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

First report of the genetic background of Marfan syndrome in Polish patients

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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

ICB-DCM/PEtab: PEtab v0.0.0a13

PEtab format updates: Add description of visualization table Cleanup Python package updates: Add visualization functions Add support for condition-specific dynamic parameter

Optimization efficiency of the proposed method.

Each scatter point shows the total computation time required for one multi-start optimization using standard ASA (x-axis) or ssASA (y-axis) for sensitivities computation (a) Blasi et al., 2016 model, (b) Zheng et al., 2012 model, (c) Fröhlich et al., 2018 model. Points on the diagonal correspond to multi-starts that took equal time with both approaches. (d) Computation speedup of optimizations using ssASA for sensitivities computation compared to using standard ASA for sensitivities. Each bar height corresponds to a mean of multi-start local optimization computation speedups and each error bar correspond to the sample standard deviations.</p

Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infection rates

Contento L, Castelletti N, Raimúndez E, et al. Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infection rates. medRxiv. 2021

Steady-state simulation scenarios, and computation methods for ASA.

(a) Different scenarios requiring computation of the model steady state. Top: The pre-equilibration case, where the system is at steady state at the beginning of the experiment. This means that by t = t0 the system has reached its steady state (x*) under some condition (ue) (blue line). At t = t0 the system was perturbed and measurements were taken at time points tj > t0 (orange crosses). The pre-equilibration steady state (x*) is the initial state under the experimental conditions (u), i.e. x0(u) = x*. Bottom: The post-equilibration case, where at some point after the beginning of the experiment, the system reaches its steady state (x*) and measurements for this time point (t = t′′) are available (orange cross at t = t′′). Measurements of the transient state may also be available (orange crosses in t t′′). (b) Alternative approaches for computing sensitivities. Top: The standard ASA approach that requires numerical integration until convergence to the steady state and subsequent backward integration of the adjoint state (p) ODE on the same time interval. Bottom: The proposed adjoint method that circumvents backward numerical integration and requires solving a system of linear algebraic equations instead.</p

Simulation efficiency of the proposed method.

Each point corresponds to a total simulation time with ASA (x-axis) and ssASA (y-axis) for sensitivities computation. Points on the diagonal correspond to simulations that took equal time with both approaches. (a) Blasi et al., 2016, (b) Zheng et al., 2012 and (c) Fröhlich et al., 2018 models. (d) Computation speedup of simulations using ssASA for sensitivities computation compared to using standard ASA. Each bar height corresponds to a mean of computation speedups of all simulations and each error bar correspond to the sample standard deviations.</p