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This article includes a list of references, related reading or external links, but its sources remain unclear because it lacks inline citations. Please improve this article by introducing more precise citations where appropriate. (October 2008) High-throughput screening (HTS) is a method for scientific experimentation especially used in drug discovery and relevant to the fields of biology and chemistry. Using robotics, data processing and control software, liquid handling devices, and sensitive detectors, High-Throughput Screening allows a researcher to quickly conduct millions of chemical, genetic or pharmacological tests. Through this process one can rapidly identify active compounds, antibodies or genes which modulate a particular biomolecular pathway. The results of these experiments provide starting points for drug design and for understanding the interaction or role of a particular biochemical process in biology. Contents 1 Assay plate preparation 2 Reaction observation 3 Automation systems 4 Experimental design and data analysis 4.1 Quality control 4.2 Hit selection 5 Techniques for increased throughput and efficiency 5.1 Recent advances 6 Increasing lab utilization of HTS 7 See also 8 Further reading 9 References 10 External links Assay plate preparation The key labware or testing vessel of HTS is the microtiter plate: a small container, usually disposable and made of plastic, that features a grid of small, open divots called wells. Modern (circa 2008) microplates for HTS generally have either 384, 1536, or 3456 wells. These are all multiples of 96, reflecting the original 96 well microplate with 8 x 12 9mm spaced wells. Most of the wells contain experimentally useful matter, often an aqueous solution of dimethyl sulfoxide (DMSO) and some other chemical compound, the latter of which is different for each well across the plate. (The other wells may be empty, intended for use as optional experimental controls.) A screening facility typically holds a library of stock plates, whose contents are carefully catalogued, and each of which may have been created by the lab or obtained from a commercial source. These stock plates themselves are not directly used in experiments; instead, separate assay plates are created as needed. An assay plate is simply a copy of a stock plate, created by pipetteing a small amount of liquid (often measured in nanoliters) from the wells of a stock plate to the corresponding wells of a completely empty plate. Reaction observation To prepare for an assay, the researcher fills each well of the plate with some logical entity that he or she wishes to conduct the experiment upon, such as a protein, or an animal embryo. After some incubation time has passed to allow the biological matter to absorb, bind to, or otherwise react (or fail to react) with the compounds in the wells, measurements are taken across all the plate's wells, either manually or by a machine. Manual measurements are often necessary when the researcher is using microscopy to (for example) seek changes or defects in embryonic development caused by the wells' compounds, looking for effects that a computer could not easily determine by itself. Otherwise, a specialized automated analysis machine can run a number of experiments on the wells (such as shining polarized light on them and measuring reflectivity, which can be an indication of protein binding). In this case, the machine outputs the result of each experiment as a grid of numeric values, with each number mapping to the value obtained from a single well. A high-capacity analysis machine can measure dozens of plates in the space of a few minutes like this, generating thousands of experimental datapoints very quickly. Depending on the results of this first assay, the researcher can perform follow up assays within the same screen by "cherrypicking" liquid from the source wells that gave interesting results (known as "hits") into new assay plates, and then re-running the experiment to collect further data on this narrowed set, confirming and refining observations. Automation systems Automation is an important element in HTS's usefulness. Typically, an integrated robot system consisting of one or more robots transports assay-microplates from station to station for sample and reagent addition, mixing, incubation, and finally readout or detection. An HTS system can usually prepare, incubate, and analyze many plates simultaneously, further speeding the data-collection process. HTS robots currently exist which can test up to 100,000 compounds per day.