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 codes freely available.
Our main software toolboxes are:
AMICI – Advanced multilanguage 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.
https://github.com/AMICI-dev/AMICI
PESTO – Parameter EStimation TOolbox for MATLAB
n.b.: superceded by pyPESTO
PESTO is a widely applicable and highly customizable MATLAB toolbox for parameter estimation. It offers state-of-the art algorithms for optimization and uncertainty analysis, which work in a very generic manner, treating the objective function as a black box. Hence, PESTO can be used for any parameter estimation problem, which provides an objective function in MATLAB.
https://github.com/ICB-DCM/PESTO
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.
https://github.com/ICB-DCM/pyPESTO
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.
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.
https://github.com/ICB-DCM/pyABC
Benchmark Models – Collection of benchmark problems for dynamical modelling in systems biology
This repository provides a collection of SBML models and experimental data. This collection can
be used for the benchmarking of existing and new methodologies for data-based modeling, for instance optimization and uncertainty analysis methods.
https://github.com/Benchmarking-Initiative/Benchmark-Models
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.
