Computational methods for estimating the kinetic parameters of biological systems
著者
書誌事項
Computational methods for estimating the kinetic parameters of biological systems
（Methods in molecular biology / John M. Walker, series editor, 2385）（Springer protocols）
Humana Press, c2022
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
1. Current Approaches of Building Mechanistic Pharmacodynamic Drug-Target Binding Models
Jingyi Liang, Vi Ngoc-Nha Tran, Colin Hemez, and Pia Abel zur Wiesch
2. An Extended Model Including Target Turnover, Ligand-Target Complex Kinetics, and Binding Properties to Describe Drug-Receptor Interactions
Lambertus A. Peletier
3. Beyond the Michaelis-Menten: Bayesian Inference for Enzyme Kinetic Analysis
Hyukpyo Hong, Boseung Choi, and Jae Kyoung Kim
4. Multi-Objective Optimization Tuning Framework for Kinetic Parameter Selection and Estimation
Yadira Boada, Jesus Pico, and Alejandro Vignoni
5. Relationship between Dimensionality and Convergence of Optimization Algorithms: A Comparison between Data-Driven Normalization and Scaling Factor-Based Methods Using PEPSSBI
Andrea Degasperi, Lan K. Nguyen, Dirk Fey, and Boris N. Kholodenko
6. Dynamic Optimization Approach to Estimate Kinetic Parameters of Monod-Based Microalgae Growth Models
Siti S. Jamaian, Fathul H. Zulkifli, and Kim S. Ling
7. Automatic Assembly and Calibration of Models of Enzymatic Reactions Based on Ordinary Differential Equations
Jure Stojan, Milan Hodoscek, and Dusanka Janezic
8. Data Processing to Probe the Cellular Hydrogen Peroxide Landscape
Fernando Antunes and Paula Brito
9. Computational Methods for Structure-Based Drug Design through Systems Biology
Aman Chandra Kaushik, Shakti Sahi, and Dong-Qing Wei
10. Model Setup and Procedures for Prediction of Enzyme Reaction Kinetics with QM-Only and QM:MM Approaches
Michal Glanowski, Sangita Kachhap, Tomasz Borowski, and Maciej Szaleniec
11. The Role of Ligand Rebinding and Facilitated Dissociation on the Characterization of Dissociation Rates by Surface Plasmon Resonance (SPR) and Benchmarking Performance Metrics
Aykut Erbas and Fatih Inci
12. Computational Tools for Accurate Binding Free Energy Prediction
Maria M. Reif and Martin Zacharias
13. Computational Alanine Scanning Reveals Common Features of TCR/pMHC Recognition in HLA-DQ8-Associated Celiac Disease
Linqiong Qiu, Jianing Song, and John Z.H. Zhang
14. Umbrella Sampling-Based Method to Compute Ligand-Binding Affinity
Son Tung Ngo and Minh Quan Pham
15. Creating Maps of the Ligand Binding Landscape for Kinetics-Based Drug Discovery
Tom Dixon, Samuel D. Lotz, and Alex Dickson
16. Prediction of Protein-Protein Binding Affinities from Unbound Protein Structures
Alberto Meseguer, Patricia Bota, Narcis Fernandez-Fuentes, and Baldo Oliva
17. Parameter Optimization for Ion Channel Models: Integrating New Data with Known Channel Properties
Marco A. Navarro, Marzie Amirshenava, Autoosa Salari, Mirela Milescu, and Lorin S. Milescu
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