This project will be focused on creating a deep learning framework for tracking individual molecules and proteins as they move within a cell under various conditions. Using total internal reflection (TIRF) microscopy, we have accumulated more than 10 million trajectories over dozens of experimental preparations with differences in both the imaging approaches as well as the biological context. In our experiments we have captured particles under a wide variety of conditions including increased protein expression level, and a range of drug concentrations. Our biggest challenge is being able to stably track the movement of a particle as it passes by other particles or groups of particles, and to do this in a way that generalizes over novel conditions. The Data Science Institute Scholar chosen for this project would work with scientists in the Javitch laboratory and others across the Columbia campus to conceive of an approach for efficiently and effectively tracking particles. The resulting work would be of great interest to an increasing number of scientists working in this field who currently rely on methods based on feature engineering that are often inaccurate or inflexible compared to modern deep learning methods.

Selected candidate(s) will receive a stipend directly from the faculty advisor. Amount is subject to available funding.

Faculty Advisor

  • Professor: Jonathan A. Javitch
  • Department/School: Department of Psychiatry and Pharmacology
  • Location: CUIMC Kolb Annex
  • We aim to understand the structural bases of agonist and antagonist binding and specificity in dopamine D2-like receptors (which are the primary targets for schizophrenia treatment) as well as related biogenic amine receptors, how agonist binding is transduced into G protein activation, and the structural basis for G protein-coupled receptor (GPCR) oligomerization and its role in signaling. In the pursuit of these objectives we are carrying out research on the dopamine D2 receptor as well as several other GPCRs.

Project Timeline

  • Earliest starting date: 3/1/2020
  • End date: 9/1/2020
  • Number of hours per week of research expected during Spring 2020: ~6
  • Number of hours per week of research expected during Summer 2020: ~20

Candidate requirements

  • Skill sets: Experience with data analysis in Python OR MATLAB, familiarity with machine learning, basic statistics
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible