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Catalyzing Inquiry at the Interface of Computing and Biology 4 Computational Tools As a factual science, biological research involves the collection and analysis of data from potentially billions of members of millions of species, not to mention many trillions of base pairs across different species. As data storage and analysis devices, computers are admirably suited to the task of supporting this enterprise. Also, as algorithms for analyzing biological data have become more sophisticated and the capabilities of electronic computers have advanced, new kinds of inquiries and analyses have become possible. 4.1 THE ROLE OF COMPUTATIONAL TOOLS Today, biology (and related fields such as medicine and pharmaceutics) are increasingly data-intensive—a trend that arguably began in the early 1960s.1 To manage these large amounts of data, and to derive insight into biological phenomena, biological scientists have turned to a variety of computational tools. As a rule, tools can be characterized as devices that help scientists do what they know they must do. That is, the problems that tools help solve are problems that are known by, and familiar to, the scientists involved. Further, such problems are concrete and well formulated. As a rule, it is critical that computational tools for biology be developed in collaboration with biologists who have deep insights into the problem being addressed. The discussion below focuses on three generic types of computational tools: (1) databases and data management tools to integrate large amounts of heterogeneous biological data, (2) presentation tools that help users comprehend large datasets, and (3) algorithms to extract meaning and useful information from large amounts of data (i.e., to find meaningful a signal in data that may look like noise at first glance). (Box 4.1 presents a complementary view of advances in computer sciences needed for next-generation tools for computational biology.) 1 The discussion in Section 4.1 is derived in part from T. Lenoir, “Shaping Biomedicine as an Information Science,” Proceedings of the 1998 Conference on the History and Heritage of Science Information Systems, M.E. Bowden, T.B. Hahn, and R.V. Williams, eds., ASIS Monograph Series, Information Today, Inc., Medford, NJ, 1999, pp. 27-45.
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Catalyzing Inquiry at the Interface of Computing and Biology Box 4.1 Tool Challenges for Computer Science Data Representation Next-generation genome annotation system with accuracy equal to or exceeding the best human predictions Mechanism for multimodal representation of data Analysis Tools Scalable methods of comparing many genomes Tools and analyses to determine how molecular complexes work within the cell Techniques for inferring and analyzing regulatory and signaling networks Tools to extract patterns in mass spectrometry datasets Tools for semantic interoperability Visualization Tools to display networks and clusters at many levels of detail Approaches for interpreting data streams and comparing high-throughput data with simulation output Standards Good software-engineering practices and standard definitions (e.g., a common component architecture) Standard ontology and data-exchange format for encoding complex types of annotation Databases Large repository for microbial and ecological literature relevant to the “Genomes to Life” effort. Big relational database derived by automatic generation of semantic metadata from the biological literature Databases that support automated versioning and identification of data provenance Long-term support of public sequence databases SOURCE: U.S. Department of Energy, Report on the Computer Science Workshop for the Genomes to Life Program, Gaithersburg, MD, March 6-7, 2002; available at http://DOEGenomesToLife.org/compbio/. These examples are drawn largely from the area of cell biology. The reason is not that these are the only good examples of computational tools, but rather that a great deal of the activity in the field has been the direct result of trying to make sense out of the genomic sequences that have been collected to date. As noted in Chapter 2, the Human Genome Project—completed in draft in 2000—is arguably the first large-scale project of 21st century biology in which the need for powerful information technology was manifestly obvious. Since then, computational tools for the analysis of genomic data, and by extension data associated with the cell, have proliferated wildly; thus, a large number of examples are available from this domain. 4.2 TOOLS FOR DATA INTEGRATION2 As noted in Chapter 3, data integration is perhaps the most critical problem facing researchers as they approach biology in the 21st century. 2 Sections 4.2.1, 4.2.4, 4.2.6, and 4.2.8 embed excerpts from S.Y. Chung and J.C. Wooley, “Challenges Faced in the Integration of Biological Information,” in Bioinformatics: Managing Scientific Data, Z. Lacroix and T. Critchlow, eds., Morgan Kaufmann, San Francisco, CA, 2003. (Hereafter cited as Chung and Wooley, 2003.)
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Catalyzing Inquiry at the Interface of Computing and Biology 4.2.1 Desiderata If researcher A wants to use a database kept and maintained by researcher B, the “quick and dirty” solution is for researcher A to write a program that will translate data from one format into another. For example, many laboratories have used programs written in Perl to read, parse, extract, and transform data from one form into another for particular applications.3 Depending on the nature of the data involved and the structure of the source databases, writing such a program may require intensive coding. Although such a fix is expedient, it is not scalable. That is, point-to-point solutions are not sustainable in a large community in which it is assumed that everyone wants to share data with everyone else. More formally, if there are N data sources to be integrated, and point-to-point solutions must be developed, N (N − 1)/2 translation programs must be written. If one data source changes (as is highly likely), N − 1 programs must be updated. A more desirable approach to data integration is scalable. That is, a change in one database should not necessitate a change on the part of every research group that wants to use those data. A number of approaches are discussed below, but in general, Chung and Wooley argue that robust data integration systems must be able to Access and retrieve relevant data from a broad range of disparate data sources; Transform the retrieved data into a common data model for data integration; Provide a rich common data model for abstracting retrieved data and presenting integrated data objects to the end-user applications; Provide a high-level expressive language to compose complex queries across multiple data sources and to facilitate data manipulation, transformation, and integration tasks; and Manage query optimization and other complex issues. Sections 4.2.2, 4.2.4, 4.2.5, 4.2.6, and 4.2.8 address a number of different approaches to dealing with the data integration problem. These approaches are not, in general, mutually exclusive, and they may be usable in combination to improve the effectiveness of a data integration solution. Finally, biological databases are always changing, so integration is necessarily an ongoing task. Not only are new data being integrated within the existing database structure (a structure established on the basis of an existing intellectual paradigm), but biology is a field that changes quickly—thus requiring structural changes in the databases that store data. In other words, biology does not have some “classical core framework” that is reliably constant. Thus, biological paradigms must be redesigned from time to time (on the scale of every decade or so) to keep up with advances, which means that no “gold standards” to organize data are built into biology. Furthermore, as biology expands its attention to encompass complexes of entities and events as well as individual entities and events, more coherent approaches to describing new phenomena will become necessary—approaches that bring some commonality and consistency to data representations of different biological entities—so that relationships between different phenomena can be elucidated. As one example, consider the potential impact of “-omic” biology, biology that is characterized by a search for data completeness—the complete sequence of the human genome, a complete catalog of proteins in the human body, the sequencing of all genomes in a given ecosystem, and so on. The possibility of such completeness is unprecedented in the history of the life sciences and will almost certainly require substantial revisions to the relevant intellectual frameworks. 3 The Perl programming language provides powerful and easy-to-use capabilities to search and manipulate text files. Because of these strengths, Perl is a major component of much bioinformatics programming. At the same time, Perl is regarded by many computer scientists as an unsafe language in which it is easy to make programs do dangerous things. In addition, many regard the syntax and structure of most Perl programs to be of a nature that is hard to understand much after the fact.
