WSC 2006 Abstracts


Poster Session (B) Track


Monday 5:00:00 PM 6:30:00 PM
Poster Session B

Chair: Victor Chan (Rensselaer Polytechnic Institute)

Capacity Analysis of Mechanical Test Laboratory from the CST Hot Strip Mills
Ricardo Antonio Ramos (Companhia Siderúrgica de Tubarão (CST-ARCELOR Brasil))

Abstract:
This case study in the CST-ARCELOR steel making plant shows how the capacity analysis of mechanical test laboratory from the Hot Strip Mills was made using simulation with the software “ARENA”. The goals of the study were: identify the actual capacity of the laboratory for different scenarios (production mix); analyze the system performance (queue analyses, resources utilization, answer times, etc); identify bottlenecks to invest in new production resources to improve the actual capacity and give support to the future situation (production expanding). Based on the results, it was possible to test and measure the gains of the new resources inclusions for the future production. Afterward, new configurations may be created to attend the expanding production. KEYWORDS: Simulation, ARENA, Hot Strip Mills

Genomics Pattern Matching Using Wavelet Approximations
Mamta C. Padole (Department of Computer Science & Engineering, The M.S.University of Baroda)

Abstract:
Solution to any BioInformatics problem is based on the efficiency of analyzing the gene sequence. The prime activity in the sequence analysis process is matching the pattern of sequences. Heuristic approach is generally used to compare two genomic sequences. String based search techniques are usually slow and sensitive. In such queries, techniques based on Fast Fourier Transforms (FFT) or Fast Discrete Wavelet Approximations (FDWA) can improve the speed and can guarantee optimal alignment, since it uses digital approach. In this poster, the use of Fast Discrete Wavelet Approximations for sequence searching in BioInformatics database is discussed.

Simulation of Retailer's Ordering Model with Both Changeable and Unchangeable Consumer Behavior
Yoshiaki Sato (Hokkai-Gakuen University) and Ikuya Horie (Sapporo University Women's Junior College)

Abstract:
Stocks with a short shelf life are wanted to have as few as possible to the extent of being not out of stock. And orders of such products are made at the predetermined times, therefore order and inventory costs are thought as fixed. If a product is out of stock in a store, consumers are reluctant to repeat buying there in a while. Considering such consumer behavior changes, Awe already proposed a retailer's ordering model deriving the number of ordered products from the estimate of demand multiplied by a coefficient minus estimated stocks at delivered time. Now we newly propose a more realistic model which includes both consumers groups, which demands comes from, with negative effects by stock out and without ones, and describe how opportunity losses influence consumer behavior, and relate to demands and supplies through a Monte Carlo simulation.

Scene Simulation’ Based on Intellectual Assets Management (IAM)
Kazutaka Kitamori, Yumi Honno, and Tetsuya Furukawa (Hokkaido Institute of Technology)

Abstract:
Using e-learning systems for child carers as a case study, this research aims to improve the overall quality and reliability of simulation thinking, by creating hybridized meta-simulations based on multiple-micro situations. It is hypothesized that there is a need to improve the integrity and consistency of stored knowledge used in simulation programming when creating composite simulations made up of multiple knowledge bases. It is also hypothesized that when storing information to be deployed as a knowledge base – such as in e-learning for child carer educational regimes - that such knowledge be packaged in the form of situational scenarios, referred to in this research as ‘scene simulations’. Such scene simulations, combine animation-derived techniques with situational knowledge ‘stories’, and are delivered for effective training as Intellectual Assets Management (IAM) strategies. We propose a new simulation-based learning system by applying Landolt’s Method typically used in eyesight testing.

Kinetic Modeling Method for Spatially Restricted Reactions
Noriko Hiroi and Akira Funahashi (JST) and Hiroaki Kitano (SBI)

Abstract:
Many of biochemical reactions occur in spatially restricted manner. Reactants naturally restrict movements of each other before the reaction take place between them. The reaction which undergoes in restricted space is far from an assumption which classic modeling method for in vitro experiments are based on (i.e. reactants do not affect each other until they collides). This leads to a requirement of new biochemical modeling method for spatial restricted reactions. We have developed a modeling method for biochemical reaction proceeding under spatially restricted environment called dimension restricted reaction kinetics (DRRK). By using DRRK, we succeeded to describe a model including spatially restricted reactions which fulfill existing experimental results. Also, our method enables to obtain an exact simulation parameter by biochemical experiments which suggest a detailed behavior of reactants (how the reaction space is restricted). We analyzed experimental data of FRAP analysis. Through this analysis, we could find the time-dependent reaction rate coefficient of the fluorescence recovery rate by applying the DRRK method. Experimental data indicated that the overall reaction order of GFP diffusion in intra-cellular environment is around 2.8~3.2. This result suggests that the intra-cellular environment is highly crowded compared with free 3 dimensional reaction spaces, and also restricted than a 2 dimensional sheet. The modeling method we used to represent a reaction proceeding under spatial restricted environment will have a significant role when we consider in vivo reactions.

