Science Score: 67.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 6 DOI reference(s) in README -
○Academic publication links
-
✓Committers with academic emails
1 of 6 committers (16.7%) from academic institutions -
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (18.6%) to scientific vocabulary
Keywords
Repository
Patient-Specific Modeling in Python
Basic Info
- Host: GitHub
- Owner: pasmopy
- License: apache-2.0
- Language: Python
- Default Branch: master
- Homepage: https://pasmopy.readthedocs.io
- Size: 5.09 MB
Statistics
- Stars: 18
- Watchers: 3
- Forks: 7
- Open Issues: 2
- Releases: 22
Topics
Metadata Files
README.md
Pasmopy is a scalable toolkit to identify prognostic factors for cancers based on intracellular signaling dynamics generated from personalized kinetic models. It is compatible with biomass and offers the following features:
- Construction of mechanistic models from text
- Personalization of the model using transcriptome data
- Prediction of patient outcome based on in silico signaling dynamics
- Sensitivity analysis for prediction of potential drug targets
Documentation
Online documentation is available at https://pasmopy.readthedocs.io.
You can also build the documentation locally by running the following commands:
shell
$ cd docs
$ make html
The site will live in _build/html/index.html.
Installation
The latest stable release (and required dependencies) can be installed from PyPI:
$ pip install pasmopy
Pasmopy requires Python 3.8+ to run.
Example
Building mathematical models of biochemical systems from text
This example shows you how to build a simple Michaelis-Menten two-step enzyme catalysis model with Pasmopy.
E + S ⇄ ES → E + P
An enzyme, E, binding to a substrate, S, to form a complex, ES, which in turn releases a product, P, regenerating the original enzyme.
```python import os from pasmopy import Text2Model, createmodel, runsimulation
Prepare a text file describing the biochemical reactions (e.g., michaelis_menten.txt)
reactions = """\ E + S ⇄ ES | kf=0.003, kr=0.001 | E=100, S=50 ES → E + P | kf=0.002 """
observables = """ @obs Substrate: u[S] @obs Efree: u[E] @obs Etotal: u[E] + u[ES] @obs Product: u[P] @obs Complex: u[ES] """
simulation_condition = """ @sim tspan: [0, 100] """
with open("michaelismenten.txt", mode="w") as f: f.writelines(reactions) f.writelines(observables) f.writelines(simulationcondition)
Convert the text into an executable model
description = Text2Model("michaelismenten.txt") description.convert() assert os.path.isdir("michaelismenten")
Run simulation
model = createmodel("michaelismenten") run_simulation(model) ```
For more examples, please refer to the Documentation.
Personalized signaling models for cancer patient stratification
Using Pasmopy, we built a mechanistic model of ErbB receptor signaling network, trained with protein quantification data obtained from cultured cell lines, and performed in silico simulation of the pathway activities on breast cancer patients using The Cancer Genome Atlas (TCGA) transcriptome datasets. The temporal activation dynamics of Akt, extracellular signal-regulated kinase (ERK), and c-Myc in each patient were able to accurately predict the difference in prognosis and sensitivity to kinase inhibitors in triple-negative breast cancer (TNBC).
For further details, please see https://pasmopy.readthedocs.io/en/latest/personalized_model.html.
References
Imoto, H., Yamashiro, S. & Okada, M. A text-based computational framework for patient -specific modeling for classification of cancers. iScience 25, 103944 (2022). https://doi.org/10.1016/j.isci.2022.103944
Imoto, H., Yamashiro, S., Murakami, K. & Okada, M. Protocol for stratification of triple-negative breast cancer patients using in silico signaling dynamics. STAR Protocols 3, 101619 (2022). https://doi.org/10.1016/j.xpro.2022.101619
Author
License
Owner
- Name: Pasmopy
- Login: pasmopy
- Kind: organization
- Repositories: 2
- Profile: https://github.com/pasmopy
Using patient-specific in silico model to pave the way for personalized treatments.
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use Pasmopy in a scientific publication, please cite the following paper:"
preferred-citation:
type: article
authors:
- family-names: "Imoto"
given-names: "Hiroaki"
orcid: "https://orcid.org/0000-0002-6817-642X"
- family-names: "Yamashiro"
given-names: "Sawa"
orcid: "https://orcid.org/0000-0001-8889-6398"
- family-names: "Okada"
given-names: "Mariko"
orcid: "https://orcid.org/0000-0002-6210-8223"
doi: "10.1016/j.isci.2022.103944"
journal: "iScience"
title: "A text-based computational framework for patient -specific modeling for classification of cancers"
issue: 3
volume: 25
start: 103944
end: 103944
year: 2022
url: "https://www.cell.com/iscience/fulltext/S2589-0042(22)00214-0"
GitHub Events
Total
- Watch event: 1
- Push event: 4
- Pull request event: 1
- Create event: 1
Last Year
- Watch event: 1
- Push event: 4
- Pull request event: 1
- Create event: 1
Committers
Last synced: almost 3 years ago
All Time
- Total Commits: 305
- Total Committers: 6
- Avg Commits per committer: 50.833
- Development Distribution Score (DDS): 0.213
Top Committers
| Name | Commits | |
|---|---|---|
| himoto | 3****o@u****m | 240 |
| Hiroaki Imoto | h****o@p****p | 48 |
| pre-commit-ci[bot] | 6****]@u****m | 9 |
| Hiroaki Imoto | h****o@u****e | 5 |
| Johannes Nicolaus | j****s@g****m | 2 |
| lgtm-com[bot] | 4****]@u****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 0
- Total pull requests: 1
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 1
Past Year
- Issues: 0
- Pull requests: 1
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 1
Top Authors
Issue Authors
- himoto (1)
Pull Request Authors
- himoto (4)
- pre-commit-ci[bot] (3)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 86 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 20
- Total maintainers: 1
pypi.org: pasmopy
Patient-Specific Modeling in Python
- Documentation: https://pasmopy.readthedocs.io/
- License: Apache-2.0
-
Latest release: 0.5.0
published almost 3 years ago
Rankings
Maintainers (1)
Dependencies
- actions/checkout v3 composite
- actions/setup-python v4 composite
- actions/checkout v3 composite
- github/codeql-action/analyze v2 composite
- github/codeql-action/autobuild v2 composite
- github/codeql-action/init v2 composite
- actions/checkout v2 composite
- actions/setup-python v2 composite
- biomass >=0.10
- numpy >=1.17
- pandas >=0.24
- scipy >=1.6
- seaborn >=0.11.2
- tqdm >=4.50.2