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Catalyzing Inquiry at the Interface of Computing and Biology 4.2.2 Data Standards One obvious approach to data integration relies on technical standards that define representations of data and hence provide an understanding of data that is common to all database developers. For obvious reasons, standards are most relevant to future datasets. Legacy databases, which have been built around unique data definitions, are much less amenable to a standards-driven approach to data integration. Standards are indeed an essential element of efforts to achieve data integration of future datasets, but the adoption of standards is a nontrivial task. For example, community-wide standards for data relevant to a certain subject almost certainly differ from those that might be adopted by individual laboratories, which are the focus of the “small-instrument, multi-data-source” science that characterizes most public-sector biological research. Ideally, source data from these projects flow together into larger national or international data resources that are accessible to the community. Adopting community standards, however, entails local compromises (e.g., nonoptimal data structuring and semantics, greater expense), and the budgets that characterize small-instrument, single-data-source science generally do not provide adequate support for local data management and usually no support at all for contributions to a national data repository. If data from such diverse sources are to be maintained centrally, researchers and laboratories must have incentives and support to adopt broader standards in the name of the community’s greater good. In this regard, funding agencies and journals have considerable leverage and through techniques such as requiring researchers to deposit data in conformance to community standards may be able to provide such incentives. At the same time, data standards cannot resolve the integration problem by themselves even for future datasets. One reason is that in some fast-moving and rapidly changing areas of science (such as biology), it is likely that the data standards existing at any given moment will not cover some new dimension of data. A novel experiment may make measurements that existing data standards did not anticipate. (For example, sequence databases—by definition—do not integrate methylation data; and yet methylation is an essential characteristic of DNA that falls outside primary sequence information.) As knowledge and understanding advance, the meaning attached to a term may change over time. A second reason is that standards are difficult to impose on legacy systems, because legacy datasets are usually very difficult to convert to a new data standard and conversion almost always entails some loss of information. As a result, data standards themselves must evolve as the science they support changes. Because standards cannot be propagated instantly throughout the relevant biological community, database A may be based on Version 12.1 of a standard, and database B on Version 12.4 of the “same” standard. It would be desirable if the differences between Versions 12.1 and 12.4 were not large and a basic level of integration could still be maintained, but this is not ensured in an environment in which options vary within standards, different releases and versions of products, and so on. In short, much of the devil of ensuring data integration is in the detail of implementation. Experience in the database world suggests that standards gaining widespread acceptance in the commercial marketplace tend to have a long life span, because the marketplace tends to weed out weak standards before they become widely accepted. Once a standard is widely used, industry is often motivated to maintain compliance with this accepted standard, but standards created by niche players in the market tend not to survive. This point is of particular relevance in a fragmented research environment and suggests that standards established by strong consortia of multiple players are more likely to endure. 4.2.3 Data Normalization4 An important issue related to data standards is data normalization. Data normalization is the process through which data taken on the “same” biological phenomenon by different instruments, procedures, or researchers can be rendered comparable. Such problems can arise in many different contexts: 4 Section 4.2.3 is based largely on a presentation by C. Ball, “The Normalization of Microarray Data,” presented at the AAAS 2003 meeting in Denver, Colorado.
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Catalyzing Inquiry at the Interface of Computing and Biology Microarray data related to a given cell may be taken by multiple investigators in different laboratories. Ecological data (e.g., temperature, reflectivity) in a given ecosystem may be taken by different instruments looking at the system. Neurological data (e.g., timing and amplitudes of various pulse trains) related to a specific cognitive phenomenon may be taken on different individuals in different laboratories. The simplest example of the normalization problem is when different instruments are calibrated differently (e.g., a scale in George’s laboratory may not have been zeroed properly, rendering mass measurements from George’s laboratory noncomparable to those from Mary’s laboratory). If a large number of readings have been taken with George’s scale, one possible fix (i.e., one possible normalization) is to determine the extent of the zeroing required and to add or subtract that correction to the already existing data. Of course, this particular procedure assumes that the necessary zeroing was constant for each of George’s measurements. The procedure is not valid if the zeroing knob was jiggled accidentally after half of the measurements had been taken. Such biases in the data are systematic. In principle, the steps necessary to deal with systematic bias are straightforward. The researcher must avoid it as much as possible. Because complete avoidance is not possible, the researcher must recognize it when it occurs and then take steps to correct for it. Correcting for bias entails determining the magnitude and effect of the bias on data that have been taken and identifying the source of the bias so that the data already taken can be modified and corrected appropriately. In some cases, the bias may be uncorrectable, and the data must be discarded. However, in practice, dealing with systematic bias is not nearly so straightforward. Ball notes that in the real world, the process goes something like this: Notice something odd with data. Try a few methods to determine magnitude. Think of many possible sources of bias. Wonder what in the world to do next. There are many sources of systematic bias, and they differ depending on the nature of the data involved. They may include effects due to instrumentation, sample (e.g., sample preparation, sample choice), or environment (e.g., ambient vibration, current leakage, temperature). Section 3.3 describes a number of the systematic biases possible in microarray data, as do several references provided by Ball.5 There are many ways to correct for systematic bias, depending on the type of data being corrected. In the case of microarray studies, these ways include use of dye swap strategies, replicates and reference samples, experimental controls, consistent techniques, and sensible array and experiment design. Yet all 5 Ball’s AAAS presentation includes the following sources: T.B. Kepler, L. Crosby, and K.T. Morgan, “Normalization and Analysis of DNA Microarray Data by Self-consistency and Local Regression,” Genome Biololgy 3(7), RESEARCH0037.1- RESEARCH0037.12, 2002. Available at http://genomebiology.org/2002/3/7/research/0037.1; R. Hoffmann, T. Seidl, M. Dugas. “Profound Effect of Normalization on Detection of Differentially Expressed Genes in Oligonucleotide Microarray Data Analysis,” Genome Biolology 3(7):RESEARCH0033.1-RESEARCH0033.1-11. Available at http://genomebiology.com/2002/3/7/research/0033; C. Colantuoni, G. Henry, S. Zeger, and J. Pevsner, “Local Mean Normalization of Microarray Element Signal Intensities Across an Array Surface: Quality Control and Correction of Spatially Systematic Artifacts,” Biotechniques 32(6):1316-1320, 2002; B.P. Durbin, J.S. Hardin, D.M. Hawkins, and D.M. Rocke, “A Variance-Stabilizing Transformation for Gene-Expression Microarray Data,” Bioinformatics 18 (Suppl. 1):S105-S110, 2002; P.H. Tran, D.A. Peiffer, Y. Shin, L.M. Meek, J.P. Brody, and K.W. Cho, “Microarray Optimizations: Increasing Spot Accuracy and Automated Identification of True Microarray Signals,” Nucleic Acids Research 30(12):e54, 2002, available at http://nar.oupjournals.org/cgi/content/full/30/12/e54; M. Bilban, L.K. Buehler, S. Head, G. Desoye, and V. Quaranta, “Normalizing DNA Microarray Data,” Current Issues in Molecular Biology 4(2):57-64, 2002; J. Quackenbush, “Microarray Data Normalization and Transformation,” Nature Genetics Supplement 32:496-501, 2002.
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Catalyzing Inquiry at the Interface of Computing and Biology of these approaches are labor-intensive, and an outstanding challenge in the area of data normalization is to develop approaches to minimize systematic bias that demand less labor and expense. 4.2.4 Data Warehousing Data warehousing is a centralized approach to data integration. The maintainer of the data warehouse obtains data from other sources and converts them into a common format, with a global data schema and indexing system for integration and navigation. Such systems have a long track record of success in the commercial world, especially for resource management functions (e.g., payroll, inventory). These systems are most successful when the underlying databases can be maintained in a controlled environment that allows them to be reasonably stable and structured. Data warehousing is dominated by relational database management systems (RDBMS), which offer a mature and widely accepted database technology and a standard high-level standard query language (SQL). However, biological data are often qualitatively different from the data contained in commercial databases. Furthermore, biological data sources are much more dynamic and unpredictable, and few public biological data sources use structured database management systems. Data warehouses are often troubled by a lack of synchronization between the data they hold and the original database from which those data derive because of the time lag involved in refreshing the data warehouse store. Data warehousing efforts are further complicated by the issue of updates. Stein writes:6 One of the most ambitious attempts at the warehouse approach [to database integration] was the Integrated Genome Database (IGD) project, which aimed to combine human sequencing data with the multiple genetic and physical maps that were the main reagent for human genomics at the time. At its peak, IGD integrated more than a dozen source databases, including GenBank, the Genome Database (GDB) and the databases of many human genetic-mapping projects. The integrated database was distributed to end-users complete with a graphical front end…. The IGD project survived for slightly longer than a year before collapsing. The main reason for its collapse, as described by the principal investigator on the project (O. Ritter, personal communication, as relayed to Stein), was the database churn issue. On average, each of the source databases changed its data model twice a year. This meant that the IGD data import system broke down every two weeks and the dumping and transformation programs had to be rewritten—a task that eventually became unmanageable. Also, because of the breadth and volume of biological databases, the effort involved in maintaining a comprehensive data warehouse is enormous—and likely prohibitive. Such an effort would have to integrate diverse biological information, such as sequence and structure, up to the various functions of biochemical pathways and genetic polymorphisms. Still, data warehousing is a useful approach for specific applications that are worth the expense of intense data cleansing to remove potential errors, duplications, and semantic inconsistency.7 Two current examples of data warehousing are GenBank and the International Consortium for Brain Mapping (ICBM) (the latter is described in Box 4.2). 4.2.5 Data Federation The data federation approach to integration is not centralized and does not call for a “master” database. Data federation calls for scientists to maintain their own specialized databases encapsulating their particular areas of expertise and retain control of the primary data, while still making it available to other researchers. In other words, the underlying data sources are autonomous. Data federation often 6 Reprinted by permission from L.D. Stein, “Integrating Biological Databases,” Nature Reviews Genetics 4(5)337-345, 2003. Copyright 2005 Macmillan Magazines Ltd. 7 R. Resnick, “Simplified Data Mining,” pp. 51-52 in Drug Discovery and Development, 2000. (Cited in Chung and Wooley, 2003.)
