Posters will be presented during the poster session on Monday, 13th October, afternoon (programme). A couple of selected posters will be selected for short talks.

List of accepted posters:

• Christian Rohr, Monika Heiner and Wolfgang Marwan
  

  Petri net modeling of cellular signal transduction based on a stochastic version of Snoopy.
  ABSTRACT: Snoopy is a tool to design and animate/simulate graphs. Its generic design facilitates the addition of new graph classes and the graphical user interface adopts dynamically to the graph type. The graph editor provides logical nodes and supports the construction of hierarchies by sub-graphs. The graph classes place/transition Petri net, extended Petri net, continuous Petri net, stochastic Petri net and some more are
  currently available to the user. The simulation of the continuous Petri nets is done by one of 12 implemented ODE solvers and the stochastic Petri nets are simulated by the exact Gillespie algorithm.
  The aim is to design and implement a building block based Petri net creation in Snoopy. After that Petri net modules for Natural/Biology systems need to be identified and a model of the molecular network controlling phototaxis in Halobacterium salinarum should gathered by means of such components. Finally the model will be investigated by exchanging elementary reaction types via building blocks.

  
  

• Sarala Dissanayake, Matt Halstead and Poul Nielsen
  Visualization of CellML Models.
  ABSTRACT: CellML is an implementation-independent model description language which is mainly used for representing the dynamics of complex biological processes. Even though the CellML model structure and mathematics provide a powerful method for describing the dynamics of biological processes, the language has limited support for capturing higher level biological information as the biological knowledge is implicit in the mathematics and structure of the model. The objective of this project is to develop a framework for annotating and visualizing biophysical concepts covered in CellML models.
  The basic pipeline for generating diagrams requires representing CellML models in web ontology language (OWL) format. Bindings are then formed from these CellML OWL models to biological ontologies to provide biological meaning to CellML entities. A visual template ontology is developed to provide mappings to a common graphical notation and bindings are formed from the biological ontologies to the visual template ontology. Using these ontological mappings, the relevant biological model of a CellML model can be represented and visualized.

• Marit S. Bratlie, Jostein Johansen and Finn Drablos
  Why do bacterial genes form operons?
  ABSTRACT: Prokaryotes can coordinate transcription of gene sets by the formation of operons. Genes in the same operon are usually separated by very short intergene distances and often conserved across species by vertical inheritance. However, few operons remain intact over long periods of time, and operon dispersal is evident in many genomes. It has been proposed that reduced regulatory complexity and coordinated formation of multi-protein complexes are important driving forces for operon formation. We have used sequence alignment and clustering of proteomes from 113 bacterial genomes together with data analysis to analyse important properties of conserved operons. The analysis shows that coordination of multi-step or multi-domain processes is a driving force for operon formation. Some multi-protein systems (e.g. ribosomes) interact with additional proteins from non-operon genes, most likely representing additional levels of regulation.

• Sylvain Pradalier
  Encoding reactive-base language into agent-base language and possible applications.
  ABSTRACT: The nanok calclus is a reactive based language with explicit binding between agent designed for modelling, simulations and analysis of molecular machines. We first present an encoding from the nanok calculus into a subset of the stochastic pi-calculus: the Spim language. The transitions of a solution S are related by the following property: S --lambda--> T iff [[S]] --lambda--> [[T]]. Such a strong correctness property permit to reuse the classical theory and tools of the process algebra settings for the nanok calculus . This last point is illustrated by sketching our on-going work on how to model check the nanok calculus .
  We believe that our approach can be adapted to other reactive-based language. The most constraining seems to be that reactions should be binary.

• Pawel Banasik, Mikolaj Rybinski and Anna GambinTaverna services for systems biology.
  ABSTRACT: Taverna workbench is a computational biology tool for facilitating the design and execution of in silico experiments described by formal workflows (connected sets of services). Thanks to the latter, the experiments are fully documented and easily repeatable. Currently, vast majority of Web Services available for use with Taverna are related to sequence analysis. In order to bridge the gap between Taverna and systems biology we present a services and example workflows for conducting sensitivity analysis of SBML models. Sensitivity analysis, as a part of model parametrization, is essential for assessing the most crucial parameters for the system's behavior. Presented work is illustrated by the example application to simple enzymatic reaction model.

• Xin Lai, Julio Vera González and Olaf Wolkenhauer
  Use of sensitivity analysis to detect critical biochemical processes in a mathematical model linking intracellular and cell population dynamics in erythropoiesis.
  ABSTRACT: Sensitivity analysis has been used in the analysis of biochemical systems to study how the variation in the output signal can be apportioned to different sources of variation, mainly uncertainties associated with parameter values in the model.
  In this work we investigate the use of sensitivity analysis for the detection of critical (most sensitive) processes in a biochemical system. The perturbation of these processes very often relates to pathological states of the system, but could also be used to detect potential drug targets. We first employ sensitivity analysis to identify and rank the most influential parameters with respect to given dynamical features. Next, this information is used together with bifurcation analysis to assess in the design of numerical simulations, which aim is to predict the effect that the perturbation of those processes has on the dynamics of the system. We apply this idea to detect the critical biochemical process in a mathematical model linking intracellular and cell population dynamics in erythropoiesis.