Synthetic Modeling and Simulation of Leukocyte Rolling on P-selectin Substrate In Vitro
Jonathan Tang and C. Anthony Hunt (University of California, San Francisco)

Abstract:
We have constructed an in silico model for representing the dynamics of leukocyte rolling in flow chambers. We use the synthetic modeling method. Object-oriented software components are designed, verified, and then plugged together and operated in ways that represent the mechanisms and processes that are believed to influence leukocyte rolling and adhesion. Our first objective was to test the model¡¦s ability to represent¡Xmimic¡Xthe essential jerky characteristics of leukocyte rolling. Our simulation results compare well to data from flow chamber experiments of leukocyte rolling on P-selectin. These results provide a necessary and essential foundation for future simulation studies of leukocyte rolling, activation by GRO-alphaƒnchemokine, and adhesion on P-selectin and VCAM-1 substrate.

A Schema Matching Architecture for the Bioinformatics Domain
Dagmar Köhn (Insitute of Computer Science, University of Rostock) and Lena Strömbäck (Department of Computer and Information Science, Linköpings Universitet)

Abstract:
One of the main goals in bioinformatics research today is to understand how various organisms function as biological systems. In order to find this out, one must understand the reactions taking place within the organism going down to interactions within molecules. Here, integration of data from various sources are important and various standards for representation are available, e.g., SBML, PSI MI, and BioPAX. This means there is a need for transformations of those standards into each other. The common representation formats for standards within the area are XML or OWL and a way of mapping them would be of high interest for system biology researchers. In this abstract we propose a solution for the mentioned problems and introduce a possible future architecture for this solution.

Simulation-Based Optimization of a Complex Mail Transportation Network
Anna Persson (Centre for Intelligent Automation) and Henrik Grimm and Amos Ng (University of Skövde)

Abstract:
The Swedish Postal Services receives and distributes over 22 million pieces of mail every day. Mail transportation takes place overnight by airplanes, trains, trucks, and cars. The transportation network comprises about 12 freight centres and a huge number of possible routes. A discrete-event simulation model of the transportation network has been developed for testing and analysis of different transportation plans. To support generation and optimization of transportations plans, there is now ongoing work in developing efficient metaheuristic optimization strategies to be coupled with the simulation model. The vast transportation network in combination with a large number of possible transportation configurations make the optimization problem very challenging. Complex real-world problems like this tend to be computationally expensive as a large number of simulation evaluations are needed before an acceptable solution can be found. To address this problem, a computationally cheap surrogate is used to offload the optimization process.

DNA Formation And Synthesis Based On Agent-Directed Simulation
Sunwoo Park (University of California, San Francisco), Sean H.J. Kim (University of California, Berkeley & San Francisco) and C. Anthony Hunt (University of California, San Francisco & Berkeley)

Abstract:
We present agent-directed molecular-level modeling and simulation (M&S) of deoxyribonucleic acid (DNA) formation and synthesis. The formation of DNA structure is postulated as a result of dynamical spatiotemporal interactions between molecular constituents. Covalent and hydrogen bonds comprise most of intermolecular interactions that lead to DNA formation. We model basic molecular building blocks -- adenine, guanine, thymine, cytosine, 2'-deoxyribose, and phosphoric acids -- as autonomous agents. Covalent and hydrogen bonds between agents are dynamically created based on their spatiotemporal properties. Formation of nucleosides, nucleotides, and polynucleotide chains are described using universal interaction mechanism. DNA polymerase, primer, and deoxynucleoside triphosphate are also modeled as agents that produce dynamic interaction specifications for DNA synthesis. Simulation results demonstrate that we can formally describe various dynamic spatiotemporal activities that occur during DNA formation and synthesis. It is expected that a similar method can be applied to RNA and protein synthesis.

BacGrid: Large-Scale Systems Biology Simulation on the Grid
Brian Logan, Michael Lees, and John King (University of Nottingham)

Abstract:
We argue that the challenges of large scale simulation development for systems biology can be met by a combination of two emerging standards and their supporting middleware: the Grid and the High Level Architecture (HLA). To evaluate the suitability of the HLA as a standard for systems biology simulations on the Grid, we are developing a prototype HLA-compliant, Grid-based simulator for systems biology which we call BacGrid. We outline the systems biology problem we have adopted as a test case, briefly describe the simulation model and the architecture of the simulator and summarise the current state of the implementation.

