Capacity Analysis of Mechanical Test Laboratory
from the CST Hot Strip Mills
Ricardo Antonio Ramos (Companhia
Siderrgica de Tubaro (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
Landolts 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 models ability to representXmimicXthe
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-alphanchemokine, and adhesion on P-selectin and VCAM-1 substrate.
A Schema Matching Architecture for the
Bioinformatics Domain
Dagmar Khn (Insitute of Computer Science,
University of Rostock) and Lena Strmbck (Department of Computer and
Information Science, Linkpings 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 Skvde)
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 others 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 Takaharas 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 Rosens 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) Rosens 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
organizations 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
systems 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, Andr Mller, and Michael Khl (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-Jrg 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)
