Release 0.11.17

Merge pull request #1511 from AMICI-dev/release_0.11.17

.github/workflows/release_biosimulators.yml

@@ -29,4 +29,4 @@ jobs:
             -u ${GH_ISSUE_USERNAME}:${GH_ISSUE_TOKEN} \
             -H "Accept: application/vnd.github.v3+json" \
               https://api.github.com/repos/${DOWNSTREAM_REPOSITORY}/actions/workflows/${WORKFLOW_FILE}/dispatches \
-            -d "{\"inputs\": {\"simulatorVersion\": \"${PACKAGE_VERSION}\", \"simulatorVersionLatest\": \"true\"}}"
+            -d "{\"ref\": \"dev\", \"inputs\": {\"simulatorVersion\": \"${PACKAGE_VERSION}\", \"simulatorVersionLatest\": \"true\"}}"

.github/workflows/test_install.yml

@@ -75,4 +75,6 @@ jobs:
       run: |
         pip3 install -v --user $(ls -t python/sdist/dist/amici-*.tar.gz | head -1)
 
-
+    - name: Test import
+      run: |
+        python -m amici

.github/workflows/test_pypi.yml

@@ -0,0 +1,45 @@
+name: PyPI installation
+on:
+  workflow_dispatch:
+  schedule:
+    - cron:  '48 4 * * *'
+
+jobs:
+  pypi:
+    name: PyPI installation
+
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: [3.7, 3.8, 3.9]
+        os: [ubuntu-20.04, macos-latest]
+
+    runs-on: ${{ matrix.os }}
+
+    steps:
+      - name: apt
+        run: |
+          if [[ ${{ matrix.os }} == ubuntu* ]] ; then \
+            sudo apt-get update \
+            && sudo apt-get install -y \
+              g++ \
+              libatlas-base-dev \
+              libhdf5-serial-dev \
+              swig
+          fi
+
+      - name: homebrew
+        run: |
+          if [[ ${{ matrix.os }} == macos* ]] ; then \
+            brew install hdf5 swig gcc libomp
+          fi
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - run: pip install --upgrade pip
+      - run: pip install -v amici
+      - run: python -c "from amici import _amici; print(_amici)"
+      - run: python -m amici

CHANGELOG.md

@@ -2,6 +2,24 @@
 
 ## v0.X Series
 
+### v0.11.17 (2021-05-30)
+
+Fixes:
+* Fix "maybe-uninitialized" compiler warning (#1495)
+* Fix substitution of expressions in `drootdt_total (#1512)
+* C++: Fix serialization and == operator (#1493)
+* C++: Avoid `w` in `root` and `stau` headers (refactor) (#1503)
+
+Documentation:
+* Updated OpenBLAS Windows installation instructions (#1496)
+* Updated how-to-cite to Bioinformatics paper (#1499)
+* Updated list of papers using AMICI (#1509)
+
+Other:
+* Remove sllh computation from `petab_objective.simulate_petab` (#1498)
+* Add __main__.py to Python package to provide info on AMICI installation
+  via `python -m amici` (#1500)
+
 ### v0.11.16 (2021-04-13)
 
 Fixes:

INSTALL.md

@@ -231,14 +231,14 @@ This will download openBLAS and compile it, creating
 You will also need to define two environment variables:
 
     BLAS_LIBS="/LIBPATH:C:/BLAS/lib openblas.lib"
-    BLAS_CFLAGS="/IC:/BLAS/OpenBLAS-v0.3.12/OpenBLAS-0.3.12"
+    BLAS_CFLAGS="/IC:/BLAS/OpenBLAS-0.3.12/OpenBLAS-0.3.12"
 
 One way to do that is to run a PowerShell script with the following commands:
 
