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 passive electrical or “dielectric” properties of biological cells have been studied since the last century [1]. Important advances in our knowledge that have accrued from the application of dielectric spectroscopy to biological systems include the recognition of the molecular thickness of biological membranes [2] and of the existence of voltage-gated ion channels as embodied in the Hodgkin-Huxley [3] equations. However, as judged by its complete omission from a book on Biological Spectroscopy [4], the technique has not achieved as widespread a recognition and exploitation in biophysics as it warrants [5]. Notwithstanding, and due in large part to advances in instrumentation which make the acquisition, display and interpretation of biological dielectric spectra much more convenient than has historically been the case, the last 10 or 15 years have seen a significant increase in the use of the technique [5–18a].

The low-frequency dielectric properties of biological cells

The enormous variety of substances which may be added to forage in order to manipulate and improve the ensilage process presents an empirical, combinatorial optimization problem of great complexity. To investigate the utility of genetic algorithms for designing effective silage additive combinations, a series of small-scale proof of principle silage experiments were performed with fresh ryegrass. Having established that significant biochemical changes occur over an ensilage period as short as 2 days, we performed a series of experiments in which we used 50 silage additive combinations (prepared by using eight bacterial and other additives, each of which was added at six different levels, including zero [i.e., no additive]). The decrease in pH, the increase in lactate concentration, and the free amino acid concentration were measured after 2 days and used to calculate a “fitness” value that indicated the quality of the silage (compared to a control silage made without additives). This analysis also included a “cost” element to account for different total additive levels. In the initial experiment additive levels were selected randomly, but subsequently a genetic algorithm program was used to suggest new additive combinations based on the fitness values determined in the preceding experiments. The result was very efficient selection for silages in which large decreases in pH and high levels of lactate occurred along with low levels of free amino acids. During the series of five experiments, each of which comprised 50 treatments, there was a steady increase in the amount of lactate that accumulated; the best treatment combination was that used in the last experiment, which produced 4.6 times more lactate than the untreated silage. The additive combinations that were found to yield the highest fitness values in the final (fifth) experiment were assessed to determine a range of biochemical and microbiological quality parameters during full-term silage fermentation. We found that these combinations compared favorably both with uninoculated silage and with a commercial silage additive. The evolutionary computing methods described here are a convenient and efficient approach for designing silage additives.

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Efficient Improvement of Silage Additives by Using Genetic Algorithms

The reduction of proline by Clostridium sporogenes NCIB8053 is coupled to transmembrane proton translocation in an uncoupler-sensitive fashion (and might therefore conserve free energy). This finding serves to explain the increase in the growth yield of this organism when proline is added to a defined growth medium containing glucose as the catabolic substrate.

Proline reduction by Clostridium sporogenes is coupled to vectorial proton ejection

It is an interesting fact (Macfarlane, 1984) that 58 years elapsed between the publication of the studies of Burdon Sanderson and of Lister showing the antibacterial action of members of the genus Penicillium (Burdon Sanderson, 187 1 ; Lister, 187 1) and Fleming’s somewhat more celebrated paper (Fleming, 1929) to the same effect. Coincidentally enough, a similar period links this latter date with the present year. When seeking to account for Fleming’s apparent ignorance of the prior art, Macfarlane (1984) notes that Papacostas & Gat6 (1928) had just published a book which contained a 60 page section, headed ‘Antibiosis’, on the extensive subject of bacterial inhibition by moulds and by other bacteria, devoted to previously published observations on this phenomenon and with seuerul hundred historical references! The evident moral to be drawn from this tale is that the state of the art as perceived by the co:lective consciousness (or its foremost representatives) often differs markedly from what has actually been widely recorded in the literature. Thus, given the impossibility of being allpervasive, one must often, and especially in an overview of the present type, concentrate upon the crucial principles at the expense of the details. I state this by way of an excuse for doing that very thing in what follows, since I shall be seeking, in the most eclectic manner, to summarize my own analysis of several areas of what constitutes a huge field of enquiry: the question of what controls the growth and metabolic rates of bacteria. Now, the first distinction which we make when we seek to analyse the mechanisms by which bacteria contrive to control the rates at which they grow or metabolize is that between catabolism and anabolism, which are of course linked predominantly by means of the adenine nucleotide system (Fig. 1). Here we see, in a formal sense, the conceptual separation between the provision and utilization of free energy in the living bacterial cell, so that the cell is modelled as a non-equilibrium thermodynamic energy converter. The negative of the free energy change per extent of reaction for the catabolic reactions constitutes the input force (or affinity; see e.g. Prigogine, 1967; Welch, 1985a) to the adenine nucleotide system, whilst the output force is represented by the free energy change (per extent of reaction) for the formation of biomass. The relevant fluxes are constituted by the rates at which the catabolic and anabolic processes take place. It is particularly important to note that not all the free energy that is generated by catabolism is actually used to drive anabolism, and this is represented formally as an uncoupled ATP hydrolase reaction. To quantify this contribution in particular, we must define the efficiency of microbial growth.

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Forces, Fluxes and the Control of Microbial Growth and Metabolism: The Twelfth Fleming Lecture

Mendes, P., Kell, D. B. (2001). MEG (Model Extender for Gepasi): a program for the modelling of complex, heterogeneous, cellular systems. Bioinformatics, 17, (3), 288-290

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Douglas B. Kell

Papers

The low-frequency dielectric properties of biological cells

Efficient Improvement of Silage Additives by Using Genetic Algorithms

Proline reduction by Clostridium sporogenes is coupled to vectorial proton ejection

Forces, Fluxes and the Control of Microbial Growth and Metabolism: The Twelfth Fleming Lecture

MEG (Model Extender for Gepasi): a program for the modelling of complex, heterogeneous, cellular systems.