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Catalyzing Inquiry at the Interface of Computing and Biology Box 4.2 The International Consortium for Brain Mapping (ICBM): A Probabilistic Atlas and Reference System for the Human Brain In the human population, the brain varies structurally and functionally in currently undefined ways. It is clear that the size, shape, symmetry, folding pattern, and structural relationships of the systems in the human brain vary from individual to individual. This has been a source of considerable consternation and difficulty in research and clinical evaluations of the human brain from both the structural and the functional perspective. Current atlases of the human brain do not address this problem. Cytoarchitectural and clinical atlases typically use a single brain or even a single hemisphere as the reference specimen or target brain to which other brains are matched, typically with simple linear stretching and compressing strategies. In 1992, John Mazziotta and Arthur Toga proposed the concept of developing a probabilistic atlas from a large number of normal subjects between the ages of 18 and 90. This data acquisition has now been completed, and the value of such an atlas is being realized for both research and clinical purposes. The mathematical and software machinery required to develop this atlas of normal subjects is now also being applied to patient populations including individuals with Alzheimer’s disease, schizophrenia, autism, multiple sclerosis, and others. Talairach Atlas To date, more than 7,000 normal subjects have been entered into the Talairach atlas project and a wide range of datasets. These datasets contain detailed demographic histories of the subjects, results of general medical and neurological examinations, neuropsychiatric and neuropsychological evaluations, quantitative “handedness measurements”, and imaging studies. The imaging studies include multispectra 1 mm3 voxel-size magnetic resonance imaging (MRI) evaluations of the entire brain (T1, T2, and proton density pulse sequences). A subset of individuals also undergo functional MRI, cerebral blood flow position emission tomography (PET) and electroencephalogram (EEG) examinations (evoked potentials). Of these subjects, 5,800 individuals have also had their DNA collected and stored for future genotyping. As such, this database represents the most comprehensive evaluation of the structural and functional imaging phenotypes of the human brain in the normal population across a wide age span and very diverse social, economic, and racial groups. Participating laboratories are widely distributed geographically from Asia to Scandinavia, and include eight laboratories, in seven countries, on four continents. World Map of Sites A component of the World Map of Sites project involves the post mortem MRI imaging of individuals who have willed their bodies to science. Subsequent to MRI imaging, the brain is frozen and sectioned at a resolution of approximately 100 microns. Block face images are stored, and the sectioned tissue is stained for cytoarchitectural, chemoarchitectural, and differential myelin to produce microscopic maps of cellular anatomy, neuroreceptor or transmitter systems, and white matter tracts. These datasets are then incorporated into a target brain to which the in vivo brain studies are warped in three dimensions and labeled automatically. The 7,000 datasets are then placed in the standardized space, and probabilistic estimates of structural boundaries, volumes, symmetries, and shapes are computed for the entire population or any subpopulation (e.g., age, gender, race). In the current phase of the program, information is being added about in vivo chemoarchitecture (5-HT2A [5-hydroxytryptamine-2A] in vivo PET receptor imaging), in vivo white matter tracts (MRI-diffusion tensor imaging), vascular anatomy (magnetic resonance angiography and venography), and cerebral connections (transcranial magnetic stimulation-PET cerebral blood flow measurements). Target Brain The availability of 342 twin pairs in the dataset (half monozygotic and half dizygotic) along with DNA for genotyping provides the opportunity to understand structure-function relationships related to genotype and, therefore, provides the first large-scale opportunity to relate phenotype-genotype in behavior across a wide range of individuals in the human population.
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Catalyzing Inquiry at the Interface of Computing and Biology The development of similar atlases to evaluate patients with well-defined disease states allows the opportunity to compare the normal brain with brains of patients having cerebral pathological conditions, thereby potentially leading to enhanced clinical trials, automated diagnoses, and other clinical applications. Such examples have already emerged in patients with multiple sclerosis and epilepsy. An example in Alzheimer’s disease relates to a current hotly contested research question. Individuals with Alzheimer’s disease have a greater likelihood of having the genotype ApoE 4 (as opposed to ApoE 2 or 3). Having this genotype, however, is neither sufficient nor required for the development of Alzheimer’s disease. Individuals with Alzheimer’s disease also have small hippocampi, presumably because of atrophy of this structure as the disease progresses. The question of interest is whether individuals with the high-risk genotype (ApoE 4) have small hippocampi to begin with. This would be a very difficult hypothesis to test without the dataset described above. With the ICBM database, it is possible to study individuals from, for example, ages 20 to 40 and identify those with the smallest (lowest 5 percent) and largest (highest 5 percent) hippocampal volumes. This relatively small number of subjects could then be genotyped for ApoE alleles. If individuals with small hippocampi all had the genotype ApoE 4 and those with large hippocampi all had the genotype ApoE 2 or 3, this would be strong support for the hypothesis that individuals with the high-risk genotype for the development of Alzheimer’s disease have small hippocampi based on genetic criteria as a prelude to the development of Alzheimer’s disease. Similar genotype-imaging phenotype evaluations could be undertaken across a wide range of human conditions, genotypes, and brain structures. SOURCE: Modified from John C. Mazziotta and Arthur W. Toga, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, personal communication to John Wooley, February 22, 2004. calls for the use of object-oriented concepts to develop data definitions, encapsulating the internal details of the data associated with the heterogeneity of the underlying data sources.8 A change in the representation or definition of the data then has minimal impact on the applications that access those data. An example of a data federation environment is BioMOBY, which is based on two ideas.9 The first is the notion that databases provide bioinformatics services that can be defined by their inputs and outputs. (For example, BLAST is a service provided by GenBank that can be defined by its input—that is, an uncharacterized sequence—and by its output, namely, described gene sequences deposited in GenBank.) The second idea is that all database services would be linked to a central registry (MOBY Central) of services that users (or their applications) would query. From MOBY Central, a user could move from one set of input-output services to the next—for example, moving from one database that, given a sequence (the input), postulates the identity of a gene (the output), and from there to a database that, given a gene (the input), will find the same gene in multiple organisms (the output), and so on, picking up information as it moves through database services. There are limitations to the BioMOBY system’s ability to discriminate database services based the descriptions of inputs and outputs, and MOBY Central must be up and running 24 hours a day.10 8 R.G.G. Cattell, Object Data Management: Object-Oriented and Extended Relational Database Systems, revised edition, Addison-Wiley, Reading, MA, 1994. (Cited in Chung and Wooley, 2003.) 9 M.D. Wilkinson and M. Links, “BioMOBY: An Open-Source Biological Web Services Proposal,” Briefings In Bioinformatics 3(4):331-341, 2002. 10 L.D. Stein, “Integrating Biological Databases,” Nature Reviews Genetics 4(5):337-345, 2003.
