Chapter 1: Biomedical Knowledge Integration

The modern biomedical research and healthcare delivery domains have seen an unparalleled increase in the rate of innovation and novel technologies over the past several decades. Catalyzed by paradigm-shifting public and private programs focusing upon the formation and delivery of genomic and personalized medicine, the need for high-throughput and integrative approaches to the collection, management, and analysis of heterogeneous data sets has become imperative. This need is particularly pressing in the translational bioinformatics domain, where many fundamental research questions require the integration of large scale, multi-dimensional clinical phenotype and bio-molecular data sets. Modern biomedical informatics theory and practice has demonstrated the distinct benefits associated with the use of knowledge-based systems in such contexts. A knowledge-based system can be defined as an intelligent agent that employs a computationally tractable knowledge base or repository in order to reason upon data in a targeted domain and reproduce expert performance relative to such reasoning operations. The ultimate goal of the design and use of such agents is to increase the reproducibility, scalability, and accessibility of complex reasoning tasks. Examples of the application of knowledge-based systems in biomedicine span a broad spectrum, from the execution of clinical decision support, to epidemiologic surveillance of public data sets for the purposes of detecting emerging infectious diseases, to the discovery of novel hypotheses in large-scale research data sets. In this chapter, we will review the basic theoretical frameworks that define core knowledge types and reasoning operations with particular emphasis on the applicability of such conceptual models within the biomedical domain, and then go on to introduce a number of prototypical data integration requirements and patterns relevant to the conduct of translational bioinformatics that can be addressed via the design and use of knowledge-based systems.

What to Learn in This Chapter

• Understand basic knowledge types and structures that can be applied to biomedical and translational science;
• Gain familiarity with the knowledge engineering cycle, tools and methods that may be used throughout that cycle, and the resulting classes of knowledge products generated via such processes;
• An understanding of the basic methods and techniques that can be used to employ knowledge products in order to integrate and reason upon heterogeneous and multi-dimensional data sets; and
• Become conversant in the open research questions/areas related to the ability to develop and apply knowledge collections in the translational bioinformatics domain.

This article is part of the “Translational Bioinformatics” collection for PLOS Computational Biology.

     

8-Substituted Purine Ribosides: Synthesis and Biological Activity

Abstract

The syntheses of a series of 8-substituted purine ribofuranosides are described. Reductive deaminalion of 8-bromoadenosine triacetate (1) provides the key intermediate (2) for subsequent displacement reactions. Four compounds show significant activity in two cell culture Screens.     

On the Limitations of Biological Knowledge

Scientific knowledge is grounded in a particular epistemology and, owing to the requirements of that epistemology, possesses limitations. Some limitations are intrinsic, in the sense that they depend inherently on the nature of scientific knowledge; others are contingent, depending on the present state of knowledge, including technology. Understanding limitations facilitates scientific research because one can then recognize when one is confronted by a limitation, as opposed to simply being unable to solve a problem within the existing bounds of possibility. In the hope that the role of limiting factors can be brought more clearly into focus and discussed, we consider several sources of limitation as they apply to biological knowledge: mathematical complexity, experimental constraints, validation, knowledge discovery, and human intellectual capacity.     

The Quiet Revolution: A New Synthesis of Biological Knowledge

A method is described for preparing thin-layer chromatography plates for use in the classroom. Glass plates are coated with adsorbent using a technique which is cheap, rapid and reliable and which avoids the need for expensive, commercially-available apparatus. It can be adapted to preparative use. The main adsorbent described is silicic acid H and G but other adsorbents such as cellulose can be spread by the same method. Points to watch for in setting up plates for thin-layer chromatography and applying solute samples are discussed.     

MFG—E8的生物学功能及应用

乳脂球表皮生长因子8（milk fat globuleEGF factorⅧ,MFG—E8）是一种乳汁脂肪小球表面的亲脂性糖蛋白,广泛参与多种细胞问的相互作用,如介导精子与卵子外膜问的结合、维持附睾上皮细胞与精子细胞膜的黏附、参与肠上皮细胞的维持与修复、促进乳腺支化形成和成人组织新生血管形成、增强树突状细胞外泌体功能以及参与吞噬清除凋亡细胞的过程。此外,MFG—E8也显示良好的应用前景,有可能作为潜在的治疗和检测手段。     

Chapter 7: Pharmacogenomics

There is great variation in drug-response phenotypes, and a “one size fits all” paradigm for drug delivery is flawed. Pharmacogenomics is the study of how human genetic information impacts drug response, and it aims to improve efficacy and reduced side effects. In this article, we provide an overview of pharmacogenetics, including pharmacokinetics (PK), pharmacodynamics (PD), gene and pathway interactions, and off-target effects. We describe methods for discovering genetic factors in drug response, including genome-wide association studies (GWAS), expression analysis, and other methods such as chemoinformatics and natural language processing (NLP). We cover the practical applications of pharmacogenomics both in the pharmaceutical industry and in a clinical setting. In drug discovery, pharmacogenomics can be used to aid lead identification, anticipate adverse events, and assist in drug repurposing efforts. Moreover, pharmacogenomic discoveries show promise as important elements of physician decision support. Finally, we consider the ethical, regulatory, and reimbursement challenges that remain for the clinical implementation of pharmacogenomics.

What to Learn in This Chapter

• Interactions between drugs (small molecules) and genes (proteins)
• Methods for pharmacogenomic discovery
  • Association- and expression-based methods
  • Cheminformatics and pathway-based methods
• Database resources for pharmacogenomic discovery and application (PharmGKB)
• Applications of pharmacogenomics into a clinical setting

This article is part of the “Translational Bioinformatics” collection for PLOS Computational Biology.

