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E-raamat: In Silico: 3D Animation and Simulation of Cell Biology with Maya and MEL

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(Institute of Medical Science, University of Toronto, Canada), (Department of Medicine, University of Toronto, Canada), (AXS Biomedical Animation Studio and Institute of Medical Science, University of Toronto, Canada)
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In Silico: 3D Animation and Simulation of Cell Biology with Maya and MELSuurem pilt
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In Silico introduces Maya programming into one of the most fascinating application areas of 3D graphics: biological visualization. In five building-block tutorials, this book prepares animators to work with visualization problems in cell biology. The book assumes no deep knowledge of cell biology or 3D graphics programming. An accompanying DVD-ROM includes code derived from the tutorials, the working Maya computer files, and sample animated movies.

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Animators will learn to work with visualization problems in cell biology using basic Maya programming
Preface xiii
Who is this book for? xiv
Why Maya? xiv
What the book offers xv
Computer hardware and software xxi
About the authors xxii
Acknowledgments xxiii
Part 1 Setting the stage
1(68)
Introduction
3(18)
The challenge
4(1)
Wetware for seeing
5(1)
Visualization in science
6(2)
Organizational hierarchy: Keys to biology in vivo and in silico
8(5)
Enter Maya
13(6)
Endless possibilities
19(1)
References
19(2)
Computers and the organism
21(24)
Introduction
22(1)
Information and process
22(1)
Language and program
23(3)
High and low
26(1)
Interpret or compile?
27(1)
The Backus watershed
28(2)
Stored programs
30(3)
Conditional control
33(2)
The computed organism
35(1)
The computational organism
36(3)
OOPs and agents
39(2)
Summary
41(2)
References
43(2)
Animating biology
45(24)
Introduction
46(1)
Animation and film perception
46(3)
The animator's workflow
49(2)
The three-stage workflow
51(16)
Putting it all together
67(1)
References
67(2)
Part 2 A foundation in Maya
69(270)
Maya basics
71(30)
Getting started
72(6)
How Maya works (briefly)
78(4)
Maya's UI
82(17)
Summary
99(2)
Modeling geometry
101(36)
Introduction
102(1)
NURBS modeling
103(4)
Polygonal modeling
107(27)
NURBS primitive modeling
109(8)
Deform the sphere using components
117(2)
Make and deform a polygon primitive
119(3)
Construction history
122(7)
Create a NURBS ``fiber''
129(5)
Summary
134(1)
References
135(2)
Animation
137(20)
Introduction
138(1)
Animation
138(13)
A Keyframe animation
145(6)
Animation nodes in the Hypergraph and Attribute Editor
151(3)
A simple procedural animation
151(3)
Summary
154(3)
Dynamics
157(30)
Introduction
158(2)
The Dynamics module
160(25)
Rigid body dynamics
166(7)
Particles in a container
173(11)
Create a playblast
184(1)
Summary
185(2)
Shading
187(28)
Introduction
188(2)
The Render menu set
190(1)
Shading
191(23)
Shading
203(11)
Summary
214(1)
Cameras
215(16)
Maya Cameras
217(13)
A camera on hemoglobin
222(8)
Summary
230(1)
Lighting
231(12)
Lighting
232(9)
Lighting the hemoglobin scene
235(6)
Summary
241(2)
Action! Maya rendering
243(18)
Rendering
244(5)
Advanced rendering techniques with the mental ray for Maya renderer
249(10)
Batch rendering
252(5)
Playback using fCheck
257(2)
Summary
259(2)
MEL scripting
261(50)
Introduction
262(1)
The origins of MEL
263(1)
In a word: Scripting
264(2)
Getting started
266(3)
MEL syntax
269(1)
Values
270(1)
variables
271(6)
Mathematical and logical expressions
277(3)
The MEL command
280(6)
Attributes in MEL
286(2)
Conditional statements
288(1)
Loops
289(2)
Procedures
291(1)
Animation expressions
292(9)
Putting it all together: The MEL script
301(5)
Building a MEL script
302(4)
Debugging your scripts
306(2)
Random number generation in Maya
308(1)
Summary
309(2)
Data input/output
311(28)
Introduction
312(1)
Translators
313(2)
Reading and writing files with MEL
315(22)
Visualizing cell migration
322(15)
Summary
337(2)
Part 3 Biology in silico---Maya in action
339(246)
Building a protein
341(42)
Introduction
342(4)
Problem overview
346(8)
Methods:Algorithm design
354(1)
Methods: Encoding the algorithm
354(14)
Results: Running the script
368(4)
Results: Rendering your molecule
372(8)
Summary
380(1)
References
381(2)
Self-assembly
383(60)
Introduction
384(1)
Problem overview
385(9)
Methods: Actin geometry
394(5)
Methods: Diffusion and reaction events
399(4)
Methods: Reaction rates and probabilities
403(6)
Methods: Algorithm design
409(3)
Methods: Encoding the algorithm
412(25)
Results: Running your simulation
437(4)
Summary
441(1)
References
442(1)
Modeling a mobile cell
443(36)
Introduction
444(1)
Problem overview
445(4)
Model definition
449(2)
Methods: Generating pseudopods
451(2)
Methods: Algorithm design
453(1)
Methods: A cell locomotion engine
454(12)
Methods: Encoding the algorithm
466(9)
Methods: Loading the script
475(1)
Results: Running the script
476(1)
Summary
477(1)
References
477(2)
Growing an ECM scaffold
479(40)
Introduction
480(1)
Problem overview
481(2)
Model definition
483(3)
Methods: Algorithm design
486(8)
Methods: Encoding the algorithm
494(18)
Methods: Grow your scaffold!
512(4)
Results: Parameter effects
516(1)
Summary
517(1)
References
517(2)
Scaffold invasions: Modeling 3D populations of mobile cells
519(56)
Introduction
520(1)
Problem overview
521(4)
Model definition
525(3)
Methods: Model design
528(10)
Methods: Encoding the algorithm
538(27)
Methods: Running the simulation
565(7)
Results: Data output
572(1)
Summary
573(1)
References
573(2)
Conclusion: A new kind of seeing
575(10)
Explanations, simulations, speculations
576(2)
Maya's role
578(1)
The path so far
578(1)
The future
579(3)
References
582(3)
Further reading 585(8)
Glossary 593(14)
Index 607
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