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Catalyzing Inquiry at the Interface of Computing and Biology 4.2.6 Data Mediators/Middleware In the middleware approach, an intermediate processing layer (a “mediator”) decouples the underlying heterogeneous, distributed data sources and the client layer of end users and applications.11 The mediator layer (i.e., the middleware) performs the core functions of data transformation and integration, and communicates with the database “wrappers” and the user application layer. (A “wrapper” is a software component associated with an underlying data source that is generally used to handle the tasks of access to specified data sources, extraction and retrieval of selected data, and translation of source data formats into a common data model designed for the integration system.) The common model for data derived from the underlying data sources is the responsibility of the mediator. This model must be sufficiently rich to accommodate various data formats of existing biological data sources, which may include unstructured text files, semistructured XML and HTML files, and structured relational, object-oriented, and nested complex data models. In addition, the internal data model must facilitate the structuring of integrated biological objects to present to the user application layer. Finally, the mediator also provides services such as filtering, managing metadata, and resolving semantic inconsistency in source databases. There are many flavors of mediator approaches in life science domains. IBM’s DiscoveryLink for the life sciences is one of the best known.12 The Kleisli system provides an internal nested complex data model and a high-power query and transformation language for data integration.13 K2 shares many design principles with Kleisli in supporting a complex data model, but adopts more object-oriented features.14 OPM supports a rich object model and a global schema for data integration.15 TAMBIS provides a global ontology (see Section 4.2.8 on ontologies) to facilitate queries across multiple data sources.16 TSIMMIS is a mediation system for information integration with its own data model (Object-Exchange Model, OEM) and query language.17 4.2.7 Databases as Models A natural progression for databases established to meet the needs and interests of specialized communities, such as research on cell signaling pathways or programmed cell death, is the evolution of 11 G. Wiederhold, “Mediators in the Architecture of Future Information Systems,” IEEE Computer 25(3):38-49, 1992; G. Wiederhold and M. Genesereth, “The Conceptual Basis for Mediation Services,” IEEE Expert, Intelligent Systems and Their Applications 12(5):38-47, 1997. (Both cited in Chung and Wooley, 2003.) 12 L.M. Haas et al., “DiscoveryLink: A System for Integrated access to Life Sciences Data Sources,” IBM Systems Journal 40(2):489-511, 2001. 13 S. Davidson, C. Overton, V. Tannen, and L. Wong, “BioKleisli: A Digital Library for Biomedical Researchers,” International Journal of Digital Libraries 1(1):36-53, 1997; L. Wong, “Kleisli, a Functional Query System,” Journal of Functional Programming 10(1):19-56, 2000. (Both cited in Chung and Wooley, 2003.) 14 J. Crabtree, S. Harker, and V. Tannen, “The Information Integration System K2,” available at http://db.cis.upenn.edu/K2/K2.doc; S.B. Davidson, J. Crabtree, B.P. Brunk, J. Schug, V. Tannen, G.C. Overton, and C.J. Stoeckert, Jr., “K2/Kleisli and GUS: Experiments in Integrated Access to Genomic Data Sources,” IBM Systems Journal 40(2):489-511, 2001. (Both cited in Chung and Wooley, 2003.) 15 I-M.A. Chen and V.M. Markowitz, “An Overview of the Object-Protocol Model (OPM) and OPM Data Management Tools,” Information Systems 20(5):393-418, 1995; I-M.A. Chen, A.S. Kosky, V.M. Markowitz, and E. Szeto, “Constructing and Maintaining Scientific Database Views in the Framework of the Object-Protocol Model,” Proceedings of the Ninth International Conference on Scientific and Statistical Database Management, Institute of Electrical and Electronic Engineers, Inc., New York, 1997, pp. 237–248. (Cited in Chung and Wooley, 2003.) 16 N.W. Paton, R. Stevens, P. Baker, C.A. Goble, S. Bechhofer, and A. Brass, “Query Processing in the TAMBIS Bioinformatics Source Integration System,” Proceedings of the 11th International Conference on Scientific and Statistical Database Management, IEEE, New York 1999, pp. 138-147; R. Stevens, P. Baker, S. Bechhofer, G. Ng, A. Jacoby, N.W. Paton, C.A. Goble, and A. Brass, “TAMBIS: Transparent Access to Multiple Bioinformatics Information Sources,” Bioinformatics 16(2):184-186, 2000. (Both cited in Chung and Wooley, 2003.) 17 Y. Papakonstantinou, H. Garcia-Molina, and J. Widom, “Object Exchange Across Heterogeneous Information Sources,” Proceedings of the IEEE Conference on Data Engineering, IEEE, New York, 1995, pp. 251-260. (Cited in Chung and Wooley, 2003.)
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Catalyzing Inquiry at the Interface of Computing and Biology databases into models of biological activity. As databases become increasingly annotated with functional and other information, they lay the groundwork for model formation. In the future, such “database models” are envisioned as the basis of informed predictions and decision making in biomedicine. For example, physicians of the future may use biological information systems (BISs) that apply known interactions and causal relationships among proteins that regulate cell division to changes in an individual’s DNA sequence, gene expression, and proteins in an individual tumor.18 The physician might use this information together with the BIS to support a decision on whether the inhibition of a particular protein kinase is likely to be useful for treating that particular tumor. Indeed, a major goal in the for-profit sector is to create richly annotated databases that can serve as testbeds for modeling pharmaceutical applications. For example, Entelos has developed PhysioLab, a computer model system consisting of a large set (more than 1,000) of ordinary nonlinear differential equations.19 The model is a functional representation of human pathophysiology based on current genomic, proteomic, in vitro, in vivo, and ex vivo data, built using a top-down, disease-specific systems approach that relates clinical outcomes to human biology and physiology. Starting with major organ systems, virtual patients are explicit mathematical representations of a particular phenotype, based on known or hypothesized factors (genetic, life-style, environmental). Each model simulates up to 60 separate responses previously demonstrated in human clinical studies. In the neuroscience field, Bower and colleagues have developed the Modeler’s Workspace,20 which is based on a notion that electronic databases must provide enhanced functionality over traditional means of distributing information if they are to be fully successful. In particular, Bower et al. believe that computational models are an inherently more powerful medium for the electronic storage and retrieval of information than are traditional online databases. The Modeler’s Workspace is thus designed to enable researchers to search multiple remote databases for model components based on various criteria; visualize the characteristics of the components retrieved; create new components, either from scratch or derived from existing models; combine components into new models; link models to experimental data as well as online publications; and interact with simulation packages such as GENESIS to simulate the new constructs. The tools contained in the Workspace enable researchers to work with structurally realistic biological models, that is, models that seek to capture what is known about the anatomical structure and physiological characteristics of a neural system of interest. Because they are faithful to biological anatomy and physiology, structurally realistic models are a means of storing anatomical and physiological experimental information. For example, to model a part of the brain, this modeling approach starts with a detailed description of the relevant neuroanatomy, such as a description of the three-dimensional structure of the neuron and its dendritic tree. At the single-cell level, the model represents information about neuronal morphology, including such parameters as soma size, length of interbranch segments, diameter of branches, bifurcation probabilities, and density and size of dendritic spines. At the neuronal network level, the model represents the cell types found in the network and the connectivity among them. The model must also incorporate information regarding the basic physiological behavior of the modeled structure—for example, by tuning the model to replicate neuronal responses to experimentally derived data. With such a framework in place, a structural model organizes data in ways that make manifestly obvious how those data are related to neural function. By contrast, for many other kinds of databases it is not at all obvious how the data contained therein contribute to an understanding of function. Bower 18 R. Brent and D. Endy, “Modelling Cellular Behaviour,” Nature 409:391-395, 2001. 19 See, for example, http://www.entelos.com/science/physiolabtech.html. 20 M. Hucka, K. Shankar, D. Beeman, and J.M. Bower, “The Modeler’s Workspace: Making Model-Based Studies of the Nervous System More Accessible,” Computational Neuroanatomy: Principles and Methods, G.A. Ascoli, ed., Humana Press, Totowa, NJ, 2002, pp. 83-103.
