umimic 0.0.2

Unified Mechanistic Inference from Multimodal Imaging and Counts

U-MIMIC

Unified Mechanistic Inference from Multimodal Imaging and Counts

Developed by: Data Analysis Team @KaziLab.se

U-MIMIC is a Python package for modeling and analyzing tumor population dynamics under drug treatment. It combines mechanistic simulation with statistical inference to support both in-vitro and in-vivo experimental workflows.

Features

• Mechanistic dynamics -- ODE, Gillespie SSA, and tau-leaping simulation of P/Q/A/R cell-state models with dose-response modulation
• Pharmacokinetics -- One- and two-compartment PK models with flexible dosing schedules, unified via ExposureProfile
• Observation models -- Cell counts (Negative Binomial), bioluminescence imaging, tumor volume, and biomarkers; combinable via MultimodalObservation
• Inference -- MLE (multi-start), MCMC (emcee/PyMC), SMC, Kalman filtering, and hierarchical Bayesian estimation
• Public datasets -- Loaders for BESTDR, PhenoPop, Hafner/Niepel GR, TSHS Tumor Growth, and NCI-60
• CLI -- umimic simulate, fit, generate, dashboard commands with YAML configuration and run logging

Installation

pip install .

With optional extras:

pip install ".[inference]"    # MCMC backends (emcee, PyMC)
pip install ".[dashboard]"    # Streamlit dashboard
pip install ".[all]"          # Everything

Requires Python >= 3.11.

Quickstart

CLI

# Run a dose-response simulation
umimic simulate --drug-type cytotoxic --output results/sim

# Fit model to data
umimic fit --config experiment.yaml --data data.csv --output results/fit

# Generate synthetic data
umimic generate --config experiment.yaml --output results/synthetic

Python API

from umimic.pipeline.config import ExperimentConfig
from umimic.pipeline.experiment import Experiment

config = ExperimentConfig(
    dosing={"concentrations": [0, 0.1, 1, 10]},
    simulation={"method": "ode", "t_max": 72.0},
)
exp = Experiment(config)

# Simulate
results = exp.simulate(concentrations=[0, 0.1, 1, 10])

# Generate synthetic data and fit
dataset = exp.generate_synthetic()
result = exp.fit(dataset)
print(result.mle.parameters)

Configuration

U-MIMIC uses YAML configuration validated by Pydantic. See docs/configuration.md for the full reference.

Minimal example:

name: my_experiment
dynamics:
  states: [P, Q]
dosing:
  concentrations: [0, 0.1, 0.3, 1, 3, 10, 30]
simulation:
  method: gillespie
  t_max: 72.0
inference:
  mode: mle
  n_restarts: 5

Run Logging

CLI commands support execution logging:

umimic simulate --output results/sim --log-file results/sim/run.log --log-level DEBUG

If --log-file is not provided, a timestamped log file is written to the command output directory. Logging uses a package-scoped logger (umimic) and does not interfere with other logging configuration.

Documentation

Full documentation is available on Read the Docs. To build locally:

pip install -e ".[docs]"
sphinx-build -b html docs docs/_build/html

• Quickstart
• Configuration Reference
• API Reference
• Read the Docs Setup
• PyPI Trusted Publishing

Contributing

See CONTRIBUTING.md for development setup, code style, and how to add new simulation methods, observation models, or dataset loaders.

Changelog

See CHANGELOG.md for version history.

License

MIT