[1] The term uHTS or ultra high throughput screening refers (circa 2008) to screening in excess of 100,000 compounds per day. Experimental design and data analysis With the ability of rapid screening of diverse compounds (such as small molecules or siRNAs) to identify active compounds, HTS has led to an explosion in the rate of data generated in recent years [2]. Consequently, one of the most fundamental challenges in HTS experiments is to glean biochemical significance from mounds of data, which relies on the development and adoption of appropriate experimental designs and analytic methods for both quality control and hit selection [3]. HTS research is one of the fields which have a feature described by Eisenstein as follows: soon, if a scientist does not understand some statistics or rudimentary data-handling technologies, he or she may not be considered to be a true molecular biologist and thus will simply become a dinosaur [4]. Quality control High-quality HTS assays are critical in HTS experiments. The development of high-quality HTS assays requires the integration of both experimental and computational approaches for quality control (QC). Three important means of QC are (i) good plate design, (ii) the selection of effective positive and negative chemical/biological controls, and (iii) the development of effective QC metrics to measure the degree of differentiation so that assays with inferior data quality can be identified [5] . A good plate design helps to identify systematic errors (especially those linked with well position) and determine what normalization should be used to remove/reduce the impact of systematic errors on both QC and hit selection [3]. Effective analytic QC methods serve as a gatekeeper for excellent quality assays. In a typical HTS experiment, a clear distinction between a positive control and a negative reference such as a negative control is an index for good quality. Many quality assessment measures have been proposed to measure the degree of differentiation between a positive control and a negative reference. Signal-to-background ratio, signal-to-noise ratio, signal window, assay variability ratio, and Z-factor have been adopted to evaluate data quality [6] [3]. Strictly standardized mean difference (SSMD) has recently been proposed for assessing data quality in HTS assays [7] [8] . Hit selection A compound with a desired size of effects in an HTS screen is called a hit. The process of selecting hits is called hit selection. The analytic methods for hit selection in screens without replicates (usually in primary screens) differ from those with replicates (usually in confirmatory screens). For example, the z-score method is suitable for screens without replicates whereas the t-statistic is suitable for screens with replicate. The calculation of SSMD for screens without replicates also differs from that for screens with replicates [3]. For hit selection in primary screens without replicates, the easily interpretable ones are average fold change, mean difference, percent inhibition, and percent activity. However, they do not capture data variability effectively. The z-score method or SSMD, which can capture data variability based on an assumption that every compound has the same variability as a negative reference in the screens [9] [10]. However, outliers are common in HTS experiments, and methods such as z-score are sensitive to outliers and can be problematic. Consequently, robust methods such as the z*-score method, SSMD*, B-score method, and quantile-based method have been proposed and adopted for hit selection [11] [12] [3]. In a screen with replicates, we can directly estimate variability for each compound; consequently, we should use SSMD or t-statistic that does not rely on the strong assumption that the z-score and z*-score rely on. One issue with the use of t-statistic and associated p-values is that they are affected by both sample size and effect size [13] . They come from testing for no mean difference, thus are not designed to measure the size of compound effects. For hit selection, the major interest is the size of effect in a tested compound. SSMD directly assesses the size of effects [14] . SSMD has also been shown to be better than other