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https://github.com/diging/sloppycell

https://github.com/diging/sloppycell

Science Score: 8.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
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    Academic publication links
  • ○
    Academic email domains
  • ✓
    Institutional organization owner
    Organization diging has institutional domain (diging.asu.edu)
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (6.9%) to scientific vocabulary
Last synced: 5 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: diging
  • License: bsd-3-clause
  • Language: Python
  • Default Branch: master
  • Size: 12.9 MB
Statistics
  • Stars: 0
  • Watchers: 2
  • Forks: 1
  • Open Issues: 3
  • Releases: 0
Fork of GutenkunstLab/SloppyCell
Created almost 5 years ago · Last pushed about 4 years ago
Metadata Files
Readme License

README.rst 

SloppyCell
==========

SloppyCell is a software environment for simulation and analysis of biomolecular networks. A particular strength of SloppyCell is estimating parameters by fitting experimental data and then calculating the resulting uncertainties on parameter values and model predictions.

SloppyCell was initially developed by Ryan Gutenkunst in the research group of `Jim Sethna `_. Ryan now has his `own group `_, where SloppyCell continues to be developed. Most recently, an XML interface was been developed in collaboration with `Michael Blinov `_, to facilitate integration with other modeling frameworks, such as `The Virtual Cell `_.

Note
----
Recently, there appears to have been a change to Python distutils such that networks cannot be compiled in an imported module. If attempt is made to compile a network in an imported module, SloppyCell will hang.

Features
--------
* support for much of the Systems Biology Markup Language (SBML) level 2 version 3
* deterministic and stochastic dynamical simulations
* sensitivity analysis without finite-difference derviatives
* optimization methods to fit parameters to experimental data
* simulation of multiple related networks sharing common parameters
* stochastic Bayesian analysis of parameter space to estimate uncertainties associated with optimal fits

Owner

  • Name: ASU Digital Innovation Group
  • Login: diging
  • Kind: organization
  • Location: Tempe, Arizona

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