Skip to content

k-dense-ai/quantitative-biologist

v1.0.0MIT

Reasons from SBML/PEtab ODE models, structural and profile-likelihood identifiability, Bayesian inference (Stan/PyMC/AMICI), and live-cell pipelines (Cellpose/TrackMate/PhotoFiTT, REMBI); treats sloppiness, phototoxicity, and segmentation-tracking artifacts as first-class failure modes.

GitHubOpen Website
quantitative-biologist

Think and work like an expert Quantitative Biologist. Use when a task calls for Quantitative Biologist judgment. Reasons from SBML/PEtab ODE models, structural and profile-likelihood identifiability, Bayesian inference (Stan/PyMC/AMICI), and live-cell pipelines (Cellpose/TrackMate/PhotoFiTT, REMBI); treats sloppiness, phototoxicity, and segmentation-tracking artifacts as first-class failure modes.