Abstracts

Integrative biological modelling in silico

Andrew D. McCulloch and Gary Huber

Department of Bioengineering, The Whitaker Institute of Biomedical Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0412, USA

In silico models of biological systems provide a powerful tool for integrative analysis of physiological function. Using the computational models of the heart as examples, we discuss three types of integration: structural integration implies integration across physical scales of biological organization from protein molecule to whole organ; functional integration of interacting physiological processes such as signalling, metabolism, excitation and contraction; and the synthesis of experimental observation with physicochemical and mathematical principles.

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© 2002 Novartis Foundation

Advances in computing, and their impact on scientific computing

Mike Giles

Oxford University Computing Laboratory, Wolfson Building, Parks Road, Oxford, OX1 3QD, UK

This paper begins by discussing the developments and trends in computer hardware, starting with the basic components (microprocessors, memory, disks, system interconnect, networking and visualization) before looking at complete systems (death of vector supercomputing, slow demise of large shared-memory systems, rapid growth in very large clusters of PCs). It then considers the software side, the relative maturity of shared-memory (OpenMP) and distributed-memory (MPI) programming environments, and new developments in 'grid computing'. Finally, it touches on the increasing importance of software packages in scientific computing, and the increased importance and difficulty of introducing good software engineering practices into very large academic software development projects.

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© 2002 Novartis Foundation

From physics to phenomenology. Levels of description and levels of selection

David Krakauer

Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA

Formal models in biology are traditionally of two types: simulation models in which individual components are described in detail with extensive empirical support for parameters, and phenomenological models, in which collective behaviour is described in the hope of identifying critical variables and parameters. The advantage of simulation is greater realism but at a cost of limited tractability, whereas the advantage of phenomenological models, is greater tractability and insight but at a cost of reduced predictive power. Simulation models and phenomenological models lie on a continuum, with phenomenological models being a limiting case of simulation models. I survey these two levels of model description in genetics, molecular biology, immunology and ecology. I suggest that evolutionary considerations of the levels of selection provides an important justification for many phenomenological models. In effect, evolution reduces the dimension of biological systems by promoting common paths towards increased fitness.

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© 2002 Novartis Foundation

Making sense of complex phenomena in biology

Philip K. Maini

Centre for Mathematical Biology, Mathematical Institute, 24-29 St Giles, Oxford OX1 3LB

The remarkable advances in biotechnology over the past two decades have resulted in the generation of a huge amount of experimental data. It is now recognized that, in many cases, to extract information from this data requires the development of computational models. Models can help gain insight on various mechanisms and to be used to process outcomes of complex biological interactions. To do the latter, models must become increasingly complex and, in many cases, they also become mathematically intractable. With the vast increase in computing power these models can now be numerically solved and can be made more and more sophisticated. A number of models can now successfully reproduce detailed observed biological phenomena and make important testable predictions. This naturally raises the question of what we mean by understanding a phenomenon by modelling it computationally. This paper briefly considers some selected examples of how simple mathematical models have provided deep insights into complicated chemical and biological phenomena and addresses the issue of what role, if any, mathematics has to play in computational biology.

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© 2002 Novartis Foundation

On ontologies for biologists: the Gene Ontology-untangling the web

Michael Ashburner and Suzanna Lewis

Department of Genetics, University of Cambridge and EMBL-European Bioinformatics Institute, Hinxton, Cambridge, UK and Berkeley Drosophila Genome Project, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA 94720, USA

The mantra of the 'post-genomic' era is 'gene function'. Yet surprisingly little attention has been given to how functional and other information concerning genes is to be captured, made accessible to biologists or structured in a computable form. The aim of the Gene Ontology (GO) Consortium is to provide a framework for both the description and the organisation of such information. The GO Consortium is presently concerned with three structured controlled vocabularies which can be used to describe three discrete biological domains, building structured vocabularies which can be used to describe the molecular function, biological roles and cellular locations of gene products.
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© 2002 Novartis Foundation

The KEGG database

Minoru Kanehisa

Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan

KEGG (http://www.genome.ad.jp/kegg/) is a suite of databases and associated software for understanding and simulating higher-order functional behaviours of the cell or the organism from its genome information. First, KEGG computerizes data and knowledge on protein interaction networks (PATHWAY database) and chemical reactions (LIGAND database) that are responsible for various cellular processes. Second, KEGG attempts to reconstruct protein interaction networks for all organisms whose genomes are completely sequenced (GENES and SSDB databases). Third, KEGG can be utilized as reference knowledge for functional genomics (EXPRESSION database) and proteomics (BRITE database) experiments. I will review the current status of KEGG and report on new developments in graph representation and graph computations.

