List of software

Our research group contributes to the development of several open-source software packages and repositories. We are committed to the FAIR principles and make all code freely available. Our main software toolboxes are:

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© The AMICI logo is released under the Creative Commons CC0 1.0.

AMICI - Advanced multi-language interface for CVODES and IDAS

AMICI provides a multilanguage (Python, C++, Matlab) interface for the SUNDIALS solvers CVODES (for ordinary differential equations) and IDAS (for algebraic differential equations). AMICI allows the user to read differential equation models specified as SBML and automatically compiles such models as .mex simulation files, C++ executables or python modules. Beyond forward integration, the compiled simulation file also allows for forward and adjoint sensitivity analysis.

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© The pyABC logo is free for use under the creative commons CC0 license.

pyABC – Approximate Bayesian Computing toolbox for python

pyABC is a python toolbox for Approximate Bayesian Computation - Sequential Monte Carlo (ABC-SMC) sampling for parameter estimation of complex stochastic models. It is very flexible, customizable and facilitates massively parallel computations. This facilitates the analysis of computationally very demanding inference problems. Applications used already up to 10^5 cores and several thousand core hours.

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Benchmark Models – Collection of  problems for dynamical modelling in systems biology 

A repository provides a collection of SBML models and experimental data. It can be used for the benchmarking of existing and new methodologies for data-based modeling, for instance optimization and uncertainty analysis methods.

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FitMultiCell  - Integrated platform for data-driven modeling of multi-cellular systems

An Integrated Platform for Data-Driven Modeling of Multi-Cellular Processes. It delivers that by combining the simulation tool Morpheus and the likelihood-free inference tool pyABC.

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PEtab – A data format for specifying parameter estimation problems in systems biology

PEtab describes a data format for specifying parameter estimation problems in systems biology, and provides a Python library for easy access and validation of PEtab files. The format is used in pyPESTO and the collection of benchmark problems.

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pyPESTO – Parameter EStimation TOolbox for python

pyPESTO is a widely applicable and highly customizable python toolbox for parameter estimation. It is based on PESTO but offers additional functionalities. Furthermore, is tightly integrated with SBML and PEtab to streamline the data-driven modelling of cellular pathways.

For a complete list of software packages and some useful functions, please check out our Github page: https://github.com/ICB-DCM/

If you have questions or comments regarding our tools, please feel free to post issues on our GitHub pages.
 
 
 

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