WSC 2006 Abstracts

Innovation in Software for Systems Biology. Is There Any?
Herbert M Sauro (Keck Graduate Institute)

Abstract:
Software for systems biology has been under development since the 1950s and has accelerated considerably in the last five years. However, much of the development has been repetitive and there has been little genuine innovation. In this talk I wish to briefly discuss the history of software provision in systems biology and will try to address the question why the academic community has found it so difficult to innovate. In many cases the software developed today is little different in functionality from the first packages that were written in the 1950s. Ironically is it industry that seems to be taking the lead. In particular, Microsoft is funding new theoretical developments at their systems biology centers and MathWorks has developed a substantial dynamics software package (SimBiology 2.0) for their Matlab product.

Development and Implementation of the PSI MI Standard for Molecular Interaction
Samuel Kerrien and Henning Hermjakob (European Bioinformatics Institute)

Abstract:
The pool of molecular interaction data is growing fast but nevertheless remains fragmented. Combining together data coming from heterogeneous sources is a crucial step towards a deeper understanding of the cell machinery. The Proteomics Standard Initiative offers mature standards (PSI-MI) to facilitate the exchange and analysis of Molecular Interaction data. After introducing the details of the latest version of the PSI-MI data model, we will present the implementation of PSI-MI in the IntAct project, which offers a platform for management and analysis of interaction data. Finally we will give some insight into realistically using molecular interaction data as a foundation for other research.

A Corpus-Driven Approach for Design, Evolution and Alignment of Ontologies
Thomas Waechter (Biotec, Dresden University of Technology), He Tan (Linköpings Universitet), Andre Wobst (Biotec, Dresden University of Technology), Patrick Lambrix (Linköpings Universitet) and Michael Schroeder (Biotec, Dresden University of Technology)

Abstract:
Bio-ontologies are hierarchical vocabularies, which are used to annotate other data sources such as sequence and structure databases. With the wide use of ontologies their integration, design, and evolution becomes an important problem. We show how textmining on relevant text corpora can be used to identify matching ontology terms of two separate ontologies and to propose new ontology terms for a given term. We evaluate these approaches on the GeneOntology.

A Method for Comparison of Standardized Information Within Systems Biology
Lena Strömbäck (Department of Computer and Information Science, Linköpings Universitet, Sweden)

Abstract:
Standards and standardized data representation to allow efficient exchange of information is an important topic within systems biology. Within this area there is currently a rapid development of new standards as well as a need for import of datasets into various computer tools for further analysis. As the number of available standards within systems biology is large, tools for comparison and translation of standards are of high interest. In this paper we present a method for comparison of standards. We illustrate how the method works by providing an analysis of the three standards SBML, PSI MI and BioPAX. The analysis gives information on how similar the three standards are and it also gives pointers on how to build tools to aid a user in the analysis of a standard.

Nonuniform Sampling for Global Optimization of Kinetic Rate Constants in Biological Pathways
Steven H. Kleinstein (Yale University School of Medicine), Dean Bottino, Anna Georgieva, and Ramesh Sarangapani (Novartis Pharmaceuticals Corporation) and G. Scott Lett (The BioAnalytics Group, LLC)

Abstract:
Global optimization has proven to be a powerful tool for solving parameter estimation problems in biological applications, such as the estimation of kinetic rate constants in pathway models. These optimization algorithms sometimes suffer from slow convergence, stagnation or misconvergence to a non-optimal local minimum. Here we show that a nonuniform sampling method (implemented by running the optimization in a transformed space) can improve convergence and robustness for evolutionary-type algorithms, specifically Differential Evolution and Evolutionary Strategies. Results are shown from two case studies exemplifying the common problems of stagnation and misconvergence.

