Cellomics 2P
Cellomics 2-P: Time-Lapse Cellomics for Root Growth Dynamics
Analyze 3D time-lapse microscopy of plant roots to discover cellular motion patterns that drive growth.
Mentor Details:
Prof. Ilan Tsarfaty
Mentor Details:
Requirments:
Build an end-to-end pipeline from raw images to interpretable trajectory phenotypes linked to growth dynamics.
Problem Statement
Root growth mechanisms are challenging to infer directly from raw volumetric microscopy data.
Project Objectives
Build an end-to-end pipeline from raw images to interpretable trajectory phenotypes linked to growth dynamics.
Technical Scope
Analyze time-lapse 3D microscopy of plant roots to discover cellular motion patterns that drive growth. You’ll build an end-to-end pipeline (data management → segmentation/tracking → trajectory representation → clustering/phenotyping) and connect behavior to biological function.
Required Knowledge and Prerequisites
Core Requirements:
Python, data pipelines, basic ML.
Recommended Background:
3D microscopy, Docker, trajectory analysis.
Project Difficulty and Expected Level
Overall Difficulty: varies
This project is well-suited for:
Teams of 2–4 students
Expected Outcomes
A transferable toolkit for any time-lapse biology problem (plants, embryos, organoids, cancer).
Hands-on experience in scalable data systems (MongoDB) and time-series learning tailored to trajectories.
A portfolio-ready project showing you can move from raw images to interpretable quantitative biology.