     

iBrick: A New Standard for Iterative Assembly of Biological Parts with Homing Endonucleases

The BioBricks standard has made the construction of DNA modules easier, quicker and cheaper. So far, over 100 BioBricks assembly schemes have been developed and many of them, including the original standard of BBF RFC 10, are now widely used. However, because the restriction endonucleases employed by these standards usually recognize short DNA sequences that are widely spread among natural DNA sequences, and these recognition sites must be removed before the parts construction, there is much inconvenience in dealing with large-size DNA parts (e.g., more than couple kilobases in length) with the present standards. Here, we introduce a new standard, namely iBrick, which uses two homing endonucleases of I-SceI and PI-PspI. Because both enzymes recognize long DNA sequences (>18 bps), their sites are extremely rare in natural DNA sources, thus providing additional convenience, especially in handling large pieces of DNA fragments. Using the iBrick standard, the carotenoid biosynthetic cluster (>4 kb) was successfully assembled and the actinorhodin biosynthetic cluster (>20 kb) was easily cloned and heterologously expressed. In addition, a corresponding nomenclature system has been established for the iBrick standard.     

Chapter 4: Protein Interactions and Disease

Proteins do not function in isolation; it is their interactions with one another and also with other molecules (e.g. DNA, RNA) that mediate metabolic and signaling pathways, cellular processes, and organismal systems. Due to their central role in biological function, protein interactions also control the mechanisms leading to healthy and diseased states in organisms. Diseases are often caused by mutations affecting the binding interface or leading to biochemically dysfunctional allosteric changes in proteins. Therefore, protein interaction networks can elucidate the molecular basis of disease, which in turn can inform methods for prevention, diagnosis, and treatment. In this chapter, we will describe the computational approaches to predict and map networks of protein interactions and briefly review the experimental methods to detect protein interactions. We will describe the application of protein interaction networks as a translational approach to the study of human disease and evaluate the challenges faced by these approaches.

What to Learn in This Chapter

• Experimental and computational methods to detect protein interactions
• Protein networks and disease
• Studying the genetic and molecular basis of disease
• Using protein interactions to understand disease

This article is part of the “Translational Bioinformatics” collection for PLOS Computational Biology.

     

Biological role of 8-oxo-2'-deoxyguanosine

The review presents currently available data on the biological role of 8-oxo-2’-deoxyguanosine. This compound for a long time has been used successfully as a biomarker of oxidative stress and diseases associated with it. However, in recent years, an increasing number of publications has appeared reporting that biological role of 8-oxo-dG is not only a mutagenic one; it also is involved in the regulation of gene expression, in some processes of DNA repair, in the control of inflammatory and autoimmune reactions, and in the activation of antioxidant systems. Probably, 8-oxo-2’-deoxyguanosine could prospectively be used as a drug.     

Chapter 3: Small Molecules and Disease

“Big” molecules such as proteins and genes still continue to capture the imagination of most biologists, biochemists and bioinformaticians. “Small” molecules, on the other hand, are the molecules that most biologists, biochemists and bioinformaticians prefer to ignore. However, it is becoming increasingly apparent that small molecules such as amino acids, lipids and sugars play a far more important role in all aspects of disease etiology and disease treatment than we realized. This particular chapter focuses on an emerging field of bioinformatics called “chemical bioinformatics” – a discipline that has evolved to help address the blended chemical and molecular biological needs of toxicogenomics, pharmacogenomics, metabolomics and systems biology. In the following pages we will cover several topics related to chemical bioinformatics. First, a brief overview of some of the most important or useful chemical bioinformatic resources will be given. Second, a more detailed overview will be given on those particular resources that allow researchers to connect small molecules to diseases. This section will focus on describing a number of recently developed databases or knowledgebases that explicitly relate small molecules – either as the treatment, symptom or cause – to disease. Finally a short discussion will be provided on newly emerging software tools that exploit these databases as a means to discover new biomarkers or even new treatments for disease.

What to Learn in This Chapter

• The meaning of chemical bioinformatics
• Strengths and limitations of existing chemical bioinformatic databases
• Using databases to learn about the cause and treatment of diseases
• The Small Molecule Pathway Database (SMPDB)
• The Human Metabolome Database (HMDB)
• DrugBank
• The Toxin and Toxin-Target Database (T3DB)
• PolySearch and Metabolite Set Enrichment Analysis

This article is part of the “Translational Bioinformatics” collection for PLOS Computational Biology.

     

Interpretation of Genomic Variants Using a Unified Biological Network Approach

The decreasing cost of sequencing is leading to a growing repertoire of personal genomes. However, we are lagging behind in understanding the functional consequences of the millions of variants obtained from sequencing. Global system-wide effects of variants in coding genes are particularly poorly understood. It is known that while variants in some genes can lead to diseases, complete disruption of other genes, called ‘loss-of-function tolerant’, is possible with no obvious effect. Here, we build a systems-based classifier to quantitatively estimate the global perturbation caused by deleterious mutations in each gene. We first survey the degree to which gene centrality in various individual networks and a unified ‘Multinet’ correlates with the tolerance to loss-of-function mutations and evolutionary conservation. We find that functionally significant and highly conserved genes tend to be more central in physical protein-protein and regulatory networks. However, this is not the case for metabolic pathways, where the highly central genes have more duplicated copies and are more tolerant to loss-of-function mutations. Integration of three-dimensional protein structures reveals that the correlation with centrality in the protein-protein interaction network is also seen in terms of the number of interaction interfaces used. Finally, combining all the network and evolutionary properties allows us to build a classifier distinguishing functionally essential and loss-of-function tolerant genes with higher accuracy (AUC = 0.91) than any individual property. Application of the classifier to the whole genome shows its strong potential for interpretation of variants involved in Mendelian diseases and in complex disorders probed by genome-wide association studies.