-    [System.Environment]::SetEnvironmentVariable("BLAS_LIBS", "/LIBPATH:C:\BLAS\lib openblas.lib", [System.EnvironmentVariableTarget]::User)
-    [System.Environment]::SetEnvironmentVariable("BLAS_LIBS", "/LIBPATH:C:\BLAS\lib openblas.lib", [System.EnvironmentVariableTarget]::Process)
-    [System.Environment]::SetEnvironmentVariable("BLAS_CFLAGS", "-IC:\BLAS\OpenBLAS-v0.3.12\OpenBLAS-0.3.12", [System.EnvironmentVariableTarget]::User)
-    [System.Environment]::SetEnvironmentVariable("BLAS_CFLAGS", "-IC:\BLAS\OpenBLAS-v0.3.12\OpenBLAS-0.3.12", [System.EnvironmentVariableTarget]::Process)
+    [System.Environment]::SetEnvironmentVariable("BLAS_LIBS", "/LIBPATH:C:/BLAS/lib openblas.lib", [System.EnvironmentVariableTarget]::User)
+    [System.Environment]::SetEnvironmentVariable("BLAS_LIBS", "/LIBPATH:C:/BLAS/lib openblas.lib", [System.EnvironmentVariableTarget]::Process)
+    [System.Environment]::SetEnvironmentVariable("BLAS_CFLAGS", "-IC:/BLAS/OpenBLAS-0.3.12/OpenBLAS-0.3.12", [System.EnvironmentVariableTarget]::User)
+    [System.Environment]::SetEnvironmentVariable("BLAS_CFLAGS", "-IC:/BLAS/OpenBLAS-0.3.12/OpenBLAS-0.3.12", [System.EnvironmentVariableTarget]::Process)
 
 The call ending in `Process` sets the environment variable in the current process, and it is no longer in effect in the next process. The call ending in `User` is permanent, and takes effect the next time the user logs on.
 

README.md

@@ -33,6 +33,8 @@ constrained optimization problems.
 
 <a href="https://badge.fury.io/py/amici">
   <img src="https://badge.fury.io/py/amici.svg" alt="PyPI version"></a>
+<a href="https://github.com/AMICI-dev/AMICI/actions/workflows/test_pypi.yml">
+  <img src="https://github.com/AMICI-dev/AMICI/actions/workflows/test_pypi.yml/badge.svg" alt="PyPI installation"></a>
 <a href="https://travis-ci.com/AMICI-dev/AMICI">
   <img src="https://travis-ci.com/AMICI-dev/AMICI.svg?branch=master" alt="Build Status"></a>
 <a href="https://codecov.io/gh/AMICI-dev/AMICI">
@@ -127,14 +129,20 @@ If you used AMICI in your work, we are happy to include
 your project, please let us know via a Github issue.
 
 When using AMICI in your project, please cite
-* Fröhlich, F., Weindl, D., Schälte, Y., Pathirana, D., Paszkowski, Ł., Lines, G.T., Stapor, P. and Hasenauer, J., 2020. 
-  AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models. arXiv preprint [arXiv:2012.09122](https://arxiv.org/abs/2012.09122).
+* Fröhlich, F., Weindl, D., Schälte, Y., Pathirana, D., Paszkowski, Ł., Lines, G.T., Stapor, P. and Hasenauer, J., 2021.
+  AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models. Bioinformatics, btab227,
+  [DOI:10.1093/bioinformatics/btab227](https://doi.org/10.1093/bioinformatics/btab227).
 ```
 @article{frohlich2020amici,
   title={AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models},
   author={Fr{\"o}hlich, Fabian and Weindl, Daniel and Sch{\"a}lte, Yannik and Pathirana, Dilan and Paszkowski, {\L}ukasz and Lines, Glenn Terje and Stapor, Paul and Hasenauer, Jan},
-  journal={arXiv preprint arXiv:2012.09122},
-  year={2020}
+  journal = {Bioinformatics},
+  year = {2021},
+  month = {04},
+  issn = {1367-4803},
+  doi = {10.1093/bioinformatics/btab227},
+  note = {btab227},
+  eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btab227/36866220/btab227.pdf},
 }
 ```
 

