Generic building blocks for simulation modelling of stochastic continuous systems

The key objective is to present the generic building blocks of a methodology that can be used to model stochastic continuous systems efficiently. The original simulation model of a real-world system is used as the basis for the development of a generic modelling methodology. The generic building blocks of the methodology are used to construct two new simulation models using two different simulation software packages (Arena and Simul8). The evaluation method, the determination of adequate sample sizes and the verification and validation of the models are discussed. The models and software packages are compared and conclusions are presented.Die hoofdoelwit is om die generiese boublokke van ‘n metodiek voor te hou wat gebruik kan word om stogastiese kontinue stelsels doeltreffend te modelleer. Die oorspronklike simulasiemodel van ‘n werklike-wêreld-stelsel word gebruik as die basis vir die ontwikkeling van ‘n generiese modelleringsmetodiek. Die generiese boublokke van die metodiek word gebruik om twee nuwe simulasiemodelle te konstrueer met twee verskillende simulasiesagtewarepakkette (Arena en Simul8). Die evaluasiemetode, die vasstelling van voldoende monstergroottes en die verifikasie en validering van die modelle word bespreek. Die modelle en sagtewarepakkette word vergelyk en gevolgtrekkings word voorgehou.This paper is a more detailed version of a paper titled: “Generic Modelling of a Stochastic Continuous System” that was presented at the 16th European Simulation Multiconference (ESM’2002) that was held from 3 to 5 June 2002 in Darmstadt, Germany.http://sajie.journals.ac.z

Generic simulation modelling of stochastic continuous systems

The key objective of this research is to develop a generic simulation modelling methodology that can be used to model stochastic continuous systems effectively. The generic methodology renders simulation models that exhibit the following characteristics: short development and maintenance times, user-friendliness, short simulation runtimes, compact size, robustness, accuracy and a single software application. The research was initiated by the shortcomings of a simulation modelling method that is detailed in a Magister dissertation. A system description of a continuous process plant (referred to as the Synthetic Fuel plant) is developed. The decision support role of simulation modelling is considered and the shortcomings of the original method are analysed. The key objective, importance and limitations of the research are also discussed. The characteristics of stochastic continuous systems are identified and a generic methodology that accommodates these characteristics is conceptualised and developed. It consists of the following eight methods and techniques: the variables technique, the iteration time interval evaluation method, the event-driven evaluation method, the Entity-represent-module method, the Fraction-comparison method, the iterative-loop technique, the time “bottleneck” identification technique and the production lost “bottleneck” identification technique. Five high-level simulation model building blocks are developed. The generic methodology is demonstrated and validated by the development and use of two simulation models. The five high-level building blocks are used to construct identical simulation models of the Synthetic Fuel plant in two different simulation software packages, namely: Arena and Simul8. An iteration time interval and minimum sufficient sample sizes are determined and the simulation models are verified, validated, enhanced and compared. The simulation models are used to evaluate two alternative scenarios. The results of the scenarios are compared and conclusions are presented. The factors that motivated the research, the process that was followed and the generic methodology are summarised. The original method and the generic methodology are compared and the strengths and weaknesses of the generic methodology are discussed. The contribution to knowledge is explained and future developments are proposed. The possible range of application and different usage perspectives are presented. To conclude, the lessons learnt and reinforced are considered.Thesis (PhD (Industrial Engineering))--University of Pretoria, 2004.Industrial and Systems Engineeringunrestricte

Analysis of signalling pathways using the prism model checker

We describe a new modelling and analysis approach for signal transduction networks in the presence of incomplete data. We illustrate the approach with an example, the RKIP inhibited ERK pathway [1]. Our models are based on high level descriptions of continuous time Markov chains: reactions are modelled as synchronous processes and concentrations are modelled by discrete, abstract quantities. The main advantage of our approach is that using a (continuous time) stochastic logic and the PRISM model checker, we can perform quantitative analysis of queries such as if a concentration reaches a certain level, will it remain at that level thereafter? We also perform standard simulations and compare our results with a traditional ordinary differential equation model. An interesting result is that for the example pathway, only a small number of discrete data values is required to render the simulations practically indistinguishable

