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.

/pdf/one-step-inactivation-of-chromosomal-genes-in-escherichia-25avsrekpc.pdf

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

Several recent developments are brought together: (i) the new availability of a consensus, curated human metabolic network reconstruction (Recon2), approximately a third of whose steps are represented by transporters, (ii) the recognition that most successful (marketed) drugs, as well as natural products, bear significant similarities to the metabolites in Recon2, (iii) the recognition that to get into and out of cells such drugs hitchhike on the transporters that are part of normal intermediary metabolism, and the consequent recognition that for intact biomembrane Phospholipid Bilayer diffusion Is Negligible (PBIN), and (iv) the consequent recognition that we need to exploit this and to use more phenotypic assays to understand how drugs affect cells and organisms. I show in particular that lipophilicity is a very poor predictor of drug permeability, and that we need to (and can) bring together our knowledge of both pharmacology and systems biology modelling into a new systems pharmacology.

The transporter-mediated cellular uptake of pharmaceutical drugs is based on their metabolite-likeness and not on their bulk biophysical properties: Towards a systems pharmacology ☆

Motivation: Most experimental evidence on kinetic parameters is buried in the literature, whose manual searching is complex, time consuming and partial. These shortcomings become particularly acute in systems biology, where these parameters need to be integrated into detailed, genome-scale, metabolic models. These problems are addressed by KiPar, a dedicated information retrieval system designed to facilitate access to the literature relevant for kinetic modelling of a given metabolic pathway in yeast. Searching for kinetic data in the context of an individual pathway offers modularity as a way of tackling the complexity of developing a full metabolic model. It is also suitable for large-scale mining, since multiple reactions and their kinetic parameters can be specified in a single search request, rather than one reaction at a time, which is unsuitable given the size of genome-scale models.

Results: We developed an integrative approach, combining public data and software resources for the rapid development of large-scale text mining tools targeting complex biological information. The user supplies input in the form of identifiers used in relevant data resources to refer to the concepts of interest, e.g. EC numbers, GO and SBO identifiers. By doing so, the user is freed from providing any other knowledge or terminology concerned with these concepts and their relations, since they are retrieved from these and cross-referenced resources automatically. The terminology acquired is used to index the literature by mapping concepts to their synonyms, and then to textual documents mentioning them. The indexing results and the previously acquired knowledge about relations between concepts are used to formulate complex search queries aiming at documents relevant to the user's information needs. The conceptual approach is demonstrated in the implementation of KiPar. Evaluation reveals that KiPar performs better than a Boolean search. The precision achieved for abstracts (60%) and full-text articles (48%) is considerably better than the baseline precision (44% and 24%, respectively). The baseline recall is improved by 36% for abstracts and by 100% for full text. It appears that full-text articles are a much richer source of information on kinetic data than are their abstracts. Finally, the combined results for abstracts and full text compared with the curated literature provide high values for relative recall (88%) and novelty ratio (92%), suggesting that the system is able to retrieve a high proportion of new documents.

Availability: Source code and documentation are available at: http://www.mcisb.org/resources/kipar/

Contact:i.spasic@manchester.ac.uk; dbk@manchester.ac.uk

Supplementary information:Supplementary data are available at Bioinformatics online.

/pdf/kipar-a-tool-for-systematic-information-retrieval-regarding-4bth6i5v3i.pdf

KiPar, a tool for systematic information retrieval regarding parameters for kinetic modelling of yeast metabolic pathways

http://dbkgroup.org/Papers/kell_morris_buffer_jbbm80.pdf

Formulation and some biological uses of a buffer mixture whose buffering capacity is relatively independent of pH in the range pH 4–9

Bacteria that are resistant to uncouplers—what can they tell us?

The chief and largely terminal element of normal blood clotting is considered to involve the polymerisation of the mainly α-helical fibrinogen to fibrin, with a binding mechanism involving ‘knobs and holes’ but with otherwise littl change in protein secondary structure. We recognise, however, that extremely unusual mutations, or mechanical stressing, can cause fibrinogen to adopt a conformation containing extensive β-sheets. Similarly, prions can change morphology from a largely alpha-helical to a largely β-sheet conformation, and the latter catalyses both the transition and the self-organising polymerisation of the β-sheet structures. Many other proteins can do this, where it is known as amyloidogenesis. When fibrin is formed in samples from patients harbouring different diseases it can have widely varying diameters and morphologies. We here develop the idea, and summarise the evidence, that in many cases the anomalous fibrin fibre formation seen in such diseases actually amounts to amyloidogenesis. In particular, fibrin can interact withthe amyloid-β (Aβ) protein that is misfolded in Alzheimer9s disease. Seeing these unusual fibrin morphologies as true amyloids explains a great deal about fibrin(ogen) biology that was previously opaque, and provides novel strategies for treating such coagulopathies. The literature on blood clotting can usefully both inform and be informed by that on prions and on the many other widely recognised (β)-amyloid proteins. “Novel but physiologically important factors that affect fibrinolysis have seldom been discovered and characterized in recent years” [1]

https://www.biorxiv.org/content/biorxiv/early/2016/05/21/054734.full.pdf

Douglas B. Kell

Papers

The transporter-mediated cellular uptake of pharmaceutical drugs is based on their metabolite-likeness and not on their bulk biophysical properties: Towards a systems pharmacology ☆

KiPar, a tool for systematic information retrieval regarding parameters for kinetic modelling of yeast metabolic pathways

Formulation and some biological uses of a buffer mixture whose buffering capacity is relatively independent of pH in the range pH 4–9

Bacteria that are resistant to uncouplers—what can they tell us?

Substoichiometric molecular control and amplification of the initiation and nature of amyloid fibril formation: lessons from and for blood clotting