(3ie) Microbiome Engineering through Computationally-Guided Experiments

Research Interests
Microbiomes carry out important chemical transformations in most observed environments on earth. The composition (i.e. which organisms) and function (i.e. which chemical transformations) of these communities are highly dependent on both their environment and interactions between their constituent members. The complexity of microbial diversity combined with environmental variation makes manipulation of these systems challenging. My research aims to design strategies for controlling microbiome functions by first understanding which of the myriad possible interactions serve as the best “control knobs” and then uncovering the molecular mechanisms for turning those knobs. My approach utilizes computational modeling to guide experimental discovery and engineering design across various scales, from biophysical models of cellular processes to complex systems models of community behavior. My aim is to develop control strategies for microbiomes that can translate into solutions to problems in health (i.e. modulating disease impact), energy (i.e. efficient and robust bioprocesses), and environment (i.e. bioremediations restoring native ecosystems).
Microbiome interactions with their environment are incredibly complex, including hundreds of microbial species (with potential strain-level variation) that produce thousands of small molecules that can have combinatorial interactions with their environment. This complexity makes identifying simplified model systems that replicate the behavior of the real system a daunting task. My general approach is to use knowledge of the real system and specific design goals to define an expansive system (i.e. with tens to hundreds of independent variables that can co-vary leading to millions to billions of possible experiments) and then use a combination of high-throughput experiments and computational modeling to map out that space. This map can then be used to identify the most interesting experiments to explore in more practical model systems (e.g. animal models or laboratory bioreactors) which have limited experimental throughput but can lead to translational outcomes for manipulating the real system. My lab will be a world-class source of expertise in microbiome assembly and function, developing cutting-edge technology in high-throughput microbiome culturing techniques, quantification of microbiome metabolic functions, and computational modeling of microbial systems across multiple scales.

Teaching Interests
During my graduate and postdoctoral studies, I put extensive effort into learning how to be a good teacher and research mentor. I greatly enjoyed my first term serving as a TA for an undergraduate chemical engineering transport phenomena course, so for my second term, I designed a Teaching-as-Research project as an intern in UW-Madison’s Delta Program. During my first TA experience, I noticed that during group work, some students worked alone, but I was unsure if this was a preference or if it was due to a lack of familiarity with other students in the course. To test this question, I designed an experiment in which I carried out a simple 5-minute intervention in each discussion section where students were required to introduce themselves to one student in their class that they did not know. This intervention was carried out in one discussion section and skipped in another section as a control. I then surveyed the students from each section at the beginning and end of the term to quantify the number and type of relationships each student had with other students in the course. I found that students in the treatment section had a more than 2-fold increase in the number of “special peer” relationships relative to the control section and that this effect was amplified (more than 4-fold increase) in female students, an underrepresented group in engineering classrooms. I presented these results in two separate web conferences with audience members from other Universities in the Center for the Integration of Research, Teaching, and Learning network where I received valuable feedback from others carrying out their own Teaching-as-Research projects. This project taught me the value of applying my research skills to improve my teaching experiences and I plan to implement more projects like this in my teaching career. In addition to this Teaching-as-Research experience, I participated in a Delta course entitled “Improv to improve teaching and science communication” in which we practiced improvisation as a skill for communication of scientific ideas. Together, these experiences have had a major impact on my communication skills, an area I plan to improve through further teaching experiences in my career.
During my graduate work, I served as research mentor for 5 UW-Madison undergraduate chemical engineering students, two summer REU interns from the University of Puerto Rico – Mayaguez, and one summer REU intern from the University of Miami. I am currently mentoring two undergraduate students, one of whom is working on a fully computational project. Four of these students have been co-authors on one or more publications with me. I make it a focus of my mentoring to encourage students explore both academic research and industrial internships to choose a career path that most appeals to their individual goals. Four of these students have gone on to PhD programs in engineering. Three other students have begun careers as engineers in industry. My undergraduate mentees have come from a wide range of backgrounds, most from demographics underrepresented in their respective fields (very different from my own background), and I highly valued their fresh perspectives on our work. I made it a priority to grow and learn as a research mentor during my time working with these students by participating and eventually co-facilitating a research mentor training seminar where I learned the value of clear communication of expectations and learning through sharing of experiences between myself and my peers. My research mentoring interactions have been some of my most valued experiences during my research career and I am quite proud of my progress as a research mentor.
Keywords: Metabolic Engineering, Synthetic Biology, Systems Biology