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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.

In his account of the history of numerical taxonomy, P. H. A. Sneath argued that Hennigian cladistics is a side issue that has not proven its value, and that numerical taxonomy is “the greatest advance in systematics since Charles Darwin or perhaps since Carl Linnaeus.” Phenetics and cladistics might very well be viewed as contrasting methodologies, situated within the larger phenomenon of numerical taxonomy. Comparing phenetics with cladistics can be rendered simple, relating to a concept of “naturalness”: phenetics discovers taxa (classification) via the parameter “overall similarity” derived from an assessment of “similarities,” cladistics discovers taxa (classification) via the parameter “relationship,” expressed in its simplest form as A(BC), derived from an assessment of particular homologies. A significant event in the development of numerical taxonomy was the publication of J. Felsenstein's book Inferring Phylogenies. Felsenstein's forty intervening years — the “statistical, computational, and algorithmic work” — amounts to the dismissal of classification and homology, reinventing and remodeling phenetics and christening it bioinformatics, thereby reducing the subject of systematics to a set of methodologies, a mere technique.

This chapter provides a high level overview of the field of bioinformatics, covering the main areas of interest in contemporary bioinformatics and introducing the pre-eminent software tools and databases used in the field. The importance of programmatic automation of bioinformatics tasks is highlighted, and the concept of building bioinformatics solutions is explained.

This chapter explores recent examples where experimental evolution, empowered by genomic technologies and increasingly informed by systems biology, has fundamentally advanced our understanding of adaptive evolution. It focuses on genomically enabled insights into the following issues: how prevalent adaptive parallelism is; what the relative contributions of structural versus regulatory mutations are to the adaptive process; whether trade offs inevitably result from pleiotropy; how and why loss of function occurs under strong selection; and how much and how often adaptations arise from large-scale structural changes, such as gene duplications, deletions, and translocations. The chapter also offers perspective on how whole-genome sequencing and bioinformatics have been and can be fruitfully applied in the context of experimental evolution.

This chapter describes how computers played a decisive role in the sequencing and understanding of the genomes of humans and other organisms. The discovery of the structure of DNA, by Watson and Crick, opened the way to a systematic approach to biology, based on the study of the way DNA encodes the proteins in all living cells. The area that became known as computational biology, or bioinformatics, aims at modelling, simulating, and understanding the behaviour of biological systems, using computer models and algorithms. Such a deep understanding will enable us to create computer models of cells and of entire organisms and even, in the future, open the doors to the creation of entirely new lifeforms, a field known as synthetic biology.

This chapter reviews innovative practices that have originated or been advanced in California for the protection of biological diversity. California has been one of the nation's leaders in initiating new ways of implementing endangered-species laws, carrying out conservation actions on public and private lands, using biodiversity information in planning conservation and mitigation actions, and adapting conservation plans to the reality of climate change. While they are not entirely successful, these important innovations recognize the value of California's unique biological diversity.

The Human Genome Project’s open-source ethos sat uneasily next to historic commitments to biomedical privacy rights. Google-backed personal genomics (PG) company 23andMe argued that citizens should possess the right to make their biological information public. State agencies disagreed, charging 23andMe with threatening privacy rights and failing to comply with important government standards that ensured the accurate transmission of information to citizens. Drawing on semi-structured interviews conducted between 2008 and 2012 with those who worked at PG companies, as well as fieldwork in Silicon Valley, the chapter argues that the ensuing debates revealed deeper contemporary struggles over how to constitute knowledge and justice in the midst of challenges to the credibility of dominant institutions. It considers what happens to liberalism’s historic commitment to the right to control—to own—one’s body when even bodily substrates enter open-source idioms. Drawing on Arendtian readings of the importance of both the private and public realm, it argues against casting one value out in favor of another, and for articulating notions of public and private that can respond to the postgenomic condition.