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035   $a(DE-He213)978-3-030-86252-7
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100   $a20211018d2021      u|         0
101 0 $aeng
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181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aStochastic Chemical Reaction Systems in Biology /$fby Hong Qian, Hao Ge
205   $a1st ed. 2021.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021.
215   $a1 online resource (364 pages)
225 1 $aLecture Notes on Mathematical Modelling in the Life Sciences,$x2193-4797
311 08$a3-030-86251-8 
327   $a1. Introduction -- Part I Essentials of Deterministic and Stochastic Chemical Kinetics: 2. Kinetic Rate Equations and the Law of Mass Action -- 3. Probability Distribution and Stochastic Processes -- 4. Large Deviations and Kramers? rate formula -- 5. The Probabilistic Basis of Chemical Kinetics -- 6. Mesoscopic Thermodynamics of Markov Processes -- 7. Emergent Macroscopic Chemical Thermodynamics -- 8. Phase Transition and Mesoscopic Nonlinear Bistability -- Part III Stochastic Kinetics of Biochemical Systems and Processes: 9. Classic Enzyme Kinetics?The Michaelis-Menten and Briggs-Haldane Theories -- 10. Single-Molecule Enzymology and Driven Biochemical Kinetics with Chemostat -- 11. Stochastic Linear Reaction Kinetic Systems -- 12. Nonlinear Stochastic Reaction Systems with Simple Examples -- 13. Kinetics of the Central Dogma of Molecular Cell Biology -- 14. Stochastic Macromolecular Mechanics and Mechanochemistry -- Part IV Epilogue: Beyond Chemical Reaction Kinetics: 15. Landscape, Attractor-State Switching, and Differentiation -- 16. Nonlinear Stochastic Dynamics: New Paradigm and Syntheses -- References -- Index.
330   $aThis book provides an introduction to the analysis of stochastic dynamic models in biology and medicine. The main aim is to offer a coherent set of probabilistic techniques and mathematical tools which can be used for the simulation and analysis of various biological phenomena. These tools are illustrated on a number of examples. For each example, the biological background is described, and mathematical models are developed following a unified set of principles. These models are then analyzed and, finally, the biological implications of the mathematical results are interpreted. The biological topics covered include gene expression, biochemistry, cellular regulation, and cancer biology. The book will be accessible to graduate students who have a strong background in differential equations, the theory of nonlinear dynamical systems, Markovian stochastic processes, and both discrete and continuous state spaces, and who are familiar with the basic concepts of probability theory.
410  0$aLecture Notes on Mathematical Modelling in the Life Sciences,$x2193-4797
606   $aMathematics
606   $aBioinformatics
606   $aPhysical biochemistry
606   $aBiophysics
606   $aMathematics
606   $aComputational and Systems Biology
606   $aBiophysical Chemistry
606   $aBiophysics
615  0$aMathematics.
615  0$aBioinformatics.
615  0$aPhysical biochemistry.
615  0$aBiophysics.
615 14$aMathematics.
615 24$aComputational and Systems Biology.
615 24$aBiophysical Chemistry.
615 24$aBiophysics.
676   $a541.394
700   $aQian$b Hong$f1960-$01251012
702   $aGe$b Hao
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910506382903321
996   $aStochastic chemical reaction systems in biology$92899857
997   $aUNINA