In Silico Modeling Of Drug Transport Across Biological Barriers
Tai Ning Lam (University of California, San Francisco, School of Pharmacy), Lana Garmire (University of California, Berkeley) and C Anthony Hunt (University of California, San Francisco)

Abstract:
We constructed an object and aspect oriented model to represent drug permeation across biological barriers. We assembled software components in a way that represents biological mechanisms. Simulation outputs mimic measurements made of traditional wet-lab observations. The model is intended for experimentation and to further explore pharmacokinetic processes. We report simulation results that are consistent with traditional models.

SBW – A Modular Framework for Systems Biology
Frank Thomas Bergmann and Herbert Sauro (Keck Graduate Institute)

Abstract:
This poster illustrates the current state of the Systems Biology Workbench (SBW) (Sauro et. al 2003), a modular framework that connects modeling and analysis applications, enabling applications to reuse each other’s capabilities. There is a wide variety of SBW modules available, such as modeling, bifurcation analysis, frequency analysis, deterministic and stochastic simulation, and 3D visualization. SBW is a collection of loosely coupled applications that all support SBML (Hucka et. al 2003). The developer of a new software application can use these tools as a foundation instead of recreating existing functionality, which allows them to focus on novel tasks. An existing application written in any supported programming language (C/C++, Java, .NET, Python, Delphi/Kylix, Matlab and FORTRAN) can be modified to interact with SBW with minimal programming overhead. This enables other applications to use its functionality. SBW is stable-open source and free downloads are available from the project website at http://sys-bio.org.

A Visual Aproach To Enhance Discrete-Event Simulation Model Interoperability
Tai-Chi Wu (I-Shou University) and Allen Greenwood (Mississippi State University)

Abstract:
With the continued advance of discrete-event simulation technology, various software packages have evolved that greatly facilitate the development and analysis of discrete-event simulation models. However, each of these applications uses a different approach and set of terminology for representing the behavior of the systems being studied. As a result of this disparity in implementations, models developed in different applications cannot interact with other models. Also, the disparity makes it more difficult for humans to understand the models. In this work, a visual modeling approach is proposed that allows the modeler, and those not expert in simulation, to create conceptual models that are independent of the implementation application. The approach uses eight common simulation elements. This approach facilitates the model interactions at the model formulation stage and at the application stage.

A Hardware Accelerator for Biochemical Simulations
Yasunori Osana, Masato Yoshimi, Yow Iwaoka, Toshinori Kojima, and Yuri Nishikawa (Keio University)

Abstract:
Mathematical simulation of biological processes is a big challenge in both of biology and computer science. This enables biologists to approach the mechanism of life as systems, however, it's a exteremely computation intensive tasks. We've developed an FPGA-based hardware accelerator for biochemical simulations. This accelerator has a dedicated pipeline to solve ODEs by its dedicated pipeline mechanism. This mechanism is effective for parameter estimation or optimization process of biochemical models. It outperforms Intel's Pentium4 with 5x to 80x performance gain.

A Principal Limitation of Virtual Cell Simulations using Differential Equations
Olaf Wolkenhauer and Arne Bittig (University of Rostock) and Jan-Hendrik S Hofmeyr (University of Stellenbosch)

Abstract:
We propose an abstract dynamic model of a cell, rooted in Mesarovic and Takahara’s general systems theory, where a cell function is defined by the dynamic processes the cell can realize. We postulate the existence of a ‘coordination principle’, which decides upon basic cellular processes to realize cell functions, i.e., cell differentiation, proliferation etc., leading to the theorem that the coordination of cell functions is realized autonomously from within the system. Inspired by Robert Rosen’s work, who introduced ‘closure to efficient causation’ as a necessary condition for a natural system to be an organism, we show that the mathematical model of an autonomously self-organizing cell is closed to efficient causation and that the associated category of models is cartesian closed. Our theorem supports (in parts) Rosen’s argument that living cells have non-simulable properties: Conventional computer simulations of cell models fail to capture an essential property of living systems: autonomous self-organization.

