Search Results

This chapter reviews the biomedical and public health developments that will influence biostatistical research and practice in the near future, such as advances in molecular biology, and measuring DNA sequences and gene and protein expression levels. It is argued that the success of biostatistics will derive largely from a model-based approach, which uses and applies the principle of conditioning. Statistical models and inferences that are central to this model-based approach are described and contrasted with computationally-intensive strategies and a design-based approach. Increasingly complex models, different sources of uncertainty, and clustered observational units are viewed as future challenges for the model-based approach. Causal inference and statistical computing are discussed as topics believed to be central to biostatistics in the near future.

This chapter presents an overview of evolving methods for tracking and compiling information on genetic factors in disease. It discusses bioinformatics, the Human Genome Epidemiology Network (HuGENet), and genomic databases. Building the knowledge base in human genome epidemiology involves organizing, sharing, mining, interpreting, and evaluating the results of genomic research from a population perspective. This effort faces many technical, scientific, and social challenges, which can be met only by unprecedented levels of interaction across multiple levels of the research enterprise, and by cooperation among individual scientists, research groups, institutions, and agencies.

An extensive summary is provided on the methods and methodology available to analyze and image total metals in biological systems as well as to assess their speciation, in particular in metalloproteomes and as free metal ion concentrations. Discussion focuses on instrumental methods for analysis and separation and how they are complemented by genetic and bioinformatics approaches. The treatment of methods follows increased complexity and experimental challenges: from lysates and fluids, to cells and tissues, to living cells and animals, and finally to the prospects of applying extent technology to investigations in humans. Presently, method sensitivity is not sufficient for analysis of metals in the pathogen in vivo. Future analytical needs are presented to address metal ions in host–pathogen interactions for diagnosis and treatment of infectious disease. The material is presented in the context of both redistribution of metals in the host and known mechanisms of warfare to acquire the transition metal ions that are essential for growth and survival of either host or pathogen.

This chapter describes a range of methods for solving the phase problem when a structure that is similar to the one being analysed has already been determined. These methods generally rely on comparing the Patterson function of the unknown structure (calculated from its diffraction intensities) with that calculated from the known structure. For computational expedience, the comparison of the two Patterson functions is done in two stages known as the rotation function and the translation function. In the rotation function, the Patterson of the search model is rotated through various angles and compared with the target Patterson — the orientation which gives the highest correlation with the target Patterson indicates a likely solution. The correct position of the search model within the target unit cell can then be determined essentially by calculating the inter-molecular vectors at a series of trial positions and comparing them with the target Patterson — this is known as the translation function. The variables affecting these calculations are discussed and methods for verifying the results are described, as are recent developments in bioinformatics which can be exploited to optimise the search model.

The role of integrative approaches to supportive care in urology is as controversial today as at any time in the history of medicine. At present, however, people are witnessing the emergence of a new, unified scientific foundation that brings together the best of modern molecular medicine with the bioinformatics of integrative medicine. This chapter outlines a new bioinformatics model of integrative medicine that complements and is consistent with the molecular dynamics of modern medicine on the genomic, proteomic, physiological, and psychological levels. Much of integrative medicine remains controversial because the typical outcome research purporting to validate the top-down approaches of integrative medicine do not include the entire four-stage bioinformatics cycle of modern molecular medicine. Clinical-experimental research programs that document the efficacy of integrative medicine with the currently emerging standards of validation via genomic and proteomic microarray technology are now needed.

SAS is most powerful when it integrates results from other techniques such as high-resolution X-ray crystallography, nuclear magnetic resonance, electron microscopy or hydrodynamic studies such as analytical ultracentrifugation or light-scattering. A brief overview of some complementary techniques is presented describing the parameters which can be determined and how they complement the information on structure and structural changes obtained from SAS. The role of bioinformatic methods in SAS structure analysis is also discussed, as are aspects of experimental automation and the developments in high-throughput and microfluidic techniques, particularly in SAXS. Finally, recognising the lack of unique solutions to modelling SAS data, a critical discussion of SAS model validation is presented.

This chapter examines how bioinformatics can be used to advance citizen science engagement opportunities that provide the framework for research, education, and dissemination of information at global scales. It discusses the practical aspects of creating a sound cyberinfrastructure that will serve as the foundation for developing large-scale citizen science projects. It also considers various strategies for creating novel and enduring applications for delivering citizen science and its data to project participants, professional scientists, and managers over the Internet. Finally, it describes techniques for data management and archiving and explains how the Cornell Lab of Ornithology makes data discoverable via metadata. The chapter provides examples of how bioinformatics and cyberinfrastructure resources make it possible for contributors to explore, synthesize, and visualize citizen science data and for professional scientists to carry out complex analyses on the same data sets.

