Skip to main content

Alias4SBML

Alias4SBML is a Python package designed to improve the visualization of biological networks formatted in SBML (Systems Biology Markup Language). It addresses the challenge of cluttered network visualizations by generating alias nodes—duplicate representations of highly connected molecular components. These alias nodes redistribute interactions, reducing visual congestion and enhancing the clarity of biological models, particularly those with dense connectivity and overlapping interactions.

Features

  • Alias Nodes for Improved Visualization: Generate alias nodes—duplicate representations of highly connected genes, proteins, or other molecular components—to alleviate visual congestion in biological networks. This helps to distribute interactions more evenly across the network, preserving the clarity of functional relationships.

  • Seamless SBML Integration: Alias4SBML integrates with SBMLDiagrams to improve the readability of SBML-formatted models. This integration is especially useful for models lacking predefined layouts, enhancing the visual clarity of complex networks without compromising structural integrity.

  • Customizable Alias Node Generation: Provides flexibility for users to select which molecular components should be represented as alias nodes. Additionally, users can define connectivity thresholds, allowing for tailored alias node generation that fits the specific needs of their biological model.

  • Scalable and Efficient: Alias4SBML is designed to handle both small and large-scale models. Whether you are working with a network containing dozens of species or hundreds, the package significantly improves model readability by reducing edge lengths and minimizing visual clutter.

Installation

To install Alias4SBML, simply run the following command:

pip install alias4sbml

Usage

import alias4sbml

# create a model
model = '''
J0: S1 -> S2;
J1: S1 -> S3;
J2: S1 -> S4 + S5;
'''

# Load your SBML model
a4sbml = alias4sbml.load(model)

# Generate alias nodes for the model
a4sbml.create_alias(species="S1", max_species_connections=1)

# Visualize the modified model
a4sbml.draw("./test1.png")

Customization

Alias4SBML provides several options for customizing the alias node generation process. Users can specify the molecular components to include in alias nodes, set connectivity thresholds for alias node creation, and adjust the appearance of alias nodes in the network visualization.

import alias4sbml

# create a model
model = '''
J0: A -> B;
J1: A -> C;
J2: A -> B + C;
J3: B -> A + C;
J4: C -> A + B;
'''

# Load your SBML model
model = alias4sbml.load(model)

# Define the molecular components to include in alias nodes
components = ['A', 'B', 'C']

# Set the connectivity threshold for alias node generation
threshold = 1

#load the model
a4sbml = alias4sbml.load(model)
    
# Generate alias nodes with custom settings
model_with_alias_nodes = a4sbml.create_alias(components, threshold)

# Visualize the modified model
a4sbml.draw(model_with_alias_nodes)

Contributing

If you would like to contribute to Alias4SBML, please open an issue or submit a pull request on the GitHub repository.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For questions or support, please open an issue on the GitHub repository.

Metadata

Release files for alias4sbml 0.1.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for alias4sbml 0.1.0
File Interpreter ABI Platform
alias4sbml-0.1.0-py3-none-any.whl Python 3 none any Details

Release files / alias4sbml-0.1.0-py3-none-any.whl

Download URL alias4sbml-0.1.0-py3-none-any.whl
Size 8.9 kB
Tags Python 3
SHA-256 checksum
How to use checksums
4da1405f1b9f5fbe666ceda0ecd00b51858139d95c97fbf7961c4ad0a6bb17bd
BLAKE2b-256 checksum
How to use checksums
6724ba0a1dab203274e178b23e4bb3a53ea57f99245cf89ebae90663e02d5fa4
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.0.1 CPython/3.12.1

Security for alias4sbml 0.1.0

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Release history Release notifications | RSS feed

This release

0.1.0 This release

1 release file No provenance

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page