Prediction of In Vitro Hepatic Biliary Excretion Using Stochastic Agent-Based Modeling and Fuzzy Clustering
Shahab Sheikh-Bahaei (University of California, Berkeley and San Francisco) and C. Anthony Hunt (University of California, San Francisco)

Abstract:
We present a method for estimating parameter values for an agent-based model of in silico hepatocytes (ISH). Further, we make the estimation method available to the model, itself, to enable it to reasonably anticipate (predict) the biliary transport and excretion properties of a new compound based on the acceptable parameter values for previously encountered compounds. We use Fuzzy c-Means (FCM) classification algorithm to determine the degree of similarity between previously tuned compounds and the new compound. Specifically, a set of simulation parameters for enkephalin was predicted using the tuned parameter values of salicylate, taurocholate, and methotrexate. The FCM classification uses the physicochemical properties of the compounds.

Comprehensive and Realistic Modeling of Biological Systems
David Harel (The Weizmann Institiute of Science)

Abstract:
In comprehensive modeling the main purpose is to understand an entire biological system in detail, utilizing in the modeling effort all that is known about the system, and to use that understanding to analyze and predict behavior in silico. In realistic modeling the main issue is to model the behavior of actual elements, making possible totally interactive and modifiable realistic executions/simulations that reveal emergent properties. I will address the motivation for such modeling and the philosophy underlying the techniques for carrying it out, as well as the crucial question of when such models are to be deemed valid, or complete. The examples I will present will be from among the biological modeling efforts my group has been involved in: T cell development in the thymus, lymph node behavior, embryonic development of the pancreas, the C. elegans reproduction system and a generic cell model.

The Role of Composition and Aggregation in Modeling Macromolecular Regulatory Networks
Clifford A. Shaffer, Ranjit Randhawa, and John J. Tyson (Virginia Tech)

Abstract:
Today's macromolecular regulatory network models are small compared to the amount of information known about a particular cellular pathway, in part because current modeling languages and tools are unable to handle significantly larger models. Thus, most pathway modeling work today focuses on building small models of individual pathways since they are easy to construct and manage. The hope is someday to put these pieces together to create a more complete picture of the underlying molecular machinery. While efforts to make large models benefit from reusing existing components, unfortunately, there currently exists little tool or representational support for combining or composing models. We have identified four distinct modeling processes related to model composition: fusion, composition, aggregation, and flattening.

SBW - a Modular Software Framework for Systems Biology
Frank Thomas Bergmann and Herbert Martin Sauro (Keck Graduate Institute)

Abstract:
A large number of software packages are available to assist researchers in systems biology. In this paper, we describe the current state of the Systems Biology Workbench (SBW), a modular framework that connects modeling and analysis applications, enabling them to reuse each other's capabilities. We describe how users and developers will perceive SBW and then focus on currently available SBW modules. The software, tutorial manual, and test models are freely available from the Computational and Systems Biology group at Keck Graduate Institute. Source code is available from SourceForge. The software is open source and licensed under BSD.

Modeling and Analysis of Biological Processes by Mem(brane) Calculi and Systems
Nadia Busi (Dipartimento di Scienze dell'Informazione, Università di Bologna) and Claudio Zandron (Dipartimento di Informatica, Sistemistica e Comunicazione, Università di Milano-Bicocca)

Abstract:
In recent years, the modeling and analysis techniques developed in the area of formal languages and of concurrent process calculi have been successfully applied to the field of Systems Biology. In this setting, Brane Calculi and Membrane Systems are two of the most prominent approaches for the modeling of the behaviour of biological membranes. Membrane Systems have been introduced by Gh. Paun as a class of distributing parallel computing devices of a biochemical type, while Brane Calculi are a family of process calculi, based on a set of biologically inspired primitives of membrane interaction. In this paper we model the behaviour of a biological process - namely, the LDL Cholesterol Degradation Pathway - in both Brane Calculi and Membrane Systems. We also provide a brief discussion on the application of analysis techniques to this case study.

