Benchmarking of numerical integration methods for ODE models of biological systems

Städter, Philipp; Schälte, Yannik; Schmiester, Leonard; Hasenauer, Jan; Stapor, Paul

doi:10.1038/s41598-021-82196-2

Cited by 39 publications

(25 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…( 3 ). As ODEs in systems biology applications must be assumed to be stiff 41 , 68 , 69 , we employed an implicit multi-step backward differential formula scheme of variable order. This allowed adaptive time stepping and automated error control, helping to ensure the desired accuracy of the computed results 41 , 68 , 69 .…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Mini-batch optimization enables training of ODE models on large-scale datasets

et al. 2022

Self Cite

View full text Add to dashboard Cite

Quantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become computationally extremely challenging. Mini-batch optimization methods, as employed in deep learning, have better scaling properties. In this work, we adapt, apply, and benchmark mini-batch optimization for ordinary differential equation (ODE) models, thereby establishing a direct link between dynamic modelling and machine learning. On our main application example, a large-scale model of cancer signaling, we benchmark mini-batch optimization against established methods, achieving better optimization results and reducing computation by more than an order of magnitude. We expect that our work will serve as a first step towards mini-batch optimization tailored to ODE models and enable modelling of even larger and more complex systems than what is currently possible.

show abstract

Section: Methodsmentioning

confidence: 99%

“…However, it is well-known that ODE models in systems biology typically exhibit stiff dynamics. This makes it necessary to employ implicit solvers with adaptive time stepping 41 . Hence, it is essential to combine advanced methods from both fields, deep learning and ODE modelling.…”

Section: Introductionmentioning

confidence: 99%

Mini-batch optimization enables training of ODE models on large-scale datasets

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…AMICI ships with a MATLAB interface and implements a Python interface using SWIG, which can be extended to other languages. These interfaces execute simulation and sensitivity computation through the C++ library, enabling high performance, competitive with other toolboxes such as COPASI ( Städter et al , 2021 ). Simulation and compilation are highly configurable with the API documented on Read the Docs ( https://amici.readthedocs.io/ ) and in example notebooks.…”

Section: Methodsmentioning

confidence: 99%

AMICI: high-performance sensitivity analysis for large ordinary differential equation models

et al. 2021

Self Cite

View full text Add to dashboard Cite

Summary 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. Availability 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. Supplementary information Supplementary data are available at Bioinformatics online.

show abstract

“…Despite the efforts done to automatically define synthetic models, all these generators share a common drawback, that is, they have a limited flexibility and can generate only a restricted set of biochemical networks and processes. Considering the impelling necessity of defining a common benchmarking approach that allows for fairly evaluating and comparing different simulation approaches [29], we propose here a novel tool, named SMGen, designed to automatically generate synthetic yet realistic biological networks codified as RBMs, whose dynamics resemble those of real biological networks. SMGen adheres to well-defined structural characteristics based on graph theory and linear algebra properties, in particular, it exploits the definition of an undirected graph with a single connected component, which makes the whole generation process a computationally demanding task.…”

Section: Introductionmentioning

confidence: 99%

SMGen: A generator of synthetic models of biochemical reaction networks

Riva

Cazzaniga

Nobile

et al. 2021

Preprint

View full text Add to dashboard Cite

Several software tools for the simulation and analysis of biochemical reaction networks have been developed in the last decades; however, assessing and comparing their computational performance in executing the typical tasks of Computational Systems Biology can be limited by the lack of a standardized benchmarking approach. To overcome these limitations, we propose here a novel tool, named SMGen, designed to automatically generate synthetic models of biochemical reaction networks that, by construction, are characterized by both features (e.g. system connectivity, reaction discreteness) and emergent dynamics resembling real biological networks. The generation of synthetic models in SMGen is based on the definition of an undirected graph consisting in a single connected component, which generally results in a computationally demanding task. To avoid any burden in the execution time, SMGen exploits a Main-Worker paradigm to speed up the overall process. SMGen is also provided with a user-friendly Graphical User Interface that allows the user to easily set up all the parameters required to generate a set of synthetic models with any used-defined number of reactions and species. We analysed the computational performance of SMGen by generating batches of symmetric and asymmetric RBMs of increasing size, showing how a different number of reactions and/or species affects the generation time. Our results show that when the number of reactions is higher than the number of species, SMGen has to identify and correct high numbers of errors during the creation process of the RBMs, a circumstance that increases the overall running time. Though, SMGen can create synthetic models with 512 species and reactions in less than 7 seconds. The open-source code of SMGen is available on GitLab: https://gitlab.com/sgr34/smgen.

show abstract

Benchmarking of numerical integration methods for ODE models of biological systems

Cited by 39 publications

References 37 publications

Mini-batch optimization enables training of ODE models on large-scale datasets

Mini-batch optimization enables training of ODE models on large-scale datasets

AMICI: high-performance sensitivity analysis for large ordinary differential equation models

SMGen: A generator of synthetic models of biochemical reaction networks

Contact Info

Product

Resources

About