Computational Methods for Estimating the Kinetic Parameters of Biological Systems

1. Front Matter

   Pages i-xi

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2. Current Approaches of Building Mechanistic Pharmacodynamic Drug–Target Binding Models

   • Jingyi Liang, Vi Ngoc-Nha Tran, Colin Hemez, Pia Abel zur Wiesch

   Pages 1-17

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3. An Extended Model Including Target Turnover, Ligand–Target Complex Kinetics, and Binding Properties to Describe Drug–Receptor Interactions

   • Lambertus A. Peletier

   Pages 19-46

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4. Beyond the Michaelis–Menten: Bayesian Inference for Enzyme Kinetic Analysis

   • Hyukpyo Hong, Boseung Choi, Jae Kyoung Kim

   Pages 47-64

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5. Multi-Objective Optimization Tuning Framework for Kinetic Parameter Selection and Estimation

   • Yadira Boada, Jesús Picó, Alejandro Vignoni

   Pages 65-89

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6. 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, Boris N. Kholodenko

   Pages 91-115

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7. Dynamic Optimization Approach to Estimate Kinetic Parameters of Monod-Based Microalgae Growth Models

   • Siti S. Jamaian, Fathul H. Zulkifli, Kim S. Ling

   Pages 117-140

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8. Automatic Assembly and Calibration of Models of Enzymatic Reactions Based on Ordinary Differential Equations

   • Jure Stojan, Milan Hodošček, Dušanka Janežič

   Pages 141-152

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9. Data Processing to Probe the Cellular Hydrogen Peroxide Landscape

   • Fernando Antunes, Paula Brito

   Pages 153-160

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10. Computational Methods for Structure-Based Drug Design Through System Biology

    • Aman Chandra Kaushik, Shakti Sahi, Dong-Qing Wei

    Pages 161-174

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11. Model Setup and Procedures for Prediction of Enzyme Reaction Kinetics with QM-Only and QM:MM Approaches

    • Michał Glanowski, Sangita Kachhap, Tomasz Borowski, Maciej Szaleniec

    Pages 175-236

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12. The Role of Ligand Rebinding and Facilitated Dissociation on the Characterization of Dissociation Rates by Surface Plasmon Resonance (SPR) and Benchmarking Performance Metrics

    • Aykut Erbaş, Fatih Inci

    Pages 237-253

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13. Computational Tools for Accurate Binding Free-Energy Prediction

    • Maria M. Reif, Martin Zacharias

    Pages 255-292

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14. Computational Alanine Scanning Reveals Common Features of TCR/pMHC Recognition in HLA-DQ8-Associated Celiac Disease

    • Linqiong Qiu, Jianing Song, John Z. H. Zhang

    Pages 293-312

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15. Umbrella Sampling-Based Method to Compute Ligand-Binding Affinity

    • Son Tung Ngo, Minh Quan Pham

    Pages 313-323

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16. Creating Maps of the Ligand Binding Landscape for Kinetics-Based Drug Discovery

    • Tom Dixon, Samuel D. Lotz, Alex Dickson

    Pages 325-334

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17. Prediction of Protein–Protein Binding Affinities from Unbound Protein Structures

    • Alberto Meseguer, Patricia Bota, Narcis Fernández-Fuentes, Baldo Oliva

    Pages 335-351

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18. Parameter Optimization for Ion Channel Models: Integrating New Data with Known Channel Properties

    • Marco A. Navarro, Marzie Amirshenava, Autoosa Salari, Mirela Milescu, Lorin S. Milescu

    Pages 353-375

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19. Back Matter

    Pages 377-379

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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.

• Quantitative mechanistic models
• Kinetic parameters
• Machine learning
• Enzymatic reactions
• Protein-ligand interactions
• Biophysics

• Insilico Medicine Hong Kong Ltd, Pak Shek Kok, Hong Kong

  Quentin Vanhaelen

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