Gregory Babbitt Headshot

Gregory Babbitt

Associate Professor

Thomas H. Gosnell School of Life Sciences
College of Science
Bioinformatics Program Faculty

585-475-6246
Office Location

Gregory Babbitt

Associate Professor

Thomas H. Gosnell School of Life Sciences
College of Science
Bioinformatics Program Faculty

Education

BA, Ohio Wesleyan University; MS, Ph.D., University of Florida

Bio

I am a computational biologist with a focused interest in the development of modern statistical tools for comparative molecular dynamic simulation applied to the function and evolution of proteins and their interactions with other molecules in the cell. I am also broadly interested in the evolution of complex and stochastic biophysical process, across all scales, from the molecular to the ecological. For more information please visit my projects website.

585-475-6246

Areas of Expertise

Select Scholarship

Journal Paper
G.A., Babbitt, et al. "Triplet codon organization optimizes the impact of synonymous mutation on nucleic acid molecular dynamics." JOURNAL OF MOLECULAR EVOLUTION 86. (2018): 91-102. Print.
GA, Babbitt. "Can all heritable biology really be reduced to a single dimension?" GENE 578. 2 (2016): 162-168. Print.
GA, Babbitt. "Synonymous codon bias and functional constraint on GC3-related DNA backbone dynamics in the prokaryotic nucleoid." NUCLEIC ACIDS RESEARCH 42. 17 (2014): 10915-10926. Print.
GA, Babbitt. "Functional conservation of nucleosome formation selectively biases presumably neutral molecular variation in yeast genomes." GENOME BIOLOGY AND EVOLUTION 3. (2011): 15-22. Web.
Invited Article/Publication
GA, Babbitt. "Chromatin Evolving." American Scientist - cover story. (2011). Print.

Currently Teaching

BIOL-130
3 Credits
This course will explore topics in the field of bioinformatics including tools and resources used by the discipline, including direct experience with the common user environment.
BIOL-230
3 Credits
This is an introductory course in languages commonly used in bioinformatics and their application to biological data. We will investigate the use of multiple languages for processing sequence and "-omics" data, building analysis pipelines, integrating languages, managing a variety of biological data types, and providing effective interfaces to existing tools for analysis of these data. The course is largely based around live-code demonstration, in-class assisted coding assignments, and a student-designed final class project.
BIOL-295
1 - 4 Credits
This course is a faculty-directed student project or research involving laboratory work, computer modeling, or theoretical calculations that could be considered of an original nature. The level of study is appropriate for students in their first three years of study.
BIOL-298
1 - 4 Credits
This course is a faculty-directed tutorial of appropriate topics that are not part of the formal curriculum. The level of study is appropriate for student in their first three years of study.
BIOL-301
1 - 4 Credits
This course allows students to assist in a class or laboratory for which they have previously earned credit. The student will assist the instructor in the operation of the course. Assistance by the student may include fielding questions, helping in workshops, and assisting in review sessions. In the case of labs, students may also be asked to help with supervising safety practices, waste manifestation, and instrumentation.
BIOL-470
3 Credits
This course is an introduction to the probabilistic models and statistical techniques used in computational molecular biology. Examples include Markov models, such as the Jukes-Cantor and Kimura evolutionary models and hidden Markov models, and multivariate models use for discrimination and classification.
BIOL-495
1 - 4 Credits
This course is a faculty-directed student project or research involving laboratory or field work, computer modeling, or theoretical calculations that could be considered of an original nature. The level of study is appropriate for students in their final two years of study.
BIOL-498
1 - 4 Credits
This course is a faculty-directed tutorial of appropriate topics that are not part of the formal curriculum. The level of study is appropriate for student in their final two years of study.
BIOL-672
3 Credits
This course will introduce traditional multivariate statistical methods and multi-model inference, as well as iterative computational algorithms (i.e. Bayesian methods and machine learning) appropriate for graduate students conducting or planning to conduct a graduate research project. The course will focus on the proper application of methods to a sample data sets using statistical programming software and graphics and will forego the more in-depth analytical mathematical exposition that you might see in a math course, so that we can cover a larger variety of methods and spend more time implementing them in code. Practical examples will often derive from the fields of biology, environmental science, or medicine, however the statistical methods we cover will also have much broader application within modern data science. The ultimate goal will be to learn when and where to correctly apply a given method to real questions about real data. Class time will be devoted to introductory lecture, programming language demonstrations with a common dataset, and open discussions of potential applications, including in-class studio hours to help with homework. Students should be prepared to learn to write code scripts that will manipulate statistical tests and graphical output. However, no background experience with programming is assumed. All software used in the course is open-source and students will be required to set up and run weekly assignments on their own laptop computer or on a computer borrowed from the library or RIT’s computer lab.
BIOL-798
1 - 4 Credits
This course is a faculty-directed, graduate level tutorial of appropriate topics that are not part of the formal curriculum.