Hybrid representation and simulation of stiff biochemical networks

Herajy, Mostafa; Heiner, Monika

doi:10.1016/j.nahs.2012.05.004

Cited by 34 publications

(51 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to [7,14,[16][17][18][19][20], the pdf of the next firing of a slow reaction ∈ R slow finally becomes slow ( , | x, ) = (X ( + ))…”

Section: Hybrid Rejection-based Stochastic Simulation Algorithm (Hrssa)mentioning

confidence: 99%

“…Hybrid simulation therefore needs to synchronize the progress of different simulation strategies in order to guarantee the exactness of the simulation of slow reactions. This requires considering time-varying transition propensities, which in turn require the computation of integrals to calculate the exact firing time of slow reactions [7,14,[16][17][18][19][20] (see also Section 2.5 and (19)). This step is computationally demanding and practical implementation necessarily approximates the integral computation by a numerical method that affects the accuracy of the simulation of slow reactions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

HSimulator: Hybrid Stochastic/Deterministic Simulation of Biochemical Reaction Networks

2017

View full text Add to dashboard Cite

HSimulator is a multithread simulator for mass-action biochemical reaction systems placed in a well-mixed environment. HSimulator provides optimized implementation of a set of widespread state-of-the-art stochastic, deterministic, and hybrid simulation strategies including the first publicly available implementation of the Hybrid Rejection-based Stochastic Simulation Algorithm (HRSSA). HRSSA, the fastest hybrid algorithm to date, allows for an efficient simulation of the models while ensuring the exact simulation of a subset of the reaction network modeling slow reactions. Benchmarks show that HSimulator is often considerably faster than the other considered simulators. The software, running on Java v6.0 or higher, offers a simulation GUI for modeling and visually exploring biological processes and a Javadoc-documented Java library to support the development of custom applications. HSimulator is released under the COSBI Shared Source license agreement (COSBI-SSLA).

show abstract

“…According to [7,14,[16][17][18][19][20], the pdf of the next firing of a slow reaction ∈ R slow finally becomes slow ( , | x, ) = (X ( + ))…”

Section: Hybrid Rejection-based Stochastic Simulation Algorithm (Hrssa)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HSimulator: Hybrid Stochastic/Deterministic Simulation of Biochemical Reaction Networks

2017

View full text Add to dashboard Cite

show abstract

“…The formal semantics including the enabling and firing rules as well as the conflict resolution are given in Herajy and Heiner (2012).…”

Section: Formal Definitionmentioning

confidence: 99%

“…Such interplay is implemented by updating and monitoring the rates of stochastic transitions The crucial point for our paper is how stochastic transitions are simulated when mixed with continuous ones. So the next section focuses in particular on the simulation of stochastic transitions, while numerically solving the set of ODEs induced by the continuous transitions (for more details see Herajy and Heiner, 2012). By this way, accurate results are obtained during simulation.…”

mentioning

confidence: 99%

Modeling and simulation of multi-scale environmental systems with Generalized Hybrid Petri Nets

Herajy

Heiner

2015

Front. Environ. Sci.

