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IBM Journal of Research and Development

Issue 6 • Date Nov. 2006

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Displaying Results 1 - 14 of 14
  • Message

    Publication Year: 2006 , Page(s): 526 - 527
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    Freely Available from IEEE
  • Preface

    Publication Year: 2006 , Page(s): 527 - 528
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (41 KB)  

    This issue of the IBM Journal of Research and Development focuses on the growing field of systems biology. While the precise definition of the term “systems biology” is continually undergoing refinement and is open for discussion, researchers agree that the vast quantities of data generated by high-throughput biology, and related techniques used to study living systems, ensure that computation and computers will play an ever-increasing role in biological research. This computer-based approach gives systems biology researchers the opportunity to investigate the ways in which various kinds of data interrelate in order to go beyond an understanding of individual parts to an understanding of the function of larger biological systems. Using computational tools, the computational biologist analyzes, models, and simulates biological systems. Biology, once considered primarily as an observational science, has become quite quantitative. View full abstract»

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  • An assessment of the role of computing in systems biology

    Publication Year: 2006 , Page(s): 529 - 543
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    Systems biology is a burgeoning field in which researchers are investigating a flood of new data that is gathered in high-throughput genomics, proteomics, and related analyses. Systems biologists focus on what this data reveals about the functioning of living systems. The large volume of data, and the complexity of living systems, ensures that computing plays a central role in analyzing, modeling, and simulating these systems. In this paper, we discuss some of the key challenges in the field of computational systems biology. We also discuss possible ways in which the field of systems biology may evolve in coming years, along with some of the demands that systems biology research places on computing resources. View full abstract»

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  • Graph data management for molecular and cell biology

    Publication Year: 2006 , Page(s): 545 - 560
    Cited by:  Papers (2)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (358 KB)  

    As high-throughput biology begins to generate large volumes of systems biology data, the need grows for robust, efficient database systems to support investigations of metabolic and signaling pathways, chemical reaction networks, gene regulatory networks, and protein interaction networks. Network data is frequently represented as graphs, and researchers need to navigate, query and manipulate this data in ways that are not well supported by standard relational database management systems (RDBMSs). Current approaches to managing graphs in an RDBMS rely on either external procedural logic to execute the graph algorithms or clumsy and inefficient algorithms implemented in Structured Query Language (SQL). In this paper we describe the Systems Biology Graph Extender, a research prototype that extends the IBM RDBMS—DB2® Universal Database software—with graph objects and operations to support declarative SQL queries over biological networks and other graph structures. Supported operations include neighborhood queries, shortest path queries, spanning trees, graph transposition, and graph matching. In a federated database environment, graph operations may be applied to data stored in any format, whether remote or local, relational or nonrelational. A single federated query may include both graph-based predicates and predicates over related data sources, such as microarray expression levels, clinical prognosis and outcome, or the function of orthologous proteins (i.e., proteins that are evolutionarily related to those in another species) in mouse disease models. View full abstract»

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  • The Pathway Editor: A tool for managing complex biological networks

    Publication Year: 2006 , Page(s): 561 - 573
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (369 KB)  

    Biological networks are systems of biochemical processes inside a cell that involve cellular constituents such as DNA, RNA, proteins, and various small molecules. Pathway maps are often used to represent the structure of such networks with associated biological information. Several pathway editors exist, and they vary according to specific domains of knowledge. This paper presents a review of existing pathway editors, along with an introduction to the Edinburgh Pathway Editor (EPE). EPE was designed for the annotation, visualization, and presentation of a wide variety of biological networks that include metabolic, genetic, and signal transduction pathways. EPE is based on a metadata-driven architecture. The editor supports the presentation and annotation of maps, in addition to the storage and retrieval of reaction kinetics information in relational databases that are either local or remote. EPE also has facilities for linking graphical objects to external databases and Web resources, and is capable of reproducing most existing graphical notations and visual representations of pathway maps. In summary, EPE provides a highly flexible tool for combining visualization, editing, and database manipulation of information relating to biological networks. EPE is open-source software, distributed under the Eclipse open-source application platform license. View full abstract»

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  • Visualization of complementary systems biology data with parallel heatmaps

    Publication Year: 2006 , Page(s): 575 - 581
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (554 KB)  

    The interpretation of large-scale biological data can be aided by the use of appropriate visualization tools. Heatmaps—pattern-revealing aggregate views of data—have emerged as a preferred technique for the display of genomics data, since they provide an extra dimension of information in a two-dimensional display. However, an increasing focus on the integration of data from multiple sources has created a need for the display of additional dimensions. To improve the identification of relationships between co-expressed genes identified in microarray experiments, a parallel dataset heatmap viewer has been developed for four-dimensional data display. The flexible data entry structure of the parallel heatmap viewer facilitates the display of both continuous and discrete data. Specific examples are presented for the analysis of diverse functional genomics yeast data related to gene regulation, expression, and annotation. The parallel heatmap viewer enables knowledgeable life science researchers to observe patterns and properties within high-throughput genomics data in order to rapidly identify biologically logical relationships. View full abstract»

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  • A spatially detailed myofilament model as a basis for large-scale biological simulations

    Publication Year: 2006 , Page(s): 583 - 600
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (880 KB)  

