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Evolution@Vanderbilt

CoEvoD Courses

The six courses listed below are required activities for students in the CoEvoD program. At the bottom, find a list of elective courses that fit into this program as well.

Evolutionary Medicine (3 credits)

Course Description: This course will introduce applications of evolutionary biology to medicine. Biomedical issues ranging from the evolution of antibiotic resistance, to aging, obesity, and cancer will be discussed using an evolutionary framework to better understand how these issues came to be, and what can be done to mitigate them. The targeted learning outcomes for students are that they will be able to (a) understand how evolutionary theory is being applied to medicine, (b) appreciate the full spectrum of “normal” physiology and human variation, as well as its sources, (c) assess the evidence required to test hypotheses in evolutionary medicine, and (d) become familiar with the fundamentals of rigorous experimental design, reproducibility, and responsible conduct of research in evolutionary medicine. Attendance and active participation in class discussions will be mandatory. Required readings from the primary literature will accompany most meetings. (Fall semesters)

Course Instructor / Coordinator: Prof. Amanda Lea

Computational Evolutionary Methods for Studying the Biology of Disease (3 credits)

 Course Description: Advances in genomics and related fields have transformed evolutionary studies of genetic and infectious diseases into a fully-quantitative, theory-rich science. Thus, it is imperative that 21st century biology students are as fluent in computational evolutionary approaches employed to study the biology of disease as they are at the bench. The goal of this course will be to enable the students to: (a) understand the concepts and ideas behind the most-commonly used computational evolutionary algorithms in the study of disease, (b) get hands-on experience on how to apply software to retrieve biological data from databases and perform standard analyses, and (c) use and understand the resources that will allow them to develop their own CoEvoD-related projects (including ensuring that their work has rigorous experimental design, is reproducible, and has been conducted in a fair and ethical manner). The course will be an even mixture of lectures and practicals. Students will be introduced to a new computational evolutionary concept each Tuesday (e.g., molecular phylogenetic analysis) and acquire hands-on practical experience in applying tools that employ this concept each Thursday (e.g., inferring the phylogeny of SARS-CoV-2 using RAxML, a popular software for maximum likelihood phylogenetic inference). (Spring semesters)

Course Instructor / Coordinator: Prof. Antonis Rokas

Computational Evolutionary Approaches to Disease Journal Club (1 credit)

Course description: In-depth discussions of high-profile or foundational preprints and publications on CoEvoD-related topics. These discussions are led by graduate student trainees, training faculty, and researchers in the community and are meant to foster critical thinking and lead to vibrant and rigorous discussions. At least one discussion per semester will be devoted to responsible conduct of research and a second to rigor and reproducibility. (Both Fall and Spring semesters)

Course Instructor / Coordinator: Prof. Paul Durst

Evolutionary Studies Initiative Seminar Series (1 credit)

Course description: Seminar series featuring external (typically) and internal speakers on CoEvoD-related topics. Each year, at least one seminar will be devoted to responsible conduct of research and another on diversity, equity, and inclusion topics. For example, we recently hosted a seminar by Dr. Elisabeth Bik, the world-renowned image forensics detective who left her paid job in industry to search for and report biomedical articles that contain errors or data of concern (and recipient of the 2021 John Maddox Prize). (Both Fall and Spring semesters)

 Course instructor: Prof. Megan Behringer

Graduate Seminar in Biological Sciences I (1 credit)

Course description: The focus of this course is critical thinking, presentation skills, and professional conduct. Each week, one student presents a critical review of a recent (<5 years) publication in a top-tier journal. All students have one week to read the paper and to prepare questions and comments for discussion. Records are kept of every student’s participation in the discussion and the course grade is based on this participation. The presenting student gives a PowerPoint presentation to the class and answers questions from the other students concerning the paper. All students give anonymous feedback to the presenter on presentation skills. After the presentation, the instructor meets privately with the student for about a half hour to give feedback on presentation skills and to discuss the science of the paper further. Each student presents 1-2 times, depending on the number of students in the course that semester. (Fall semesters)

Course Instructor / Coordinator: Prof. Gianni Castiglione

Graduate Seminar in Biological Sciences II (1 credit)

Course description: This course focuses on scientific writing skills. All students have one week to read, write, or edit materials and prepare questions and comments for discussion. Each week, students work in groups, read each other’s works, and provide peer-review. The meeting topics include course goals and writing tools; outlining methods; figure types, how to choose a figure type, how to make a clear figure; how, when, and why to cite; structured editing; data availability, dissemination, and reproducibility; transparency in reporting data; manuscript writing and submission; responding to reviewers; publication ethics, figure criticism and improvement workshop; writing for grant applications; text criticism and improvement workshop. (Spring semesters)

Course Instructor / Coordinator: Prof. Gianni Castiglione

Other Elective Courses

In addition to the above elective courses, our students will also have the choice to take relevant electives on a variety of topics related to CoEvoD, such as:

  • Foundations of Bioinformatics
  • Molecular Evolution
  • Practical Python Programming and Algorithms for Data Analysis
  • Experimental Design, Statistical Methodology, and Responsible Conduct
  • Principles of Human Disease
  • Statistical Methods in Biology
  • Exploratory Data Analysis
  • Principles of Programming and Simulation
  • Human Evolutionary Genetics
  • Ecology and Evolution of Infectious Disease
  • Data Science Rights and Responsibility
  • Genome Science
  • Modeling and Machine Learning I
  • Modeling and Machine Learning II
  • Mathematical Modeling in Biology and Medicine
  • Biobank Study Design