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Designing microbial communities using control theory principles

Opening for a Postdoctoral Research Fellow to join the Gibson Lab at Harvard Medical School and Brigham and Women’s Hospital. We leverage tools from machine learning and control theory to understand biological systems. Control theoretic concepts are integrated both in the design of our optimization schemes and statistical machine learning models, as well as in the design of our in vitro and in vivo experiments. Our main area of focus is the microbiome and microbial dynamics more specifically. Applications include the design of bacteriotherapies (bugs-as-drugs), developing methods to learn microbial dynamics at ecosystem-scale, studying the impact of phages on microbial communities, methods for tracking low abundance pathogens, and methods for single cell biology. We focus on Bayesian methods that propagate measurement uncertainty throughout the model so that we can access confidence in model parameters and to help prioritize follow-up experiments. ML techniques applied include variational inference, Bayesian non-parametric models, and relaxation techniques (for making discrete models differentiable).

This specific call is for someone who is interested in designing bacteriotherapies using control theory principles. Using our dense time series gnotobiotic and Bayesian models we have generated ecological scale microbial interaction networks Using predictions from those models we wish to design cocktails of bacteria that can manipulate a complex microbiome (for therapeutic applications). Simply adding the therapeutic taxa isn’t sufficient, however, as they may not robustly colonize the gut or may have large variations in carrying capacity depending on the background microbiome of the host. Using notions of robustness and stability from control theory we intend to augment bacterial cocktails with other microbial members to allow for more stable and robust colonization of the therapeutic taxa. One simple example could be the inclusion of a bacteria in negative feedback with the primary therapeutic taxa to regulate its abundance. The candidate will not only design these bacterial communities but will help in the design of the mouse experiments, which would then be carried out by staff in the germ-free mouse facility. The candidate does not need to have experience with germ free studies and will not physically handle the mice. The candidate will however be in charge of all aspects of the experimental design. They will also likely contribute to the further development of our Bayesian models, expanding their capabilities, which may include the integration of multiple data modalities (e.g. spatial omics data) or exploring different inference techniques (e.g. variational inference).


About the lab environment

The Gibson Lab is located in the Division of Computational Pathology at Brigham and Women’s Hospital (BWH), a Harvard Medical School teaching hospital, which is the second largest non-university recipient of NIH research funding. The broad mandate of the Division of Computational Pathology is to develop and apply advanced computational methods for furthering the understanding, diagnosis, and treatment of human diseases. The Division is situated within the BWH Department of Pathology, which houses over 40+ established investigators, 50+ postdoctoral research fellows, and 100+ research support staff. In addition, BWH is part of the greater Longwood Medical Area in Boston, a rich, stimulating environment conducive to intellectual development and research collaborations, which includes the Harvard Medical School quad, Harvard School of Public Health, Boston Children’s Hospital, and the Dana Farber Cancer Institute. Many of our lab members also have appointments at the Massachusetts Institute of Technology and the Broad Institute.

Applications Process

Submit: (1) brief research statement (not to exceed 2 pages); (2) curriculum vitae; (3) two most relevant publications; (4) names and contact information of three individuals who can serve as references to: Travis Gibson, If you wish to chat briefly over Zoom before submitting materials to learn more details about our ongoing work, please inquire about this possibility.

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.