Handbook of Research on Computational Grid Technologies for Life Sciences, Biomedicine, and Healthcare (2 Volumes)

Handbook of Research on Computational Grid Technologies for Life Sciences, Biomedicine, and Healthcare (2 Volumes)

Mario Cannataro (University Magna Graecia of Catanzaro, Italy)
Release Date: May, 2009|Copyright: © 2009 |Pages: 960|DOI: 10.4018/978-1-60566-374-6
ISBN13: 9781605663746|ISBN10: 1605663743|EISBN13: 9781605663753
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Grids are currently used in many sectors of life sciences, including basic sciences such as genomics, proteomics, and bioinformatics.

The Handbook of Research on Computational Grid Technologies for Life Sciences, Biomedicine, and Healthcare brings together state-of-the art methodologies and developments of grid technologies applied in different fields of life sciences. This Handbook of Research considers the use of grid technologies to support research and application of each information level where life science research takes place - a useful reference source for academicians, medical practitioners, and researchers involved in all areas of healthcare technologies.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

  • Biomedical application
  • Data mining in proteomics
  • Data provenance in scientific workflows
  • Docking process in drug discovery
  • Grid-based epidemic surveillance system
  • Grid-based tool for comparative genomics
  • Heart failure awareness management system
  • Managing uncertain data
  • Molecular structure determination
  • Multiple repositories for digital content
  • Polymorph prediction data for drug development
  • Resource discovery in health grids

Reviews and Testimonials

The book surveys state-of-the art research, methodologies and applications of Grid technology in different fields of Life Sciences, from basic science to clinical and healthcare applications.

– Mario Cannataro, University Magna Graecia of Catanzaro, Italy

Table of Contents and List of Contributors

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Grid technologies emerged from specific needs in basic sciences demanding high-computing power and large store repositories like physics, and from the availability of high-speed high-bandwidth networks and high-performance computers. In the last few years the term Grid has evolved toward a concept of ubiquitous and transparent computing that extends the network paradigm offered by the Internet and offers a new way to think and use computing resources and databases. As this evolution continued new terms like Data Grid, Knowledge Grid, and Semantic Grid emerged, including new functionalities and capabilities regarding, respectively, the efficient management of data stores, the discovery and sharing of knowledge, and the use of semantic-rich services and workflows.

Presently Grids start to be available as a commodity and have reached a maturity that are now available for different application domains, as it happened with the Internet in recent years. In particular, Grids are currently used in many sectors of Life Sciences, from basic sciences such as genomics, proteomics, and bioinformatics, to biomedical research and applications such as drug discovery, biomedical imaging, and biomedicine, and to clinical research and healthcare. So, the application of Grid technologies to Life Sciences, yielded new terms like Bioinformatics Grid, Biomedical Grid, and Health Grid.

The term “HealthGrid” is used to describe the application of Grid technology in the field of biomedical and healthcare informatics. The scientific and industrial community recognized the importance of governing and coordinating technical, standardization and organization activities of HealthGrids by establishing a set of international initiatives.

The international HealthGrid Association (http://www.healthgrid.org/) is an organization based in Europe that promotes the application of Grid technologies in the biomedical sector. With the HealthGrid White Paper (http://whitepaper.healthgrid.org), it established an agenda and defined the concepts, the opportunities and the likely benefits of such an approach. During the last few years it has been working to develop a strong relationship with clinicians and researchers to gain feedback from the use of Grid in the biomedical and healthcare sectors. The HealthGrid.US Alliance (http://www.healthgrid.us), an affiliate of the international HealthGrid Association, is a partnership of scientific, medical and technology professionals whose goal is to promote the application of advanced information technology to solve cutting-edge problems in biomedical science and Healthcare.

Starting from the HealthGrid vision, the European SHARE project (http://www.eu-share.org) identified the key developments - technical advances, social actions, economic investments and ethical or legal initiatives - needed to achieve wide adoption and deployment of HealthGrids throughout Europe. The full road map (http://roadmap.healthgrid.org) analyses several case studies and discusses technical, ethical, legal, social and economic issues which may impede early deployment of HealthGrids. Relevant projects are addressing these challenges worldwide and include, among others, sharing datasets for cancer cure (caBIG - https://cabig.nci.nih.gov/, ACGT - http://www.eu-acgt.org/), science portals for better visualization of brain morphology (BIRN - http://www.nbirn.net/), collaborative shar ing of mammograms (MammoGrid), 3D spatio-temporal visualization of the mammalian cells (Virtual Cell - http://www.visiblecell.com/), accurate multi-scale computational models of the heart and of cancer tumours (Integrative Biology - http://www.integrativebiology.ox.ac.uk/), “virtual” research repository of clinical, laboratory and genetic data sets spread within hospitals and research organisations (MMIM - http://www.mmim.org.au/). These and other projects have begun to demonstrate the power and potential of the Grid approach in biomedicine.

