The Systems Biology Simulation Core Library

Panchiwala, Hemil; Shah, Shital; Planatscher, Hannes; Zakharchuk, Mykola; König, Matthias; Dräger, Andreas

doi:10.20944/preprints202012.0296.v1

Cited by 5 publications

(6 citation statements)

References 10 publications

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“…SBMLWebApp uses AJAX software within the communication configuration between frontend and backend (GWT, http: //www.gwtproject.org). The time course simulation servlet is executed using COPASI [9], SBSCL [18,19] with JSBML [20] as its internal data structure, and LibSBMLSim [21] that is based on libSBML [22]. The steady-state analysis servlet and parameter estimation servlet use COPASI [9].…”

Section: Methodsmentioning

confidence: 99%

SBMLWebApp: Web-based Simulation, Steady-State Analysis, and Parameter Estimation of Systems Biology Models

Yamada¹,

Kaito²,

König³

et al. 2021

Preprint

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In systems biology, biological phenomena are often modeled by ODE and distributed in the de facto standard file format SBML. The primary analyses performed with such models are dynamic simulation, steady-state analysis, and parameter estimation. These methodologies are mathematically formalized, and libraries for such analyses have been published. Several tools exist to create, simulate, or visualize models encoded in SBML. However, setting up and establishing analysis environments is a crucial hurdle for non-modelers. Therefore, easy access to perform fundamental analyses of ODE models is a significant challenge. We developed SBMLWebApp, a web-based service to execute SBML-based simulations, steady-state analysis, and parameter estimation directly in the browser without the need for any setup or prior knowledge to address this issue. SBMLWebApp visualizes the result and numerical table of each analysis and provides a download of the results. SBMLWebApp allows users to select and analyze SBML models directly from the BioModels Database. Taken together, SBMLWebApp provides barrier-free access to an SBML analysis environment for simulation, steady-state analysis, and parameter estimation for SBML models. SBMLWebApp is implemented in Java™ based on an Apache Tomcat® web server using COPASI, the SBSCL, and LibSBMLSim as simulation engines. SBMLWebApp is licensed under MIT with source code available from https://github.com/TakahiroYamada/SBMLWebApp. The program runs online at http://simulate-biology.org.

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Section: Methodsmentioning

confidence: 99%

SBMLWebApp: Web-based Simulation, Steady-State Analysis, and Parameter Estimation of Systems Biology Models

Yamada¹,

Kaito²,

König³

et al. 2021

Preprint

Self Cite

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“…fine-and coarse-grained acceleration are GPU-ODM [23], which is based on a variant of SSA called the Optimized Direct Method, and the SSA proposed by Sumiyoshi et al [24]. Besides these GPU-powered tools, other solutions have been recently proposed, such as the Stochastic Simulation as a Service (StochSS) [25], and the Systems Biology Simulation Core Library (SBSCL) [26]. StochSS is an integrated development environment that can be used to model and simulate both deterministic and discrete stochastic biochemical systems in up to three dimensions.…”

Section: Plos Computational Biologymentioning

confidence: 99%

FiCoS: A fine-grained and coarse-grained GPU-powered deterministic simulator for biochemical networks

et al. 2021

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Mathematical models of biochemical networks can largely facilitate the comprehension of the mechanisms at the basis of cellular processes, as well as the formulation of hypotheses that can be tested by means of targeted laboratory experiments. However, two issues might hamper the achievement of fruitful outcomes. On the one hand, detailed mechanistic models can involve hundreds or thousands of molecular species and their intermediate complexes, as well as hundreds or thousands of chemical reactions, a situation generally occurring in rule-based modeling. On the other hand, the computational analysis of a model typically requires the execution of a large number of simulations for its calibration or to test the effect of perturbations. As a consequence, the computational capabilities of modern Central Processing Units can be easily overtaken, possibly making the modeling of biochemical networks a worthless or ineffective effort. To the aim of overcoming the limitations of the current state-of-the-art simulation approaches, we present in this paper FiCoS, a novel “black-box” deterministic simulator that effectively realizes both a fine-grained and a coarse-grained parallelization on Graphics Processing Units. In particular, FiCoS exploits two different integration methods, namely, the Dormand–Prince and the Radau IIA, to efficiently solve both non-stiff and stiff systems of coupled Ordinary Differential Equations. We tested the performance of FiCoS against different deterministic simulators, by considering models of increasing size and by running analyses with increasing computational demands. FiCoS was able to dramatically speedup the computations up to 855×, showing to be a promising solution for the simulation and analysis of large-scale models of complex biological processes.

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“…Popular tools for the development, simulation and analysis of models specified in SBML include COPASI ( Hoops et al , 2006 ), the systems biology workbench (SBW) ( Bergmann and Sauro, 2006 ), the systems biology simulation core library ( Panchiwala et al , 2022 ), libSBMLSim ( Takizawa et al , 2013 ), iBioSim ( Myers et al , 2009 ), PySB ( Lopez et al , 2013 ), PySCeS ( Olivier et al , 2005 ) and VirtualCell ( Moraru et al , 2008 ), as well as many legacy tools that have been superseded by more modern software. Some of these applications are stand-alone packages designed for interactive use.…”

Section: Introductionmentioning

confidence: 99%

libRoadRunner 2.0: a high performance SBML simulation and analysis library

et al. 2022

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Motivation This paper presents libRoadRunner 2.0, an extensible, high-performance, cross-platform, open-source software library for the simulation and analysis of models expressed using the Systems Biology Markup Language (SBML). Results libRoadRunner is a self-contained library, able to run either as a component inside other tools via its C ++, C and Python APIs, or interactively through its Python or Julia interface. libRoadRunner uses a custom Just-In-Time (JIT) compiler built on the widely-used LLVM JIT compiler framework. It compiles SBML-specified models directly into native machine code for a large variety of processors, making it fast enough to simulate extremely large models or repeated runs in reasonable timeframes. libRoadRunner is flexible, supporting the bulk of the SBML specification (except for delay and nonlinear algebraic equations) as well as several SBML extensions such as hierarchical composition and probability distributions. It offers multiple deterministic and stochastic integrators, as well as tools for steady-state, sensitivity, stability and structural analyses. Availability libRoadRunner binary distributions for Windows, Mac OS, and Linux, Julia and Python bindings, source code, and documentation are all available at https://github.com/sys-bio/roadrunner, and Python bindings are also available via pip. The source code can be compiled for the supported systems as well as in principle any system supported by LLVM-13, such as ARM-based computers like the Raspberry Pi. The library is licensed under the Apache License Version 2.0.

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The Systems Biology Simulation Core Library

Cited by 5 publications

References 10 publications

SBMLWebApp: Web-based Simulation, Steady-State Analysis, and Parameter Estimation of Systems Biology Models

SBMLWebApp: Web-based Simulation, Steady-State Analysis, and Parameter Estimation of Systems Biology Models

FiCoS: A fine-grained and coarse-grained GPU-powered deterministic simulator for biochemical networks

libRoadRunner 2.0: a high performance SBML simulation and analysis library

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