pyPESTO: a modular and scalable tool for parameter estimation for dynamic models.

SUMMARY: 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. pyPESTO is 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. AVAILABILITY AND IMPLEMENTATION: 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).

Atrial Adaptations in Athletes Heart.

BACKGROUND: Intensive training efforts are associated with hemodynamic changes accompanied by increases in cardiac output and stroke volume related to higher peak oxygen consumption and better athletic performance during exercise. These hemodynamic changes induce an enlargement of cardiac chambers, but also of the atria and may result in an athletes' heart (AH). Data from large studies about atrial enlargement in AH are sparse. METHODS: Competitive athletes aged ≥18 years, who presented for pre-participation screening 04/2020-10/2021 were included in this study and stratified for AH (defined as physiologically increased heart volume >13.0 in males and >12.0 mL/kg in females). RESULTS: Overall, 646 athletes aged ≥18 years (median age 24.0 [20.0/31.0] years; 206 [31.9%] females) were included in our study 04/2020-10/2021; among these, 118 (18.3%) had an AH. The computed absolute heart volume was 969.4 (853.1/1083.0) mL in athletes with AH and 841.3 (707.4/966.3) mL in those without AH (p < 0.001). AH was associated with larger left ventricular mass (206.6 ± 39.0 vs. 182.7 ± 44.2 g, p < 0.001). LA area (15.4 [13.7/18.2] vs. 14.3 [12.0/16.3] cm2, p < 0.001) and RA area (15.8 [13.8/18.6] vs. 14.5 [12.3/17.0] cm2, p < 0.001) were enlarged in AH versus those athletes without AH. The logistic regressions confirmed an independent association of AH on LV mass (OR 1.05 [95% CI 1.04-1.06], p < 0.001). LA area (OR 1.29 [95% CI 1.19-1.39], p < 0.001) as well as RA area (OR 1.28 [95% CI 1.19-1.38], p < 0.001) were afflicted by AH. CONCLUSION: An AH is accompanied by significant enlargement of the atria as well as increased cardiac muscle mass.

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.

Benchmarking of numerical integration methods for ODE models of biological systems.

Ordinary differential equation (ODE) models are a key tool to understand complex mechanisms in systems biology. These models are studied using various approaches, including stability and bifurcation analysis, but most frequently by numerical simulations. The number of required simulations is often large, e.g., when unknown parameters need to be inferred. This renders efficient and reliable numerical integration methods essential. However, these methods depend on various hyperparameters, which strongly impact the ODE solution. Despite this, and although hundreds of published ODE models are freely available in public databases, a thorough study that quantifies the impact of hyperparameters on the ODE solver in terms of accuracy and computation time is still missing. In this manuscript, we investigate which choices of algorithms and hyperparameters are generally favorable when dealing with ODE models arising from biological processes. To ensure a representative evaluation, we considered 142 published models. Our study provides evidence that most ODEs in computational biology are stiff, and we give guidelines for the choice of algorithms and hyperparameters. We anticipate that our results will help researchers in systems biology to choose appropriate numerical methods when dealing with ODE models.