Our world is digitized. Thanks to pioneers such as Turing, Shannon, and von Neumann, we have the amazing technologies of today. We have strong mathematical foundations for remarkable silicon machines that run amazing software. We have connections to everyone in the world through easy-to- use interfaces. We even have intelligent machines that help us in ways we could never have dreamed. The journey has only just begun. Every year new pioneers exploit the technologies of computer science for startling and imaginative applications. Artists and musicians use computers to entertain us, biologists use computers to understand us, doctors use computers to heal us. We even use computers to protect ourselves from crime. It’s an exciting time. . . . The young artist enters the large room, rolls of drawings under his arm. There are twelve large cube-shaped computer monitors on desks surrounding him, apparently connected to mainframe computers on the floor below. Blinds cover all the windows, darkening the room, and allowing strange graphics on the screens to shine brightly. The artist nervously unrolls his drawings on the floor of the computer lab, filling the space with his carefully drawn renderings. One large sheet looks like a strange abstract swirling piece of Hindu art with snakes or tentacles fl owing outwards from the centre. Another looks like a weird bug-collector’s display with a myriad differently shaped bugs placed randomly—except that similar bugs are always next to each other. Another looks like an evolutionary tree of abstract shapes, from Viking helmets to beehives, each morphing into another. The watching group of scientists and computer programmers have never seen anything quite like this. The artist had struggled to gain acceptance for his ideas from his own community. The art world was not ready for his use of computers to help generate his art. Would this audience be any different? William Latham looks up at his audience of scientists, and begins to explain his work. Latham was no ordinary artist. Born in 1961, he grew up in Blewbury, England. It was a rural setting, but because of the nearby Harwell Atomic Energy Research Establishment, he came into contact with scientists throughout his childhood.

The Human Genome Project was launched in the early 1990s to map, sequence, and study the function of genomes derived from humans and a number of model organisms such as mouse, rat, fruit fly, worm, yeast, and Escherichia coli. This ambitious project was made possible by advances in high-speed DNA sequencing technology (Hunkapiller et al., 1991). To date, the Human Genome Project and other large-scale sequencing projects have been enormously successful. The complete genomes of several microbes (such as Hemophilus influenzae Rd, Mycoplasma genitalium, and Methanococcus jannaschii) have been completely sequenced. The genome of bacteriophage T4 is complete, and the 4.6-megabase sequence of E. coli and the 13-megabase genome of Saccharomyces cerevisiae have just recently also been completed. There are 71 megabases of the nematode Caenorhabditis elegans available. Six megabases of mouse and 60 megabases of human genomic sequence have been finished, which represent 0.2% and 2% of their respective genomes. Finally, more than 1 million expressed sequence tags derived from human and mouse complementary DNA expression libraries are publicly available. These public data, in addition to private and proprietary DNA sequence databases, represent an enormous information-processing challenge and data-mining opportunity. The need for common interfaces and query languages to access heterogeneous sequence databases is well documented, and several good systems are well underway to provide those interfaces (Woodsmall and Benson, 1993; Marr, 1996). Our own work on database and program interoperability in this domain and in computational chemistry (Gushing, 1995) has shown, however, that providing the interface is but the first step toward making these databases fully useful to the researcher. (Here, the term “database” means a collection of data in electronic form, which may not necessarily be physically deposited in a database management system [DBMS]. A scientist’s database could thus be a collection of flat files, where the term “database” means “data stored in a DBMS” is clear from the context.) Deciphering the genomes of sequenced organisms falls into the realm of analysis; there is now plenty of sequence data. The most common form of sequence analysis involves the identification of homologous relationships among similar sequences.

Genomic information (nucleic acid and amino acid sequences) completely determines the characteristics of the nucleic acid and protein molecules that express a living organism’s function. One of the greatest challenges in which computation is playing a role is the prediction of higher order structure from the one-dimensional sequence of genes. Rules for determining macromolecule folding have been continually evolving. Specifically in the case of RNA (ribonucleic acid) there are rules and computer algorithms/systems (see below) that partially predict and can help analyze the secondary and tertiary interactions of distant parts of the polymer chain. These successes are very important for determining the structural and functional characteristics of RNA in disease processes and hi the cell life cycle. It has been shown that molecules with the same function have the potential to fold into similar structures though they might differ in their primary sequences. This fact also illustrates the importance of secondary and tertiary structure in relation to function. Examples of such constancy in secondary structure exist in transfer RNAs (tRNAs), 5s RNAs, 16s RNAs, viroid RNAs, and portions of retroviruses such as HIV. The secondary and tertiary structure of tRNA Phe (Kim et al., 1974), of a hammerhead ribozyme (Pley et al., 1994), and of Tetrahymena (Cate et al., 1996a, 1996b) have been shown by their crystal structure. Currently little is known of tertiary interactions, but studies on tRNA indicate these are weaker than secondary structure interactions (Riesner and Romer, 1973; Crothers and Cole, 1978; Jaeger et al., 1989b). It is very difficult to crystallize and/or get nuclear magnetic resonance spectrum data for large RNA molecules. Therefore, a logical place to start in determining the 3D structure of RNA is computer prediction of the secondary structure. The sequence (primary structure) of an RNA molecule is relatively easy to produce. Because experimental methods for determining RNA secondary and tertiary structure (when the primary sequence folds back on itself and forms base pairs) have not kept pace with the rapid discovery of RNA molecules and their function, use of and methods for computer prediction of secondary and tertiary structures have increasingly been developed.