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Catalyzing Inquiry at the Interface of Computing and Biology and colleagues argue that “as models become more sophisticated, so does the representation of the data. As models become more capable, they extend our ability to explore the functional significance of the structure and organization of biological systems.”21 4.2.8 Ontologies Variations in language and terminology have always posed a great challenge to large-scale, comprehensive integration of biological findings. In part, this is due to the fact that scientists operate, with a data- and experience-driven intuition that outstrips the ability of language to describe. As early as 1952, this problem was recognized: Geneticists, like all good scientists, proceed in the first instance intuitively and … their intuition has vastly outstripped the possibilities of expression in the ordinary usages of natural languages. They know what they mean, but the current linguistic apparatus makes it very difficult for them to say what they mean. This apparatus conceals the complexity of the intuitions. It is part of the business of genetical methodology first to discover what geneticists mean and then to devise the simplest method of saying what they mean. If the result proves to be more complex than one would expect from the current expositions, that is because these devices are succeeding in making apparent a real complexity in the subject matter which the natural language conceals.22 In addition, different biologists use language with different levels of precision for different purposes. For instance, the notion of “identity” is different depending on context.23 Two geneticists may look at a map of human chromosome 21. A year later, they both want to look at the same map again. But to one of them, “same” means exactly the same map (same data, bit for bit); to the other, it means the current map of the same biological object, even if all of the data in that map have changed. To a protein chemist, two molecules of beta-hemoglobin are the same because they are composed of exactly the same sequence of amino acids. To a biologist, the same two molecules might be considered different because one was isolated from a chimpanzee and the other from a human. To deal with such context-sensitive problems, bioinformaticians have turned to ontologies. An ontology is a description of concepts and relationships that exist among the concepts for a particular domain of knowledge.24 Ontologies in the life sciences serve two equally important functions. First, they provide controlled, hierarchically structured vocabularies for terminology that can be used to describe biological objects. Second, they specify object classes, relations, and functions in ways that capture the main concepts of and relationships in a research area. 188.8.131.52 Ontologies for Common Terminology and Descriptions To associate concepts with the individual names of objects in databases, an ontology tool might incorporate a terminology database that interprets queries and translates them into search terms consistent with each of the underlying sources. More recently, ontology-based designs have evolved from static dictionaries into dynamic systems that can be extended with new terms and concepts without modification to the underlying database. 21 M. Hucka, K. Shankar, D. Beeman, and J.M. Bower, “The Modeler’s Workspace,” 2002. 22 J.H. Woodger, Biology and Language, Cambridge University Press, Cambridge, UK, 1952. 23 R.J. Robbins, “Object Identity and Life Science Research,” position paper submitted for the Semantic Web for Life Sciences Workshop, October 27-28 2004, Cambridge, MA, available at http://lists.w3.org/Archives/Public/public-swls-ws/2004Sep/att-0050/position-01.pdf. 24 The term “ontology” is a philosophical term referring to the subject of existence. The computer science community borrowed the term to refer to “specification of a conceptualization” for knowledge sharing in artificial intelligence. See, for example, T.R. Gruber, “A Translation Approach to Portable Ontology Specification,” Knowledge Acquisition 5(2):199-220, 1993. (Cited in Chung and Wooley, 2003.)
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Catalyzing Inquiry at the Interface of Computing and Biology Beyond studies of protein structure is the problem of describing a solvent environment (such as water) and its influence on a protein’s conformational behavior. The importance of hydration in protein stability and folding is widely accepted. Models are needed to incorporate the effects of solvents in protein three-dimensional structure. 4.4.10 Protein Identification and Quantification from Mass Spectrometry A second important problem in proteomics is protein identification and quantification. That is, given a particular biological sample, what specific proteins are present and in what quantities? This problem is at the heart of studying protein–protein interactions at proteomic scale, mapping various organelles, and generating quantitative protein profiles from diverse species. Making inferences about protein identification and abundance in biological samples is often challenging, because cellular proteomes are highly complex and because the proteome generally involves many proteins at relatively low abundances. Thus, highly sensitive analytical techniques are necessary. Today, techniques based on mass spectrometry increasingly fill this need. The mass spectrometer works on a biological sample in ionized gaseous form. A mass analyzer measures the mass-to-charge ratio (m/z) of the ionized analytes, and a detector measures the number of ions at each m/z value. In the simplest case, a procedure known as peptide mass fingerprinting (PMF) is used. PMF is based on the fact that a protein is composed of multiple peptide groups, and identification of the complete set of peptides will with high probability characterize the protein in question. After enzymatically breaking up the protein into its constituent peptides, the mass spectrometer is used to identify individual peptides, each of which has a known mass. The premise of PMF is that only a very few (one in the ideal case) proteins will correspond to any particular set of peptides, and protein identification is effected by finding the best fit of the observed peptide masses to the calculated masses derived from, say, a sequence database. Of course, the “best fit” is an algorithmic issue, and a variety of approaches have been taken to determine the most appropriate algorithms. The applicability of PMF is limited when samples are complex (that is, when they involve large numbers of proteins at low abundances). The reason is that only a small fraction of the constituent peptides are typically ionized, and those that are observed are usually from the dominant proteins in the mixture. Thus, for complex samples, multiple (tandem) stages of mass spectrometry may be necessary. In a typical procedure, peptides from a database are scored on the likelihood of their generating a tandem mass spectrum, and the top scoring peptide is chosen. This computational approach has shown great success, and contributed to the industrialization of proteomics. However, much remains to be done. First, the generation of the spectrum is a stochastic process governed by the peptide composition, and the mass spectrometer. By mining data to understand these fragmentation propensities, scoring and identification can be further improved. Second, if the peptide is not in the database, de novo or homology-based methods must be developed for identification. Many proteins are post-translationally modified, with the modifications changing the mass composition. Enumeration and scoring of all modifications leads to a combinatorial explosion that must be addressed using novel computational techniques. It is fair to say that computation will play an important role in the success of mass spectrometry as the tool of choice for proteomics. Mass spectrometry is also coming into its own for protein expression studies. The major problem here is that the intensity of a peak depends not only on the peptide abundance, but also on the physicochemical properties of the peptide. This makes it difficult to measure expression levels directly. However, relative abundance can be measured using the proven technique of stable-isotope dilution. This method makes use of the facts that pairs of chemically identical analytes of different stable-isotope composition can be differentiated in a mass spectrometer owing to their mass difference, and that the ratio of signal intensities for such analyte pairs accurately indicates the abundance ratio for the two analytes. This approach shows great promise. However, computational methods are needed to correlate data across different experiments. If the data were produced using liquid chromatography coupled with
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Catalyzing Inquiry at the Interface of Computing and Biology mass spectrometry, a peptide pair could be approximately labeled by its retention time in the column, and its mass-to-charge ratio. Such pairs can be matched across experiments using geometric matching. Combining the relative abundance levels from different experiments using statistical methods will greatly help in improving the reliability of this approach. 4.4.11 Pharmacological Screening of Potential Drug Compounds139 The National Cancer Institute (NCI) has screened more than 60,000 compounds against a panel of 60 human cancer cell lines. The extent to which any single compound inhibits growth in any given cell line is simply one data point relevant to that compound-cell line combination—namely the concentration associated with a 50 percent inhibition in the growth of that cell line. However, the pattern of such values across all 60 cell lines can provide insight into the mechanisms of drug action and drug resistance. Combined with molecular structure data, these activity patterns can be used to explore the NCI database of 460,000 compounds for growth-inhibiting effects in these cell lines, and can also provide insight into potential target molecules and modulators of activity in the 60 cell lines. Based on this approach, five compounds have been screened in this manner and selected for entry into clinical trials. This approach to drug discovery and molecular pharmacology serves a number of useful functions. According to Weinstein et al., It suggests novel targets and mechanisms of action or modulation. It detects inhibition of integrated biochemical pathways not adequately represented by any single molecule or molecular interaction. (This feature of cell-based assays is likely to be more important in the development of therapies for cancer than it is for most other diseases; in the case of cancer, one is fighting the plasticity of a poorly controlled genome and the selective evolutionary pressures for development of drug resistance.) It provides candidate molecules for secondary testing in biochemical assays; conversely, it provides a well-characterized biological assay in vitro for compounds emerging from biochemical screens. It ‘‘fingerprints’’ tested compounds with respect to a large number of possible targets and modulators of activity. It provides such fingerprints for all previously tested compounds whenever a new target is assessed in many or all of the 60 cell lines. (In contrast, if a battery of assays for different biochemical targets were applied to, for example, 60,000 compounds, it would be necessary to retest all of the compounds for any new target or assay.) It links the molecular pharmacology with emerging databases on molecular markers in microdissected human tumors—which, under the rubric of this article, constitute clinical (C) databases. It provides the basis for pharmacophore development and searches of an S [structure] database for additional candidates. If an agent with a desired action is already known, its fingerprint patterns of activity can be used by … [various] pattern-recognition technologies to find similar compounds. Box 4.6 provides an example of this approach. 4.4.12 Algorithms Related to Imaging Biological science is rich in images. Most familiar are images taken through optical microscopes, but there are many other imaging modalities—electron microscopes, computed tomography scans, X-rays, magnetic resonance imaging, and so on. For most of the history of life science research, images have 139 Section 4.4.11 is based heavily on J.N. Weinstein, T.G. Myers, P.M. O’Connor, S.H. Friend, A.J. Fornace, Jr., K.W. Kohn, T. Fojo, et al., “An Information-Intensive Approach to the Molecular Pharmacology of Cancer,” Science 275(5298):343-349, 1997.
