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Stochastic System identification Toolkit

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@Munsky Munsky released this 19 Oct 22:04
· 56 commits to main since this release
v1.0.3
97ad550

We are excited to announce the release of the Stochastic System Identification Toolkit (#SSIT, https://github.com/MunskyGroup/SSIT).

(v1.0.3 Corrects minor bug and adds .html for additional published examples).

The SSIT is an #OpenSource #Matlab Package for formulating, solving, and comparing continuous time, discrete stochastic models to single-cell gene regulation data.

The package includes support for many model development and inference tasks, including (but not limited to):

  • (1) Model Generation, Modification, Saving, and Loading (#simBiology, #SBML)

  • (2) Model Solution Schemes including deterministic analyses (#ODE), Stochastic Simulations (#SSA), Finite State Projections (#FSP).

  • (3) Support for time-inhomogeneous stochastic processes with time-varing upstream signals.

  • (4) Support for Hybrid Models that combine deterministic and stochastic reactions.

  • (5) Parametric Sensitivity analyses.

  • (6) First passage time calculations for complex trajectories

  • (7) Data-driven parameter estimation and uncertainty quantification using Maximum Likelihood Calculations (#MLE) and posterior sampling under #Bayesian priors.

  • (8) Multiple Model Fitting using shared hyper-parameters to capture heterogeneous data types from different experiments.

  • (9) Support for Optimal Experiment Design using #FisherInformation and for iterative Bayesian Experiment Design using sample posteriors from previous experiments.

  • (10) Support to analyze and reject non-Gaussian measurement errors during model fitting and experiment design analyses.

  • (11) Support for model reduction schemes based on Eigenvalue Decompositions, Principle Orthogonal Decompositions, Coarse Graining, Quasi-Steady State Approximations on Fast Species.

  • (12) Graphical User Interface (#GUI) for model development, data analysis and parameter estimation.

  • (13) Multiple Examples provided as templates to quickly get started to build your own models and fit your own experimental data.

We are always interested to hear from new collaborators who may be looking to use or extend these open source tools. If you have data you would like to fit, or if you have Master Equation solution approaches you would like to test, please contact us.

We would be happy to set up in person or zoom tutorials on how to build your own models, fit existing data, and design your next round of single-cell experiments.

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