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Computational Cell Biology 1st ed 2002. Corr. 3rd printing 2005 [Kietas viršelis]

4.50/5 (19 ratings by Goodreads)
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  • Formatas: Hardback, 468 pages, aukštis x plotis: 254x178 mm, weight: 1900 g, XX, 468 p., 1 Hardback
  • Serija: Interdisciplinary Applied Mathematics 20
  • Išleidimo metai: 09-Jul-2002
  • Leidėjas: Springer-Verlag New York Inc.
  • ISBN-10: 0387953698
  • ISBN-13: 9780387953694
Kitos knygos pagal šią temą:
  • Formatas: Hardback, 468 pages, aukštis x plotis: 254x178 mm, weight: 1900 g, XX, 468 p., 1 Hardback
  • Serija: Interdisciplinary Applied Mathematics 20
  • Išleidimo metai: 09-Jul-2002
  • Leidėjas: Springer-Verlag New York Inc.
  • ISBN-10: 0387953698
  • ISBN-13: 9780387953694
Kitos knygos pagal šią temą:
This textbook provides an introduction to dynamic modeling in cell biology, emphasizing computational approaches based on realistic molecular mechanisms. It is designed to introduce cell biology and neuroscience students to computational modeling, and applied mathematics students, theoretical biologists, and engineers to many of the problems in dynamical cell biology. This volume was conceived of and begun by Professor Joel Keizer based on his many years of teaching and research together with his colleagues. The project was expanded and finished by his students and friends after his untimely death in 1999. Carefully selected examples are used to motivate the concepts and techniques of computational cell biology, through a progression of increasingly more complex and demanding cases. Illustrative exercises are included with every chapter, and mathematical and computational appendices are provided for reference. This textbook will be useful for advanced undergraduate and graduate theoretical biologists, and for mathematics students and life scientists who wish to learn about modeling in cell biology.Royalties from this book will be donated to the Joel E. Keizer memorial endowment for collaborative interdisciplinary research in the life sciences.

This textbook provides an introduction to dynamic modeling in molecular cell biology, taking a computational and intuitive approach. Detailed illustrations, examples, and exercises are included throughout the text. Appendices containing mathematical and computational techniques are provided as a reference tool.

Recenzijos

From the reviews:



Nature Cell Biology, Vol. 5/2, February 2003 



"The authors and editors of this volume are outstanding mathematical biologists. Computational Cell Biology introduces the principles, techniques, tools and insights of mathematical biology through detailed exposition of ion channels, calcium signaling, transporters, cellular endocrinology, gap junctions, cell cycle controls and molecular motors. If you want to see the tools of applied mathematics at work in some key areas of cell physiology and cell biology, this is the book to read. Your understanding of rate laws, ordinary and partial differential equations, numerical methods, phase portraits, stability analysis, Brownian ratchets, multiple time scales, bifurcations and stochastic processes will all be enhanced. You will have become significantly bilingual. The authors reach out valiantly from the blue of mathematical biology toward the red of experimental biology. Perhaps the time has finally arrived when experimental cell biologists can reach back and shake hands on an effective partnership. It's an epochal moment; illuminating the shadowy complexity of cell biology will surely require white light."



BIOINFORMATICS



"a very didactic, easy to read and excellent introduction to the subjectthe book is remarkable for it pedagogical clarityThe book is strong in presenting the in-action techniques, with state-of-the art models of realistic biological situation, where their usefulness is easily appreciated. Another strength of the book is to provide examples of the comprehensive modeling, from the initial descriptive molecular model to the full analysis of the equationsMany modeling approaches presented in the book can be easily adapted or serve as templates to other biological problemsThe mathematically-able should find [ the book] a nice complement to the classical biology textbookan attractive introduction to a number of mathematicaltechniques whose existence is simply unknown to many biologists. The remarkable clarity of the presentation makes it an unique self-teaching tool for scientists who would like to model their own experimental data, or to be able to appreciate modelinga modern and valuable textbook full of simple yet useful analytical and numerical knowledge. It will please the mature scientist by its topics, and the student by its didactic style. Coming from a strong teaching practice, it is also the perfect support for a lecture series on mathematical modeling in biology."



"This textbook address students of either mathematics or the biological sciences interested in the other topic. It aims to introduce to computational cell biology, i.e. the question, whether and how mathematical modelling can contribute to a description or understanding of cellular phenomena. the book should be well readable and provide essential guide to the methodology of modelling in biology." (H. Muthsam, Monatshefte für Mathematik, Vol. 143 (4), 2004)



"Computational Cell Biology has been carefully designed as a text for an introductory course in cellular dynamics, in which the students are to be drawn from both biology and applied mathematics. Computational Cell Biology is an imaginatively conceived, carefully written, and well-edited book, which is strongly recommended as the text for advanced and early graduate courses in mathematical biology. Both students and teachers will enjoy learning from it, and future quantitative biologists and biomathematicians will remember it fondly." (Alwyn Scott, SIAM Review, Vol. 45 (2), 2003)



"Joel E. Keizer was the pioneer in computational cell biology. This textbook represents the combined efforts of friends and colleagues to complete a task that Joel was unable to end. In its pages one can easily sense the enthusiasm for this subject that he generated in others. It is a tribute that each chapter has been written by awell-recognized expert in the field. The result is a work that I believe should be on the desktop of all who appreciate good science." (John G. Milton, Mathematical Reviews, 2003 j)



"Computational Cell Biology is a recent introductory textbook for dynamic modelling in cell biology. The result is a very didactic, easy to read and excellent introduction to the subject. the book is remarkable for its pedagogical clarity. Another strength of the book is to provide examples of the comprehensive modelling . It will please the mature scientist by its topics, and the student by its didactic style. it is also the perfect support for a lecture series ." (Francois Nedelec, Bioinformatics, July, 2003)



"Computational Cell Biology presents us with some elegant presentation of fundamental topics. Many of the chapters are absolute gems. In short, Computational Cell Biology is a valuable addition to the literature, filling a number of gaps and presenting the material in a way that will be useful for students. It will have a place on my bookshelf, and in my required reading list." (James Sneyd, UK Nonlinear News, November, 2002)

