Bioscrape — Biological Modeling, Simulation, and Parameter Estimation

A Python toolbox to simulate, analyze, and learn biological system models

Build Status PyPI version status

  • Getting started with Bioscrape: Bioscrape Core

  • Bioscrape analysis features: Bioscrape Sensitivity Analysis

  • Parameter inference with Bioscrape: Bioscrape Inference

Bioscrape is a Systems Biology Markup Language (SBML) simulator written in Cython for speed and Python compatibility. It can be used for deterministic, stochastic, or single cell simulation and also has parameter inference capabilities.

Example 1: Simulating an SBML

Bioscrape allows for deterministic and stochastic simulation of SBML models:

from bioscrape.types import Model
# Load an SBML file repressilator.xml 
# (you can find this file in `examples/models` directory)
M = Model(sbml_filename = 'repressilator_sbml.xml')
# Simulate the model
from bioscrape.simulator import py_simulate_model
import numpy as np
tp = np.linspace(0,256,100)
result = py_simulate_model(timepoints=tp, Model=M, stochastic=True)
# Plot the simulation result (the result is a Pandas dataframe)
import matplotlib.pyplot as plt
plt.plot(tp, result['X'])

Example 2: Run Bayesian inference with Bioscrape

Bioscrape can be used to identify model parameters using experimental data. In the example below, we show the user-friendly plug-and-play nature of bioscrape inference. We load the data as a Pandas dataframe and the model as an SBML file. The Bayesian inference is implemented as a wrapper for Python emcee that implements Markov Chain Monte Carlo (MCMC) sampler. Bioscrape inference provides various features such as: multiple data conditions, multiple data trajectories, deterministic inference, automatic visualization of posteriors, convergence checking tools, built-in and customizable priors, and lots more!

from bioscrape.types import Model
import pandas as pd
from bioscrape.inference import py_inference

# Load an SBML model 
# (you can get this file in `inference examples/models/` directory)
M = Model(sbml_filename='toy_sbml_model.xml')

# Load experimental data 
# (you can find test data in `inference examples/data/` directory)
df = pd.read_csv('test_data.csv', delimiter = '\t', 
                 names = ['X','time'], skiprows = 1)

# Use built-in priors, 
# For 'd1': a Gaussian distribution of mean 0.2 and standard deviation of 20,
# while ensuring the parameter remains positive
# For 'k1': a Uniform distribution with minimum value 0 and maximum value 100

prior = {'d1' : ['gaussian', 0.2, 20, 'positive'], 'k1' : ['uniform', 0, 100]}

# Run Bayesian inference
sampler, pid = py_inference(Model = M, exp_data = df, measurements = ['X'], 
                            time_column = ['time'], nwalkers = 20, nsteps = 5500,
                            params_to_estimate = ['d1', 'k1'], prior = prior)
# A sampler object containing all samples is returned.
# The pid object consists of various utilities for further analysis.
# This will plot the resulting posterior parameter distributions as well.

All examples can be found in the examples, the inference examples, and the lineage examples folders. If you prefer to run the package without installing the package, please use the Google Colab links above. If you want a local installation for bioscrape (recommended for faster speeds), follow the steps below:

Documentation

Read more about the package, the tutorial-style examples, and the Bioscrape API in the Bioscrape ReadTheDocs.

Installation

Install the latest version of Bioscrape::

$ pip install bioscrape

Please note that Bioscrape is a Cython extension module and requires a C++ compiler to be set up on your computer for installation. With the PyPi distribution, you can only install the core Bioscrape without the additional lineages package. To install lineages, clone the GitHub repository and run python setup.py install lineage from the bioscrape directory.

Try online without installing, open self-explanatory jupyter notebooks with Google Colab (linked at the top of this README).

Further details about the installation process can be found in the Bioscrape docs.

Bugs and Contributing to Bioscrape

Please report any bugs that you find here. Or, even better, fork the repository on GitHub, and create a pull request (PR). We welcome all changes, big or small, and we will help you make the PR if you are new to git (just ask on the issue). The CONTRIBUTING.md file has more detailed set of instructions on contributing to the software.

Versions

Bioscrape versions:

  • 1.4.0 (latest release): To install run pip install bioscrape
  • 1.2.2 (tagged stable release): To install run pip install bioscrape==1.2.2
  • 1.0.4 (beta release): To install run pip install bioscrape==1.0.4

License

Released under the MIT License (see LICENSE)

Copyright (c) 2022, Biocircuits, California Institute of Technology. All rights reserved.

Project links

Data verified by PyPI on Aug 20, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

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  • Released:
Newer release available (1.4.1)

3 maintainers

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Credits

Author: Ayush Pandey, William Poole, Anandh Swaminathan, Richard M Murray

GitHub Statistics

Data verified by PyPI on Aug 20, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

BSD License (MIT License)

Requires

Python >=3.8

Provides Extra

test

Tags

SBML synthetic biology modeling Chemical Reaction Network CRN simulator stochastic parameter inference

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