scispace - formally typeset
Search or ask a question
Author

Douglas B. Kell

Bio: Douglas B. Kell is an academic researcher from University of Liverpool. The author has contributed to research in topics: Dielectric & Systems biology. The author has an hindex of 111, co-authored 634 publications receiving 50335 citations. Previous affiliations of Douglas B. Kell include Max Planck Society & University of Wales.


Papers
More filters
Journal ArticleDOI
30 Oct 2014
TL;DR: It is shown that the RBCs of PD patients are indeed rather dramatically deranged in their morphology, exhibiting eryptosis (a kind of programmed cell death), and this morphological indicator may have useful diagnostic and prognostic significance.
Abstract: A major trend in recent Parkinson's disease (PD) research is the investigation of biological markers that could help in identifying at-risk individuals or to track disease progression and response to therapies. Central to this is the knowledge that inflammation is a known hallmark of PD and of many other degenerative diseases. In the current work, we focus on inflammatory signalling in PD, using a systems approach that allows us to look at the disease in a more holistic way. We discuss cyclooxygenases, prostaglandins, thromboxanes and also iron in PD. These particular signalling molecules are involved in PD pathophysiology, but are also very important in an aberrant coagulation/hematology system. We present and discuss a hypothesis regarding the possible interaction of these aberrant signalling molecules implicated in PD, and suggest that these molecules may affect the erythrocytes of PD patients. This would be observable as changes in the morphology of the RBCs and of PD patients relative to healthy controls. We then show that the RBCs of PD patients are indeed rather dramatically deranged in their morphology, exhibiting eryptosis (a kind of programmed cell death). This morphological indicator may have useful diagnostic and prognostic significance.

81 citations

Journal ArticleDOI
TL;DR: In this article, both the sensitivity analysis and robust experimental design strategies for the IκB-NF-κB signal transduction model were developed for cellular networks based on sensitivity analysis, and the initial IKK intensity was calculated using an optimal experimental design process.
Abstract: Experimental design for cellular networks based on sensitivity analysis is studied in this work. Both optimal and robust experimental design strategies are developed for the IκB-NF-κB signal transduction model. Based on local sensitivity analysis, the initial IKK intensity is calculated using an optimal experimental design process, and several scalarization measures of the Fisher information matrix are compared. Global sensitivity analysis and robust experimental design techniques are then developed to consider parametric uncertainties in the model. The modified Morris method is employed in global sensitivity analysis, and a semidefinite programming method is exploited to implement the robust experimental design for the problem of measurement set selection. The parametric impacts on the oscillatory behavior of NF-κB in the nucleus are also discussed. © 2008 Wiley Periodicals, Inc. Int J Chem Kinet 40: 730–741, 2008

80 citations

Journal ArticleDOI
TL;DR: A whirlwind tour of recent projects to transform scholarly publishing paradigms, culminating in Utopia and the Semantic Biochemical Journal experiment, and an experiment to rescue data from the dormant pages of published documents.
Abstract: We live in interesting times. Portents of impending catastrophe pervade the literature, calling us to action in the face of unmanageable volumes of scientific data. But it isn't so much data generation per se, but the systematic burial of the knowledge embodied in those data that poses the problem: there is so much information available that we simply no longer know what we know, and finding what we want is hard – too hard. The knowledge we seek is often fragmentary and disconnected, spread thinly across thousands of databases and millions of articles in thousands of journals. The intellectual energy required to search this array of data-archives, and the time and money this wastes, has led several researchers to challenge the methods by which we traditionally commit newly acquired facts and knowledge to the scientific record. We present some of these initiatives here – a whirlwind tour of recent projects to transform scholarly publishing paradigms, culminating in Utopia and the Semantic Biochemical Journal experiment. With their promises to provide new ways of interacting with the literature, and new and more powerful tools to access and extract the knowledge sequestered within it, we ask what advances they make and what obstacles to progress still exist? We explore these questions, and, as you read on, we invite you to engage in an experiment with us, a real-time test of a new technology to rescue data from the dormant pages of published documents. We ask you, please, to read the instructions carefully. The time has come: you may turn over your papers…