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Catalyzing Inquiry at the Interface of Computing and Biology Box 4.6 An Information-intensive Approach to Cancer Drug Discovery Given one compound as a “seed,” [an algorithm known as] COMPARE searches the database of screened agents for those most similar to the seed in their patterns of activity against the panel of 60 cell lines. Similarity in pattern often indicates similarity in mechanism of action, mode of resistance, and molecular structure…. A formulation of this approach in terms of three databases [includes databases for] the activity patterns [A], … molecular structural features of the tested compounds [S], and … possible targets or modulators of activity in the cells [T]…. The (S) database can be coded in terms of any set of two-dimensional (2D) or 3D molecular structure descriptors. The NCI’s Drug Information System (DIS) contains chemical connectivity tables for approximately 460,000 molecules, including the 60,000 tested to date. 3-D structures have been obtained for 97% of the DIS compounds, and a set of 588 bitwise descriptors has been calculated for each structure by use of the Chem-X computational chemistry package. This data set provides the basis for pharmacophoric searches; if a tested compound, or set of compounds, is found to have an interesting pattern of activity, its structure can be used to search for similar molecules in the DIS database. In the target (T) database, each row defines the pattern (across 60 cell lines) of a measured cell characteristic that may mediate, modulate, or otherwise correlate with the activity of a tested compound. When the term is used in this general shorthand sense, a “target” may be the site of action or part of a pathway involved in a cellular response. Among the potential targets assessed to date are oncogenes, tumor-suppressor genes, drug resistance-mediating transporters, heat shock proteins, telomerase, cytokine receptors, molecules of the cell cycle and apoptotic pathways, DNA repair enzymes, components of the cytoarchitecture, intracellular signaling molecules, and metabolic enzymes. In addition to the targets assessed one at a time, others have been measured en masse as part of a protein expression database generated for the 60 cell lines by 2D polyacrylamide gel electrophoresis. Each compound displays a unique “fingerprint” pattern, defined by a point in the 60D space (one dimension for each cell line) of possible patterns. In information theoretic terms, the transmission capacity of this communication channel is very large, even after one allows for experimental noise and for biological realities that constrain the compounds to particular regions of the 60D space. Although the activity data have been accumulated over a 6-year period, the experiments have been reproducible enough to generate … patterns of coherence. SOURCE: Reprinted by permission from J.N. Weinstein, T.G. Myers, P.M. O’Connor, S.H. Friend, A.J. Fornace, Jr., K.W. Kohn, T. Fojo, et al., “An Information-intensive Approach to the Molecular Pharmacology of Cancer,” Science 275(5298):343-349, 1997. Copyright 1997 AAAS. been a source of qualitative insight.140 While this is still true, there is growing interest in using image data more quantitatively. Consider the following applications: Automated identification of fungal spores in microscopic digital images and automated estimation of spore density;141 Automated analysis of liver MRI images from patients with putative hemochromatosis to determine the extent of iron overload, avoiding the need for an uncomfortable liver biopsy;142 140 Note also that biological imaging itself is a subset of the intersection between biology and visual techniques. In particular, other biological insight can be found in techniques that consider spectral information, e.g., intensity as a function of frequency and perhaps a function of time. Processing microarray data (discussed further in Section 7.2.1) ultimately depends on the ability to extract interesting signals from patterns of fluorescing dots, as does quantitative comparison of patterns obtained in two-dimensional polyacrylamide gel electrophoresis. (See S. Veeser, M.J. Dunn, and G.Z. Yang, “Multiresolution Image Registration for Two-dimensional Gel Electrophoresis,” Proteomics 1(7):856-870, 2001, available at http://vip.doc.ic.ac.uk/2d-gel/2D-gel-final-revision.pdf.) 141 T. Bernier and J.A. Landry, “Algorithmic Recognition of Biological Objects,” Canadian Agricultural Engineering 42(2):101-109, 2000. 142 George Reeke, Rockefeller University, personal communication to John Wooley, October 8, 2004.
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Catalyzing Inquiry at the Interface of Computing and Biology Box 4.7 The Open Microscopy Environment1 Responding to the need to manage a large number of multispectral movies of mitotic cells in the late 1990s, Sorger and Swedlow began work on the open microscopy environment (OME). The OME is designed as infrastructure that manages optical microscopy images, storing both the primary image data and appropriate metadata on those images, including data on the optics of the microscope, the experimental setup and sample, and information derived by analysis of the images. OME also permits data federation that allows information from multiple sources (e.g., genomic or chemical databases) to be linked to image records. In addition, the OME provides an extensible environment that enables users to write their own applications for image analysis. Consider, for example, the task of tracking labeled vesicles in a time-lapse movie. As noted by Swedlow et al., this problem requires the following: a segmentation algorithm to find the vesicles and to produce a list of centroids, volumes, signal intensities, and so on; a tracker to define trajectories by linking centroids at different time points according to a predetermined set of rules; and a viewer to display the analytic results overlaid on the original movie.2 OME provides a mechanism for linking together various analytical modules by specifying data semantics that enable the output of one module to be accepted as input to another. These semantic data types of OME describe analytic results such as “centroid,” “trajectory,” and “maximum signa,” and allow users, rather than a predefined standard, to define such concepts operationally, including in the machine-readable definition and the processing steps that produce it (e.g., the algorithm and the various parameter settings used). 1 See www.openmicroscopy.org. 2 J.R. Swedlow, I. Goldberg, E. Brauner, and P.K. Sorger, “Informatics and Quantitative Analysis in Biological Imaging,” Science 300(5616):100-102, 2003. SOURCE: Based largely on the paper by Swedlow et al. cited in Footnote 145 and on the OME Web page at www.openmicroscopy.org. Fluorescent speckle microscopy, a technique for quantitatively tracking the movement, assembly, and disassembly of macromolecules in vivo and in vitro, such as those involved in cytoskeleton dynamics;143 and Establishing metrics of similarity between brain images taken at different times.144 These applications are only an infinitesimal fraction of those that are possible. Several research areas associated with increasing the utility of biological images are discussed below. Box 4.7 describes the open microscopy environment, an effort intended to automate image analysis, modeling, and mining of large sets of biological images obtained from optical microscopy.145 As a general rule, biologists need to develop better imaging methods that are applicable across the entire spatial scale of interest, from the subcellular to the organismal. (In this context, “better” means imaging that occurs in real time (or nearly so) with the highest possible spatial and temporal resolution.) These methods will require new technologies (such as the multiphoton microscope) and also new protein and nonprotein reporter molecules that can be expressed or introduced into cells or organisms. 143 C.M. Waterman-Storer and G. Danuser, “New Directions for Fluorescent Speckle Microscopy,” Current Biology 12(18):R633-R640, 2002. 144 M.I. Miller, A. Trouve, and L. Younes, “On the Metrics and Euler-Lagrange Equations of Computational Anatomy,” Annual Review of Biomedical Engineering 4:375-405, 2002, available at http://www.cis.jhu.edu/publications/papers_in_database/EulerLagrangeEqnsCompuAnatomy.pdf. 145 J.R. Swedlow, I. Goldberg, E. Brauner, and P.K. Sorger, “Informatics and Quantitative Analysis in Biological Imaging,” Science 300(5616):100-102, 2003.