Extended Robustness Analyzes Indicates Stable Behaviour of the Wnt/b-catenin Pathway
Christian Wawra and Michael Kuehl (University of Ulm), Paolo Frasconi (University of Florence) and Hans A Kestler (University of Ulm)

Abstract:
Wnt proteins are extracellular glycoproteins that can activate different intracellular signalling pathways. The canononical Wnt/b-catenin pathway is characterized by the stabilization of cytoplasmic b-catenin upon pathway stimulation. A has been established and experimentally confirmed by Lee and co-workers and includes the pathway's core components, quantitative measurements of protein concentrations in Xenopus egg extracts and required estimations. It is likely, however that these concentrations differ in a varity of other model systems such as other cell lines or other organisms. Hence it is questionable if a model with changed initial parameters and conditions, will result in a similar behaviour of the pathway. On the other hand the Wnt pathway is highly conserved in different species, which makes a robust behaviour under different conditions reasonable. We therefore investigated the influence of simultaneously perturbed pathway parameters and changed initial concentrations and conditions of the pathway model. Lee E etal (2003) PloS Biol

Efficient computation of conserved moieties in large biochemical networks
Ravishanakar Rao Vallabhajosyula and Herbert M Sauro (Keck Graduate Institute)

Abstract:
Large biochemical networks pose a unique challenge from the point of view of evaluating conserved moieties. The computational problem in most cases exceeds the capability of available software tools, often resulting in inaccurate computation of the number and form of conserved cycles. Such errors have profound effects on subsequent calculations, particularly in the evaluation of the Jacobian which is a critical quantity in many other calculations. The goal of this paper is to outline a new algorithm that is computationally efficient, and robust at extracting the correct conservation laws for very large biochemical networks. This algorithm is based on the application of Householder QR method to the transpose of the stoichiometry matrix to compute the link matrix that relates the independent and dependent species. The analysis can be extended further to obtain the other matrices pertinent to the study of biochemical networks. Repeating the analysis directly on the stoichiometry matrix yields the null space of the system, which is a useful component in studying biochemical networks. We demonstrate the robustness of the algorithm by computing the conservation laws for four large whole-genome network models of E.coli, H.pylori and S.cerevisiae, and show how it is better than other algorithms in use. The validity of the conserved moieties obtained from this analysis is established by carrying out five validation tests to ensure their correctness. This approach guarantees that the conserved moieties thus obtained are correct. The results are further corroborated by comparing them with other applications as well as LU decomposition with full and partial pivoting. Finally, we also discuss applications of the algorithm to reduce network dimensionality for simulation, and in bifurcation analysis to obtain parameter ranges of network stability. Further details of the algorithm, including methodology and results can be obtained from the paper, published in Bioinformatics [1]. References 1. R.R. Vallabhajosyula, V.Chickarmane, H.M.Sauro (2006) Conservation analysis of large biochemical networks. Bioinformatics,22:3, 346-353

Simulation of the Kinetically Controlled Riboswitch Folding in 5' Untranslated mRNA Regions
Carsten Maus (University of Rostock)

Abstract:
Ribonucleic acids are involved in a huge amount of biological processes. They can fold into complex 3-dimensional structures which have often a very important function. In lots of cases the biological function is not given by the thermodynamical optimal structure but by a kinetically stabilized, suboptimal structure. The aim of simulations of kinetically controlled foldings is to identify such metastable structures. We analyzed the folding and function of purine riboswitches. Riboswitches are untranslated regions of mRNAs which can bind directly small metabolite molecules without a cofactor. They regulate gene expression by a conformational change of the RNA structure as a consequence of metabolite binding. The first natural riboswitch was found in 2002. Till now lots of additional riboswitch classes where discovered in all three kingdoms of life (bacteria, archaebacteria and eukaryotes). This is a strong evidence for a very old regulation mechanism from an ancient RNA world. We present a method for directed stabilization of defined secondary structure elements during simulation of the sequential folding. Because of that a prediction of the conformational change of the RNA structure induced by ligand binding is possible and we can get information for the detailed regulation mechanism of such molecular switches.

Executable Symbolic Modeling of Neural Processes
M Sriram Iyengar (University of Texas Health Science Center at Houston), Carolyn Talcott (SRI International) and Riccardo Mozzachiodi (University of Texas Health Science Center at Houston)

Abstract:
Neuroscience is currently experiencing explosive growth in detailed high-quality experimental information on neural processes underlying learning, memory and behavior. Consequently there is a need for computational models that can manage this outpouring of information, derive knowledge from information, and to generate novel, testable hypotheses. In this paper we describe an application of Pathway Logic, using the rewriting-logic specification system Maude, to model the behavior of a neural circuit involved in feeding behavior of a marine mollusk. This approach, intended to augment existing modeling techniques in neuroscience, has potential advantages of scalability, and robustness with regard to system parameters. It yields expressive models capable of simulating known neural circuit behaviors and performing in silico experiments including knock-outs, 'what-if's and others.