Self Cite

View full text Add to dashboard Cite

Predicting and studying the dynamics and properties of environmental systems necessitates the construction and simulation of mathematical models entailing different levels of complexities. Such type of computational experiments often require the combination of discrete and continuous variables as well as processes operating at different time scales. Furthermore, the iterative steps of constructing and analyzing environmental models might involve researchers with different background. Hybrid Petri nets may contribute in overcoming such challenges as they facilitate the implementation of systems integrating discrete and continuous dynamics. Additionally, the visual depiction of model components will inevitably help to bridge the gap between scientists with distinct expertise working on the same problem. Thus, modeling environmental systems with hybrid Petri nets enables the construction of complex processes while keeping the models comprehensible for researchers working on the same project with significantly divergent educational background. In this paper we propose the utilization of a special class of hybrid Petri nets, Generalized Hybrid Petri Nets (GHPN ), to model and simulate environmental systems exposing processes interacting at different time-scales. GHPN integrate stochastic and deterministic semantics as well as some other types of special basic events. To this end, we present a case study illustrating the use of GHPN in constructing and simulating multi-timescale environmental scenarios.Keywords: modeling and simulation, Hybrid Petri Nets, multi-scale environmental systems, Chagas disease, Triatoma infestans IntroductionThe process of constructing and analyzing environmental systems is increasingly becoming a complex procedure (Seppelt et al., 2009;Uusitalo et al., 2015). On the one hand, it can require the amalgamation of different simulation techniques to accurately and efficiently find a solution to the problem under consideration (see e.g., Gillet, 2008;Gregorio et al., 1999). On the other hand, complex environmental systems require the collection and analysis of various data and information that cannot be tackled by researchers coming from just one area of expertise (Seppelt et al., 2009).While the ordinary differential equations (ODEs) approach is widely used to construct and simulate many problems in the environmental domain, certain classes of such problems cannot be adequately addressed using this approach alone. For instance in Khoury et al. (2013) construct a simple, but elegant ODEs model to study food and population dynamics in honey bee colonies. However, such a continuous approach cannot capture the effect of seasonal variations Herajy and Heiner GHPN for multi-scale environmental systems on many parameters. Contrary, in Schmickl and Crailsheim (2007) and Russell et al. (2013) a discrete simulation of recurrence and difference equations has been deployed to emulate the discrete changes in bee population taking into account seasonal variations. Nevertheless, certain scenarios necessitate ...

show abstract

“…Recently, generalised hybrid Petri nets have been introduced for systems biology modelling, particularly for stiff systems [47] together with both static partitioning and dynamic partitioning simulation algorithms. There are many other algorithmic hybrid approaches that use stochastic simulation and a second technique faster than stochastic simulation, both with and without dynamic partitioning of reactions [3,36,58].…”

Section: Related Workmentioning

confidence: 99%

Hybrid semantics for Bio-PEPA

Galpin

2014

Information and Computation

View full text Add to dashboard Cite

This paper investigates the stochastic, continuous and instantaneous (hybrid) modelling of systems defined in Bio-PEPA, a quantitative process algebra for biological modelling. This is achieved by mapping a Bio-PEPA model to a model in stochastic HYPE, a process algebra that models these three behaviour types in a compositional and structured manner. The novel mapping between process algebras provides another method of analysis for Bio-PEPA models and presents the modeller with a well-structured stochastic HYPE model which can itself be easily modified and is only a small constant larger in size than the Bio-PEPA model. The structure of the stochastic HYPE model generated has desirable properties and also gives a general framework for modelling biochemical systems where the advantages of both stochastic and deterministic simulation are required. Thresholds are introduced for each reaction, and when all values are above these thresholds, the reaction is treated deterministically. However, if a relevant value is below a threshold, the reaction is treated stochastically (as are the changes in species quantities as a result of that reaction). It is proved that in the purely deterministic case and in the purely stochastic case, the stochastic HYPE model has the same behaviour as the Bio-PEPA model when considered purely deterministically and purely stochastically, respectively. Furthermore, addition of instantaneous events in the style of Bio-PEPA with events is illustrated, and a proposal for mapping Bio-PEPA with delays (Bio-PEPAd) to stochastic HYPE is presented.

show abstract

Hybrid representation and simulation of stiff biochemical networks

Cited by 34 publications

References 28 publications

HSimulator: Hybrid Stochastic/Deterministic Simulation of Biochemical Reaction Networks

HSimulator: Hybrid Stochastic/Deterministic Simulation of Biochemical Reaction Networks

Modeling and simulation of multi-scale environmental systems with Generalized Hybrid Petri Nets

Hybrid semantics for Bio-PEPA

Contact Info

Product

Resources

About