documentation/amici_refs.bib

@@ -549,7 +549,7 @@ @Article{LinesPas2019
 @Article{VillaverdeRai2019,
   author   = {Alejandro F. Villaverde and Elba Raimúndez and Jan Hasenauer and Julio R. Banga},
   journal  = {IFAC-PapersOnLine},
-  title    = {A Comparison of Methods for Quantifying Prediction Uncertainty in Systems Biology⁎⁎This research has received funding from the European Unions Horizon 2020 research and innovation program under grant agreement No 686282 (CanPathPro) and the German Ministry of Education and Research (BMBF) under the grant agreement No 01ZX1310B (SYS-Stomach) and No 01ZX1705A (INCOME).},
+  title    = {A Comparison of Methods for Quantifying Prediction Uncertainty in Systems Biology},
   year     = {2019},
   issn     = {2405-8963},
   note     = {8th Conference on Foundations of Systems Biology in Engineering FOSBE 2019},
@@ -711,7 +711,7 @@ @Article{Schaelte2020.01.30.927004
   url          = {https://www.biorxiv.org/content/early/2020/01/31/2020.01.30.927004},
 }
 
-@Article{Adlung866871,
+@Article{AdlungSta2019,
   author       = {Adlung, Lorenz and Stapor, Paul and T{\"o}nsing, Christian and Schmiester, Leonard and Schwarzm{\"u}ller, Luisa E. and Wang, Dantong and Timmer, Jens and Klingm{\"u}ller, Ursula and Hasenauer, Jan and Schilling, Marcel},
   journal      = {bioRxiv},
   title        = {Cell-to-cell variability in JAK2/STAT5 pathway components and cytoplasmic volumes define survival threshold in erythroid progenitor cells},
@@ -725,10 +725,10 @@ @Article{Adlung866871
   url          = {https://www.biorxiv.org/content/early/2019/12/07/866871},
 }
 
-@Article{PITT201872,
+@Article{PittGom2018,
   author    = {Jake Alan Pitt and Lucian Gomoescu and Constantinos C. Pantelides and Benoît Chachuat and Julio R. Banga},
   journal   = {IFAC-PapersOnLine},
-  title     = {Critical Assessment of Parameter Estimation Methods in Models of Biological Oscillators⁎⁎This project has received funding from the European Unions Horizon 2020 research and innovation program under grant agreement 675585 (Marie Sklodowska-Curie ITN SyMBioSys). The authors JAP and LG are Marie Sklodowska-Curie Early Stage Researchers at IIM-CSIC (Spain) and PSE Ltd (UK) respectively.},
+  title     = {Critical Assessment of Parameter Estimation Methods in Models of Biological Oscillators},
   year      = {2018},
   issn      = {2405-8963},
   note      = {7th Conference on Foundation of Systems Biology in Engineering FOSBE 2018},
@@ -752,7 +752,7 @@ @MastersThesis{Watanabe2019
   url       = {https://hdl.handle.net/20.500.12380/256855},
 }
 
-@Article{Erdem2020.11.09.373407,
+@Article{ErdemBen2020,
   author       = {Erdem, Cemal and Bensman, Ethan M. and Mutsuddy, Arnab and Saint-Antoine, Michael M. and Bouhaddou, Mehdi and Blake, Robert C. and Dodd, Will and Gross, Sean M. and Heiser, Laura M. and Feltus, F. Alex and Birtwistle, Marc R.},
   journal      = {bioRxiv},
   title        = {A Simple and Efficient Pipeline for Construction, Merging, Expansion, and Simulation of Large-Scale, Single-Cell Mechanistic Models},
@@ -811,6 +811,80 @@ @Article{RaimundezDud2021
   url       = {https://www.sciencedirect.com/science/article/pii/S1755436521000037},
 }
 