commonly used effect sizes [15]. The population value of SSMD is comparable across experiments and thus we can use the same cutoff for the population value of SSMD to measure the size of compound effects [16]. Techniques for increased throughput and efficiency Unique distributions of compounds across one or many plates can be employed to increase either the number of assays per plate, or to reduce the variance of assay results, or both. The simplifying assumption made in this approach is that any N compounds in the same well will not typically interact with each other, or the assay target, in a manner that fundamentally changes the ability of the assay to detect true hits. For example, imagine a plate where compound A is in wells 1-2-3, compound B is in wells 2-3-4, and compound C is in wells 3-4-5. In an assay of this plate against a given target, a hit in wells 2, 3, and 4 would indicate that compound B is the most likely agent, while also providing three measurements of compound B's efficacy against the specified target. Commercial applications of this approach involve combinations in which no two compounds ever share more than one well, to reduce the (second-order) possibility of interference between pairs of compounds being screened. Recent advances In March 2010 research was published demonstrating an HTS process allowing 1,000 times faster screening (100 million reactions in 10 hours) at 1 millionth the cost (using 10−7 times the reagent volume) than conventional techniques using drop-based microfluidics.[17] Drops of fluid separated by oil replace microplate wells and allow analysis and hit sorting while reagents are flowing through channels. In 2010 researchers developed a silicon sheet of lenses that can be placed over microfluidic arrays to allow the fluorescence measurement of 64 different output channels simultaneously with a single camera.[18] This process can analyze 200,000 drops per second. Increasing lab utilization of HTS HTS is a relatively recent innovation, made lately feasible through modern advances in robotics and high-speed computer technology. It still takes a highly specialized and expensive screening lab to run an HTS operation, so in many cases a small-to-moderately sized research institution will use the services of an existing HTS facility rather than set up one for itself. There is a trend in academia to be their own drug discovery enterprise. ( High-throughput screening goes to school) Facilities which normally only industry had can now increasingly be found as well at universities. UCLA for example, features an HTS laboratory (Molecular Screening Shared Resources (MSSR, UCLA) which can screen more than 100,000 compounds a day on a routine basis. The University of Illinois also has a facility for HTS, as does the University of Minnesota. The Rockefeller University, has an open access (infrastructure) HTS Resource Center HTSRC (The Rockefeller University, HTSRC) which offers a library of over 165,000 compounds. Northwestern University's High Throughput Analysis Laboratory supports target identification, validation, assay development, and compound screening. In the United States, the National Institute of Health or NIH has created a nationwide consortium of small molecule screening centers that has been recently funded to produce innovative chemical tools for use in biological research. The Molecular Libraries Screening Center Network or MLSCN performs HTS on assays provided by the research community, against a large library of small molecules maintained in a central molecule repository.([1]) For more information see Laboratory automation See also Drug discovery hit to lead Virtual high throughput screening High-content screening Drug discovery Z-factor SSMD Dual-flashlight plot Compound management Synthetic genetic array Yeast two-hybrid screening Further reading Staff (2008-08-01). "High-Throughput Screening Challenges". Genetic Engineering & Biotechnology News (Mary Ann Liebert) 28 (14): pp. 26–27. ISSN 1935-472X. http://www.genengnews.com/articles/chitem.aspx?aid=2571. Retrieved 2008-10-01.  Zhang XHD (2011) "Optimal High-Throughput Screening: Practical Experimental Design and Data Analysis for Genome-scale RNAi Research, Cambridge University Press" References ^ Hann MM, Oprea TI (June 2004). "Pursuing the leadlikeness concept in pharmaceutical research". Curr Opin 8 (3): 255–63. doi:10.1016/j.cbpa.2004.04.003. PMID 15183323.  ^ Howe D, Costanzo M, Fey P, Gojobori T, Hannick L, Hide W, Hill DP, Kania R, Schaeffer M, Pierre SS, Twigger S, White O, Rhee SY (2008). "Big data: The future of biocuration". Nature 455 (7209): 47-50. Bibcode 2008Natur.455...47H. doi:10.1038/455047a.  ^ a b c d e Zhang XHD (2011). Optimal High-Throughput Screening: Practical Experimental Design and Data Analysis for Genome-scale RNAi Research. Cambridge University Press. ISBN 978-0-521-73444-8.  ^ Eisenstein M (2006). "Quality control". Nature 442 (7106): 1067-70. Bibcode 2006Natur.442.1067E. doi:10.1038/4421067a.  ^ Zhang XHD, Espeseth AS, Johnson EN, Chin J, Gates A, Mitnaul LJ, Marine SD, Tian J, Stec EM, Kunapuli P, Holder DJ, Heyse JF, Strulocivi B, Ferrer M (2008). "Integrating experimental and analytic approaches to improve data quality in genome-scale RNAi screens". Journal of Biomolecular Screening 13: 378-89. doi:10.1177/1087057108317145.  ^ Zhang JH, Chung TDY, Oldenburg KR (1999). "A simple statistical parameter for use in evaluation and validation of high throughput screening assays". Journal of Biomolecular Screening 4: 67-73. doi:10.1177/108705719900400206.  ^ Zhang, XHD (2007). "A pair of new statistical parameters for quality control in RNA interference high-throughput screening assays". Genomics 89: 552-61. doi:10.1016/j.ygeno.2006.12.014.  ^ Zhang XHD (2008). "Novel analytic criteria and effective plate designs for quality control in genome-scale RNAi screens". Journal of Biomolecular Screening 13: 363-77. doi:10.1177/1087057108317062.  ^ Zhang XHD (2007). "A new method with flexible and balanced control of false negatives and false positives for hit selection in RNA interference high-throughput screening assays". Journal of Biomolecular Screening 12: 645-55. doi:10.1177/1087057107300645.  ^ Zhang XHD, Ferrer M, Espeseth AS, Marine SD, Stec EM, Crackower MA, Holder DJ, Heyse JF, Strulovici B (2007). "The use of strictly standardized mean difference for hit selection in primary RNA interference high-throughput screening experiments". Journal of Biomolecular Screening 12: 645-55. doi:10.1177/1087057107300646.  ^ Zhang XHD, Yang XC, Chung N, Gates A, Stec E, Kunapuli P, Holder DJ, Ferrer M, Espeseth AS (2006). "Robust statistical methods for hit selection in RNA interference high-throughput screening experiments". Pharmacogenomics 7: 299-09. doi:10.2217/14622416.7.3.299.  ^ Brideau C, Gunter G, Pikounis B, Liaw A (2003). "Improved statistical methods for hit selection in high-throughput screening". Journal of Biomolecular Screening 8: 634-47. doi:10.1177/1087057103258285.  ^ Cohen J (1994). "The Earth Is Round (P-Less-Than.05)". American Psychologist 49: 997-1003. doi:10.1037/0003-066X.49.12.997. ISSN 0003-066X.  ^ Zhang XHD (2009). "A method for effectively comparing gene effects in multiple conditions in RNAi and expression-profiling research". Pharmacogenomics 10: 345-58. doi:10.2217/PGS.09.1.  ^ Zhang XHD (2010). "Strictly standardized mean difference, standardized mean difference and classical t-test for the comparison of two groups". Statistics in Biopharmaceutical Research 2: 292-99. doi:10.1198/sbr.2009.0074.  ^ Zhang XHD (2010). "Assessing the size of gene or RNAi effects in multifactor high-throughput experiments". Pharmacogenomics 11: 199-213. doi:10.2217/PGS.09.136.  ^ Agrestia JJ, Antipovc E, Abatea AR, Ahna K, Rowata AC, Barete JC, Marquezf M, Klibanovc AM, Griffiths AD, Weitz DA (2010). "Ultrahigh-throughput screening in drop-based microfluidics for directed evolution". Proceedings of the National Academy of Sciences 107 (9): 4004–4009. Bibcode 2010PNAS..107.4004A. doi:10.1073/pnas.0910781107. PMC 2840095. PMID 20142500. Lay summary – Physorg.com.  ^ Ethan Schonbrun, Adam R. Abate, Paul E. Steinvurzel, David. A. Weitz and Kenneth B. Crozier (2010). "High-throughput fluorescence detection using an integrated zone-plate array". Lab on a Chip 10 (7): 852–856. doi:10.1039/b923554j. Lay summary – scienceDaily.com.  edit External links Molecular Screening Shared Resources (MSSR, UCLA) Advanced Cell Classifier (ACC) project for high-throughput screen evaluation (ETH, Zurich) Yale Center for High Throughput Cell Biology Biohts: HTS resources and links- Biohts Society for Biomolecular Sciences - links (SBS) Open Screening Environment Setting up High-Throughput Screening Laboratory (Koppal, Lab Manager Magazine) Assay Guidance Manual (NIH, NCGC) [2] (Aurora Biomed High-Throughput Screening]