The human microbiome is associated with different diseases, but the metabolic mechanisms through which it can modulate health are mostly unknown. Understanding these mechanisms is of paramount importance for prevention and treatment. While metagenomics analysis provides associations between microbial presence and specific diseases, metabolomics analysis can highlight metabolic alterations. None of the two, however, can unveil microbiome metabolic mechanisms associated with these detected alterations. In an attempt to fill this knowledge gap, several microbiome metabolic modeling methods were recently developed. An accurate evaluation of the accuracy of such methods in relation to different pathologies and microbiomes was never conducted.

The objective of this project is to evaluate existing metabolic modeling approaches using several datasets in which both metabolites and microbes were profiled from the same sample. The microbiome data would be used to generate the models, and the metabolites data to evaluate their accuracy. We will assess the benefits and deficiencies of each method in different contexts.

This project is eligible for a matching fund stipend from the Data Science Institute. This not a guarantee of payment, and the total amount is subject to available funding.

Faculty Advisor

  • Professor: Tal Korem
  • Department/School: Systems Biology
  • Location: Presbyterian Hospital, 18-200
  • We develop analytic approaches and algorithm to analyze data from the human microbiome, the collection of bacteria that live in and on our bodies. We use these algorithm to pursue clinical questions where microbiome analysis could practically benefit patient care.

Project Timeline

  • Earliest starting date: 10/1/2020
  • End date: 6/1/2020
  • Number of hours per week of research expected during Fall 2020: ~12

Candidate requirements

  • Skill sets: Students should have some experience in R, MATLAB, or python. Familiarity with basic microbiology concepts is a plus
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible
  • Academic Credit Possible: No