Preface vii
Contributors xi
I Introductory Course 1(168)
Dynamic Phenomena in Cells
3(18)
Scope of Cellular Dynamics
3(5)
Computational Modeling in Biology
8(3)
Cartoons, Mechanisms, and Models
8(1)
The Role of Computation
9(1)
The Role of Mathematics
10(1)
A Simple Molecular Switch
11(2)
Solving and Analyzing Differential Equations
13(7)
Numerical Integration of Differential Equations
15(3)
Introduction to Numerical Packages
18(2)
Exercises
20(1)
Voltage Gated Ionic Currents
21(32)
Basis of the Ionic Battery
23(4)
The Nernst Potential: Charge Balances Concentration
24(2)
The Resting Membrane Potential
26(1)
The Membrane Model
27(2)
Equations for Membrane Electrical Behavior
28(1)
Activation and Inactivation Gates
29(5)
Models of Voltage-Dependent Gating
29(2)
The Voltage Clamp
31(3)
Interacting Ion Channels: The Morris-Lecar Model
34(11)
Phase Plane Analysis
36(2)
Stability Analysis
38(2)
Why Do Oscillations Occur?
40(3)
Excitability and Action Potentials
43(1)
Type I and Type II Spiking
44(1)
The Hodgkin-Huxley Model
45(2)
FitzHugh-Nagumo Class Models
47(2)
Summary
49(1)
Exercises
50(3)
Transporters and Pumps
53(24)
Passive Transport
54(3)
Transporter Rates
57(8)
Algebraic Method
59(1)
Diagrammatic Method
60(2)
Rate of the GLUT Transporter
62(3)
The Na+/Glucose Cotransporter
65(5)
SERCA Pumps
70(3)
Transport Cycles
73(3)
Exercises
76(1)
Fast and Slow Time Scales
77(24)
The Rapid Equilibrium Approximation
78(4)
Asymptotic Analysis of Time Scales
82(1)
Glucose-Dependent Insulin Secretion
83(5)
Ligand Gated Channels
88(2)
The Neuromuscular Junction
90(1)
The Inositol Trisphosphate (IP3) receptor
91(3)
Michaelis-Menten Kinetics
94(4)
Exercises
98(3)
Whole-Cell Models
101(39)
Models of ER and PM Calcium Handling
102(5)
Flux Balance Equations with Rapid Buffering
103(3)
Expressions for the Fluxes
106(1)
Calcium Oscillations in the Bullfrog Sympathetic Ganglion Neuron
107(8)
Ryanodine Receptor Kinetics: The Keizer-Levine Model
108(3)
Bullfrog Sympathetic Ganglion Neuron Closed-Cell Model
111(2)
Bullfrog Sympathetic Ganglion Neuron Open-Cell Model
113(2)
The Pituitary Gonadotroph
115(13)
The ER Oscillator in a Closed Cell
116(6)
Open-Cell Model with Constant Calcium Influx
122(2)
The Plasma Membrane Oscillator
124(2)
Bursting Driven by the ER in the Full Model
126(2)
The Pancreatic Beta Cell
128(8)
Chay-Keizer Model
129(4)
Chay-Keizer with an ER
133(3)
Exercises
136(4)
Intercellular Communication
140(29)
Electrical Coupling and Gap Junctions
141(5)
Synchronization of Two Oscillators
142(1)
Asynchrony Between Oscillators
143(1)
Cell Ensembles, Electrical Coupling Length Scale
144(2)
Synaptic Transmission Between Neurons
146(4)
Kinetics of Postsynaptic Current
147(1)
Synapses: Excitatory and Inhibitory; Fast and Slow
148(2)
When Synapses Might (or Might Not) Synchronize Active Cells
150(3)
Neural Circuits as Computational Devices
153(6)
Large-Scale Networks
159(6)
Exercises
165(4)
II Advanced Material 169(209)
Spatial Modeling
171(27)
One-Dimensional Formulation
173(6)
Conservation in One Dimension
173(2)
Fick's Law of Diffusion
175(1)
Advection
176(1)
Flux of Ions in a Field
177(1)
The Cable Equation
177(1)
Boundary and Initial Conditions
178(1)
Important Examples with Analytic Solutions
179(5)
Diffusion Through a Membrane
179(1)
Ion Flux Through a Channel
180(1)
Voltage Clamping
181(1)
Diffusion in a Long Dendrite
181(2)
Diffusion into a Capillary
183(1)
Numerical Solution of the Diffusion Equation
184(2)
Multidimensional Problems
186(3)
Conservation Law in Multiple Dimensions
186(1)
Fick's Law in Multiple Dimensions
187(1)
Advection in Multiple Dimensions
188(1)
Boundary and Initial Conditions for Multiple Dimensions
188(1)
Diffusion in Multiple Dimensions: Symmetry
188(1)
Traveling Waves in Nonlinear Reaction-Diffusion Equations
189(6)
Traveling Wave Solutions
190(2)
Traveling Wave in the Fitzhugh-Nagumo Equations
192(3)
Exercises
195(3)
Modeling Intracellular Calcium Waves and Sparks
198(32)
Microfluorometric Measurements
198(2)
A Model of the Fertilization Calcium Wave
200(2)
Including Calcium Buffers in Spatial Models
202(1)
The Effective Diffusion Coefficient
203(1)
Simulation of a Fertilization Calcium Wave
204(1)
Simulation of a Traveling Front
204(4)
Calcium Waves in the Immature Xenopus Oocycte
208(1)
Simulation of a Traveling Pulse
208(2)
Simulation of a Kinematic Wave
210(3)
Spark-Mediated Calcium Waves
213(1)
The Fire-Diffuse-Fire Model
214(6)
Modeling Localized Calcium Elevations
220(2)
Steady-State Localized Calcium Elevations
222(5)
The Steady-State Excess Buffer Approximation (EBA)
224(1)
The Steady-State Rapid Buffer Approximation (RBA)
225(1)
Complementarity of the Steady-State EBA and RBA
226(1)
Exercises
227(3)
Biochemical Oscillations
230(31)
Biochemical Kinetics and Feedback
232(4)
Regulatory Enzymes
236(3)
Two-Component Oscillators Based on Autocatalysis
239(4)
Substrate-Depletion Oscillator
240(2)
Activator-Inhibitor Oscillator
242(1)
Three-Component Networks Without Autocatalysis
243(4)
Positive Feedback Loop and the Routh-Hurwitz Theorem
244(1)
Negative Feedback Oscillations
