Implementing biological hybrid systems: Allowing composition and avoiding stiffness

Assar, Rodrigo; Sherman, David James

doi:10.1016/j.amc.2013.08.012

Cited by 6 publications

(5 citation statements)

References 33 publications

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“…Another application where LIQSS methods exhibit advantages over classic discrete time algorithms is that of certain biological models. 17 However, in some of these models, the Jacobian matrix contains large entries at both sides of the main diagonal and LIQSS methods provoke spurious oscillations. A case where this happens is the classic Tyson model of cdc2 and cyclin interactions, 18 that represents the creation and degradation of cyclin in a cell.…”

Section: Examples and Resultsmentioning

confidence: 99%

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Improving Linearly Implicit Quantized State System Methods

2018

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In this article we propose a modification to Linearly Implicit Quantized State System Methods (LIQSS), a family of methods for solving stiff Ordinary Differential Equations (ODEs) that replace the classic time discretization by the quantization of the state variables. LIQSS methods were designed to efficiently simulate stiff systems, but they only work when the system has a particular structure. The proposed modification overcomes this limitation, allowing the algorithms to efficiently simulate stiff systems with more general structures. Besides describing the new methods and their algorithmic descriptions, the article analyzes the algorithms performance in the simulation of some complex systems.

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Section: Examples and Resultsmentioning

confidence: 99%

“…x j and € x j (lines [16][17] and the calculation of the next quantized state change in line 18 involves solving a quadratic equation. This additional cost is compensated by the fact that QSS2 can perform much larger steps, achieving better error bounds.…”

Section: Quantized State System Methodsmentioning

confidence: 99%

Improving Linearly Implicit Quantized State System Methods

2018

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“…QSS integration methods have certain features (sparsity exploitation, efficient discontinuities handling [8], explicit stiff integration [9]) that reduce the computational costs in the simulation of large scale systems [10,11,12]. These facts motivated the usage of QSS methods in several applications, including Building and Power Systems simulation [13,14] (where there are also plans to include these algorithm in the EnergyPlus software package [15]), as well as large biological models [16], water distribution models [17], wildfire propagation [18], and simulation of high energy particles [19] (where there is also a preliminary implementation of QSS algorithms in the software Geant4).…”

Section: Introductionmentioning

confidence: 99%

A parallel Quantized State System Solver for ODEs

Fernandez

Kofman

Bergero

2017

Journal of Parallel and Distributed Computing

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Section: Introductionmentioning

confidence: 99%

“…For these reasons, there are several applications where QSS methods simulate much faster than the most efficient discrete time algorithms, including power electronic circuits [17,22,21], biological models [13,1], Heating, Ventilation, and Air Conditioning (HVAC) systems [23,27], among other systems that combine different features under which QSS methods are efficient. The easiest way of implementing the QSS algorithms is through the use of a DEVS (Discrete EVent System Specification) [28] simulation engine.…”

Section: Introductionmentioning

confidence: 99%

A stand-alone quantized state system solver for continuous system simulation

Pastor

Kofman

2014

SIMULATION

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This article introduces a stand-alone implementation of the Quantized State System (QSS) integration methods for continuous and hybrid system simulation. QSS methods replace the time discretization of classic numerical integration by the quantization of the state variables. These algorithms lead to discrete event approximations of the original continuous systems and show some advantages over classic numerical integration schemes.For simplicity reasons, most implementations of QSS methods were confined to discrete event simulation engines. The problem is that they were not fully efficient as they wasted much of the computational load in the discrete event simulation mechanism. The Stand-Alone QSS solver presented here overcomes this problem, improving in more than one order of magnitude the computation times of the previous discrete event implementations.Besides describing the solver structure and functionality, the article analyzes four different models and compares the performance of the new solver with that of the discrete event implementation, and with that of different classic solvers.

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Implementing biological hybrid systems: Allowing composition and avoiding stiffness

Cited by 6 publications

References 33 publications

Improving Linearly Implicit Quantized State System Methods

Improving Linearly Implicit Quantized State System Methods

A parallel Quantized State System Solver for ODEs

A stand-alone quantized state system solver for continuous system simulation

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