Brenae L Bailey
 Senior Lecturer
Contact
 (520) 6268536
 Mathematics, Rm. 318
 Tucson, AZ 85721
 bbailey@arizona.edu
Degrees
 Ph.D. Applied Mathematics
 University of Arizona, Tucson, Arizona, United States
 Stochastic Modeling of 1 Programmed Ribosomal Frameshifting
 M.S. Applied Mathematics
 University of Arizona, Tucson, Arizona, United States
 M.S. Astrophysics
 University of Wyoming, Laramie, Wyoming, United States
 XRay Emissions in Dwarf Galaxies
 B.A. Mathematics
 Oberlin College, Oberlin, Ohio, United States
Work Experience
 University of Arizona, Tucson, Arizona (2023  Ongoing)
 The University of Arizona (2017  2023)
 Summer Upward Bound Program, University of Wyoming (2014  2020)
 The University of Arizona (2012  2017)
 University of Wyoming, Laramie, Wyoming (2000  2004)
 Ivimila Secondary School (1997  1999)
Awards
 Teaching and Service Award
 Department of Mathematics, Spring 2019
Interests
Research
Stochastic Modeling, Teaching and Learning
Teaching
Algebra, Calculus, Discrete Mathematics, Statistics, Mathematics Education
Courses
202425 Courses

Calculus Preparation
MATH 120R (Fall 2024) 
FirstSemester Calculus
MATH 122B (Fall 2024)
202324 Courses

Calculus Preparation
MATH 120R (Spring 2024) 
Col Alg Cncpts+Aplcns
MATH 112 (Spring 2024) 
Patterns, Functions & Modeling
MATH 106 (Spring 2024) 
Col Alg Cncpts+Aplcns
MATH 112 (Fall 2023) 
Discrete Mathematics
MATH 243 (Fall 2023) 
Patterns, Functions & Modeling
MATH 106 (Fall 2023)
202223 Courses

Discrete Mathematics
MATH 243 (Spring 2023) 
Patterns, Functions & Modeling
MATH 106 (Spring 2023) 
Preceptorship
MATH 391 (Spring 2023) 
Col Alg Cncpts+Aplcns
MATH 112 (Fall 2022) 
Patterns, Functions & Modeling
MATH 106 (Fall 2022)
202122 Courses

Discrete Mathematics
MATH 243 (Summer I 2022) 
Calculus Preparation
MATH 120R (Spring 2022) 
Discrete Math Cmptr Sci
MATH 243 (Spring 2022) 
Patterns, Functions & Modeling
MATH 106 (Spring 2022) 
Calculus Preparation
MATH 120R (Fall 2021) 
Discrete Math Cmptr Sci
MATH 243 (Fall 2021) 
Patterns, Functions & Modeling
MATH 106 (Fall 2021)
202021 Courses

Discrete Math Cmptr Sci
MATH 243 (Spring 2021) 
Patterns, Functions & Modeling
MATH 106 (Spring 2021) 
Calculus Preparation
MATH 120R (Fall 2020) 
Discrete Math Cmptr Sci
MATH 243 (Fall 2020) 
Patterns, Functions & Modeling
MATH 106 (Fall 2020)
201920 Courses

Discrete Math Cmptr Sci
MATH 243 (Summer I 2020) 
Col Alg Cncpts+Aplcns
MATH 112 (Spring 2020) 
Discrete Math Cmptr Sci
MATH 243 (Spring 2020) 
Col Alg Cncpts+Aplcns
MATH 112 (Fall 2019) 
Discrete Math Cmptr Sci
MATH 243 (Fall 2019) 
Functions for Calculus
MATH 122A (Fall 2019)
201819 Courses

Col Alg Cncpts+Aplcns
MATH 112 (Spring 2019) 
Discrete Math Cmptr Sci
MATH 243 (Spring 2019) 
Col Alg Cncpts+Aplcns
MATH 112 (Fall 2018) 
Discrete Math Cmptr Sci
MATH 243 (Fall 2018)
201718 Courses

Col Alg Cncpts+Aplcns
MATH 112 (Spring 2018) 
Discrete Math Cmptr Sci
MATH 243 (Spring 2018) 
Calc II SI Seminar
MATH 196N (Fall 2017) 
Col Alg Cncpts+Aplcns
MATH 112 (Fall 2017) 
Discrete Math Cmptr Sci
MATH 243 (Fall 2017) 
Vector Calculus SI Seminar
MATH 196V (Fall 2017)
201617 Courses

Calc II SI Seminar
MATH 196N (Spring 2017) 
Col Alg Cncpts+Aplcns
MATH 112 (Spring 2017) 
Discrete Math Cmptr Sci
MATH 243 (Spring 2017) 
Functions for Calculus
MATH 122A (Spring 2017) 
Calc II SI Seminar
MATH 196N (Fall 2016) 
Col Alg Cncpts+Aplcns
MATH 112 (Fall 2016) 
FirstSemester Calculus
MATH 122B (Fall 2016) 
Functions for Calculus
MATH 122A (Fall 2016)
201516 Courses