+@Article{vanRosmalenSmi2021,
+  author   = {R.P. {van Rosmalen} and R.W. Smith and V.A.P. {Martins dos Santos} and C. Fleck and M. Suarez-Diez},
+  journal  = {Metabolic Engineering},
+  title    = {Model reduction of genome-scale metabolic models as a basis for targeted kinetic models},
+  year     = {2021},
+  issn     = {1096-7176},
+  pages    = {74-84},
+  volume   = {64},
+  abstract = {Constraint-based, genome-scale metabolic models are an essential tool to guide metabolic engineering. However, they lack the detail and time dimension that kinetic models with enzyme dynamics offer. Model reduction can be used to bridge the gap between the two methods and allow for the integration of kinetic models into the Design-Built-Test-Learn cycle. Here we show that these reduced size models can be representative of the dynamics of the original model and demonstrate the automated generation and parameterisation of such models. Using these minimal models of metabolism could allow for further exploration of dynamic responses in metabolic networks.},
+  doi      = {https://doi.org/10.1016/j.ymben.2021.01.008},
+  keywords = {Metabolic engineering, DBTL cycle, Model reduction, Model optimisation, Model-driven design, Synthetic biology},
+  url      = {https://www.sciencedirect.com/science/article/pii/S1096717621000161},
+}
+
+@Article{StenPod2021,
+  author       = {Sten, Sebastian and Pod{\'e}us, Henrik and Sundqvist, Nicolas and Elinder, Fredrik and Engstr{\"o}m, Maria and Cedersund, Gunnar},
+  journal      = {bioRxiv},
+  title        = {A multi-data based quantitative model for the neurovascular coupling in the brain},
+  year         = {2021},
+  abstract     = {The neurovascular coupling (NVC) forms the foundation for functional imaging techniques of the brain, since NVC connects neural activity with observable hemodynamic changes. Many aspects of the NVC have been studied both experimentally and with mathematical models: various combinations of blood volume and flow, electrical activity, oxygen saturation measures, blood oxygenation level-dependent (BOLD) response, and optogenetics have been measured and modeled in rodents, primates, or humans. We now present a first inter-connected mathematical model that describes all such data types simultaneously. The model can predict independent validation data not used for training. Using simulations, we show for example how complex bimodal behaviors appear upon stimulation. These simulations thus demonstrate how our new quantitative model, incorporating most of the core aspects of the NVC, can be used to mechanistically explain each of its constituent datasets.Competing Interest StatementThe authors have declared no competing interest.},
+  doi          = {10.1101/2021.03.25.437053},
+  elocation-id = {2021.03.25.437053},
+  eprint       = {https://www.biorxiv.org/content/early/2021/03/26/2021.03.25.437053.full.pdf},
+  publisher    = {Cold Spring Harbor Laboratory},
+  url          = {https://www.biorxiv.org/content/early/2021/03/26/2021.03.25.437053},
+}
+
+@PhdThesis{Gaspari2021,
+  author  = {Gaspari, Erika},
+  school  = {Wageningen University},
+  title   = {Model-driven design of Mycoplasma as a vaccine chassis},
+  year    = {2021},
+  address = {Wageningen},
+  comment = {WU thesis 7758 Includes bibliographical references. - With summaries in English, Italian and Spanish
+10.18174/539593},
+  doi     = {10.18174/539593},
+  issn    = {9789463956864},
+  url     = {https://edepot.wur.nl/539593},
+}
+
+@Article{VanhoeferMat2021,
+  author    = {Jakob Vanhoefer and Marta R. A. Matos and Dilan Pathirana and Yannik Schälte and Jan Hasenauer},
+  journal   = {Journal of Open Source Software},
+  title     = {yaml2sbml: Human-readable and -writable specification of ODE models and their conversion to SBML},
+  year      = {2021},
+  number    = {61},
+  pages     = {3215},
+  volume    = {6},
+  doi       = {10.21105/joss.03215},
+  publisher = {The Open Journal},
+  url       = {https://doi.org/10.21105/joss.03215},
+}
+
+@Misc{VillaverdePat2021,
+  author        = {Alejandro F. Villaverde and Dilan Pathirana and Fabian Fröhlich and Jan Hasenauer and Julio R. Banga},
+  title         = {A protocol for dynamic model calibration},
+  year          = {2021},
+  archiveprefix = {arXiv},
+  eprint        = {2105.12008},
+  primaryclass  = {q-bio.QM},
+}
+@article {Froehlich2021.05.20.445065,
+	author = {Fr{\"o}hlich, Fabian and Sorger, Peter K.},
+	title = {Fides: Reliable Trust-Region Optimization for Parameter Estimation of Ordinary Differential Equation Models},
+	elocation-id = {2021.05.20.445065},
+	year = {2021},
+	doi = {10.1101/2021.05.20.445065},
+	publisher = {Cold Spring Harbor Laboratory},
+	abstract = {Motivation Because they effectively represent mass action kinetics, ordinary differential equation models are widely used to describe biochemical processes. Optimization-based calibration of these models on experimental data can be challenging, even for low-dimensional problems. However, reliable model calibration is a prerequisite for many subsequent analysis steps, including uncertainty analysis, model selection and biological interpretation. Although multiple hypothesis have been advanced to explain why optimization based calibration of biochemical models is challenging, there are few comprehensive studies that test these hypothesis and tools for performing such studies are also lacking.Results We implemented an established trust-region method as a modular python framework (fides) to enable structured comparison of different approaches to ODE model calibration involving Hessian approximation schemes and trust-region subproblem solvers. We evaluate fides on a set of benchmark problems that include experimental data. We find a high variability in optimizer performance among different implementations of the same algorithm, with fides performing more reliably that other implementations investigated. Our investigation of possible sources of poor optimizer performance identify shortcomings in the widely used Gauss-Newton approximation. We address these shortcomings by proposing a novel hybrid Hessian approximation scheme that enhances optimizer performance.Availability Fides is published under the permissive BSD-3-Clause license with source code publicly available at https://github.com/fides-dev/fides. Citeable releases are archived on Zenodo.Contact fabian_froehlich{at}hms.harvard.edu and peter_sorger{at}hms.harvard.eduSupplementary information Supplementary data are available at Bioinformatics online and at https://github.com/fides-dev/fides-benchmark.Competing Interest StatementPKS is a member of the SAB or BOD member of Applied Biomath, RareCyte Inc., and Glencoe Software, which distributes a commercial version of the OMERO database; PKS is also a member of the NanoString SAB. In the last five years the Sorger lab has received research funding from Novartis and Merck. Sorger declares that none of these relationships have related to the content of this manuscript.},
+	URL = {https://www.biorxiv.org/content/early/2021/05/22/2021.05.20.445065},
+	eprint = {https://www.biorxiv.org/content/early/2021/05/22/2021.05.20.445065.full.pdf},
+	journal = {bioRxiv}
+}
+
 @Comment{jabref-meta: databaseType:bibtex;}
 