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Catalyzing Inquiry at the Interface of Computing and Biology The discussion below focuses only on a narrow slice of the very general problem of biological imaging, as a broader discussion would go beyond the scope of this report. 184.108.40.206 Image Rendering146 Images have been central to the study of biological phenomena ever since the invention of the microscope. Today, images can be obtained from many sources, including tomography, MRI, X-rays, and ultrasound. In many instances, biologists are interested in the spatial and geometric properties of components within a biological entity. These properties are often most easily understood when viewed through an interactive visual representation that allows the user to view the entity from different angles and perspectives. Moreover, a single analysis or visualization session is often not sufficient, and processing across many image volumes is often required. The requirement that a visual representation be interactive places enormous demands on the computational speed of the imaging equipment in use. Today, the data produced by imaging equipment are quickly outpacing the capabilities offered by the image processing and analysis software currently available. For example, the GE EVS-RS9 CT scanner is able to generate image volumes with resolutions in the 20-90 mm range, which results in a dataset size of multiple gigabytes. Datasets of such size require software tools specifically designed for the imaging datasets of today and tomorrow (see Figure 4.5) so that researchers can identify subtle features that can otherwise be missed or misrepresented. Also with increasing dataset resolution comes increasing dataset size, which translates directly to lengthening dataset transfer, processing, and visualization times. New algorithms that take advantage of state-of-the-art hardware in both relatively inexpensive workstations and multiprocessor supercomputers must be developed and moved into easy-to-access software systems for the clinician and researcher. An example is ray-tracing, a method commonly used in computer graphics that supports highly efficient implementations on multiple processors for interactive visualization. The resulting volume rendition permits direct inspection of internal structures, without a precomputed segmentation or surface extraction step, through the use of multidimensional transfer functions. As seen in the visualizations in Figure 4.6, the resolution of the CT scan allows subtleties such as the definition of the cochlea, the modiolus, the implanted electrode array, and the lead wires that connect the array to a head-mounted connector. The co-linear alignment of the path of the cochlear nerve with the location of the electrode shanks and tips is the necessary visual confirmation of the correct surgical placement of the electrode array. In both of the studies described in Figure 4.5 and Figure 4.6, determination of three-dimensional structure and configuration played a central role in biological inquiry. Volume visualization created detailed renderings of changes in bone morphology due to a Pax3 mutation in mice, and it provided visual confirmation of the precise location of an electrode array implanted in the feline skull. The scientific utility of volume visualization will benefit from further improvements in its interactivity and flexibility, as well as simultaneous advances in high-resolution image acquisition and the development of volumetric image-processing techniques for better feature extraction and enhancement. 220.127.116.11 Image Segmentation147 An important problem in automated image analysis is image segmentation. Digital images are recorded as a set of pixels in a two- or three-dimensional array. Images that represent natural scenes usually contain different objects, so that, for example, a picture of a park may depict people, trees, and 146 Section 18.104.22.168 is based on material provided by Chris Johnson, University of Utah. 147 Section 22.214.171.124 is adapted from and includes excerpts from National Research Council, Mathematics and Physics of Emerging Biomedical Imaging, National Academy Press, Washington, DC, 1996.
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Catalyzing Inquiry at the Interface of Computing and Biology FIGURE 4.5 Visualizations of mutant (left) and normal (right) mice embryos. CT values are inspected by maximum intensity projection in (a) and with standard isosurface rendering in (b). Volume rendering (c) using multidimensional opacity functions allows more accurate bone emphasis, depth cueing, and curvature-based transfer functions to enhance bone contours in image space. In this case, Drs. Keller and Capecchi are investigating the birth defects caused by a mutation in the Pax3 gene, which controls musculoskeletal development in mammalian embryos. In their model, they have activated a dominantly acting mutant Pax3 gene and have uncovered two of its effects: (1) abnormal formation of the bones of the thoracolumbar spine and cartilaginous rib cage and (2) cranioschisis, a more drastic effect in which the dermal and skeletal covering of the brain is missing. Imaging of mutant and normal mouse embryos was performed at the University of Utah Small Animal Imaging Facility, producing two 1.2 GB 16-bit volumes of 769 × 689 × 1173 samples, with resolution of 21 × 21 × 21 microns. SOURCE: Courtesy of Chris Johnson, University of Utah; see also http://www.sci.utah.edu/stories/2004/spr_imaging.html.
