of the immune system: NetLogo, a promising framework. Biomed Res Int 2014:1–6. https://doi.org/10.1155/2014/907171
Chou CS, Friedman A (2016) Introduction to mathematical biology: modeling, analysis and simulation. Springer, Switzerland
Eager EA, Peirce J, Barlow P (2014) Math bio or biomath? Flipping the mathematical biology classroom. Lett Biomath 1:139–155. https://doi.org/10.1080/23737867.2014.11414476
Edelstein-Keshet L (2005) Mathematical models in biology. SIAM, Philadelphia
Feser J, Vasaly H, Herrera J (2013) On the edge of mathematics and biology integration: improving quantitative skills in undergraduate biology education. Cell Biol Educ 12:124–128. https://doi.org/10.1187/cbe.13-03-0057
Furner JM, Kumar DD (2007) The mathematics and science integration argument: a stand for teacher education. Eur J Math Sci Technol Educ 3:185–189
Ganusov VV (2016) Strong inference in mathematical modeling: a method for robust science in the twenty-first century. Front Microbiol. https://doi.org/10.3389/fmicb.2016.01131
Goins G, Chen M, White C, Clemence D, Redd T, Kelkar V (2010) An initiative to broaden diversity in undergraduate biomathematics training. CBE Life Sci Educ 9:241–247. https://doi.org/10.1187/cbe.10-03-0043
Griffith AL (2010) Persistence of women and minorities in STEM field majors: is it the school that matters? Econ Educ Rev 29:911–922. https://doi.org/10.1016/j.econedurev.2010.06.010
Gross LJ (2017) Points of view: the interface of mathematics and biology. Cell Biol Educ 3
Guiffrida D (2005) To break away or strengthen ties to home: a complex issue for african american college students attending a predominantly white institution. Equity Excell Educ 38:49–60. https://doi.org/10.1080/10665680590907864
Hass H, Kipkeew F, Gauhar A, Bouche E, May P, Timmer J, Bock HH (2017) Mathematical model of early Reelin-induced Src family kinase-mediated signaling. PLoS ONE 12:e0186927. https://doi.org/10.1371/journal.pone.0186927
Hofstein A, Lunetta VN (1982) The role of the laboratory in science teaching: neglected aspects of research. Rev Educ Res 52:201–217. https://doi.org/10.3102/00346543052002201
Hoops S et al (2006) COPASI—a complex pathway simulator. Bioinformatics 22:3067–3074. https://doi.org/10.1093/bioinformatics/btl485
Hoppensteadt F (1995) Getting started in mathematical biology. Not AMS 42:972–975
Hunter P (2010) Biology is the new physics. EMBO Rep 11:350–352. https://doi.org/10.1038/embor.2010.55
Integrated Postsecondary Education Data System (IPEDS) (2017) Institute of Education Sciences, National Center for Education Statistics
Jackson A (2007) A labor of love: the mathematics genealogy project. Not AMS 54:1002–1003
Kurepa A (2019) The design and implementation of a mathematics learning community. Int J High Educ 8(3):77–82
Lauffenburger DA, Kennedy CR (1983) Localized bacterial infection in a distributed model for tissue inflammation. J Math Biol 16:141–163. https://doi.org/10.1007/bf00276054
Lunch C, Crall A, Jones MA, Jones KD (2019) Diversity, equity, and inclusion in data science: introducing the environmental data science inclusion network (EDSIN). AGUFM, 2019, ED21E − 1068
Madlung A, Bremer M, Himelblau E, Tullis A (2011) A study assessing the potential of negative effects in interdisciplinary math–biology instruction. CBE Life Sci Educ 10:43–54. https://doi.org/10.1187/cbe.10-08-0102
Margot KC, Kettler T (2019) Teachers’ perception of STEM integration and education: a systematic literature review. Int J STEM Educ 6(1):2
Matthews KE, Adams P, Goos M (2009) Putting it into perspective: mathematics in the undergraduate science curriculum. Int J Math Educ Sci Technol 40:891–902. https://doi.org/10.1080/00207390903199244
Meyer MR (1989) Equity: the missing element in recent agendas for mathematics education. Peabody J Educ 66:6–21. https://doi.org/10.1080/01619568909538636
Miller LA, Alben S (2012) Interfacing mathematics and biology: a discussion on training, research, collaboration, and funding. Integr Comp Biol 52:616–621. https://doi.org/10.1093/icb/ics097
Miller JE, Walston T (2010) Interdisciplinary training in mathematical biology through team-based undergraduate research and courses. CBE Life Sci Educ 9:284–289. https://doi.org/10.1187/cbe.10-03-0046
Miriti MN (2020) The elephant in the room: race and STEM diversity. Bioscience 70(3):237–242. https://doi.org/10.1093/biosci/biz167
Museus SD, Yi V, Saelua N (2017) The impact of culturally engaging campus environments on sense of belonging. Rev High Educ 40:187–215. https://doi.org/10.1353/rhe.2017.0001
National Academies of Sciences, Engineering, and Medicine (2019) Minority serving institutions: America’s underutilized resource for strengthening the STEM workforce. National Academies Press, New York
