Computational Methods for Estimating the Kinetic Parameters of Biological Systems
portes grátis
Computational Methods for Estimating the Kinetic Parameters of Biological Systems
Vanhaelen, Quentin
Springer-Verlag New York Inc.
12/2022
379
Mole
Inglês
9781071617694
15 a 20 dias
743
Descrição não disponível.
Current Approaches of Building Mechanistic Pharmacodynamic Drug-Target Binding Models.- An Extended Model Including Target Turnover, Ligand-Target Complex Kinetics, and Binding Properties to Describe Drug-Receptor Interactions.- Beyond the Michaelis-Menten: Bayesian Inference for Enzyme Kinetic Analysis.- Multi-Objective Optimization Tuning Framework for Kinetic Parameter Selection and Estimation.- Relationship between Dimensionality and Convergence of Optimization Algorithms: A Comparison between Data-Driven Normalization and Scaling Factor-Based Methods Using PEPSSBI.- Dynamic Optimization Approach to Estimate Kinetic Parameters of Monod-Based Microalgae Growth Models.- Automatic Assembly and Calibration of Models of Enzymatic Reactions Based on Ordinary Differential Equations.- Data Processing to Probe the Cellular Hydrogen Peroxide Landscape.- Computational Methods for Structure-Based Drug Design through Systems Biology.- Model Setup and Procedures for Prediction of Enzyme Reaction Kinetics with QM-Only and QM:MM Approaches.- The Role of Ligand Rebinding and Facilitated Dissociation on the Characterization of Dissociation Rates by Surface Plasmon Resonance (SPR) and Benchmarking Performance Metrics.- Computational Tools for Accurate Binding Free Energy Prediction.- Computational Alanine Scanning Reveals Common Features of TCR/pMHC Recognition in HLA-DQ8-Associated Celiac Disease.- Umbrella Sampling-Based Method to Compute Ligand-Binding Affinity.- Creating Maps of the Ligand Binding Landscape for Kinetics-Based Drug Discovery.- Prediction of Protein-Protein Binding Affinities from Unbound Protein Structures.- Parameter Optimization for Ion Channel Models: Integrating New Data with Known Channel Properties.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Quantitative mechanistic models;Kinetic parameters;Machine learning;Enzymatic reactions;Protein-ligand interactions;Biophysics
Current Approaches of Building Mechanistic Pharmacodynamic Drug-Target Binding Models.- An Extended Model Including Target Turnover, Ligand-Target Complex Kinetics, and Binding Properties to Describe Drug-Receptor Interactions.- Beyond the Michaelis-Menten: Bayesian Inference for Enzyme Kinetic Analysis.- Multi-Objective Optimization Tuning Framework for Kinetic Parameter Selection and Estimation.- Relationship between Dimensionality and Convergence of Optimization Algorithms: A Comparison between Data-Driven Normalization and Scaling Factor-Based Methods Using PEPSSBI.- Dynamic Optimization Approach to Estimate Kinetic Parameters of Monod-Based Microalgae Growth Models.- Automatic Assembly and Calibration of Models of Enzymatic Reactions Based on Ordinary Differential Equations.- Data Processing to Probe the Cellular Hydrogen Peroxide Landscape.- Computational Methods for Structure-Based Drug Design through Systems Biology.- Model Setup and Procedures for Prediction of Enzyme Reaction Kinetics with QM-Only and QM:MM Approaches.- The Role of Ligand Rebinding and Facilitated Dissociation on the Characterization of Dissociation Rates by Surface Plasmon Resonance (SPR) and Benchmarking Performance Metrics.- Computational Tools for Accurate Binding Free Energy Prediction.- Computational Alanine Scanning Reveals Common Features of TCR/pMHC Recognition in HLA-DQ8-Associated Celiac Disease.- Umbrella Sampling-Based Method to Compute Ligand-Binding Affinity.- Creating Maps of the Ligand Binding Landscape for Kinetics-Based Drug Discovery.- Prediction of Protein-Protein Binding Affinities from Unbound Protein Structures.- Parameter Optimization for Ion Channel Models: Integrating New Data with Known Channel Properties.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