80 citations

Journal ArticleDOI
TL;DR: The recognition of an infectious component in the etiology of PE mirrors that for ulcers and other diseases that were previously considered to lack one and suggests a number of treatments, some of which have, in fact, been shown to be successful.
Abstract: Pre-eclampsia (PE) is a complex, multi-system disorder that remains a leading cause of morbidity and mortality in pregnancy. Four main classes of dysregulation accompany PE, and are widely considered to contribute to its severity. These are abnormal trophoblast invasion of the placenta, anti-angiogenic responses, oxidative stress, and inflammation. What is lacking, however, is an explanation of how these themselves are caused. We here develop the unifying idea, and the considerable evidence for it, that the originating cause of PE (and of the four classes of dysregulation) is in fact microbial infection, that most such microbes are dormant and hence resist detection by conventional (replication-dependent) microbiology, and that by occasional resuscitation and growth it is they that are responsible for all the observable sequelae, including the continuing, chronic inflammation. In particular, bacterial products such as lipopolysaccharide (LPS), also known as endotoxin, are well known as highly inflammagenic and stimulate an innate (and possibly trained) immune response that exacerbates the inflammation further. The known need of microbes for free iron can explain the iron dysregulation that accompanies PE. We describe the main routes of infection (gut, oral, urinary tract infection) and the regularly observed presence of microbes in placental and other tissues in PE. Every known proteomic biomarker of “pre-eclampsia” that we assessed has in fact also been shown to be raised in response to infection. An infectious component to PE fulfils the Bradford Hill criteria for ascribing a disease to an environmental cause, and suggests a number of treatments, some of which have in fact been shown to be successful. PE was classically referred to as endotoxaemia or toxaemia of pregnancy, and it is ironic that it seems that LPS and other microbial endotoxins really are involved. Overall, the recognition of an infectious component in the aetiology of PE mirrors that for ulcers and other diseases that were previously considered to lack one.

78 citations

Journal ArticleDOI
TL;DR: In this paper, the authors used recombinant DNA techniques to generate artificial concatamers of large numbers of internal standard peptides, which were used for the quantification of enzymes of the glycolysis pathway in the yeast Saccharomyces cerevisiae.

78 citations


Cited by
More filters
28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Journal ArticleDOI
TL;DR: A simple and highly efficient method to disrupt chromosomal genes in Escherichia coli in which PCR primers provide the homology to the targeted gene(s), which should be widely useful, especially in genome analysis of E. coli and other bacteria.
Abstract: We have developed a simple and highly efficient method to disrupt chromosomal genes in Escherichia coli in which PCR primers provide the homology to the targeted gene(s). In this procedure, recombination requires the phage lambda Red recombinase, which is synthesized under the control of an inducible promoter on an easily curable, low copy number plasmid. To demonstrate the utility of this approach, we generated PCR products by using primers with 36- to 50-nt extensions that are homologous to regions adjacent to the gene to be inactivated and template plasmids carrying antibiotic resistance genes that are flanked by FRT (FLP recognition target) sites. By using the respective PCR products, we made 13 different disruptions of chromosomal genes. Mutants of the arcB, cyaA, lacZYA, ompR-envZ, phnR, pstB, pstCA, pstS, pstSCAB-phoU, recA, and torSTRCAD genes or operons were isolated as antibiotic-resistant colonies after the introduction into bacteria carrying a Red expression plasmid of synthetic (PCR-generated) DNA. The resistance genes were then eliminated by using a helper plasmid encoding the FLP recombinase which is also easily curable. This procedure should be widely useful, especially in genome analysis of E. coli and other bacteria because the procedure can be done in wild-type cells.

14,389 citations

Journal Article
TL;DR: This book by a teacher of statistics (as well as a consultant for "experimenters") is a comprehensive study of the philosophical background for the statistical design of experiment.
Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

13,333 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal ArticleDOI
TL;DR: A practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics, which makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries.
Abstract: The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.

10,947 citations