Computational Cell Biology

Front Cover
Christopher P. Fall, Eric S. Marland, John M. Wagner, John J. Tyson
Springer Science & Business Media, Feb 15, 2005 - Science - 468 pages
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This text is an introduction to dynamical modeling in cell biology. It is not meant as a complete overview of modeling or of particular models in cell biology. Rather, we use selected biological examples to motivate the concepts and techniques used in computational cell biology. This is done through a progression of increasingly more complex cellular functions modeled with increasingly complex mathematical and c- putational techniques. There are other excellent sources for material on mathematical cell biology, and so the focus here truly is computer modeling. This does not mean that there are no mathematical techniques introduced, because some of them are absolutely vital, but it does mean that much of the mathematics is explained in a more intuitive fashion, while we allow the computer to do most of the work. The target audience for this text is mathematically sophisticated cell biology or neuroscience students or mathematics students who wish to learn about modeling in cell biology. The ideal class would comprise both biology and applied math students, who might be encouraged to collaborate on exercises or class projects. We assume as little mathematical and biological background as we feel we can get away with, and we proceed fairly slowly. The techniques and approaches covered in the ?rst half of the book will form a basis for some elementary modeling or as a lead in to more advanced topics covered in the second half of the book.
 

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Contents

Christopher P Fall
3
Theory
12
Phase Plane Analysis
18
7
49
Transporters and Pumps
53
3
54
Fast and Slow Time Scales
77
Exercises
98
9
236
4
243
6
250
Bela Novak
261
4
267
B 5
269
9
274
11
277

WholeCell Models
101
53
125
Intercellular Communication
140
Spatial Modeling 171
170
57
178
59
187
91
193
Modeling Intracellular Calcium Waves and Sparks
198
B Solving and Analyzing Dynamical Systems Using XPPAUT
210
B 3
221
Biochemical Oscillations
230
98
232
Modeling
285
15
294
29
300
Modeling Fluctuations in Macroscopic Currents with Stochastic
302
TwoState Channels Stochasticity and Discreteness in an Excitable Membrane Model 11 6 1 Phenomena Induced by Stochasticity and Discreteness
313
A Qualitative Analysis of Differential Equations
323
Numerical Algorithms
439
188
463
101
464
286
466
Copyright

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