The Operational Risk Tree Methodology for Managing Operational Risk Exposure and Measuring Capital Requirements
Henry Lee (SunTrust Bank)

Abstract:
Monte Carlo simulation is applied to measuring operational risk via a modified decision tree. The author describes a simulation based approach to measuring economic and regulatory capital for operational risk. Except in relatively rare cases where sufficient data exists, quantitative operational risk has grown to depend upon expert opinion via scenario analysis to fill in the missing data. The author explains the limitations and shortcomings of the current approach and details an alternative approach using a modified decision tree to address many of these shortcomings. While in the absence of data we still depend upon expert opinion, there is good reason to believe that the method of collecting this opinion can have great impact upon the confidence in, and reproducibility of, the results.

Hardware Design of a Stochastic Biochemical Simulator
Masato Yoshimi (Keio University)

Abstract:
We present a hardware design of a biochemical simulator executing stochastic simulation algorithm(SSA). Due to an iterative scheme of the algorithm, it takes vast computational time with PCs, and it is more effective to run a simulation on a fixed hardware. In sense of both cost and effectiveness, an FPGA is considered as a suitable solution. Designed simulator is based on Gillespie's First Reaction Method (FRM), and high throughput is achieved through deeply pipelined structure of floating point(FP) units and multiple task execution. As a result, this simulator achieved approximately 80 times higher throughput compared to Xeon 2.80 GHz processor with larger biochemical models compared to previous hardware implementations.

Multi-Level Modeling with DEVS - A Critical Inspection and Steps Towards a Feasible Approach
Roland Ewald, Enrico Gutzeit, Sebastian Schwanke, Adelinde Uhrmacher M. Uhrmacher, Christian Lange, Susanne Biermann, and Carsten Maus (University of Rostock)

Abstract:
Multi-level modeling means the description of systems at different abstraction levels. In Systems Biology, different abstraction levels that arise from considering parts of the system at macro (e.g., concentrations) and parts of the system at micro (e.g., individual) level are of particular interest. Many modelling formalisms allow the modular and hierarchical construction of models. Those have often been inspired by DEVS and its construction of hierarchical models via the coupling of other models, but this type of hierarchy does still not allow a direct description of multi-level models, as the composed model has no behavior or state of its own. In DEVS, like in other modeling formalisms, macro and micro models are constructured as modular entities interacting with each other, as if they were equal sub-models. While this approach enables the use of established formalisms, it inhibits a clear distinction between the levels of abstraction and therefore hampers reusability, clearness and expressiveness. To overcome these problems, coupled DEVS models have been enriched by newly introduced high-level models that serve as a representation of the macro-level. Our approach is based on the rhoDEVS modelling formalism, which already provides variables structures, ports, and multi-couplings as key features. As an additional feature, high-level models may completely determine their coupled model's external input and output ports and can filter all inputs and outputs. This enables us to translate macro- and micro-level events over multiple abstraction levels. Furthermore, this behaviour could be very useful for modeling membrane systems. We present the realization of this approach in James II and the individual-based simulation of the canonical Wnt-Pathway as a sample application, in which a high-level model implementing the Gillespie approach serves as a particle collision scheduler. Similarly, a high-level model could include differential equations, or any other approach to model high-level properties of the system at hand. With its ability to survey all structural activities going on at the micro-level and to directly affect individuals by scheduling own events, the upward and downward causation in biological systems is captured. Although it looks as if we left DEVS far behind with this extension, it can be shown that models based on the extended formalism are still equivalent to basic DEVS models. Hence, the DEVS advantages, like modular composition of models via coupling, are preserved. Our approach is feasible to model any number of abstraction levels and can be coupled to other DEVS extensions (including hybrid models). We hope it eases the development of spatial biological models, including membranes, compartments and multiple abstraction levels.

Executable Symbolic Modeling of Neural Processes
M Sriram Iyengar (School of HEalth Information Sciences, University of Texas, Houston), Carolyn Talcott (SRI International), Riccardo Mozzachiodi (Medical School, University of Texas, Houston) and Douglas Baxter (Medical School, Univeristy of Texas, Houston)

Abstract:
Neuroscience is currently experiencing explosive growth in detailed high-quality experimental information on neural processes underlying learning, memory and behavior. Consequently there is a need for computational models that can manage this outpouring of information, derive knowledge from information, and to generate novel, testable hypotheses. In this paper we describe an application of Pathway Logic, using the rewriting-logic specification system Maude, to model the behavior of a neural circuit involved in feeding behavior of a marine mollusk. This approach, intended to augment existing modeling techniques in neuroscience, has potential advantages of scalability, and robustness with regard to system parameters. It yields expressive models capable of simulating known neural circuit behaviors and performing in silico experiments including knock-outs, 'what-if's and others.

