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Titlebook: Computational Methods for Estimating the Kinetic Parameters of Biological Systems; Quentin Vanhaelen Book 2022 Springer Science+Business M

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發(fā)表于 2025-3-21 17:30:52 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Computational Methods for Estimating the Kinetic Parameters of Biological Systems
編輯Quentin Vanhaelen
視頻videohttp://file.papertrans.cn/233/232715/232715.mp4
概述Includes cutting-edge techniques.Provides step-by-step details for ease of use.Contains key implementation advice from the experts
叢書名稱Methods in Molecular Biology
圖書封面Titlebook: Computational Methods for Estimating the Kinetic Parameters of Biological Systems;  Quentin Vanhaelen Book 2022 Springer Science+Business M
描述This detailed book provides an overview of various classes of computational techniques, including machine learning techniques, commonly used for evaluating kinetic parameters of biological systems. Focusing on three distinct situations, the volume covers the prediction of the kinetics of enzymatic reactions, the prediction of the kinetics of protein-protein or protein-ligand interactions (binding rates, dissociation rates, binding affinities), and the prediction of relatively large set of kinetic rates of reactions usually found in quantitative models of large biological networks. Written for the highly successful .Methods in Molecular Biology. series, chapters include the kind of expert implementation advice that leads to successful results.?.Authoritative and practical, .Computational Methods for Estimating the Kinetic Parameters of Biological Systems. will be of great interest for researchers working through the challenge of identifying the best type of algorithm and who would like to use or develop a computational method for the estimation of kinetic parameters..
出版日期Book 2022
關(guān)鍵詞Quantitative mechanistic models; Kinetic parameters; Machine learning; Enzymatic reactions; Protein-liga
版次1
doihttps://doi.org/10.1007/978-1-0716-1767-0
isbn_softcover978-1-0716-1769-4
isbn_ebook978-1-0716-1767-0Series ISSN 1064-3745 Series E-ISSN 1940-6029
issn_series 1064-3745
copyrightSpringer Science+Business Media, LLC, part of Springer Nature 2022
The information of publication is updating

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Relationship Between Dimensionality and Convergence of Optimization Algorithms: A Comparison Betweector-based methods. However, currently there is no parameter estimation software that directly supports DNS. In this chapter, we show how to apply DNS to dynamic models of systems and synthetic biology using PEPSSBI (Parameter Estimation Pipeline for Systems and Synthetic Biology). PEPSSBI is the fi
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Model Setup and Procedures for Prediction of Enzyme Reaction Kinetics with QM-Only and QM:MM Approastarts from the PDB structure of the enzyme, through MD simulation of enzyme: substrate complex and statistical analysis of MD trajectory, selection of a model of the?active site, and study of reaction pathways. We show how theoretical predictions basing on QM-only cluster models, QM:MM model, or mu
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Creating Maps of the Ligand Binding Landscape for Kinetics-Based Drug Discovery, describe the other steps in more detail. The end result of this protocol is a map of the ligand–protein binding landscape that identifies transition states of the ligand binding pathway, as well as alternative bound poses that could be stabilized with modifications to the ligand.
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Subcellular Basis of Contractile Failure objectives had been defined appropriately. The methodology gives accurate identification results, as?it provides clear orientation on the effect of the parameter values over the system’s behavior even under different experimental scenarios. It is particularly useful for easily combining time-course
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