Molecular Techniques in Ecology and Evolution
Steven Kimble, Towson University
Microbiomes are the huge communities of microbes that live in and on host organisms, and are typically intimately involved with the host in myriad ways, including in immune, metabolic, and behavior functions. As ecosystems, these microbiomes are sensitive to changes in their environments, such as host aging, disease state, or contact with pollutants. They could therefore be used as bioindicators of host health, but the membership and functions of microbiomes are poorly understood in almost all creatures, especially reptiles and amphibians. In this CURE we use modern field, laboratory, and bioinformatic tools to describe and analyze the microbiomes of non-model organisms such as frogs, turtles, and reef fish.
- Learn about the nature and practice of science. Before their first authentic research experience, undergraduates have typically only experience "canned" laboratory experiments where outcomes are already known and results are unambiguous. They have typically not be exposed to the more realistic aspects of the scientific method in practice: experimental failure, ambiguous results, science as a social endeavor, self-teaching, and the need to invent new processes and techniques. The goal is to help students learn that these are integral to how science is done.
- Engage in science practices. An authentic research experience is often the first time that undergraduate students have experienced a large part of the scientific process, from hypothesis development to experimental design to data collection. They get some experience with all of these in the course, plus more general skills, including data visualization, scientific communication, and writing. The goal is to give students experience with these skill sets.
- Contribute to the knowledge of microbiomes in non-model species. As an authentic research experience, the course aims to contribute to the knowledge of microbiomes in non-model species. Ideally, students learn with every activity in the course; in practice they contribute something meaningful to scientific understanding even if their role is a minor one. The goal is for students to understand that they can contribute to science.
- Characterize and compare the microbiome composition of non-model species. The microbes that make up these communities are typically not at all quantified for most wildlife species and understanding who they are and what they might do is a necessary first step in conservation.
- Explore factors that drive microbiome community composition. So far, we have compared microbiomes across space, body condition, trophic level, exposure to pollutants, sex, age, and viral load.
Typically, 10-14 students are enrolled in the course. It is a 400-level undergraduate (though graduate students can enroll) course and is typically mostly seniors and a few juniors. The course lasts one semester, though sometimes interested students continue to work on the project afterwards. The course is 6 contact hours per week, with 1-4 outside hours required. Students need to have had some ecology and genetics training before taking this class.
Target Audience: Major, Upper Division
CURE Duration:A full term
The research theme at the heart of the CURE is the identification and quantification of microbiomes in on-model organisms, especially reptiles and amphibians. The course helps students ask their own scientific questions by giving them enough structure that the amount of prior information they need to identify research gaps can be digested in the first third of the course. In this way they can be expected to develop hypotheses that are testable within the confines of the course (but extendable by themselves or future students). The course is structured to ensure success by providing scaffolding, reiteration, multiple deadlines, performance, feedback, and being assigned project partners based on personality.
Outside stakeholders include colleagues who share their microbiome data with my lab for purposes of this class, species managers, and other students in my lab. Data are shared informally and by presentations, reports, and publications.
Core Competencies:Analyzing and interpreting data, Asking questions (for science) and defining problems (for engineering), Constructing explanations (for science) and designing solutions (for engineering), Developing and using models, Planning and carrying out investigations, Using mathematics and computational thinking
Nature of Research:Applied Research, Informatics/Computational Research, Wet Lab/Bench Research
Tasks that Align Student and Research Goals
Student Goals ↓
-troubleshoot coding errors
-interpret non-significant test results
-search for solutions to common roadblocks
-search for solutions to novel roadblocks
-work with peers
-find appropriate statistical techniques
-develop data visualizations
-working with peers
-collect and curate data
-use bioinformatic approaches to characterize microbiomes
-use statistical approaches such as regression to understand relationships
-present research findings
-present research findings
Attached are a copy of the syllabus and a screenshot of a typical Module. A typical module includes activities for the week: readings, self-paced tutorials, activities, and an assessment. Activities can vary widely among modules (see syllabus). Equipment includes standard laboratory materials for microbial propagation, aseptic techniques, DNA extraction, PCR, electrophoresis, and DNA quantification. Students also need a standard laptop with access to the internet and the ability to connect to remote compute resources via Terminal (macs) or a free ssh client, e.g.,. PUTTY https://www.putty.org/. In this class I have used a free education allocation at the NSF-supported ACCESS network https://access-ci.org/.
Syllabus (Acrobat (PDF) 401kB Apr26 23)
Paper critique rubric (Acrobat (PDF) 105kB Apr26 23)
Final presentation rubric (Acrobat (PDF) 174kB Apr26 23)
The class is supported by a staff member, and by one or two Undergraduate Learning Assistants (ULA)s, preferably versed in both the benchwork and in bioinformatics.
Steven Kimble, Towson University
I teach a CURE class to achieve two primary goals. The first is to afford undergraduate research opportunities to a larger number of students in a classroom setting. There is higher demand for authentic undergraduate research experiences than can be met with the traditional apprentice-style mentoring relationship, so the CURE allows me to simultaneously mentor a dozen or more students at once, and many students achieve more sciencing in a group setting. The second primary goal for teaching the CURE is to further my own research program. With so many students working a research problem, I can explore research avenues that may be riskier but more rewarding. The CURE is also a great way to recruit students for further research.
Advice for Implementation
Managing expectations is important in a CURE. There will be lots more failure, ambiguity, and mistakes than the typical undergraduate student is used to. Positive attitudes include, "negative results are really results, too", and "this failure rate is totally normal", and constant reminders that their grade is based on their engagement and effort, not on "successful" experiments. About 10% of students react negatively to this ambiguous type of environment, but it's good that they learn this about themselves before graduation.
Students are highly involved in trouble-shooting. For example, their typical response to a computer error is to immediately seek help from a ULA or the Instructor; our first response is to point out that someone has likely had this problem before and to search the internet for help with this error. This encourages them to teach themselves to apply answers from various internet help sites to their own error message. On a practical note, much of the bioinformatics work gets done late at night when students do not have ULA or Instructor support, encouraging reliance on themselves, the internet, or their labmates. When research does not go as planned, the class changes accordingly. Again, managing expectations is important when an activity originally scheduled for one week takes three.
Using CURE Data
Their data are saved to a shared drive and used to answer the research questions in several ways: they often can be used to gauge if certain research questions are worthy of further research by other students or are dead-ends; they may also be used directly in manuscript preparation. Findings are regularly reported to other stakeholders, typically at the end of each semester. Authorship is earned by work in the course AND in some continued work in manuscript preparation after the semester, since once semester is not enough time to include this aspect.
See syllabus for list of papers.
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