Stochastic Joint Replenishment Problem with Renewal Demand
Ulku Gurler and Emre Berk (Bilkent University) and Banu Yuksel-Ozkaya (ISYE)

Abstract:
We consider the stochastic joint replenishment problem with renewal demands. We use simulation to assess the performance of the available policies. Our numerical studies illustrate the operational settings where each policy considered may be most suitable.

Why Stochastic Modeling and Simulation for Analyzing Avian Flu Impacts on Continuity of Operations and Contingency Plan Effectiveness?
Robert Brigantic (Pacific Northwest National Laboratory)

Abstract:
There are various national, state, and city initiatives to develop contingency plans to deal with an avian flu pandemic. Likewise, there are similar organizational endeavors that seek to ensure continuity of operations under a pandemic based on a variety of assumptions and personnel absenteeism levels. Such initiatives should be applauded as they seek to anticipate and prepare for a pandemic vice being in a reactive mode should such a crisis strike. The premise of this presentation is that such planning must also include stochastic simulation modeling as such an approach would lend even more value in identifying critical impacts of a pandemic on an organization’s operations and the effectiveness of mitigation plans to deal with these impacts. The key element of our approach is that humans and their capabilities/functions/interactions must be included in a model in order to address the impact of their unavailability.

Global Epidemic Model (GEM) as a Tool For Studying the Spread and Containment of Pandemic Flu.
Georgiy V. Bobashev and D. Michael Goedecke (RTI International), Joshua M. Epstein (Brookings Institution) and Feng Yu, Diane K. Wagener, and Robert J. Morris (RTI International)

Abstract:
We use a stochastic equation-based epidemic model to study the global transmission of pandemic flu. Under a range of assumptions regarding the initial time and location of an outbreak, we project the worldwide, and U.S.-specific, impacts of an unmitigated pandemic and then study the effect of selected interventions such as travel restrictions, vaccination, and the combination of these interventions. When the epidemic starts in Asia, travel restrictions can substantially delay the arrival of flu to the US. If, in the time afforded, control measures such as vaccination are instituted, the result is a significant reduction in cases worldwide and in the U.S. We show that accounting for seasonality in the transmission rate is critical for making the decision on the optimal combination of the interventions at the global scale.

Quantitative Measures to Characterize Properties
Simone Frey, Thomas Millat, Katja Rateitschak, and Olaf Wolkenhauer (University of Rostock)

Abstract:
Signalling cascades in cells are used to translate external stimuli into a specific response. Quantitative measures to characterize properties of kinase-phosphatase cascades were developed by Heinrich et al. [1], including average signalling time (ast), average signalling duration (atd) and average concentration (ac). In a weakly activated system these measures directly describe the influence of the system’s parameters on the cascade dynamics. This is possible through the assumption that the signal eventually returns to its initial zero state. The purpose of the present work is to generalize this for more complex cascades, including double phosphorylations and arbitrary initial states. Towards this end we investigate extensions of the simplified model in [1], including enzyme-kinetics and Michaelis-Menten type representations of the MAPK-cascade [2, 3]. A conclusion of [1] was that longer signalling cascades are capable of giving faster signals (smaller ast). Using numerical simulation studies we investigate this by relating the signal speed to different lengths of a cascade by keeping the ac in each case constant for all cascade lengths. Furthermore we investigate quantitative measures for the control of signal amplitude, duration and integral strength as introduced in [4]. References [1] Heinrich, R.; Neel, B. G.; Rapoport, T.A; Mathematical Models of Protein Kinase Signal Transduction. Mol. Cell 2002, 9, 957-970. [2] Huang, C-Y. F.; Ferrell, J. E., Jr.; Ultrasensitivity in the Mitogen-Activated Protein Kinase Cascade. Proc. Natl. Acad. Sc U. S. A. 1996, 93, 10078-10083. [3] Kholodenko, B. N.; Negative feedback and ultrasensitivity can bring about oscillations in the mitogen-activated protein kinase cascades. Eur. J. Biochem. 2000, 267, 1583-1588. [4] Hornberg, J.J.; Bruggeman, F.J.; Binder, B.; Geest, C.R.; Marjolein Bij de Vaate, A.J.; Lankelma, J.; Heinrich, R.; Westerhoff, H.V.; Principles behind the multifarious control of signal transduction. ERK phosphorylation and kinase/phosphatase control. FEBS J. 2005, 272, 244-258.