Analysis of signalling pathways using continuous time Markov chains

We describe a quantitative modelling and analysis approach for signal transduction networks. We illustrate the approach with an example, the RKIP inhibited ERK pathway [CSK+03]. Our models are high level descriptions of continuous time Markov chains: proteins are modelled by synchronous processes and reactions by transitions. Concentrations are modelled by discrete, abstract quantities. The main advantage of our approach is that using a (continuous time) stochastic logic and the PRISM model checker, we can perform quantitative analysis such as what is the probability that if a concentration reaches a certain level, it will remain at that level thereafter? or how does varying a given reaction rate affect that probability? We also perform standard simulations and compare our results with a traditional ordinary differential equation model. An interesting result is that for the example pathway, only a small number of discrete data values is required to render the simulations practically indistinguishable

Modelling and analysis of biochemical signalling pathway cross-talk

Signalling pathways are abstractions that help life scientists structure the coordination of cellular activity. Cross-talk between pathways accounts for many of the complex behaviours exhibited by signalling pathways and is often critical in producing the correct signal-response relationship. Formal models of signalling pathways and cross-talk in particular can aid understanding and drive experimentation. We define an approach to modelling based on the concept that a pathway is the (synchronising) parallel composition of instances of generic modules (with internal and external labels). Pathways are then composed by (synchronising) parallel composition and renaming; different types of cross-talk result from different combinations of synchronisation and renaming. We define a number of generic modules in PRISM and five types of cross-talk: signal flow, substrate availability, receptor function, gene expression and intracellular communication. We show that Continuous Stochastic Logic properties can both detect and distinguish the types of cross-talk. The approach is illustrated with small examples and an analysis of the cross-talk between the TGF-b/BMP, WNT and MAPK pathways

Certainty Closure: Reliable Constraint Reasoning with Incomplete or Erroneous Data

Constraint Programming (CP) has proved an effective paradigm to model and solve difficult combinatorial satisfaction and optimisation problems from disparate domains. Many such problems arising from the commercial world are permeated by data uncertainty. Existing CP approaches that accommodate uncertainty are less suited to uncertainty arising due to incomplete and erroneous data, because they do not build reliable models and solutions guaranteed to address the user's genuine problem as she perceives it. Other fields such as reliable computation offer combinations of models and associated methods to handle these types of uncertain data, but lack an expressive framework characterising the resolution methodology independently of the model. We present a unifying framework that extends the CP formalism in both model and solutions, to tackle ill-defined combinatorial problems with incomplete or erroneous data. The certainty closure framework brings together modelling and solving methodologies from different fields into the CP paradigm to provide reliable and efficient approches for uncertain constraint problems. We demonstrate the applicability of the framework on a case study in network diagnosis. We define resolution forms that give generic templates, and their associated operational semantics, to derive practical solution methods for reliable solutions.Comment: Revised versio

Modelling of priority pollutants releases from urban areas

In the framework of the EU project ScorePP (Source Control Options for Reducing Emissions of Priority Pollutants), dynamic PPs (priority pollutants) fate models are being developed to assess appropriate strategies for limiting the release of PPs from urban sources and for treating PPs on a variety of spatial scales. Different possible sources of PP releases were mapped and both their release pattern and their loads were quantified as detailed as possible. This paper focuses on the link between the gathered PP sources data and the dynamic models of the urban environment. This link consists of: (1) a method for the quantitative and structured storage of temporal emission pattern information, (2) the coupling of GIS-based spatial emission source data with temporal emission pattern information and (3) the generation of PP release time series to feed the dynamic sewer catchment model. Steps 2 and 3 were included as the main features of a dedicated software tool. Finally, this paper also illustrates the method’s applicability to generate model input timeseries for generic pollutants (N, P and COD/BOD) in addition to priority pollutants

Process algebra modelling styles for biomolecular processes

We investigate how biomolecular processes are modelled in process algebras, focussing on chemical reactions. We consider various modelling styles and how design decisions made in the definition of the process algebra have an impact on how a modelling style can be applied. Our goal is to highlight the often implicit choices that modellers make in choosing a formalism, and illustrate, through the use of examples, how this can affect expressability as well as the type and complexity of the analysis that can be performed

Interest rate models with Markov chains

Imperial Users onl