GPU-powered model analysis with PySB/cupSODA

Harris, Leonard A.; Nobile, Marco S.; Pino, James C.; Lubbock, Alexander L R; Besozzi, Daniela; Mauri, Giancarlo; Cazzaniga, Paolo; López, Carlos F.

doi:10.1093/bioinformatics/btx420

Cited by 15 publications

(14 citation statements)

References 26 publications

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“…For applications which benefit from a high degree of parallelization, GPU acceleration may also be beneficial. Approaches in this direction are implemented in, e.g., the toolboxes LASSIE 32 and cupSODA 29 , 33 .…”

Section: Methodsmentioning

confidence: 99%

Benchmarking of numerical integration methods for ODE models of biological systems

Städter

Schälte

Schmiester

et al. 2021

Sci Rep

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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.

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Section: Methodsmentioning

confidence: 99%

Benchmarking of numerical integration methods for ODE models of biological systems

Städter

Schälte

Schmiester

et al. 2021

Sci Rep

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“…Parallel computing paradigm may be used on multi-core CPUs, many-core processing units (such as, GPUs [77]), re-configurable hardware platforms (such as, FPGAs), or over distributed infrastructure (such as, cluster, Grid, or Cloud). While multi-core CPUs are suitable for general-purpose tasks, many-core processors (such as the Intel Xeon Phi [24] or GPU [85]) comprise a larger number of lower frequency cores and perform well on scalable applications (such as, DNA sequence analysis [71], biochemical simulation [53,76,81,123] or deep learning [129]).…”

Section: High Performance Computing and Big Datamentioning

confidence: 99%

“…3.1 for some examples). In this context, GPUs [77] were already successfully employed to achieve a considerable reduction in the computational times required by the simulation of both deterministic [53,76,123] and stochastic models [81,150]. Besides accelerating single simulations of such models, these methods prove to be particularly useful when there is a need of running multiple independent simulations of the same model.…”

Section: High Performance Computing and Big Datamentioning

confidence: 99%

Towards Human Cell Simulation

Spolaor

Gribaudo

Iacono

et al. 2019

Lecture Notes in Computer Science

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The faithful reproduction and accurate prediction of the phenotypes and emergent behaviors of complex cellular systems are among the most challenging goals in Systems Biology. Although mathematical models that describe the interactions among all biochemical processes in a cell are theoretically feasible, their simulation is generally hard because of a variety of reasons. For instance, many quantitative data (e.g., kinetic rates) are usually not available, a problem that hinders the execution of simulation algorithms as long as some parameter estimation methods are used. Though, even with a candidate parameterization, the simulation of mechanistic models could be challenging due to the extreme computational effort required. In this context, model reduction techniques and High-Performance Computing infrastructures could be leveraged to mitigate these issues. In addition, as cellular processes are characterized by multiple scales of temporal and spatial organization, novel hybrid simulators able to harmonize different modeling approaches (e.g., logic-based, constraint-based, continuous deterministic, discrete stochastic, spatial) should be designed. This chapter describes a putative unified approach to tackle these challenging tasks, hopefully paving the way to the definition of large-scale comprehensive models that aim at the comprehension of the cell behavior by means of computational tools.

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“…Models of biological networks play important roles in our understanding of disease biology [22,23], cancer [24], drug discovery [25], metabolic regulation [26], and many other subjects. However, simulation of large kinetic network models continues to be a major challenge, despite recent progress in high-performance simulation software [27][28][29]. The growth in size and complexity of biological pathway models has exceeded the growth of simulation hardware and software.…”

Section: Introductionmentioning

confidence: 99%

“…Large-scale examples of kinetic simulations also arise in genome-scale kinetic models [31,32]. Common simulation bottlenecks (Award #3835) and the Alfred P. Sloan Foundation (Award #2013- [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.…”

Section: Introductionmentioning

confidence: 99%

A compiler for biological networks on silicon chips

et al. 2020

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The explosive growth in semiconductor integrated circuits was made possible in large part by design automation software. The design and/or analysis of synthetic and natural circuits in living cells could be made more scalable using the same approach. We present a compiler which converts standard representations of chemical reaction networks and circuits into hardware configurations that can be used to simulate the network on specialized cytomorphic hardware. The compiler also creates circuit-level models of the target configuration, which enhances the versatility of the compiler and enables the validation of its functionality without physical experimentation with the hardware. We show that this compiler can translate networks comprised of mass-action kinetics, classic enzyme kinetics (Michaelis-Menten, Briggs-Haldane, and Botts-Morales formalisms), and genetic repressor kinetics, thereby allowing a large class of models to be transformed into a hardware representation. Rule-based models are particularly well-suited to this approach, as we demonstrate by compiling a MAP kinase model. Development of specialized hardware and software for simulating biological networks has the potential to enable the simulation of larger kinetic models than are currently feasible or allow the parallel simulation of many smaller networks with better performance than current simulation software.

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GPU-powered model analysis with PySB/cupSODA

Cited by 15 publications

References 26 publications

Benchmarking of numerical integration methods for ODE models of biological systems

Benchmarking of numerical integration methods for ODE models of biological systems

Towards Human Cell Simulation

A compiler for biological networks on silicon chips

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