Symbolic Modeling of Signal Transduction in Pathway Logic
Carolyn Talcott (SRI International)

Abstract:
Pathway Logic is a step towards a vision of symbolic systems biology. It is an approach to modeling cellular processes based on formal methods. In particular, formal executable models of processes such as signal transduction, metabolic pathways, and immune system cell-cell signaling are developed using the rewriting logic language Maude and a variety of formal tools are used to query these models. An important objective of Pathway logic is to reflect the ways that biologists think about problems using informal models, and to provide bench biologists with tools for computing with and analyzing these models that are natural. In this paper we describe the Pathway Logic approach to the modeling and analysis of signal transduction, and the use of the Pathway Logic Assistant tool to browse and query these models. The Rac1 signaling pathway is used to illustrate the concepts.

Simulation and Verification for Computational Modelling of Signalling Pathways
Marta Zofia Kwiatkowska, Gethin Norman, David Parker, Oksana Tymchyshyn, John Heath, and Eamonn Gaffney (University of Birmingham)

Abstract:
Modelling of the dynamics of biochemical reaction networks typically proceeds by solving ordinary differential equations or stochastic simulation via the Gillespie algorithm. More recently, computational methods such as process algebra techniques have been successfully applied to the analysis of signalling pathways. One advantage of these is that they enable automatic verification of the models, via model checking, against qualitative and quantitative temporal logic specifications, for example, "what is the probability that the protein eventually degrades"? Such verification is exhaustive, that is, the analysis is carried out over all paths, producing exact quantitative measures. In this paper, we give an overview of the simulation, verification and differential equation approaches to modelling biochemical reaction networks. We discuss the advantages and disadvantages of the respective methods, using as an illustration a fragment of the FGF signalling pathway.

Executable Biology
Jasmin Fisher and Thomas A. Henzinger (Swiss Federal Institute of Technology (EPFL))

Abstract:
Computational modeling of biological systems is becoming increasingly common as scientists attempt to understand biological phenomena in their full complexity. Here we distinguish between two types of biological models -mathematical and computational- according to their different representations of biological phenomena and their diverse potential. We call the approach of constructing computational models of biological systems Executable Biology, as it focuses on the design of executable computer algorithms that mimic biological phenomena. We give an overview of the main modeling efforts in this direction, and discuss some of the new challenges that executable biology poses for computer science and biology. We argue that for executable biology to reach its full potential as a mainstream biological technique, formal and algorithmic approaches must be integrated into biological research, driving biology towards a more precise engineering discipline.

Approaches to Complexity Reduction in a Systems Biology Research Environment (SYCAMORE)
Irina Surovtsova, Sven Sahle, Juergen Pahle, and Ursula Kummer (EML-Research gGmbH)

Abstract:
Due to the complexity of biochemical reaction networks, so-called complexity reduction algorithms play a crucial role for making simulations efficient and for dissecting biochemical networks into meaningful subnetworks for analysis. Here, different approaches are presented, which we are developing in the context of a computational research environment for systems biology (SYCAMORE). These approaches are based on time-scale decomposition, sensitivity analysis, and hybrid simulation methods.

Complexity Reduction of Biochemical Networks
Ravishankar Rao Vallabhajosyula (Keck Graduate Insitute) and Herbert Martin Sauro (Keck Graduate Institute)

Abstract:
This paper discusses two broad approaches for reducing the complexity of large cellular network models. The first approach involves exploiting conservation and time-scale separation and allows the dimension of the model to be significantly reduced. The second approach involves identifying subnetworks that carry out well defined functions and replacing these with simpler representations. Examples include identification of functional subnetworks such as oscillators or bistable switches and replacing these with a simplified mathematical construct. This enables complex networks to be rationalized as a series of hierarchical modules and greatly simplifies our ability to understand the dynamics of complex networks.

