Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

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Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks

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

http://bartellab.wi.mit.edu/publication_reprints/Bartel_Cell09.pdf

MicroRNAs: Target Recognition and Regulatory Functions

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

Background: Currently, a lack of consensus exists on how best to perform and interpret quantitative real-time PCR (qPCR) experiments. The problem is exacerbated by a lack of sufficient experimental detail in many publications, which impedes a reader’s ability to evaluate critically the quality of the results presented or to repeat the experiments.

Content: The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines target the reliability of results to help ensure the integrity of the scientific literature, promote consistency between laboratories, and increase experimental transparency. MIQE is a set of guidelines that describe the minimum information necessary for evaluating qPCR experiments. Included is a checklist to accompany the initial submission of a manuscript to the publisher. By providing all relevant experimental conditions and assay characteristics, reviewers can assess the validity of the protocols used. Full disclosure of all reagents, sequences, and analysis methods is necessary to enable other investigators to reproduce results. MIQE details should be published either in abbreviated form or as an online supplement.

Summary: Following these guidelines will encourage better experimental practice, allowing more reliable and unequivocal interpretation of qPCR results.

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The MIQE Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments

http://www.cell.com/article/S2372770522000341/pdf

NAD+ depletion enhances reovirus-induced oncolysis in multiple myeloma

/pdf/fat-cells-directly-sense-temperature-to-activate-3enrhphsik.pdf

Fat cells directly sense temperature to activate

/pdf/the-harvard-community-has-made-this-article-openly-available-z432ixth1u.pdf

The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters.

Author response: Scaffold-mediated gating of Cdc42 signalling flux

Abstract While targeted treatment against BRAF(V600E) improve survival for melanoma patients, many will see their cancer recur. Here we provide data indicating that epigenetic suppression of PGC1α defines an aggressive subset of chronic BRAF-inhibitor treated melanomas. A metabolism-centered pharmacological screen further identifies statins (HMGCR inhibitors) as a collateral vulnerability within PGC1α-suppressed BRAF-inhibitor resistant melanomas. Lower PGC1α levels mechanistically causes reduced RAB6B and RAB27A expression, whereby their combined re-expression reverses statin vulnerability. BRAF-inhibitor resistant cells with reduced PGC1α have increased integrin-FAK signaling and improved extracellular matrix detached survival cues that helps explain their increased metastatic ability. Statin treatment blocks cell growth by lowering RAB6B and RAB27A prenylation that reduces their membrane association and affects integrin localization and downstream signaling required for growth. These results suggest that chronic adaptation to BRAF-targeted treatments drive novel collateral metabolic vulnerabilities, and that HMGCR inhibitors may offer a strategy to treat melanomas recurring with suppressed PGC1α expression. 

/pdf/epigenetic-suppression-of-pgc1a-ppargc1a-causes-collateral-3fqc7xdm.pdf

Steven P. Gygi

Papers

NAD+ depletion enhances reovirus-induced oncolysis in multiple myeloma

Fat cells directly sense temperature to activate

The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters.

Author response: Scaffold-mediated gating of Cdc42 signalling flux

Epigenetic suppression of PGC1α (PPARGC1A) causes collateral sensitivity to HMGCR-inhibitors within BRAF-treatment resistant melanomas