 @Comment{jabref-meta: grouping:

documentation/how_to_cite.rst

@@ -13,17 +13,23 @@ your project, please let us know via a Github issue.
 
 When using AMICI in your project, please cite
 
-* Fröhlich, F., Weindl, D., Schälte, Y., Pathirana, D., Paszkowski, Ł., Lines, G.T., Stapor, P. and Hasenauer, J., 2020.
-  AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models. arXiv preprint `arXiv:2012.09122 <https://arxiv.org/abs/2012.09122>`_.
+* Fröhlich, F., Weindl, D., Schälte, Y., Pathirana, D., Paszkowski, Ł., Lines, G.T., Stapor, P. and Hasenauer, J., 2021.
+  AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models. Bioinformatics, btab227,
+  `DOI:10.1093/bioinformatics/btab227 <https://doi.org/10.1093/bioinformatics/btab227>`_.
 
   .. code-block:: bibtex
 
      @article{frohlich2020amici,
        title={AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models},
        author={Fr{\"o}hlich, Fabian and Weindl, Daniel and Sch{\"a}lte, Yannik and Pathirana, Dilan and Paszkowski, {\L}ukasz and Lines, Glenn Terje and Stapor, Paul and Hasenauer, Jan},
-       journal={arXiv preprint arXiv:2012.09122},
-       year={2020}
-    }
+       journal = {Bioinformatics},
+       year = {2021},
+       month = {04},
+       issn = {1367-4803},
+       doi = {10.1093/bioinformatics/btab227},
+       note = {btab227},
+       eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btab227/36866220/btab227.pdf},
+     }
 
 When presenting work that employs AMICI, feel free to use one of the icons in
 `documentation/gfx/ <https://github.com/AMICI-dev/AMICI/tree/master/documentation/gfx>`_,