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008			100917s2010 xxu| o |||| 0|eng d
020			_a9781607618423
024	7		_a10.1007/978-1-60761-842-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aRB43.8 .F58 _b2010 EB
245	0	0	_aComputational Biology _cedited by David Fenyö
250			_a1st edition 2010
264		1	_aTotowa, NJ _bHumana Press _c2010
300			_a1 recurso en línea (XI, 327 páginas) _b81 ilustraciones
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aMethods in Molecular Biology _x1940-6029 _v673
505	0		_aSequencing and Genome Assembly Using Next-Generation Technologies -- RNA Structure Prediction -- Normalization of Gene-Expression Microarray Data -- Prediction of Transmembrane Topology and Signal Peptide Given a Protein's Amino Acid Sequence -- Protein Structure Modeling -- Template-Based Protein Structure Modeling -- Automated Protein NMR Structure Determination in Solution -- Computational Tools in Protein Crystallography -- 3-D Structures of Macromolecules Using Single-Particle Analysis in EMAN -- Computational Design of Chimeric Protein Libraries for Directed Evolution -- Mass Spectrometric Protein Identification Using the Global Proteome Machine -- Unbiased Detection of Posttranslational Modifications Using Mass Spectrometry -- Protein Quantitation Using Mass Spectrometry -- Modeling Experimental Design for Proteomics -- A Functional Proteomic Study of the Trypanosoma brucei Nuclear Pore Complex: An Informatic Strategy -- Inference of Signal Transduction Networks from Double Causal Evidence -- Reverse Engineering Gene Regulatory Networks Related to Quorum Sensing in the Plant Pathogen Pectobacterium atrosepticum -- Parameter Inference and Model Selection in Signaling Pathway Models -- Genetic Algorithms and Their Application to In Silico Evolution of Genetic Regulatory Networks.
520			_aComputational biology is an interdisciplinary field that applies mathematical, statistical, and computer science methods to answer biological questions, and its importance has only increased with the introduction of high-throughput techniques such as automatic DNA sequencing, comprehensive expression analysis with microarrays, and proteome analysis with modern mass spectrometry. In Computational Biology, expert practitioners present a broad survey of computational biology methods by focusing on their applications, including primary sequence analysis, protein structure elucidation, transcriptomics and proteomics data analysis, and exploration of protein interaction networks. As a volume in the highly successful Methods in Molecular Biology™ series, this work provides the kind of detailed description and implementation advice that is crucial for getting optimal results. Authoritative and easy to use, Computational Biology is an ideal guide for all scientists interested in quantitative biology.
988			_aSpringer_Protocols_2010
650		7	_2embne _9688531 _aHibridación in situ
776	0	8	_iPrinted edition: _z9781607618416
776	0	8	_iPrinted edition: _z9781607618430
776	0	8	_iPrinted edition: _z9781493961221
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-1-60761-842-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2024 _dz _eIG _zSI