Data Science for Biophysics
Passion for physics, applied math, machine learning, data science
This is a Project for an Honors Thesis.
Current biophysical imaging and spectroscopy methods can probe time (< 10-6 s), length (10-9 m) and force (10-12 N) scales relevant to the life cycle of a cell. These methods have revealed that all steps involved in molecular biology's central dogma (transcription, translation and DNA replication) are intrinsically stochastic. Despite the wealth of experimental data, the ability to gain meaningful insight from experiments on such small scales is severely limited by fundamental challenges common to all biological systems: current methods cannot capture complex processes in their full multi-dimensional detail. Here we propose to adapt state-of-the-art tools from Data Science to probe processes relevant to life from the arrival of single photons. Students involved in this project will master tools of computational statistics, data science, and Bayesian inference.
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