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。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

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.

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One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products

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

The Design and Analysis of Experiments

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.).

Machine learning

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.

Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal

The bactericidal activity of Tinopal AN [1,1-bis(3,N-5-dimethyl-benzoxazol-2-yl)-methine p-toluene sulphonate] was shown to be due to a mechanism entirely independent of its inhibitory effects upon NADH dehydrogenase which were reported previously. Whereas the compound had no significant effect upon DNA synthesis in Escherichia coli D22, RNA and protein synthesis were immediately and markedly inhibited. In confirmation, Tinopal AN caused an immediate cessation in inducible β-galactosidase synthesis in the same organism. An in vitro assay of the transcription of calf-thymus DNA by purified E. coli RNA polymerase showed that this process was inhibited by Tinopal AN.

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The antibacterial action of Tinopal AN.

Cardiac metabolism and cardiac function are inextricably linked, with changes in cardiac metabolism during cardiac ischaemia contributing to the development of cardiac arrhythmias. Using metabolomics, we aim to identify metabolite changes occurring during cardiac ischaemia through analysis of peripheral serum to reconstruct myocardial metabolic pathways that contribute to the development of cardiac arrhythmias. Peripheral venous samples from 25 patients (and 20 patients in a validation cohort) were analysed in an untargeted fashion using LC-MS following cardiac ischaemia induced by transient coronary artery occlusion during PCI at baseline, 1 and 5 min. Following validation, 99 and 126 metabolite peaks were significantly different at 1 min and 5 min after coronary occlusion compared with baseline (p

https://heart.bmj.com/content/heartjnl/96/17/e13.1.full.pdf

BAS/BSCR3 Partial reconstruction of myocardial metabolic pathways following analysis of peripheral serum using metabolomics in early cardiac ischaemia

Coupling factor B and the bovine mitochondrial H+-ATP synthase

Rapid high-throughput detection of preeclampsia.

Let me start by saying what I am not going to do, and that is to seek to define systems biology! However, it is widely recognized that the chief differences between Systems Biology and traditional molecular biology are (i) the concentration of systems biology (and systems biologists) not on the molecules involved, but on the dynamics of their interactions, and (ii) that systems biology should involve a judicious interplay between modelling, theory, experiment and technology development 1. As the modelling element is really the key, it is this aspect that I stress here.

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Douglas B. Kell

Papers

The antibacterial action of Tinopal AN.

BAS/BSCR3 Partial reconstruction of myocardial metabolic pathways following analysis of peripheral serum using metabolomics in early cardiac ischaemia

Coupling factor B and the bovine mitochondrial H+-ATP synthase

Rapid high-throughput detection of preeclampsia.

Understanding the languages of cells: Network modelling in metabolic systems biology and biotechnology: why, how and whither