Transforming a Static into a Dynamic Protein-Protein Interaction Network Using Non-Uniform Rule-Based Random Graphs
Hans A Kestler, André Müller, and Michael Kühl (University of Ulm)

Abstract:
Cells are able to react to extracellular stimuli by activation of intracellular signal transduction pathways to modulate cellular behaviour. Protein-protein interaction maps have been generated for several organisms [1-5] and different intracellular signaling pathways including the canonical Wnt pathway [4, 5]. These interaction maps describe a static network [7] of potential interactions and do not represent dynamic changes upon stimulation of the network. Here we show that a static interaction network can be transfered into a semi-quantitative simulation model which is able to reproduce the global behavior of a whole signaling pathway which could be verified by comparing simulation results to in vivo measurements on the canonical Wnt pathway. Here we propose a new semi-quantitative method for modeling signaling pathways which is based on an extension of the standard random graph. Instead of approximating chemical reactions our approach is exclusively based on interaction probabilities between entities (receptors, enzymes, substrates, proteins). A set of local rules modifies future interaction probabilities between node pairs dependent on the history of states of the neighbour nodes leading to a discrete time-varying nonuniform random graph process. In contrast to differential equations, which require exact substrate concentrations and reaction rates, the chosen parameters were uncritical. Simulation results are in agreement with own as well as previously published in vivo measurements and also reflect the results of a previously published but partial model of the pathway based on ordinary differential equations [6]. It also correctly predicts the behaviour of the pathway in situations under which the pathway is disturbed i.e. by loss of the co-receptor LRP6 or the scaffolding protein APC. These data suggest that large protein-protein interactions maps modified by local interaction rules will be a suitable tool to predict the behavior of complex intracellular networks under physiological and pathological conditions. 1. P Uetz et al, Nature 2000, 403:623-627. 2. L Giot et al Science 2003, 302:1727 - 1736. 3. S Li et al, Science 2004, 303. 4. U Stelzl et al, Cell 2005, 122:957-968. 5. F Colland et al. Genome Res 2004, 14:1324-1332. 6. E Lee et al. PLoS Biology 2003, 1:116 - 132. 7. A-L Barabasi, ZN Zoltan, Nat Rev Genet 2004, 5(2): 101--113.

Simulation Generation Using XML-Prolog Based Knowledge Synthesis
Yohei Miyazaki, Hajime Kozen, and Kazutaka Kitamori (Hokkaido Institute of Technology)

Abstract:
We have developed an intellectual simulation development environment and an intellectual search engine "XML-Prolog" for efficient simulation development. The intellectual simulation development environment manages a cycle that consists of three phases (i.e., modeling, programming, and output review) as an XML document. The techniques used for simulation, the simulation parameters, and the knowledge making up the model are included in this XML document. The document accumulates this knowledge as an intellectual asset. The intellectual properties included in the XML document are united using XML-Prolog and a new simulation is composed. In this paper, two models, a "Monte Carlo simulation of the commodity sales stock model" and "Knowledge of the technique for straightening bias by the statistical error of counting of random numbers" are united in a logical manner using XML-Prolog. The process of generating a new simulation based on the unified models is described.

Computational approach for understanding molecular and cellular implications of defects in the Wnt pathway
Oksana Tymchyshyn and Marta Z Kwiatkowska (University of Birmingham)

Abstract:
Wnt signalling is implicated in many cellular decisions, including proliferation, differentiation and stem cell control. Yet, how Wnt induces specific cellular responses is poorly understood. We apply stochastic pi-calculus-based approach to analyze Wnt function in a multicellular system. The process calculi formalism is extended with generic multicellular abstractions. An individual-based representation of molecules is utilized to simulate stochastic kinetics of reactions integrated within pathways. Intracellular dynamics is coupled to cellular growth, division, differentiation and death. We incorporate a spatial structure representing cells and implement spatial cell-fate control. Multicellular system evolution under the influence of environment is simulated. A derived model of Wnt signalling reveals bimodal response with respect to stimulus duration. Additional control components are identified by analyzing phase transitions. The coupling of Wnt signalling to individual cell-fate decisions allows testing different hypotheses about normal homeostasis and transition to mutant regulation. Presented approach facilitates understanding the mechanisms controlling biological systems.