    The availability of increased computing power will make possible new classes of biological models that include detailed representations of proteins and protein complexes with spatial interactions. We develop such a model of the interaction of actin and myosin within one pair of thick and thin filaments in the cardiac sarcomere. The model includes explicit representations of actin, myosin, and regulatory proteins. Although this is not an atomic-scale model, as would be the case for molecular dynamics simulations, the model seeks to represent spatial interactions between protein complexes that are thought to produce characteristic cardiac muscle responses at larger scales. While the model simulates the microscopic scale, when model results are extrapolated to larger structures, the model recapitulates complex, nonlinear behavior such as the steep calcium sensitivity of developed force in muscle structures. By bridging spatial scales, the model provides a plausible and quantitative explanation for several unexplained phenomena observed at the tissue level in cardiac muscles. Model execution entails Monte-Carlo-based simulations of Markov representations of calcium regulation and actin-myosin interactions. While most of the results presented here are preliminary, we suggest that this model will be suitable to serve as a basis for larger-scale simulations of multiple fibers assembled into larger sarcomere structures. View full abstract»

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  • Multiscale biosystems integration: Coupling intracellular network analysis with tissue-patterning simulations

    Publication Year: 2006 , Page(s): 601 - 615
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (309 KB)  

    In order to achieve a comprehensive understanding of complex biological systems, researchers must develop new techniques that incorporate key features of the system across all relevant spatial and temporal scales. Recent advances in molecular biology and genetics have generated a wealth of experimental data that provides details with respect to gene-expression patterns and individual gene and protein functions, but integration of this information into meaningful knowledge of the complete system is a challenge borne by a new scientific era dependent on computational tools. In this paper, we review new computational techniques, developed to reconstruct single-cell biochemical networks for generating quantitative descriptions of network properties, and agent-based models designed to study multicell interactions important in tissue patterning. We also discuss the challenges and promises of combining these approaches in a single quantitative framework for advancing medical care for diseases that arise from a multitude of factors. View full abstract»

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  • Computational multiscale modeling in the IUPS Physiome Project: Modeling cardiac electromechanics

    Publication Year: 2006 , Page(s): 617 - 630
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (772 KB)  

    We present a computational modeling and numerical simulation framework that enables the integration of multiple physics and spatiotemporal scales in models of physiological systems. This framework is the foundation of the IUPS (International Union of Physiological Sciences) Physiome Project. One novel aspect is the use of CellML, an annotated mathematical representation language, to specify model- and simulation-specific equations. Models of cardiac electromechanics at the cellular, tissue, and organ spatial scales are outlined to illustrate the development and implementation of the framework. We quantify the computational demands of performing simulations using such models and compare models of differing biophysical detail. Applications to other physiological systems are also discussed. View full abstract»

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  • Machine learning methods for transcription data integration

    Publication Year: 2006 , Page(s): 631 - 643
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (257 KB)  

    Gene expression is modulated by transcription factors (TFs), which are proteins that generally bind to DNA adjacent to coding regions and initiate transcription. Each target gene can be regulated by more than one TF, and each TF can regulate many targets. For a complete molecular understanding of transcriptional regulation, researchers must first associate each TF with the set of genes that it regulates. Here we present a summary of completed work on the ability to associate 104 TFs with their binding sites using support vector machines (SVMs), which are classification algorithms based in statistical learning theory. We use several types of genomic datasets to train classifiers in order to predict TF binding in the yeast genome. We consider motif matches, subsequence counts, motif conservation, functional annotation, and expression profiles. A simple weighting scheme varies the contribution of each type of genomic data when building a final SVM classifier, which we evaluate using known binding sites published in the literature and in online databases. The SVM algorithm works best when all datasets are combined, producing 73% coverage of known interactions, with a prediction accuracy of almost 0.9. We discuss new ideas and preliminary work for improving SVM classification of biological data. View full abstract»

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  • Model-based design approaches in drug discovery: A parallel to traditional engineering approaches

    Publication Year: 2006 , Page(s): 645 - 651
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (201 KB)  

    Model-based design (MBD) has been successfully applied in the automotive, chemical, and aerospace industries. Here we discuss the possible application of engineering-based MBD approaches to drug discovery. One of the biggest challenges in drug discovery is the high attrition rate of new drugs in development: Many promising candidates prove ineffective or toxic in animal or human testing. More often than not, these failures are the result of a poor understanding of the molecular mechanisms of the biological systems they target. Recent advances in biological systems modeling make MBD an attractive approach to improve drug development. We elaborate on the view that the pharmaceutical industry should be able to use MBD to design new drugs more effectively. There are significant differences between drug discovery and traditional engineering that lead to specific MBD requirements. We delineate those differences and introduce suggestions to overcome them. View full abstract»

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  • Author index for papers in Volume 50

    Publication Year: 2006 , Page(s): 653 - 657
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    Freely Available from IEEE
  • Errata

    Publication Year: 2006 , Page(s): 665
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  • Subject index for papers in Volume 50

    Publication Year: 2006 , Page(s): 659 - 663
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    Freely Available from IEEE

Aims & Scope

The IBM Journal of Research and Development is a peer-reviewed technical journal, published bimonthly, which features the work of authors in the science, technology and engineering of information systems.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Clifford A. Pickover
IBM T. J. Watson Research Center