• Sayed-Amir Marashi and Alexander Bockmayr
  Revisiting Flux Coupling Analysis.
  ABSTRACT: Constraint-based analysis of metabolic networks has been shown to be a promising tool for the analysis of biochemical reaction networks. Under steady-state conditions, stoichiometric and thermodynamic constraints can be used to determine the possible flux distributions in the network. Often, in a metabolic network, some reactions are always working together. Using constraint-based analysis techniques, it is possible to find these reactions, and to determine how their corresponding fluxes are related to each other.
  Flux coupling analysis (FCA) is a method to find the relationships between the steady-state fluxes through every pair of reactions in the networks. Originally, the concept of 'enzyme subsets' was presented as groups of reactions that operate together in a constant ratio under a steady-state condition. Enzyme subsets were found to be interesting from the biological point of view, since reactions in the same enzyme subset are often reactions in the same biologically important pathway. Later, the Flux Coupling Finder algorithm was used to find coupled reactions in genome scale metabolic networks. It was shown that this approach can be useful in determining functional modules in metabolic networks, as it determines the reactions that work together. In this paper, some problems associated with flux coupling analysis are discussed. Then, it will be explained why uncoupling of flux pairs is reliable, rather than their coupling.

• Utz-Uwe Haus, Kathrin Niermann, Klaus Truemper and Robert Weismantel
  Logic Integer Programming Models for Signaling Networks.
  ABSTRACT: We propose a static and a dynamic approach to model biological signaling networks, and show how each can be used to answer relevant biological questions. For this we use the two different mathematical tools of Propositional Logic and Integer Programming. The power of discrete mathematics for handling qualitative as well as quantitative data has so far not been exploited in Molecular Biology, which is mostly driven by experimental research, relying on first-order or statistical models. The arising logic statements and integer programs are analyzed and can be solved with standard software. Additionally, a more dynamic model enables enumeration of possible time resolutions in poly-logarithmic time.

• Russ Harmer, Joshua Havumaki, Isha Antani and Gordon Webster
  Stochastic Simulation of Large, Complex Cell Signaling Pathways.
  ABSTRACT: The activation of ras is the gatekeeper for the downstream, mitogenic MAP Kinase pathway. EGFR regulates ras in two contradictory ways, promoting its acitvation via Sos and inhibiting it via RasGap. Since EGFR also promotes its own internalization and degradation, its control over ras is transient and the duration of this window of control exhibits significant stochastic variance. In simulations using a rule-based modeling platform based upon the Kappa language, we have been able to reproduce these stochastic effects that have also been observed experimentally in certain cell lines.

• Matthias Jeschke and Adelinde Uhrmacher
  Multi-resolution Spatial Simulation for Molecular Crowding.
  ABSTRACT: Spatial phenomena attract increasingly interest in computational biology. Molecular crowding, i.e. a dense population of macromolecules, is known to have a significant impact on the kinetics of molecules. However, an in-detail inspection of cell behavior in time and space is extremely costly. To balance between cost and accuracy, multi-resolution approaches offer one solution. Particularly, a combination of individual and lattice-population based algorithms promise an adequate treatment of phenomena like macromolecular crowding. In realizing such an approach, central questions are how to specify and synchronize the interaction between population and individual spatial level, and to decide what is best treated at a specific level, respectively. The presented approach is based on an algorithm which combines the Next Subvolume Method and a simple, individual-based spatial approach and we will discuss first experimental results.

• Silke Eckstein and Claudia Täubner
  

  Automatically deriving Colored Petri Net Representations for Signaling Pathways from a Manually Curated Database.
  ABSTRACT: This paper presents an approach to automatically derive Colored Petri Net representations for signal transduction pathways from a manually curated pathway database. The approach is part of a bigger project, where we provide an extendable system, that generates alternative representations of signal transduction pathways using different modeling languages.
  




• Rainer Breitling, David Gilbert and Monika Heiner
  BioModel Engineering - from Structure to Behaviour.
  ABSTRACT: Biomodel engineering is the science of designing, constructing and analyzing computational models of biological systems. It forms a systematic and powerful extension of earlier mathematical modeling approaches and has recently gained popularity in systems biology and synthetic biology. In this poster we present some of the basic concepts of successful biomodel engineering, illustrating them with a few examples, ranging from metabolic networks to cellular signaling cascades. We also sketch some of the major techniques of biomodel engineering – Petri net models – which provide a flexible and powerful tool for building, validating and exploring computational descriptions of biological systems.