The abstract operation of complex natural processes is often expressed in terms of networks of computational components such as Boolean logic gates or artificial neurons. The interaction of biological molecules and the flow of information controlling the development and behavior of organisms is particularly amenable to this approach, and these models are well established in the biological community. However, only relatively recently have papers appeared proposing the use of such systems to perform useful, human-defined tasks. Rather than merely using the network analogy as a convenient technique for clarifying our understanding of complex systems, it is now possible to harness the power of such systems for the purposes of computation. The purpose of this volume is to discuss such work. In this introductory chapter we place this work in historical context and provide an introduction to some of the underlying molecular biology. We then introduce recent developments in the field of cellular computing. Despite the relatively recent emergence of molecular computing as a distinct research area, the link between biology and computer science is not a new one. Of course, for years biologists have used computers to store and analyze experimental data. Indeed, it is widely accepted that the huge advances of the Human Genome Project (as well as other genome projects) were only made possible by the powerful computational tools available to them. Bioinformatics has emerged as the science of the 21st century, requiring the contributions of truly interdisciplinary scientists who are equally at home at the lab bench or writing software at the computer. However, the seeds of the relationship between biology and computer science were sown long ago, when the latter discipline did not even exist. When, in the 17th century, the French mathematician and philosopher René Descartes declared to Queen Christina of Sweden that animals could be considered a class of machines, she challenged him to demonstrate how a clock could reproduce. Three centuries later, with the publication of The General and Logical Theory of Automata [19] John von Neumann showed how a machine could indeed construct a copy of itself.

They obey our instructions with unlimited patience. They store the world’s knowledge and make it accessible in a split second. They are the backbone of modern society. Yet they are largely ignored. Computers. They comprise our crowning achievements to date, the pinnacle of all tools. Computer processors and software represent the most complex designs humans have ever created. The science of computers has enabled one of the most extraordinary transformations of our societies in human history. . . . You switch on your computer and launch the Internet browser. A one-word search for ‘pizza’ finds a list of pizza restaurants in your area. One click with the mouse and you are typing in your address to see if this restaurant delivers. They do! And they also allow you to order online. You choose the type of pizza you feel like, adding your favourite toppings. The restaurant even allows you to pay online, so you type in your credit card number, your address, and the time you’d like the delivery. You choose ‘as soon as possible’ and click ‘pay’. Just thirty-five minutes later there is a knock on your door. The pizza is here, smelling delicious. You tip the delivery guy and take the pizza to your table to eat. Ordering pizza is nothing unusual for many of us around the world. Although it may seem surprising, this increasingly common scenario with cheap prices, fast delivery, and access to such variety of food for millions of customers is only possible because of computers. In the situation above you might have spotted just one computer. If we take a look behind the scenes, the number of computers involved in bringing your pizza is astonishing. When you switched on your computer, you actually powered up many computers that all work together to make the display, mouse, keyboard, broadband, and main computer operate. Your computer linked itself to the Internet—which is a worldwide network of computers— with the help of computers of the phone company and Internet service provider. When you searched for ‘pizza’ the request was routed between several computers before reaching the search engine computers.

There is enormous interest in the biology of complex reaction systems, be it in metabolism, signal transduction, gene regulatory networks, protein synthesis, and many others. The field of the interpretation of experiments on such systems by application of the methods of information science, computer science, and biostatistics is called bioinformatics (see for a presentation of this subject). Part of it is an extension of the chemical approaches that we have discussed for obtaining information on the reaction mechanisms of complex chemical systems to complex biological and genetic systems. We present here a very brief introduction to this field, which is exploding with scientific and technical activity. No review is intended, only an indication of several approaches on the subject of our book, with apologies for the omission of vast numbers of publications. A few reminders: The entire complement of DNA molecules constitute the genome, which consists of many genes. RNA is generated from DNA in a process called transcription; the RNA that codes for proteins is known as messenger RNA, abbreviated tomRNA. Other RNAs code for functional molecules such as transfer RNAs, ribosomal components, and regulatory molecules, or even have enzymatic function. Protein synthesis is regulated by many mechanisms, including that for transcription initiation, RNA splicing (in eukaryotes), mRNA transport, translation initiation, post-translational modifications, and degradation of mRNA. Proteins perform perhaps most cellular functions. Advances in microarray technology, with the use of cDNA or oligonucleotides immobilized in a predefined organization on a solid phase, have led to measurements of mRNA expression levels on a genome-wide scale (see chapter 3). The results of the measurements can be displayed on a plot on which a row represents one gene at various times, a column the whole set of genes, and the time of gene expression is plotted along the axis of rows. The changes in expression levels, as measured by fluorescence, are indicated by colors, for example green for decreased expression, black for no change in expression, and red for increased expression. Responses in expression levels have been measured for various biochemical and physiological conditions. We turn now to a few methods of obtaining information on genomic networks from microarray measurements.