AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models

Ordinary differential equation models facilitate the understanding of cellular signal transduction and other biological processes. However, for large and comprehensive models, the computational cost of simulating or calibrating can be limiting. AMICI is a modular toolbox implemented in C++/Python/MATLAB that provides efficient simulation and sensitivity analysis routines tailored for scalable, gradient-based parameter estimation and uncertainty quantification. AMICI is published under the permissive BSD-3-Clause license with source code publicly available on https://github.com/AMICI-dev/AMICI. Citeable releases are archived on Zenodo

pyPESTO: A modular and scalable tool for parameter estimation for dynamic models

Mechanistic models are important tools to describe and understand biological processes. However, they typically rely on unknown parameters, the estimation of which can be challenging for large and complex systems. We present pyPESTO, a modular framework for systematic parameter estimation, with scalable algorithms for optimization and uncertainty quantification. While tailored to ordinary differential equation problems, pyPESTO is broadly applicable to black-box parameter estimation problems. Besides own implementations, it provides a unified interface to various popular simulation and inference methods. pyPESTO is implemented in Python, open-source under a 3-Clause BSD license. Code and documentation are available on GitHub (https://github.com/icb-dcm/pypesto)

PEtab -- interoperable specification of parameter estimation problems in systems biology

Reproducibility and reusability of the results of data-based modeling studies are essential. Yet, there has been -- so far -- no broadly supported format for the specification of parameter estimation problems in systems biology. Here, we introduce PEtab, a format which facilitates the specification of parameter estimation problems using Systems Biology Markup Language (SBML) models and a set of tab-separated value files describing the observation model and experimental data as well as parameters to be estimated. We already implemented PEtab support into eight well-established model simulation and parameter estimation toolboxes with hundreds of users in total. We provide a Python library for validation and modification of a PEtab problem and currently 20 example parameter estimation problems based on recent studies. Specifications of PEtab, the PEtab Python library, as well as links to examples, and all supporting software tools are available at https://github.com/PEtab-dev/PEtab, a snapshot is available at https://doi.org/10.5281/zenodo.3732958. All original content is available under permissive licenses

