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020 _a9781597452434
024 7 _a10.1007/978-1-59745-243-4
_2doi
040 _aES-MaUEC
_bspa
_cES-MaUEC
_dES-MaUEC
050 4 _aQH324.2
_b2009 EB
245 0 0 _aComputational Systems Biology
_cedited by Jason McDermott, Ram Samudrala, Roger Bumgarner, Kristina Montgomery, Reneé Ireton
250 _a1st edition 2009
264 1 _aTotowa, NJ
_bHumana Press
_c2009
300 _a1 recurso en línea (XVIII, 587 páginas)
_b130 ilustraciones, 3 ilustraciones a color
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
_v541
505 0 _aNetwork Components -- Identification of cis-Regulatory Elements in Gene Co-expression Networks Using A-GLAM -- Structure-Based Ab Initio Prediction of Transcription Factor-Binding Sites -- Inferring Protein-Protein Interactions from Multiple Protein Domain Combinations -- Prediction of Protein-Protein Interactions: A Study of the Co-evolution Model -- Computational Reconstruction of Protein-Protein Interaction Networks: Algorithms and Issues -- Prediction and Integration of Regulatory and Protein-Protein Interactions -- Detecting Hierarchical Modularity in Biological Networks -- Network Inference -- Methods to Reconstruct and Compare Transcriptional Regulatory Networks -- Learning Global Models of Transcriptional Regulatory Networks from Data -- Inferring Molecular Interactions Pathways from eQTL Data -- Methods for the Inference of Biological Pathways and Networks -- Network Dynamics -- Exploring Pathways from Gene Co-expression to Network Dynamics -- Network Dynamics -- Kinetic Modeling of Biological Systems -- Guidance for Data Collection and Computational Modelling of Regulatory Networks -- Function and Evolutionary Systems Biology -- A Maximum Likelihood Method for Reconstruction of the Evolution of Eukaryotic Gene Structure -- Enzyme Function Prediction with Interpretable Models -- Using Evolutionary Information to Find Specificity-Determining and Co-evolving Residues -- Connecting Protein Interaction Data, Mutations, and Disease Using Bioinformatics -- Effects of Functional Bias on Supervised Learning of a Gene Network Model -- Computational Infrastructure for Systems Biology -- Comparing Algorithms for Clustering of Expression Data: How to Assess Gene Clusters -- The Bioverse API and Web Application -- Computational Representation of Biological Systems -- Biological Network Inference and Analysis Using SEBINI and CABIN.
520 _aThe recent confluence of high throughput methodology for biological data gathering, genome-scale sequencing, and computational processing power has driven a reinvention and expansion of the way we identify, infer, model, and store relationships between molecules, pathways, and cells in living organisms. In Computational Systems Biology, expert investigators contribute chapters which bring together biological data and computational and/or mathematical models of the data to aid researchers striving to create a system that provides both predictive and mechanistic information for a model organism. The volume is organized into five major sections involving network components, network inference, network dynamics, function and evolutionary system biology, and computational infrastructure for systems biology. As a volume of 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. Comprehensive and up-to-date, Computational Systems Biology serves to motivate and inspire all those who wish to develop a complete description of a biological system.
988 _aSpringer_Protocols_2009
650 7 _2embne
_9160489
_aBioinformática
776 0 8 _iPrinted edition:
_z9781607610830
776 0 8 _iPrinted edition:
_z9781588299055
776 0 8 _iPrinted edition:
_z9781493956449
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-1-59745-243-4
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
998 _b09/2023
_dz
_ean
_zSI