Visualizing and Analyzing Large Systems of Differential Equations
Hans-Jörg Schulz and Heidrun Schumann (University of Rostock) and Thomas Nocke (Potsdam Institute for Climate Impact Research)

Abstract:
The biochemical reactions inside a living cell form a huge network whose properties are still unknown for the most part. Current attempts use 1.000-15.000 differential equations to realistically model this highly dynamic system. This large number of equations is virtually impossible to overlook in its written form. So, our approach is to describe only the dependencies between the different variables that occur in the equations and to visualize these dependencies as a network itself. We further propose to combine the visualization with analysis techniques from the field of graph and network theory. This provides a strong algorithmic foundation that supports the visual analysis and exploration process and permits to verify or falsify the perceived features. We have built a software prototype that realizes this combined approach. Some of the results from using it on biochemical reaction data sets are shown and commented on as well.

Automating the Analysis of Simulation Output Data
Stewart Robinson, Katy Hoad, and Ruth Davies (Warwick Business School) and Mark Elder (SIMUL8)

Abstract:
Appropriate use of a simulation model requires accurate measures of model performance. This in turn, requires decisions concerning three key areas: warm-up, run-length and number of replications. Simulation software, however, gives little or no guidance in making these decisions. A three year project sponsored by the EPSRC and SIMUL8 is investigating the development of a methodology for automatically advising a simulation user on these three decisions. This poster will outline the approach being taken and will discuss progress to-date, albeit the early stages of the work.

Which Works Best for Household Electricity Markets - Coal plus Nuclear or Natural Gas plus Nuclear?
Daniel M. Hamblin (Dan Hamblin & Associates, Inc.)

Abstract:
The poster session reports on a Monte Carlo simulation tool that uses topology to solve for nearest-to-Nash equilibrium solutions that optimize profitability for household retail electricity service providers in markets distinguished by the wholesale power available for base load generation. The low wholesale cost regime relies on coal-fired generation and nuclear benefiting from rating upgrades and embedded cost recovery. The high wholesale cost regime relies on natural gas-fired generation and nuclear benefiting from rating upgrades and embedded cost recovery. The generation sources may be local or out-of-state. Game solutions predict whether service providers profit more from high or low wholesale cost regimes, whether service differentiation by non-incumbent retailers fares differently by wholesale cost regime, the attractiveness of no-frills service by wholesale cost regime, the attractiveness of merging versus going it alone by wholesale cost regime, and situations for which cooperative Nash oligopoly is not possible in the retail market.

COPASI - A Complex Pathway Simulator
Sven Sahle (EML-Research gGmbH), Stefan Hoops (Virginia Bioinformatics Institute), Juergen Pahle, Ralph Gauges, Natalia Simus, and Ursula Kummer (EML-Research gGmbH) and Christine Lee, Mudita Singhal, Liang Xu, and Pedro Mendes (Virginia Bioinformatics Institute)

Abstract:
COPASI is a software tool for simulation and analysis of biochemical reaction networks. It is designed to be easy to use and robust and to allow access to powerful numerical algorithms even for non-expert users. It is provided as an easy to install stand-alone program for all major operating systems (Linux, Mac OSX, Windows, Solaris). Two different versions are available: One version with a graphical user interface and another version with a command line interface. The command line interface can be used for batch job processing or for interfacing COPASI with other software tools. COPASI is actively maintained as a joint project of two well established groups which ensures future development and availability. COPASI can interpret a model in two different mathematical frameworks: A deterministic framework (where the model is converted into a set of ordinary differential equations internally) and a stochastic framework (where probabilities for single reaction events are considered). Deterministic simulation uses the LSODA algorithm, stochastic simulation uses Gibson and Bruck's version of an exact stochastic algorithm. The user can switch transparently between the two model interpretations. In some cases switching from deterministic to stochastic simulation requires adaptations to the rate laws. COPASI can automatically do some of these adaptations, so that in many cases no additional changes by the user are necessary. Steady states are calculated using a combination of damped Newton method with forward and backward integration. A stability analysis of the steady state can be performed as well as a Metabolic Control Analysis (MCA). Other analysis features of COPASI include calculation of conservation relations (using an algorithm recently published by Vallabhajosyula et al.1), elementary flux modes, optimization of arbitrary functions of the model variables as well as parameter fitting (using a number of diverse optimization algorithms). Also Lyapunov exponents and the average divergence can be calculated. A graphical interface for defining repeated calculations and parameter scans is also provided. Output of simulation or analysis results can be written to a text file. The format of the output can be defined in a very flexible way. Alternatively an integrated plotting tool can be used to display the results. Provided are 2D-plots and histograms. In addition to its own file format (which allows saving of the model and all defined calculation tasks and outputs) COPASI can read and write SBML. The differential equations that are generated from the model can also be exported as plain C code or in Berkeley Madonna format. Vallabhajosyula, R.R. et al. Conservation analysis of large biochemical networks. Bioinformatics 22. 346-353 (2006)

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