244(1)
The Goodwin Oscillator
244(3)
Time-Delayed Negative Feedback
247(3)
Distributed Time Lag and the Linear Chain Trick
248(1)
Discrete Time Lag
249(1)
Circadian Rhythms
250(5)
Exercises
255(6)
Cell Cycle Controls
261(24)
Physiology of the Cell Cycle in Eukaryotes
261(2)
Molecular Mechanisms of Cell Cycle Control
263(2)
A Toy Model of Start and Finish
265(4)
Hysteresis in the Interactions Between Cdk and APC
266(1)
Activation of the APC at Anaphase
267(2)
A Serious Model of the Budding Yeast Cell Cycle
269(4)
Cell Cycle Controls in Fission Yeast
273(3)
Checkpoints and Surveillance Mechanisms
276(1)
Division Controls in Egg Cells
276(2)
Growth and Division Controls in Metazoans
278(1)
Spontaneous Limit Cycle or Hysteresis Loop?
279(2)
Exercises
281(4)
Modeling the Stochastic Gating of Ion Channels
285(35)
Single-Channel Gating and a Two-State Model
285(8)
Modeling Channel Gating as a Markov Process
286(2)
The Transition Probability Matrix
288(1)
Dwell Times
289(1)
Monte Carlo Simulation
290(1)
Simulating Multiple Independent Channels
291(1)
Gillespie's Method
292(1)
An Ensemble of Two-State Ion Channels
293(5)
Probability of Finding N Channels in the Open State
293(3)
The Average Number of Open Channels
296(1)
The Variance of the Number of Open Channels
297(1)
Fluctuations in Macroscopic Currents
298(4)
Modeling Fluctuations in Macroscopic Currents with Stochastic ODEs
302(5)
Langevin Equation for an Ensemble of Two-State Channels
304(2)
Fokker-Planck Equation for an Ensemble of Two-State Channels
306(1)
Membrane Voltage Fluctuations
307(4)
Membrane Voltage Fluctuations with an Ensemble of Two-State Channels
309(2)
Stochasticity and Discreteness in an Excitable-Membrane Model
311(6)
Phenomena Induced by Stochasticity and Discreteness
312(1)
The Ensemble Density Approach Applied to the Stochastic Morris-Lecar Model
313(1)
Langevin Formulation for the Stochastic Morris-Lecar Model
314(3)
Exercises
317(3)
Molecular Motors: Theory
320(34)
Molecular Motions as Stochastic Processes
323(7)
Protein Motion as a Simple Random Walk
323(2)
Polymer Growth
325(2)
Sample Paths of Polymer Growth
327(2)
The Statistical Behavior of Polymer Growth
329(1)
Modeling Molecular Motions
330(5)
The Langevin Equation
330(2)
Numerical Simulation of the Langevin Equation
332(1)
The Smoluchowski Model
333(1)
First Passage Time
334(1)
Modeling Chemical Reactions
335(3)
A Mechanochemical Model
338(1)
Numerical Simulation of Protein Motion
339(6)
Numerical Algorithm that Preserves Detailed Balance
340(1)
Boundary Conditions
341(1)
Numerical Stability
342(2)
Implicit Discretization
344(1)
Derivations and Comments
345(8)
The Drag Coefficient
345(1)
The Equipartition Theorem
345(1)
A Numerical Method for the Langevin Equation
346(1)
Some Connections with Thermodynamics
347(2)
Jumping Beans and Entropy
349(1)
Jump Rates
350(1)
Jump Rates at an Absorbing Boundary
351(2)
Exercises
353(1)
Molecular Motors: Examples
354(24)
Switching in the Bacterial Flagellar Motor
354(5)
A Motor Driven by a ``Flashing Potential''
359(3)
The Polymerization Ratchet
362(2)
Simplified Model of the Fo Motor
364(10)
The Average Velocity of the Motor in the Limit of Fast Diffusion
366(3)
Brownian Ratchet vs. Power Stroke
369(1)
The Average Velocity of the Motor When Chemical Reactions Are as Fast as Diffusion
369(5)
Other Motor Proteins
374(2)
Exercises
376(2)
A Qualitative Analysis of Differential Equations 378(32)
Matrix and Vector Manipulation
379(1)
A Brief Review of Power Series
380(2)
Linear ODEs
382(6)
Solution of Systems of Linear ODEs
383(2)
Numerical Solutions of ODEs
385(1)
Eigenvalues and Eigenvectors
386(2)
Phase Plane Analysis
388(7)
Stability of Linear Steady States
390(2)
Stability of a Nonlinear Steady States
392(3)
Bifurcation Theory
395(6)
Bifurcation at a Zero Eigenvalue
396(2)
Bifurcation at a Pair of Imaginary Eigenvalues
398(3)
Perturbation Theory
401(7)
Regular Perturbation
401(2)
Resonances
403(2)
Singular Perturbation Theory
405(3)
Exercises
408(2)
B Solving and Analyzing Dynamical Systems Using XPPAUT 410(29)
Basics of Solving Ordinary Differential Equations
411(11)
Creating the ODE File
411(1)
Running the Program
412(1)
The Main Window
413(1)
Solving the Equations, Graphing, and Plotting
414(2)
Saving and Printing Plots
416(2)
Changing Parameters and Initial Data
418(1)
Looking at the Numbers: The Data Viewer
419(1)
Saving and Restoring the State of Simulations
420(1)
Important Numerical Parameters
421(1)
Command Summary: The Basics
422(1)
Phase Planes and Nonlinear Equations
422(5)
Direction Fields
423(1)
Nullclines and Fixed Points
423(3)
Command Summary: Phase Planes and Fixed Points
426(1)
Bifurcation and Continuation
427(5)
General Steps for Bifurcation Analysis
427(1)
Hopf Bifurcation in the FitzHugh-Nagumo Equations
428(2)
Hints for Computing Complete Bifurcation Diagrams
430(2)
Partial Differential Equations: The Method of Lines
432(2)
Stochastic Equations
434(5)
A Simple Brownian Ratchet
434(1)
A Sodium Channel Model
434(2)
A Flashing Ratchet
436(3)
C Numerical Algorithms 439(12)
References 451(12)
Index 463