Michael C. Reed
- Professor of Mathematics
- Arts & Sciences Professor
- Bass Fellow
Professor Reed is engaged in a large number of research projects that involve the application of mathematics to questions in physiology and medicine. He also works on questions in analysis that are stimulated by biological questions. For recent work on cell metabolism and public health, go to email@example.com/metabolism.
Since 2003, Professor Reed has worked with Professor Fred Nijhout (Duke Biology) to use mathematical methods to understand regulatory mechanisms in cell metabolism. Most of the questions studied are directly related to public health questions. A primary topic of interest has been liver cell metabolism where Reed and Nijhout have created mathematical models for the methionine cycle, the folate cycle, and glutathione metabolism. The goal is to understand the system behavior of these parts of cell metabolism. The models have enabled them to answer biological questions in the literature and to give insight into a variety of disease processes and syndromes including: neural tube defects, Down’s syndrome, autism, vitamin B6 deficiency, acetaminophen toxicity, and arsenic poisoning.
A second major topic has been the investigation of dopamine and serotonin metabolism in the brain; this is collaborative work with Professor Nijhiout and with Janet Best, a mathematician at The Ohio State University. The biochemistry of these neurotransmitters affects the electrophysiology of the brain and the electrophysiology affects the biochemistry. Both affect gene expression, the endocrine system, and behavior. In this complicated situation, especially because of the difficulty of experimentation, mathematical models are an essential investigative tool that can shed like on questions that are difficult to get at experimentally or clinically. The models have shed new light on the mode of action of selective serotonin reuptake inhibitors (used for depression), the interactions between the serotonin and dopamine systems in Parkinson’s disease and levodopa therapy, and the interactions between histamine and serotonin.
Recent work on homeostatic mechanisms in cell biochemistry in health and disease have shown how difficult the task of precision medicine is. A gene polymorphism may make a protein such as an enzyme less effective but often the system compensates through a variety of homeostatic mechanisms. So even though an individual's genotype is different, his or her phenotype may not be different. The individuals with common polymorphisms tend tend to live on homeostatic plateaus and only those individuals near the edges of the plateau are at risk for disease processes. Interventions should try to enlarge the homeostatic plateau around the individual's genotype.
Other areas in which Reed uses mathematical models to understand physiological questions include: axonal transport, the logical structure of the auditory brainstem, hyperacuity in the auditory system, models of pituitary cells that make luteinizing hormone and follicle stimulating hormone, models of maternal-fetal competition, models of the owl’s optic tectum, and models of insect metabolism.
For general discussions of the connections between mathematics and biology, see his articles: ``Why is Mathematical Biology so Hard?,'' 2004, Notices of the AMS, 51, pp. 338-342, and ``Mathematical Biology is Good for Mathematics,'' 2015, Notices of the AMS, 62, pp., 1172-1176.
Often, problems in biology give rise to new questions in pure mathematics. Examples of work with collaborators on such questions follow:
Laurent, T, Rider, B., and M. Reed (2006) Parabolic Behavior of a Hyberbolic Delay Equation, SIAM J. Analysis, 38, 1-15.
Mitchell, C., and M. Reed (2007) Neural Timing in Highly Convergent Systems, SIAM J. Appl. Math. 68, 720-737.
Anderson,D., Mattingly, J., Nijhout, F., and M. Reed (2007) Propagation of Fluctuations in Biochemical Systems, I: Linear SSC Networks, Bull. Math. Biol. 69, 1791-1813.
McKinley S, Popovic L, and M. Reed M. (2011) A Stochastic compartmental model for fast axonal transport, SIAM J. Appl. Math. 71, 1531-1556.
Lawley, S. Reed, M., Mattingly, S. (2014), Sensitivity to switching rates in stochastically switched ODEs,'' Comm. Math. Sci. 12, 1343-1352.
Lawley, S., Mattingly, J, Reed, M. (2015), Stochastic switching in infinite dimensions with applications to parabolic PDE, SIAM J. Math. Anal. 47, 3035-3063.
Ulrich, CM, Nijhout, HF, and Reed, MC. "Mathematical modeling: epidemiology meets systems biology." Cancer Epidemiol Biomarkers Prev 15.5 (May 2006): 827-829. Full Text
Nijhout, HF, Reed, MC, Anderson, DF, Mattingly, JC, James, SJ, and Ulrich, CM. "Long-range allosteric interactions between the folate and methionine cycles stabilize DNA methylation reaction rate." Epigenetics 1.2 (April 2006): 81-87.
Laurent, T, Rider, B, and Reed, M. "Parabolic behavior of a hyperbolic delay equation." Siam Journal on Mathematical Analysis 38.1 (March 1, 2006): 1-15. Full Text
Nijhout, HF, Reed, MC, Anderson, DF, Mattingly, JC, James, SJ, and Ulrich, CM. "Long-Range Allosteric Interactions between the Folate and Methionine Cycles Stabilize DNA Methylation Reaction Rate (vol 2, pg 81, 2006)." EPIGENETICS 1.3 (2006): 115-115. Full Text
Nijhout, HF, Reed, MC, Budu, P, and Ulrich, CM. "A mathematical model of the folate cycle: new insights into folate homeostasis." The Journal of Biological Chemistry 279.53 (December 2004): 55008-55016. Full Text
Reed, MC. "Why is mathematical biology so hard?." Notices of the American Mathematical Society 51.3 (March 1, 2004): 338-null.
Reed, MC, Nijhout, HF, Sparks, R, and Ulrich, CM. "A mathematical model of the methionine cycle." J Theor Biol 226.1 (January 7, 2004): 33-43.
Washington, TM, Blum, JJ, Reed, MC, and Conn, PM. "A mathematical model for LH release in response to continuous and pulsatile exposure of gonadotrophs to GnRH." Theoretical Biology and Medical Modelling 1 (2004). Full Text
Reed, MC, Blum, JJ, and Mitchell, CC. "Precision of neural timing: effects of convergence and time-windowing." J Comput Neurosci 13.1 (July 2002): 35-47.
Solodovnikov, A, and Reed, MC. "Robustness of a neural network model for differencing." J Comput Neurosci 11.2 (September 2001): 165-173.