Simulation of Biochemical Networks Using COPASI-- a Complex Pathway Simulator
Stefan Hoops (Biochemical Networks Modeling Group, Virginia Bioinformatics Institute), Sven Sahle and Ralph Gauges (Bioinformatics and Computational Biochemistry, EML Research), Christine Lee (Biochemical Networks Modeling Group, Virginia Bioinformatics Institute), Juergen Pahle and Natalia Simus (Bioinformatics and Computational Biochemistry, EML Research), Mudita Singhal, Liang Xu, and Pedro Mendes (Biochemical Networks Modeling Group, Virginia Bioinformatics Institute) and Ursula Kummer (Bioinformatics and Computational Biochemistry, EML Research)

Abstract:
Simulation and modeling is becoming one of the standard approaches to understand complex biochemical processes. Therefore, there is a big need for software tools that allow access to diverse simulation and modeling methods as well as support for the usage of these methods. Here, we present a new software tool that is platform independent, user friendly and offers several unique features. In addition, we discuss numerical considerations and support for the switching between simulation methods.

CellDesigner: A Modeling Tool for Biochemical Networks
Akira Funahashi, Yukiko Matsuoka, and Akiya Jouraku (Kitano Symbiotic Systems Project, JST), Norihiro Kikuchi (Mitsui Knowledge Industry Co.,Ltd.) and Hiroaki Kitano (Kitano Symbiotic Systems Project, JST)

Abstract:
Understanding of logic and dynamics of gene-regulatory and biochemical networks is a major challenge of systems biology. To facilitate this research topic, we have developed CellDesigner, a modeling tool of gene-regulatory and biochemical networks. CellDesigner supports users to easily create such networks using solidly defined and comprehensive graphical representation (SBGN: Systems Biology Graphical Notation). CellDesigner is SBML compliant, and is SBW-enabled software so that it can import/export SBML described documents and can integrate with other SBW-enabled simulation/analysis software packages. CellDesigner also supports simulation and parameter search, which is supported by integration with SBML ODE Solver, enabling us to simulate through our sophisticated graphical user interface. We could also browse and modify existing SBML models with references to existing databases. CellDesigner is implemented in Java, thus it runs on various platforms such as Windows, Linux, and MacOS X. CellDesigner is freely available via the Web.

Think Simulation - Think Experiment: The Virtual Cell Paradigm
Ion I Moraru, James C Schaff, and Leslie M Loew (University of Connecticut Health Center)

Abstract:
The Virtual Cell modeling and simulation framework is the product of interdisciplinary research in biology that applies the diverse strengths and experiences of individuals from engineering, the physical sciences, the biological sciences, and mathematics. A key feature is the separation of layers (core technologies and abstractions) representing biological models, physical mechanisms, geometry, mathematical models and numerical methods. This reduces software complexity, allowing independent development and verification, but most importantly it clarifies the impact of modeling decisions, assumptions, and approximations. The result is a physically consistent, mathematically rigorous, spatial modeling and simulation framework for cell biology. The Virtual Cell has a rich, interactive user interface which connects to remote services providing scalable access to a modeling database and a large dedicated cluster for shared computation and storage. In addition to new modeling capabilities, future developments will emphasize data and tool interoperability, extensibility, and experimentally oriented model analysis tools.

Challenges for Modeling and Simulation Methods in Systems Biology
Herbert M. Sauro (Virginia Tech), Adelinde M. Uhrmacher (University of Rostock), David Harel (The Weizmann Institute of Science), Michael Hucka (California Institute of Technology), Marta Kwiatkowski (University of Birmingham), Pedro Mendes (Virginia Bioinformatics Institute), Clifford A. Shaffer (Virginia Tech), Lena Strömbäck (Linköpings universitet) and John J. Tyson (Virginia Tech)

Abstract:
Systems Biology is aimed at analyzing the behavior and interrelationships of biological systems and is characterized by combining experimentation, theory, and computation. Dedicated to exploring current challenges, the panel brings together people from a variety of disciplines whose perspectives illuminate diverse facets of Systems Biology and the implied challenges for modeling and simulation methods.