Computational Methods for Estimating the Kinetic Parameters of Biological Systems (Methods in Molecular Biology Book 2385)
Take your systems biology and bioinformatics expertise to the next level with Computational Methods for Estimating the Kinetic Parameters of Biological Systems, Volume 2385 in the highly regarded Methods in Molecular Biology series. Now available at Books Hub PK, this volume offers a detailed guide for researchers, computational biologists, and students focused on the quantitative modeling and simulation of biochemical systems.
Edited by Germán Bassi, this book delivers a practical, research-based overview of computational strategies used to estimate kinetic parameters—a critical component in systems biology, metabolic modeling, drug design, and synthetic biology. The volume compiles a wide range of methods, from basic algorithms to advanced optimization techniques, making it suitable for both beginners and experienced practitioners.
What You’ll Find Inside:
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Step-by-step protocols for parameter estimation from time-series data.
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Applications of deterministic and stochastic modeling approaches.
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Tutorials on tools like MATLAB, Python, and open-source platforms used in biological systems modeling.
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Techniques including genetic algorithms, Bayesian inference, machine learning, and multi-objective optimization for robust parameter estimation.
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Expert commentary and troubleshooting notes for practical use in real-world datasets.
Each chapter adheres to the Springer Protocols format, ensuring clarity and reproducibility. This book not only explains how to model biochemical pathways but also provides code examples and datasets for hands-on learning.
Ideal For:
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Computational and systems biologists
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Bioengineers and synthetic biologists
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Graduate students and postdoctoral researchers
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Anyone developing or analyzing kinetic models in biochemistry or pharmacology
Key Features:
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Over 300 pages of detailed, peer-reviewed methods
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High-quality illustrations, sample data, and software guidance
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Written by international experts in computational biology and modeling