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Catalyzing Inquiry at the Interface of Computing and Biology FIGURE 4.6 Volume renderings of electrode array implanted in feline skull. In this example, scanning produced a 131 MB 16-bit volume of 425 × 420 × 385 samples, with resolution of 21 × 21 × 21 microns. Renderings of the volume were generated using a ray-tracing algorithm across multiple processors allowing interactive viewing of this relatively large dataset. The resolution of the scan allows definition of the shanks and tips of the implanted electrode array. Volumetric image processing was used to isolate the electrode array from the surrounding tissue, highlighting the structural relationship between the implant and the bone. There are distinct CT values for air, soft tissue, bone, and the electrode array, enabling the use of a combination of ray tracing and volume rendering to visualize the array in the context of the surrounding structures, specifically the bone surface. The volume is rotated gradually upward in columns (a), (b), and (c), from seeing the side of the cochlea exterior in (a), to looking down the path of the cochlear nerve in (c). From top to bottom, each row uses different rendering styles: (1), summation projections of CT values (green) and gradients (magenta); (2), volume renderings with translucent bone, showing the electrode leads in magenta. SOURCE: Courtesy of Chris Johnson, University of Utah; see also http://www.sci.utah.edu/stories/2004/spr_imaging.html. benches. Similarly, a scanned image of a magazine page may contain text and graphics (e.g., a picture of a park). Segmentation refers to the process by which an object (or characteristics of the object) in an image is extracted from image data for purposes of visualization and measurement. (Extraction means that the pixels associated with the object of interest are isolated.) In a biological context, a typical problem in image segmentation might involve extracting different organs in a CT scan of the body. Segmentation research involves the development of automatic, computer-executable rules that can isolate enough of these pixels to produce an acceptably accurate segmentation. Segmentation is a central problem of image analysis because segmentation must be accomplished before many other interesting
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Catalyzing Inquiry at the Interface of Computing and Biology problems in image analysis can be solved, including image registration, shape analysis, and volume and area estimation. A specific laboratory example would be the segmentation of spots on two-dimensional electrophoresis gels. There is no common method or class of methods applicable to even the majority of images. Segmentation is easiest when the objects of interest have intensity or edge characteristics that allow them to be separated from the background and noise, as well as from each other. For example, an MRI image of the human body would be relatively easy to segment for bones: all pixels with intensity below a given threshold would be eliminated, leaving mostly the pixels associated with high-signal-intensity bone. Generally, edge detection depends on a search for intensity gradients. However, it is difficult to find gradients when, as is usually the case in biomedical images, intensities change only gradually between the structure of interest and the surrounding structure(s) from which it is to be extracted. Continuity and connectivity are important criteria for separating objects from noise and have been exploited quite widely. A number of different approaches to image segmentation are described in more detail by Pham et al.148 126.96.36.199 Image Registration149 Different modes of imaging instrumentation may be used on the same object because they are sensitive to different object characteristics. For example, an X-ray of an individual will produce different information than a CT scan. For various purposes, and especially for planning surgical and radiation treatment, it can be important for these images to be aligned with each other, that is, for information from different imaging modes to be displayed in the same locations. This process is known as image registration. There are a variety of techniques for image registration, but in general they can be classified based on the features that are being matched. For example, such features may be external markers that are fixed (e.g., on a patient’s body), internal anatomic markers that are identifiable on all images, the center of gravity for one or more objects in the images, crestlines of objects in the images, or gradients of intensity. Another technique is minimization of the distance between corresponding surface points of a predefined object. Image registration often depends on the identification of similar structures in the images to be registered. In the ideal case, this identification can be performed through an automated segmentation process. Image registration is well defined for rigid objects but is more complicated for deformable objects or for objects imaged from different angles. When soft tissue deforms (e.g., because a patient is lying on his side rather than on his back), elastic warping is required to transform one dataset into the other. The difficulty lies in defining enough common features in the images to enable specifying appropriate local deformations. An example of an application in which image registration is important is the Cell-Centered Database (CCDB).150 Launched in 2002, the CCDB contains structural and protein distribution information derived from confocal, multiphoton, and electron microscopy for use by the structural biology and neuroscience communities. In the case of neurological images, most of the imaging data are referenced to a higher level of brain organization by registering their location in the coordinate system of a standard brain atlas. Placing data into an atlas-based coordinate system provides one method by which data taken across scales 148 D.L. Pham, C. Xu, and J.L. Prince, “Current Methods in Medical Image Segmentation,” Annual Review of Biomedical Engineering 2:315-338, 2000. 149 Section 188.8.131.52 is adapted from National Research Council, Mathematics and Physics of Emerging Biomedical Imaging, National Academy Press, Washington, DC, 1996. 150 See M.E. Martone, S.T. Peltier, and M.H. Ellisman, “Building Grid Based Resources for Neurosciences,” unpublished paper 2003, National Center for Microscopy and Imaging Research, Department of Neurosciences, University of California, San Diego, San Diego, CA, and http://ccdb.ucsd.edu/CCDB/about.shtml.
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Catalyzing Inquiry at the Interface of Computing and Biology and distributed across multiple resources can be compared reliably. Through the use of atlases and tools for surface warping and image registration, it is possible to express the location of anatomical features or signals in terms of a standardized and quantitative coordinate system, rather by using terms that describe objects in the field of view. The expression of brain data in terms of atlas coordinates also allows it to be transformed spatially to provide alternative views that may offer additional information (e.g., flat maps or additional parcellation schemes). Finally, a standard coordinate system allows the same brain region to be sampled repeatedly to allow data to be accumulated over time. 184.108.40.206 Image Classification Image classification is the process through which a set of images can be sorted into meaningful categories. Categories can be defined through low-level features such as color mix and texture patterns or through high-level features such as objects depicted. As a rule, low-level features can be computed with little difficulty, and a number of systems have been developed that take advantage of such features.151 However, users are generally much more interested in semantic content that is not easily represented in such low-level features. The easiest method to identify interesting semantic content is simply to annotate an image manually with text, although this process is quite tedious and is unlikely to capture the full range of content in an image. Thus, automated techniques hold considerable interest. The general problem of automatic identification of such image content has not been solved. One approach described by Huang et al. relies on supervised learning to classify images hierarchically.152 This approach relies on using good low-level features and then performing feature-space reconfiguration using singular value decomposition to reduce noise and dimensionality. A hierarchical classification tree can be generated from training data and subsequently used to sort new images into categories. A second approach is based on the fact that biological images often contain branching structures. (For example, both muscle and neural tissue contain blood vessels and dendrites that are found in branching structures.) The fractal dimensionality of such structures can then be used as a measure of similarity, and images that contain structures of similar fractal dimension can be grouped into categories.153 4.5 DEVELOPING COMPUTATIONAL TOOLS The computational tools described above were once gleams in the eye of some researcher. Despite the joy and satisfaction felt when a prototype program supplies the first useful results to its developer, it is a long, long way to converting that program into a genuine product that is general, robust, and useful to others. Indeed, in his classic text The Mythical Man-Month (Addison-Wesley, Reading, MA, 1995), Frederick P. Brooks, Jr., estimates the difference in effort necessary to create a programming systems product from a program as an order of magnitude. Some of the software engineering considerations necessary to turn a program into a product include the following: Quality. The program, of course, must be as free of defects as possible, not only in the sense of running without faults, but also of precisely implementing the stated algorithm. It must be tested for all 151 See, for example, M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele, and P. Yanker, “Query by Image and Video Content: The QBIC System,” IEEE Computer 28(9):23-32, 1995, available at http://wwwqbic.almaden.ibm.com/. 152 J. Huang, S.R. Kumar, and R. Zabih, “An Automatic Hierarchical Image Classification Scheme,” ACM Conference on Multimedia, Bristol, England, September 1998. A revised version appears in EURASIP Journal on Applied Signal Processing, 2003, available at http://www.cs.cornell.edu/rdz/Papers/Archive/mm98.pdf. 153 D. Cornforth, H. Jelinek, and L. Peich, “Fractop: A Tool for Automated Biological Image Classification,” available at http://csu.edu.au/~dcornfor/Fractop_v7.pdf.
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Catalyzing Inquiry at the Interface of Computing and Biology potential inputs, and combinations of factors, and must be robust even in the face of invalid usage. The program should have well-understood and bounded resource demands, including memory, input-output, and processing time. Maintenance. When bugs are discovered, they must be tracked, patched, and provided to users. This often means that the code should be structured for maintainability; for example, Perl, which is extremely powerful, is often written in a way that is incomprehensible to programmers other than the author (and often even to the author). Differences in functionality between versions must be documented carefully. Documentation. If the program is to be usable by others, all of the functionality must be clearly documented, including data file formats, configuration options, output formats, and of course program usage. If the source code of the program is made available (as is often the case with scientific tools), the code must be documented in such a way that users can check the validity of the implementation as well as alter it to meet their needs. User interface. The program must have a user interface, although not necessarily graphical, that is unambiguous and able to access the full range of functions of the program. It should be easy to use, difficult to make mistakes, and clear in its instructions and display of state. System integration and portability. The program must be distributed to users in a convenient way, and be able to run on different platforms and operating systems in a way that does not interfere with existing software or system settings. It should be easily configurable and customizable for particular requirements, and should install easily without access to specialized software, such as nonstandard compilers. General. The program should accept a wide selection of data types, including common formats, units, precisions, ranges, and file sizes. The internal coding interfaces should have precisely defined syntax and semantics, so that users can easily extend the functionality or integrate it into other tools. Tool developers address these considerations to varying degrees, and users may initially be more tolerant of something that is more program than product if the functionality it confers is essential and unique. Over time, however; such programs will eventually become more product-like because users will not tolerate significant inconvenience. Finally, there is an issue of development methodology. A proprietary approach to development can be adopted for a number of competitive reasons, ranging from the ultimate desire to reap financial benefit to staying ahead of competing laboratories. Under a proprietary approach, source code for the tools would be kept private, so that potential competitors would be unable to exploit the code easily for their own purposes. (Source code is needed to make changes to a program.) An open approach to development calls for the source code to be publicly available, on the theory that broad community input strengthens the utility of the tools being made available and better enables one team to build on another team’s work.
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