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© 2002 Novartis Foundation

Bioinformatics of cellular signalling

Shankar Subramaniam and the Bioinformatics Core Laboratory

Departments of Bioengineering and Chemistry and Biochemistry, The University of California at San Diego and The San Diego Supercomputer Center, La Jolla, CA 92037, USA
The completion of the human genome sequencing provides a unique opportunity to understand the complex functioning of cells in terms of myriad biochemical pathways. Of special significance are pathways involved in cellular signalling. Understanding how signal transduction occurs in cells is of paramount importance to medicine and pharmacology. The major steps involved in deciphering signalling pathways are: (a) identifying the molecules involved in signalling; (b) figuring out who talks to whom, i.e. deciphering molecular interactions in a context specific manner; (c) obtaining spatiotemporal location of the signalling events; (d) reconstructing signalling modules and networks evoked in specific response to input; (e) correlating the signalling response to different cellular inputs; and (e) deciphering cross-talk between signalling modules in response single and multiple inputs. High-throughput experimental investigations offer the promise of providing data pertaining to the above steps. A major challenge, then, is the organization of this data into knowledge in the form of hypothesis, models and context-specific understanding. The Alliance for Cellular Signaling (AfCS) is a multi-institution, multidisciplinary project and its primary objective is to utilize a multitude of high throughput approaches to obtain context-specific knowledge of cellular response to input. It is anticipated that the AfCS experimental data in combination with curated gene and protein annotations, available from public repositories, will serve as a basis for reconstruction of signalling networks. It will then be possible to model the networks mathematically to obtain quantitative measures of cellular response. In this paper we describe some of the bioinformatics strategies employed in the AfCS. 

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© 2002 Novartis Foundation


Imaging-based integrative models of the heart: closing the loop between experiment and simulation

Raimond L. Winslow, Patrick Helm, William Baumgartner Jr., Srinivas Peddi, Tilak Ratnanather, Elliot McVeigh and Michael I. Miller
Center for Computational Medicine & Biology and Center for Imaging Sciences, The Whitaker Biomedical Engineering Institute and NIH Laboratory of Cardiac Energetics: Medical Imaging Section 3, Johns Hopkins University, Baltimore MD 21218, USA

We describe methodologies for: (a) mapping ventricular activation using high-density epicardial electrode arrays; (b) measuring and modelling ventricular geometry and fibre orientation at high spatial resolution using diffusion tensor magnetic resonance imaging (DTMRI); and (c) simulating electrical conduction; using comprehensive data sets collected from individual canine hearts. We demonstrate that computational models based on these experimental data sets yield reasonably accurate reproduction of measured epicardial activation patterns. We believe this ability to electrically map and model individual hearts will lead to enhanced understanding of the relationship between anatomical structure, and electrical conduction in the cardiac ventricles.

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© 2002 Novartis Foundation

The Virtual Cell project

Leslie M. Loew

Center for Biomedical Imaging Technology, Department of Physiology, University of Connecticut Health Center, Farmington, CT 06030, USA
The Virtual Cell is a modular computational framework that permits construction of models, application of numerical solvers to perform simulations, and analysis of simulation results. A key feature of the Virtual Cell is that it permits the incorporation of realistic experimental geometries within full 3D spatial models. An intuitive JAVA interface allows access via a web browser and includes options for database access, geometry definition (including directly from microscope images), specification of compartment topology, species definition and assignment, chemical reaction input and computational mesh. The system is designed for cell biologists to aid both the interpretation and the planning of experiments. It also contains sophisticated modelling tools that are appropriate for the needs of mathematical biologists. Thus, communication between these traditionally separate scientific communities can be facilitated. This paper will describe the status of the project and will survey several applications to cell biological problems. 