Among the others, HealthGrid is currently attempting to face the following questions:

  • How to address the needs of the multiple laboratories collecting genomics and post-genomics data and willing to analyse them in an up-to-date and competitive environment?
  • How to solve the need for large computation power needed in medical informatics?
  • How to address the need for easy accessible data and computing power for both scientist and medical staff?
  • How to make information on all levels, from molecule to population, accessible and understandable to the large variety of people which could benefit from such a knowledge? As will be explained in the following, the book aims to give a contribution to those and other questions regarding the methods, techniques, benefits and problems of using Grid technology in the biological, biomedical and healthcare domains.

    The book surveys state of the art research, methodologies and applications of Grid technology in different fields of Life Sciences, from basic science to clinical and healthcare applications. According to an agreed taxonomy, the fields where life science research takes place can be classified as molecule, cell, tissue, individual, and population levels.

    Considering data and computational models, bioinformatics is related to the molecule and cell; medical informatics is related to tissue/organ and individual, while public health informatics (in short healthcare informatics) is related to population.

    According to that taxonomy, the book discusses the application of Grid technology to each of the information levels where life science research takes place, by focusing on three main areas: bioinformatics for the study of molecules and cells, medical informatics for the study and simulation of tissues and organs, and healthcare informatics related to population.

    On the other hand, the Grid is a valuable tool for collaboration and cooperation, enabling the remote use of medical instruments in the so called virtual laboratory. Thus, the use of the Grid to improve collaboration among scientists and health centers and to enhance the healthcare provision to the final users is another important topic that has been addressed by the book.

    Finally, the application of Grid technology to Life Sciences poses news challenges about security and privacy of patient’s data, provenance of data, protection and reliability of information, and collaborative analysis of biomedical data. The book introduces these requirements and discusses new methodologies and techniques, such as the Virtual Laboratory concept, used to realize HealthGrids and BioGrids. So, one of the main questions the book attempts to answer is “How current Grids have to be extended to support Life Sciences and become HealthGrids?”.

    Guided by these considerations, the book’s contents are organized in a systematic way by considering different orthogonal dimensions: the complexity level of information being considered (molecule, cell, tissue/organ, individual, population), the support to collaboration and knowledge sharing (e.g. virtual laboratory), as well as the technology, infrastructures and middleware currently available for planning and building HealthGrids.

    The chapters of the book, organized accordingly to those dimensions of analysis, are divided into the following eight sections:

  • Section I Infrastructures and Services for HealthGrids and Biogrids
  • Section II Grids for Genomics and Proteomics
  • Section III Grid-Based Bioinformatics Environments
  • Section IV Grids for Medical Informatics
  • Section V Collaborative Grids for Healthcare and Clinical Applications
  • Section VI Grid-Based Virtual Laboratories for Bioinformatics and e-Science
  • Section VII Building and Deploying HealthGrids
  • Section VIII Selected Readings

    Section I introduces the main concepts and defines technical, methodological and organizational challenges of HealthGrids and BioGrids. After discussing the roadmap toward future HealthGrids (Chapter I), the Section defines basic resources and their discovery in HealthGrids (Chapter II). Then, taking into account the large use of scientific workflows in BioGrids and HealthGrids, the Section introduces the data provenance concept (Chapter III) and discusses ways to query and exploit provenance data (Chapter IV). Data protection and security, an important aspect that must be faced when considering biomedical data, is also discussed (Chapter V). Finally, a review of distributed data mining and knowledge discovery systems useful for implementing the analysis layer of HealthGrids is also presented (Chapter VI). At the molecular level, common trends are the increasing size and complexity of biological data and the increasing number and heterogeneity of collected databases. In fact, biological data is being more and more produced by different high-throughput technologies, like micro array and mass spectrometry, and such novel data must be correlated to well known sequences and structures databases, like SwissProt and PDB. Such requirements can be faced by Grids that provide the storage and the computing power needed to store, analyse and correlate such data.

    In particular, two current trends in developing BioGrids are reported in the book (Sections II and III): (a) well established bioinformatics tools like BLAST are being ported, eventually through parallelization, on the Grid, allowing to face the analysis issues of the overwhelming biological data; and (b) complete software environments allowing to combine basic bioinformatics tools through workflow of Web/Grid services are deployed on the Grid, supporting the complete life cycle of complex “in silico” experiments.

    Section II discusses the use of the Grid for the management and analysis of genomics and proteomics data, the basic data at the biological level. After describing main issues for the parallel and distributed implementation of BLAST (Chapter VII), a cornerstone of all genomics analysis, the Section discusses some significant Grid-based implementations of genomics applications (Chapters VIII to X). Then, the Section introduces proteomics with a special focus on mass spectrometry-based proteomics (Chapter XI), and different Grid-based proteomics applications, ranging from biomarker discovery to protein identification (Chapter XII) and protein classification (Chapter XIII), are discussed.

    Section III discusses Grid-based software environments for bioinformatics. Grid-based Problem Solving Environments specifically devoted for bioinformatics (Chapter XIV), as well as Grid-based implementations of relevant bioinformatics applications, such as docking (Chapter XV), bio-molecular simulation (Chapter XVI) and molecule structure determinations (Chapter XVII), are presented. Section IV focuses on Grid-based medical informatics applications. Since a core aspect of medical applications regards the acquisition and analysis of biomedical images, the Section first introduces different aspects of biomedical images visualization on the Grid (Chapter XVIII). Then, as significant applications in the field, Grid analysis of radiological data (Chapter XIX) and 3D electron microscopy reconstruction (Chapter XX) are discussed. Finally, as an example of biomedical instrumentation made available on the Grid, a hybrid mock circulatory system to test cardiovascular prostheses is presented (Chapter XXI).