Predicting blood pressure in aortas treated for coarctation

Coarctation of the aorta is a narrowing of the aorta that is present at birth. Treatments for coarctation of the aorta affect the aorta wall stiffness. This study uses a 1D model to predict the effects of altered wall stiffness. Stent treatments increase both blood pressure and the radial change of the aorta wall over a heartbeat cycle, compared to both healthy aortas and the resection and end-to-end anastomosis treatment. References A. M. Rudolph, M. A. Heymann, and U. Spitznas. Hemodynamic considerations in the development of narrowing of the aorta. Am. J. Cardiol. 30(5):514–525, 1972. doi:10.1016/0002-9149(72)90042-2 N. S. Talner and M. A. Berman. Postnatal development of obstruction in coarctation of the aorta: role of the ductus arteriosus. Pediatrics, 56(4):562–569, 1975. http://pediatrics.aappublications.org/content/56/4/562 P. S. Rao. Coarctation of the aorta. Curr. Cardiol. Rep., 7(6):425–434, 2005. doi:10.1007/s11886-005-0060-0 Z. Keshavarz-Motamed, E. R. Edelman, P. K. Motamed, J. Garcia, N. Dahdah, and L. Kadem. The role of aortic compliance in determination of coarctation severity: Lumped parameter modeling, in vitro study and clinical evaluation. J. Biomech. 48(16):4229–4237, 2015. doi:10.1016/j.jbiomech.2015.10.017 M. Campbell. Natural history of coarctation of the aorta. Br. Heart J. 32(5):633–640, 1970. doi:10.1136/hrt.32.5.633 L. M. S. Padua, L. C. Garcia, C. J. Rubira, and P. E. de Oliveira Carvalho. Stent placement versus surgery for coarctation of the thoracic aorta. Cochrane Db. Syst. Rev. 5:CD008204, 2012. doi:10.1002/14651858.CD008204.pub2 R. Jurcut, A. M. Daraban, A. Lorber, D. Deleanu, M. S. Amzulescu, C. Zara, B. A. Popescu, and C. Ginghina. Coarctation of the aorta in adults: what is the best treatment? Case report and literature review. J. Med. Life, 4(2):189–195, 2011. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3124275/ J. Alastruey, T. Passerini, L. Formaggia, and J. Peiro. Physical determining factors of the arterial pulse waveform: theoretical analysis and calculation using the 1-D formulation. J. Eng. Math. 77(1):19–37, 2012. doi:10.1007/s10665-012-9555-z J. Alastruey, M. Willemet, K. Lau, S. Epstein, and S. Vennin. Nektar1D, Haemodynamic Modelling Research Group, Kings College London, 2016. http://haemod.uk/nektar J. Alastruey, K. H. Parker, and S. J. Sherwin. Arterial pulse wave haemodynamics. 11th International Conference on Pressure Surges, pg. 401–442, 2012. http://wwwf.imperial.ac.uk/ssherw/spectralhp/papers/PulseSurges_2012.pdf N. P. Smith, A. J. Pullan, and P. J. Hunter. An anatomically based model of transient coronary blood flow in the heart. SIAM J. Appl. Math. 62(3):990–1018, 2002. doi:10.1137/S0036139999355199 N. Westerhof, F. Bosman, C. J. De Vries, and A. Noordergraaf. Analog studies of the human systemic arterial tree. J. Biomech. 2(2):121–134, 1969. doi:10.1016/0021-9290(69)90024-4 N. Xiao, J. Alastruey, and C. A. Figueroa. A systematic comparison between 1-D and 3-D hemodynamics in compliant arterial models. Int. J. Numer. Meth. Biomed. Eng. 30(2):204–231, 2014. doi:10.1002/cnm.2598 C. A. Figueroa, I. E. Vignon-Clementel, K. E. Jansen, T. J. R. Hughes, and C. A. Taylor. A coupled momentum method for modeling blood flow in three-dimensional deformable arteries. Comput. Meth. Appl. Mech. Eng. 195(41\T1\textendash 43):5685–5706, 2006. doi:10.1016/j.cma.2005.11.011 E. Boileau, P. Nithiarasu, P. J. Blanco, L. O. Muller, F. E. Fossan, L. R. Hellevik, W. P. Donders, W. Huberts, M. Willemet, and J. Alastruey. A benchmark study of numerical schemes for one-dimensional arterial blood flow modelling. Int. J. Numer. Meth. Biomed. Eng. 31(10):e02732, 2015. doi:10.1002/cnm.273

A protocol for dynamic model calibration

Ordinary differential equation models are nowadays widely used for the mechanistic description of biological processes and their temporal evolution. These models typically have many unknown and nonmeasurable parameters, which have to be determined by fitting the model to experimental data. In order to perform this task, known as parameter estimation or model calibration, the modeller faces challenges such as poor parameter identifiability, lack of sufficiently informative experimental data and the existence of local minima in the objective function landscape. These issues tend to worsen with larger model sizes, increasing the computational complexity and the number of unknown parameters. An incorrectly calibrated model is problematic because it may result in inaccurate predictions and misleading conclusions. For nonexpert users, there are a large number of potential pitfalls. Here, we provide a protocol that guides the user through all the steps involved in the calibration of dynamic models. We illustrate the methodology with two models and provide all the code required to reproduce the results and perform the same analysis on new models. Our protocol provides practitioners and researchers in biological modelling with a one-stop guide that is at the same time compact and sufficiently comprehensive to cover all aspects of the problemFinanciado para publicación en acceso aberto: Universidade de Vigo/CISUGMinisterio de Ciencia e Innovación | Ref. PID2020-117271RB-C22Ministerio de Asuntos Económicos y Transformación Digital | Ref. DPI2017-82896-C2-2-RMinisterio de Ciencia e Innovación | Ref. RYC-2019-027537-IXunta de Galicia | Ref. ED431F 2021/00

AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models

<p><strong>Deprecations</strong></p> <ul> <li>Moved PEtab-related functionality from <code>amici.petab_*</code> to the petab-subpackage <code>amici.petab.*</code>. The old public functions are still available but will be removed in a future release. by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2205, https://github.com/AMICI-dev/AMICI/pull/2211, https://github.com/AMICI-dev/AMICI/pull/2252</li> </ul> <p><strong>Features</strong></p> <ul> <li>Handle events occurring at fixed timepoints without root-finding. This avoids event-after-reinitialization errors in many cases a brings a slight performance improvement. by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2227</li> <li>Added <code>PetabProblem</code> class for handling PEtab-defined simulation conditions, making it easier to perform customized operations based on PEtab-defined simulation conditions. by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2255</li> <li>code-gen: Simplified <code>switch</code> statements, leading to reduced file sizes and faster compilation for certain models. by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2240</li> <li>Made <code>Model</code> and <code>ModelPtr</code> deepcopyable by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2247</li> <li>Made <code>Solver</code> and <code>SolverPtr</code> deepcopyable by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2245</li> <li>Added a debugging helper <code>get_model_for_preeq</code> for debugging simulation issues during pre-equilibration. by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2250</li> <li>Added <code>SwigPtrView</code> fields to <code>dir()</code> outputs by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2244</li> <li>Use proper labels for in plotting functions if IDs are available in <code>ReturnData</code>. by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2249</li> <li>Added <code>ExpData::clear_observations</code> to set all measurements/sigmas to NaN by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2258</li> </ul> <p><strong>Fixes</strong></p> <ul> <li>Fixed AMICI hiding all warnings. Previously, importing <code>amici</code> resulted in all warnings being hidden in the rest of the program. by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2243</li> <li>CMake: Fixed model debug builds by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2222</li> <li>Fixed CMake potentially using incorrect Python library for building model extension by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2220</li> <li>CMake: fixed cxx flag check by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2225</li> <li>Fixed potential out-of-bounds read in <code>Model::checkFinite</code> for matlab-imported models by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2232</li> <li>Fixed piecewise/Heaviside handling by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2234</li> <li>Deterministic order of event assignments by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2242</li> <li>Proper error message in case of unsupported state-dependent sigmas by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2239</li> <li>Fixed swig shadow warning + other linting issues by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2261</li> <li>Fixed <code>SwigPtrView.__getattr__</code> by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2259</li> <li><code>simulate_petab</code>: Avoid warning when simulating with default parameters by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2265</li> </ul> <p><strong>Documentation</strong></p> <ul> <li>Updated Python package installation instructions for Arch Linux by @willov in https://github.com/AMICI-dev/AMICI/pull/2212</li> <li>Updated <code>ExpData</code> documentation by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2254</li> <li>Documented simulation starting time <code>t0</code> by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2263</li> <li>Updated PEtab example by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2255</li> </ul> <p>...</p> <p><strong>Full Changelog</strong>: https://github.com/AMICI-dev/AMICI/compare/v0.20.0...v0.21.0</p>If you use this software, please cite both the article from preferred-citation and the software itself

AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models

<p>Fixed package configuration for PyPI upload. No further changes.</p> <p>See https://github.com/AMICI-dev/AMICI/releases/tag/v0.21.0.</p>If you use this software, please cite both the article from preferred-citation and the software itself

AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models

<p><strong>Fixes</strong></p> <ul> <li>Fixed CMake cmake_minimum_required deprecation warning by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2183</li> <li>Fixed misleading preequilibration failure messages by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2181</li> <li>Removed setuptools<64 restriction by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2180</li> <li>Fixed ExpData equality operator for Python by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2194</li> <li>Enabled deepcopy for ExpData(View) by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2196</li> <li>Allowed subsetting simulation conditions in simulate_petab by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2199</li> <li>Set CMake CMP0144 to prevent warning by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2209</li> </ul> <p><strong>Features</strong></p> <ul> <li>Possibility to evaluate and plot symbolic expressions based on simulation results by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2152</li> <li>Easier access to timepoints via ExpDataView by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2193</li> <li>Nicer <code>__repr__</code> for ReturnDataView by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2192</li> </ul> <p><strong>Documentation</strong></p> <ul> <li>Added installation instructions for Arch Linux by @stephanmg in https://github.com/AMICI-dev/AMICI/pull/2173</li> <li>Updated reference list by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2172</li> <li>Installation guide: optional requirements by @dweindl in https://github.com/AMICI-dev/AMICI/pull/2207</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/AMICI-dev/AMICI/compare/v0.19.0...v0.20.0</p>If you use this software, please cite both the article from preferred-citation and the software itself