Calculus II
MATH 129 (Spring 2016) 
Col Alg Cncpts+Aplcns
MATH 112 (Spring 2016) 
Functions for Calculus
MATH 122A (Spring 2016)
Scholarly Contributions
Journals/Publications
 Watkins, J., Visscher, K., & Bailey, B. L. (2014). A stochastic model of translation with 1 programmed ribosomal frameshifting. Physical Biology, 11(1), 116.More infoAbstract: Many viruses produce multiple proteins from a single mRNA sequence by encoding overlapping genes. One mechanism to decode both genes, which reside in alternate reading frames, is 1 programmed ribosomal frameshifting. Although recognized for over 25 years, the molecular and physical mechanism of 1 frameshifting remains poorly understood. We have developed a mathematical model that treats mRNA translation and associated 1 frameshifting as a stochastic process in which the transition probabilities are based on the energetics of local molecular interactions. The model predicts both the location and efficiency of 1 frameshift events in HIV1. Moreover, we compute 1 frameshift efficiencies upon mutations in the viral mRNA sequence and variations in relative tRNA abundances, predictions that are directly testable in experiment. © 2014 IOP Publishing Ltd.
 Bailey, B. L., Malhotra, R., & Bailey, B. L. (2009). Two dynamical classes of Centaurs. Icarus, 203(1), 155163. doi:10.1016/j.icarus.2009.03.044More infoAbstract The Centaurs are a transient population of small bodies in the outer Solar System whose orbits are strongly chaotic. These objects typically suffer significant changes of orbital parameters on timescales of a few thousand years, and their orbital evolution exhibits two types of behaviors described qualitatively as random walk and resonancesticking. We have analyzed the chaotic behavior of the known Centaurs. Our analysis has revealed that the two types of chaotic evolution are quantitatively distinguishable: (1) the random walk type behavior is well described by socalled generalized diffusion in which the rms deviation of the semimajor axis grows with time t as ∼ t H , with Hurst exponent H in the range 0.22–0.95, however (2) orbital evolution dominated by intermittent resonance sticking, with sudden jumps from one mean motion resonance to another, has poorly defined H. We further find that these two types of behavior are correlated with Centaur dynamical lifetime: most Centaurs whose dynamical lifetime is less than ∼ 22 Myr exhibit generalized diffusion, whereas most Centaurs of longer dynamical lifetimes exhibit intermittent resonance sticking. We also find that Centaurs in the diffusing class are likely to evolve into Jupiterfamily comets during their dynamical lifetimes, while those in the resonancehopping class do not.
 Dale, D. A., & Bailey, B. L. (2003). Physics in the Art Museum. The Physics Teacher, 41(2), 8283. doi:10.1119/1.1542042More infoParisian artist Paul Signac met the impressionists Claude Monet and Georges Seurat in 1884. Their influence spurred his work in pointillism (or, where the juxtaposition of small dots of color in conjunction with the limited resolving power of the human eye lead to the impression of color coalescence).1–4 To stimulate a crossdisciplinary appreciation of science and art, we used the University of Wyoming Art Museum's Signac painting “Barques de Peche a Marseilles” (see Fig. 1) to explore diffraction theory and the anatomical limitations to our vision during an optics exercise done in the museum.
 Thronson, H. A., Rapp, D., Hawarden, T. G., & Bailey, B. (1995). Ecological Niches in Infrared and SubMillimeter Space Astronomy: Expected Sensitivity as a Function of Observatory Parameters. Publications of the Astronomical Society of the Pacific, 107(717), 1099. doi:10.1086/133666More infoUsing current estimates of the celestial and atmospheric background emission, infrared source crowding, and detector performance, we estimate expected point source sensitivities for possible future infrared and submillimeter observatories on the ground, in the air, and in space. Our goal is to evaluate the effects of variations in basic observatory system parameters for typical operation over the wavelength range labmda ~3  1000 microns. For the first time in a general astronomical journal, we evaluate mission design goals for the wavelength range identified as the premier for this decade. Here we emphasize the effects of telescope temperature, aperture, and emissivity upon detectable point source signal level using diffractionlimited instrumentation, along with approximations to improvements in sensitivities using the new generation of array detectors. We find that [1] for broadband imaging, a larger aperture is more important than extremely low optical system temperature in detecting weak sources in two situations: (1) at short wavelengths, where a telescope need only be cooled to several tens of kelvins for the optical system emission to fall below that of the celestial background, and (2) at farinfrared and submillimeter wavelengths, where interstellar "cirrus" and extragalactic confusion eventually determine the limits to sensitivity, which, below some critical temperature, can only be improved upon by increasing the effective angular resolution. [2] for moderateresolution spectroscopy (lambda/deltalambda ~30  2000), the faintest signal level is always achieved when the telescope temperature is low enough so that the optical system contributes less than the celestial background. This temperature declines as the wavelength increases, so, for example, T ~ 100 microns. However, for submillimeter wavelengths, on or near the RayleighJeans side of the optical system emission, sensitivity increases less strongly with declining temperature. Consequently, large, warm (groundbased and airborne) submillimeter telescopes compete favorably with small cold ones in space and those windows accessible from within the Earth's atmosphere. [3] for highresolution spectroscopy {lambda/deltalambda >~ 105+), the thermal background from sky and optical system can be low enough that detector noise limits achievable sensitivity. If so, the telescope temperature need not be extremely low and lightgathering aperature is relatively more important. However, to take full advantage of reduced thermal background via high spectral resolution, detector performance may need to be near optimum. [4] conversely, in many circumstances, current and nearfuture detectors are overspecified in key aspects of operation compared to the high background of the farinfrared. Under such circumstances, detectors may be operated in nonoptimum conditions without significant adverse effects upon overall system sensitivity.