    Considering the research at individual and population level, the Grid is used both as a technology to enable compute intensive applications such as simulation of an organ or organism, and also as a mean to enhance collaboration among individuals and to enable interoperability among applications and databases. As an example, personalized medicine will be based on the integration of genotype, phenotype and environment information (e.g. clinical, genomic, proteomic, and drug toxicity data).

    Healthcare applications at the population level as well as applications used in the clinical context are the focus of Section V. These applications are the most difficult to port on the Grid, due to the high number of patients involved, the partitioning of data among different health centers, the privacy and security problems and the need of collaboration among various operators. The use of the Grid as a support for collaboration in epidemiology (Chapter XXII), as well as the integration and analysis of epidemiology data (Chapter XXIII) are fully described. Two important HealthGrids for paediatrics (Chapter XXIV) and brain injury (XXV) are presented showing how the Grid is a mature technology also for the clinical setting.

    Section VI discusses concepts and properties of Virtual Laboratories, an abstraction for cooperative data analysis and distributed collaboration among scientists, and their applications in Life Sciences. After describing the foundations of modern virtual laboratories (Chapter XXVI), such as formalisms for representing domain knowledge, data integration, semantic annotations and shared vocabularies, the Section describes some emergent virtual laboratories that focus on distributed collaboration and use of provenance data (Chapter XXVII), transparent use of the Grid (Chapter XXVIII) and support for specific domains like bioinformatics (Chapter XXIX).

    Section VII describes main infrastructures, middleware, and tools for building and deploying Health- Grids. After introducing the main requirements posed on Grid middleware by biomedical applications, and how these are satisfied by a well known Grid middleware (Chapter XXX), the Section describes a Grid-based portal that aims to promote collaboration and cooperation among scientists and healthcare research groups on the Grid (Chapter XXXI). The management and deployment of Grid repositories for biomedical digital contents such as mammograms is also discussed (Chapter XXXII).

    Finally, Section VIII is a short collection of suggested readings of different authors, aiming to enrich this book with others knowledge, experience, thought and insight. Chapter XXXIII introduces Cloud Computing, an emerging style of computing in which dynamically scalable resources are provided as a service, and describes the porting of applications to Grids and Clouds. Chapter XXXIV describes a bio-inspired approach for the construction of a self-organizing Grid information system. Chapter XXXV discusses Grid-based implementation of phylogenetic analysis.

    In summary, the book records the main experiences and best practices on using Grids in bioinformatics, genomics, proteomics, medical informatics, and healthcare informatics, allowing a better understanding of benefits and added values in concrete biomedical and clinical applications.

    Readers of the book can explore the power and flexibility of Grid solutions with respect to either the complexity level of life exploration or the level of cooperation among scientists.

    In particular, the book reports the main methodologies currently used for exploiting Grid’s features in Life Sciences, and it may be an important aid for engineers, computer scientists, and clinicians working on biomedicine and healthcare.

    The book contains also a comprehensive description of tools, infrastructures and organizational issues currently available for planning and building HealthGrids and BioGrids. Thus, healthcare practitioners may find several interesting cases and best practices useful to plan and build HealthGrids in real situations, as well as arguments to evaluate the impact on organization.

    Many open research issues remain in these fields and new challenges emerge continuously. I hope that this book will serve to introduce readers to the potential developments and benefits of Grid technology for Life Sciences and to the challenges posed by biomedical and healthcare applications to current Grid.

    Mario Cannataro

    University Magna Graecia of Catanzaro, Italy

    January 2009

    Author(s)/Editor(s) Biography

    Mario Cannataro has been an associate professor of computer engineering at University “Magna Græcia” (Catanzaro, Italy) since 2002. He received the Laurea Degree (cum laude) in computer engineering from the University of Calabria (Italy) in 1993. His current research interests include grid computing, bioinformatics, computational proteomics and genomics, grid-based problem solving environments, and adaptive hypermedia systems. He has published a book and more than 100 papers in international journals and conference proceedings. Mario Cannataro is a co-founder of Exeura and is a member of ACM and IEEE Computer Society.


    Editorial Board

  • Ignacio Blanquer, Universidad Politécnica de Valencia, Spain
  • Marian Bubak, AGH University of Science and Technology, Poland
  • Vincent Breton, LPC Clermont-Ferrand, France
  • Vicente Hernández, Universidad Politécnica de Valencia, Spain
  • Yannick Legre, CNRS/IN2P3 and HealthGrid, France
  • Luciano Milanesi, Institute of Biomedical Technologies – CNR, Italy
  • Paolo Romano, National Cancer Research Institute, Italy
  • Richard Sinnott, National e-Science Centre at the University of Glasgow, UK
  • Tony Solomonides, University of the West of England, UK
  • Domenico Talia, University of Calabria, Italy