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parPE

The parPE library provides functionality for solving large-scale parameter optimization problems requiring up to thousands of simulations per objective function evaluation on high performance computing (HPC) systems.

parPE offers easy integration with AMICI-generated ordinary differential equation (ODE) models.

Features

parPE offers the following features:

• MPI-based load-balancing of individual simulations (if you don't need this, and don't require a C++ library, consider using the pypesto Python package which is more mature and user-friendly)
• improved load balancing by intermingling multiple optimization runs (multi-start local optimization)
• integration with SBML models via AMICI and PEtab
  • hierarchical optimization (https://doi.org/10.1093/bioinformatics/btz581)
• interfaces to Ipopt, Ceres, FFSQP and SUMSL (CALGO/TOMS 611) optimizers
• HDF5 I/O compatible with a wide variety of programming languages
• Good parallel scaling to up to several thousand cores (highly problem dependent)

Note that this library has been developed for specific research questions and certain assumptions may not always hold. Use with caution. In particular, certain default settings may need adaptation (in particular, parallelization settings and AMICI settings such as the sensitivity method). PEtab support is patchy. Always verify your simulation results.

Getting started

Although various modules of parPE can be used independently, the most meaningful and convenient use case is parameter optimization for an SBML model specified in the PEtab format. This is described in doc/petab_model_import.md.

Dependencies

For full functionality, parPE requires the following libraries:

• CMAKE (>=3.22)
• MPI (OpenMPI, MPICH, ...)
• IPOPT (>= 1.2.7) (requires coinhsl)
• CERES (>=1.13) (requires Eigen)
• Boost (serialization, thread)
• HDF5 (>= 1.10)
• CBLAS compatible BLAS (libcblas, Intel MKL, ...)
• AMICI (included in this repository) (uses SuiteSparse, Sundials)
• C++17 compiler
• Python >= 3.10, including header files

On Debian-based systems, dependencies can be installed via:

sudo apt-get install \
  build-essential \
  cmake \
  cmake-curses-gui \
  coinor-libipopt-dev \
  curl \
  gfortran \
  libblas-dev \
  libboost-chrono-dev \
  libboost-serialization-dev \
  libboost-thread-dev \
  libceres-dev \
  libmpich-dev \
  libhdf5-dev \
  libpython3-dev \
  python3-pip

Scripts to fetch and build the remaining dependencies are provided in /ThirdParty/:

ThirdParty/installDeps.sh

NOTE: When using ThirdParty/installIpopt.sh to build Ipopt, you may have to download the HSL library separately as described at https://coin-or.github.io/Ipopt/INSTALL.html#DOWNLOAD_HSL. Place the HSL archive into ThirdParty before running ThirdParty/installIpopt.sh. If asked type in your coinhsl version (e.g. 2019.05.21 if you have coinhsl-2019.05.21.tar.gz).

Building

After having taken care of the dependencies listed above, parPE can be built:

./buildAll.sh

Other sample build scripts are provided as /build*.sh.

Recently tested compilers

• GCC 14.2.0
• Clang 18.1.3

Containers

There is a Dockerfile available in container/charliecloud/ and images can be found on dockerhub.

Documentation & further information

Some high-level documentation is available at https://parpe.readthedocs.io/en/latest/ and among GitHub issues. No extensive full-text documentation is available for the C++ interface yet. For usage of the C++ interface see examples/ and */tests.

References

parPE is being used or has been used in the following projects:

• Leonard Schmiester, Yannik Schälte, Fabian Fröhlich, Jan Hasenauer, Daniel Weindl. Efficient parameterization of large-scale dynamic models based on relative measurements. Bioinformatics, btz581, doi:10.1093/bioinformatics/btz581 (preprint: doi:10.1101/579045).

• Stapor, P., Schmiester, L., Wierling, C. et al. Mini-batch optimization enables training of ODE models on large-scale datasets. Nat Commun 13, 34 (2022). doi:10.1038/s41467-021-27374-6 (preprint: doi:10.1101/859884).

• Paul F. Lang, David R. Penas, Julio R. Banga, Daniel Weindl, Bela Novak. Reusable rule-based cell cycle model explains compartment-resolved dynamics of 16 observables in RPE-1 cells. bioRxiv (2023). doi:10.1101/2023.05.04.539349

• CanPathPro

Funding

parPE has been developed within research projects receiving external funding:

• Through the European Union's Horizon 2020 research and innovation programme under grant agreement no. 686282 (CanPathPro).

• Computer resources for testing parPE have been provided among others by the Gauss Centre for Supercomputing / Leibniz Supercomputing Centre under grant pr62li and pn72go.