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© 2002 Novartis Foundation

Modelling the bacterial chemotaxis receptor complex

Thomas Simon Shimizu and Dennis Bray
Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK
The pathway controlling chemotaxis in Escherichia coli is the simplest and most well understood cell signalling system to date. However, quantitative models based on the available data still fail to reproduce important features of the pathway. Most notably, the observed sensitivity of cells to very small changes in stimulus concentrations cannot be reproduced by conventional models based on the measured concentrations, binding affinities and rate constants of the proteins involved. This discrepancy, together with recent experimental findings, drew our attention to the spatial organization of molecules within the cell and in particular to the clusters of receptors localised at the cell poles. A stochastic simulator for chemical reactions, STOCHSIM, was previously developed to model the chemotaxis pathway at the level of individual molecular interactions. This program has now been extended to incorporate a spatial representation that allows the interaction between molecules in a two-dimensional lattice to be simulated. In silico 'experiments' using this new version of STOCHSIM demonstrate that lateral interactions between clustered receptors can significantly enhance the excitation response. The adaptation reactions may also exploit the proximity of receptor molecules, and a hypothetical mechanism by which this may occur is currently being tested.

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© 2002 Novartis Foundation

The heart cell in silico: successes, failures and prospects

Denis Noble

University Laboratory of Physiology, Parks Road, Oxford OX1 3PT, UK
The development of computer models of heart cells is used to illustrate the interaction between simulation and experimental work. At each stage, the reasons for new models are explained, as are their defects and how these were used to point the way to successor models. As much, if not more, was learnt from the way in which models failed as from their successes. The insights gained are evident in the most recent developments in this field, both experimental and theoretical. The prospects for the future are discussed. 

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© 2002 Novartis Foundation

The IUPS Physiome Project

P.J. Hunter, P.M.F. Nielsen and D. Bullivant
Bioengineering Institute, University of Auckland, Private Bag 92019, Auckland, New Zealand
Modern medicine is currently benefiting from the development of new genomic and proteomic techniques, and also from the development of ever more sophisticated clinical imaging devices. This will mean that the clinical assessment of a patient's medical condition could, in the near future, include information from both diagnostic imaging and DNA profile or protein expression data. The Physiome Project of the International Union of Physiological Sciences (IUPS) is attempting to provide a comprehensive framework for modelling the human body using computational methods which can incorporate the biochemistry, biophysics and anatomy of cells, tissues and organs. A major goal of the project is to use computational modelling to analyse integrative biological function in terms of underlying structure and molecular mechanisms. To support that goal the project is establishing web-accessible physiological databases dealing with model-related data, including bibliographic information, at the cell, tissue, organ and organ system levels. This paper discusses the development of comprehensive integrative mathematical models of human physiology based on patient-specific quantitative descriptions of anatomical structures and models of biophysical processes which reach down to the genetic level. 

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© 2002 Novartis Foundation

Using in silico biology to facilitate drug development

Jeremy M. Levin, R. Christian Penland, Andrew T. Stamps and Carolyn R. Cho
Physiome Sciences, 307 College Road East, Princeton, NJ 08540-6608, USA
G protein-coupled receptor (GPCR) mediation of cardiac excitability is often overlooked in predicting the likelihood that a compound will alter repolarization. While the areas of GPCR signal transduction and electrophysiology are rich in data, experiments combining the two are difficult. In silico modelling facilitates the integration of all relevant data in both areas to explore the hypothesis that critical associations may exist between the different GPCR signalling mechanisms and cardiac excitability and repolarization. An example of this linkage is suggested by the observation that a mutation of the gene encoding HERG, the pore- forming subunit of the rapidly activating delayed rectifier K+ current (IKr), leads to a form of long QT syndrome in which affected individuals are vulnerable to stress- induced arrhythmia following ß-adrenergic stimulation. Using Physiome's In Silico CellTM, we constructed a model integrating the signalling mechanisms of second messengers cAMP and protein kinase A with IKr in a cardiac myocyte. We analysed the model to identify the second messengers that most strongly influence IKr behaviour. Our conclusions indicate that the dynamics of regulation are multifactorial, and that Physiome's approach to in silico modelling helps elucidate the subtle control mechanisms at play. 

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© 2002 Novartis Foundation