Computational Analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular BiologistsCambridge University Press, 2000年9月4日 - 531 頁 A true understanding of genetic and metabolic function and design is facilitated by mathematical and computational methods for analyzing biochemical systems. This hands-on reference teaches biochemists and molecular biologists the use of modern computational methods for the analysis of complex biomedical systems requiring a modest mathematical background. The book begins with representations of biochemical systems, provides guidelines for setting up models, details mathematical and computational methods of parameter estimation and model analysis, and connects to the modern literature with four detailed case studies. Every step is illustrated with examples and explored with accompanying PLAS software. The volume also features over 250 exercises with about one quarter fully or partially solved. |
內容
Graphical Representation of Biochemical Systems | 11 |
Models of Biochemical Systems | 37 |
From Maps to Equations | 76 |
Computer Simulation | 97 |
Parameter Estimation | 143 |
Analytical SteadyState Evaluation | 193 |
Sensitivity Analysis | 222 |
Case Study 1 Anaerobic Fermentation Pathway in Saccharomyces cerevisiae | 260 |
Case Study 3 A Sequence of Models Describing Purine Metabolism | 326 |
Case Study 4 Algebraic Analysis of the Initial Steps of the GlycolyticGlycogenolytic Pathway in Perfused Rat Liver | 365 |
Epilogue Canonical Modeling Beyond Biochemistry | 399 |
Appendix | 413 |
Hints and Partial Solutions | 443 |
| 469 | |
| 499 | |
| 507 | |
Case Study 2 Diagnosis and Refinement of a Model of the Tricarboxylic Acid Cycle in Dictyostelium discoideum | 293 |
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常見字詞
a₁ adenylates algebraic algorithm analysis approximation b₁ biochemical systems bolus branch point canonical modeling Chapter components computed constraints corresponding Cramer's rule Curto decrease degradation dependent derivative determinant differential equations dynamics effect eigenvalues enzyme enzyme activities estimation example Exercise experimental feedback flux fructose-6-phosphate gene glucose glucose-6-phosphate glycogen GMA model graph implemented increase independent variables inhibition initial values input instance kinetic orders linear pathway log gains logarithmic gains mathematical matrix metabolites Michaelis-Menten modulation numerical values obtained operating point oscillations parameter values perturbations phosphofructokinase PLAS pool power-law predictions production protein PRPP purine metabolism pyruvate rate constant rate law reaction relative change representation represented responses S-system equations S-system model Savageau Shiraishi and Savageau simulations slope Sorribas specific steady steady-state concentrations steady-state equations steady-state values step substrate symbolic synthesis uric acid V₁ vector Vmax Voit X₁ X1 and X2 y₁ zero β₁