ArticleBioinformatics (Oxford, England)2017
GPU-powered model analysis with PySB/cupSODA.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- A biochemical necroptosis model explains cell-type-specific responses to cell death cues.Biophysical journal · 2023Article
- Programmatic modeling for biological systems.Current opinion in systems biology · 2021Article
- FiCoS: A fine-grained and coarse-grained GPU-powered deterministic simulator for biochemical networks.PLoS computational biology · 2021Article
- Benchmarking of numerical integration methods for ODE models of biological systems.Scientific reports · 2021Article
- A compiler for biological networks on silicon chips.PLoS computational biology · 2020Article
- Modeling heterogeneous tumor growth dynamics and cell-cell interactions at single-cell and cell-population resolution.Current opinion in systems biology · 2019Article
- Plant Synthetic Biology: Quantifying the "Known Unknowns" and Discovering the "Unknown Unknowns".Plant physiology · 2019Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
summaryA major barrier to the practical utilization of large, complex models of biochemical systems is the lack of open-source computational tools to evaluate model behaviors over high-dimensional parameter spaces. This is due to the high computational expense of performing thousands to millions of model simulations required for statistical analysis. To address this need, we have implemented a user-friendly interface between cupSODA, a GPU-powered kinetic simulator, and PySB, a Python-based modeling and simulation framework. For three example models of varying size, we show that for large numbers of simulations PySB/cupSODA achieves order-of-magnitude speedups relative to a CPU-based ordinary differential equation integrator. AVAILABILITY AND IMPLEMENTATION: The PySB/cupSODA interface has been integrated into the PySB modeling framework (version 1.4.0), which can be installed from the Python Package Index (PyPI) using a Python package manager such as pip. cupSODA source code and precompiled binaries (Linux, Mac OS/X, Windows) are available at github.com/aresio/cupSODA (requires an Nvidia GPU; developer.nvidia.com/cuda-gpus). Additional information about PySB is available at pysb.org. CONTACT: paolo.cazzaniga@unibg.it or c.lopez@vanderbilt.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.