Oremland M, Michels KR, Bettina AM, Lawrence C, Mehrad B, Laubenbacher R (2016) A computational model of invasive aspergillosis in the lung and the role of iron. BMC Syst Biol 10:9. https://doi.org/10.1186/s12918-016-0275-2
Otto S, Troy D (2007) A biologist’s guide to mathematical modeling in ecology and evolution. Princeton University Press, Princeton
Paleontology UoCMo (2020) Understanding science. Accessed 8 Jan 2020
Perna L, Lundy-Wagner V, Drezner ND, Gasman M, Yoon S, Bose E, Gary S (2009) The contribution of HBCUS to the preparation of African American women for stem careers: a case study. Res High Educ 50:1–23. https://doi.org/10.1007/s11162-008-9110-y
Pulley M, Rodriguez Quinones L, Kohler B, Gordillo LF (2019) Marshmallow Lab. https://digitalcommons.usu.edu/lemb/7
PCAST (2020) Prepare and inspire: K-12 Education in Science, Technology, Engineering, and Math (STEM) for America’s Future. Executive Office of the President. https://science.osti.gov/-/media/_/pdf/about/pcast/202006/PCAST_June_2020_Report.pdf
Rapoport A (1951) The aims and tasks of mathematical biology ETC. Rev Gen Semant 8:254–269
Raue A et al (2015) Data2Dynamics: a modeling environment tailored to parameter estimation in dynamical systems. Bioinformatics 31:3558–3560. https://doi.org/10.1093/bioinformatics/btv405
Reed M (2004) Why is mathematical biology so hard. Not Am Math Soc 51:338–342
Richards DAR, Awokoya JT (2012) Understanding HBCU retention and completion. Frederick D. Patterson Research Institute, Fairfax
Robeva R, Laubenbacher R (2009) Mathematical biology education: beyond calculus. Science 325:542–543. https://doi.org/10.1126/science.1176016
Scheper TO, Klinkenberg D, Pennartz C, Van Pelt J (1999) A mathematical model for the intracellular circadian rhythm generator. J Neurosci 19:40–47. https://doi.org/10.1523/jneurosci.19-01-00040.1999
Sgouralis I, Layton AT (2015) Mathematical modeling of renal hemodynamics in physiology and pathophysiology. Math Biosci 264:8–20. https://doi.org/10.1016/j.mbs.2015.02.016
Shadding CR, Whittington D, Wallace LE, Wandu WS, Wilson RK (2016) Cost-effective recruitment strategies that attract underrepresented minority undergraduates who persist to STEM doctorates. SAGE Open 6:215824401665714. https://doi.org/10.1177/2158244016657143
Smith E, Haarer S, Confrey J (1997) Seeking diversity in mathematics education: mathematical modeling in the practice of biologists and mathematicians. Sci Educ 6:441–472
Theobald EJ et al (2020) Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math. Proc Natl Acad Sci 117:6476–6483
Toldson I (2018) Why historically black colleges and universities are successful with graduating black baccalaureate students who subsequently earn doctorates in STEM (editor’s commentary). J Negro Educ 87:95. https://doi.org/10.7709/jnegroeducation.87.2.0095
Trujillo G, Tanner KD (2014) Considering the role of affect in learning: monitoring students’ self-efficacy, sense of belonging, and science identity. CBE Life Sci Educ 13:6–15. https://doi.org/10.1187/cbe.13-12-0241
Tyson JJ (1991) Modeling the cell division cycle: cdc2 and cyclin interactions. Proc Natl Acad Sci 88:7328–7332. https://doi.org/10.1073/pnas.88.16.7328
Ullah M, Schmidt H, Cho KH, Wolkenhauer O (2006) Deterministic modelling and stochastic simulation of biochemical pathways using MATLAB. IEE Proc Syst Biol 153:53. https://doi.org/10.1049/ip-syb:20050064
Upton R, Tanenbaum C (2014) The role of historically black colleges and universities as pathway providers: institutional pathways to the STEM PhD among black students (Broadening participation in STEM graduate education issue brief). American Institutes for Research (AIR)
Venturelli OS et al (2018) Deciphering microbial interactions in synthetic human gut microbiome communities. Mol Syst Biol 14:e8157. https://doi.org/10.15252/msb.20178157
Villasana M, Radunskaya A (2003) A delay differential equation model for tumor growth. J Math Biol 47:270–294. https://doi.org/10.1007/s00285-003-0211-0
Walker E (2007) Why aren’t more minorities taking advanced math? Educ Leadership 65:48–53
Wang X (2013) Why students choose STEM majors. Am Educ Res J 50:1081–1121. https://doi.org/10.3102/0002831213488622
Winkle-Wagner R, McCoy DL (2018) Feeling like an “Alien” or “Family”? Comparing students and faculty experiences of diversity in STEM disciplines at a PWI and an HBCU. Race Ethnic Educ 21:593–606. https://doi.org/10.1080/13613324.2016.1248835
Yue P, Lee S, Afkhami S, Renardy Y (2012) On the motion of super paramagnetic particles in magnetic drug targeting. Acta Mech 223:505–527. https://doi.org/10.1007/s00707-011-0577-9
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Lee, S., Clinedinst, L. Mathematical Biology: Expand, Expose, and Educate!. Bull Math Biol 82, 120 (2020). https://doi.org/10.1007/s11538-020-00796-x
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DOI: https://doi.org/10.1007/s11538-020-00796-x