celltraj
A Python toolkit for live-cell trajectory analysis, morphodynamical state decomposition, trajectory embedding, and dynamical modeling from time-lapse imaging.
Repository →Live-cell data → executable models
Measure cancer as a dynamic tissue process.
CancerDynamics.org collects open materials, publications, software, and visual explainers for measuring how cancer cells signal, interact, transition, and respond to therapy over time.
Live-cell imaging turns cancer progression into trajectories that can be measured and modeled.
Single-cell histories reveal state transitions that are invisible in static snapshots.
Framework
From movies to dynamical cancer biology.
Static assays capture endpoints. Live-cell imaging captures the path: signaling histories, cell-cell contacts, motility, death, division, and tissue organization as they unfold. The central aim is to convert those paths into models that identify transition states and control points.
Trajectory embedding. Build state descriptions from time-ordered single-cell histories rather than isolated snapshots.
Microenvironment in context. Connect cell fate to neighbors, matrix, tissue architecture, and signaling inputs.
Forward prediction. Use live-cell data to train models that simulate how tissue-scale behavior evolves.
Software and repositories
Open tools for cancer dynamics.
MMIST_Copperman_2026
Application materials for Molecular and Morphodynamics-Integrated Single-cell Trajectories, linking label-free live-cell behavior to dynamic molecular programs.
Repository →SITE_MURTHY_2026
Resources for Serial Imaging of Tumor and microEnvironment workflows: live-cell ex vivo modeling, segmentation, tracking, context mapping, and tumor-host dynamics.
Repository →Publications
Foundational papers and platform releases.
This site should become the stable landing page that connects manuscripts to code, downloadable materials, tutorial notebooks, and visual summaries.
MMIST — Molecular and Morphodynamics-Integrated Single-cell Trajectories
Cell Systems publication linking live-cell morphodynamic histories to molecular programs during cell state change.
SITE — Serial Imaging of Tumor and microEnvironment
Live-cell ex vivo platform for resolving tumor-host interactions, signaling states, spatial context, and fate dynamics across primary and metastatic cancer models.
Active boundary and signaling-state models
In-development resources connecting ERK/AKT signaling-state plasticity to cell-cell interactions, active boundary mechanics, polarity, and collective motion.
Materials
Draft resource map.
Tutorial notebooks
Walkthroughs for trajectory embedding, Markov modeling, MMIST, SITE-derived features, and model diagnostics.
Visual explainers
Short web-native narratives translating live-cell movies into state spaces, transition maps, and tissue-scale models.
Protocol links
Public protocol and methods pages for cleared SITE, LungSITE, segmentation, and tracking workflows.
Datasets and examples
Small example datasets or Zenodo-linked data objects that let readers reproduce selected analysis figures.
CancerDynamics.org is the broader methods and resource companion to the Davies Cancer Lab: a place for software, models, protocols, publications, and interactive explanations of dynamic cancer biology.