Complex Systems Science in BiomedicineThomas Deisboeck, J. Yasha Kresh Springer Science & Business Media, 13 Haz 2007 - 864 sayfa Complex Systems Science in Biomedicine Complex Systems Science in Biomedicine covers the emerging field of systems science involving the application of physics, mathematics, engineering and computational methods and techniques to the study of biomedicine including nonlinear dynamics at the molecular, cellular, multi-cellular tissue, and organismic level. With all chapters helmed by leading scientists in the field, Complex Systems Science in Biomedicine's goal is to offer its audience a timely compendium of the ongoing research directed to the understanding of biological processes as whole systems instead of as isolated component parts.
Complex Systems Science in Biomedicine is essential reading for experimental, theoretical, and interdisciplinary scientists working in the biomedical research field interested in a comprehensive overview of this rapidly emerging field. About the Editors: J. Yasha Kresh is currently Professor of Cardiothoracic Surgery and Research Director, Professor of Medicine and Director of Cardiovascular Biophysics at the Drexel University College of Medicine. An expert in dynamical systems, he holds appointments in the School of Biomedical Engineering and Health Systems, Dept. of Mechanical Engineering and Molecular Pathobiology Program. Prof. Kresh is Fellow of the American College of Cardiology, American Heart Association, Biomedical Engineering Society, American Institute for Medical and Biological Engineering. |
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3 | |
25 | |
METHODS AND TECHNIQUES OF COMPLEX SYSTEMS | 33 |
3 | 35 |
2 | 36 |
AgentBased Models | 68 |
Information | 94 |
Chapter 2 | 115 |
Model and 3 Future | 496 |
Discussion and Future | 500 |
MOTOR LEARNING | 507 |
6 | 557 |
Competition with Spatial | 563 |
17 | 564 |
26 | 570 |
3 | 573 |
Nonlinear Effects in Simple | 121 |
Spatial Structure and Network Structure 6 Discussion and Conclusions | 130 |
Chapter 3 | 138 |
Chapter 3 | 146 |
1 | 160 |
THE ARCHITECTURE OF BIOLOGICAL NETWORKS | 165 |
5 | 183 |
Complexity in Molecular Networks | 210 |
MODELING | 227 |
4 | 233 |
3 | 247 |
Contending with Multiple Independent 6 Relevance | 251 |
Medical | 259 |
2 | 265 |
Modeling | 268 |
Future Work and Its Relevance | 277 |
1 | 283 |
Molecular Biology and Complex System 2 Complexity in Living Systems Sciences | 284 |
5 | 294 |
2 | 311 |
MODELS FROM | 333 |
Modeling and Computational Analysis of Autocrine and Paracrine Networks | 341 |
Conclusions and Outlook | 349 |
1 | 358 |
Discussion | 368 |
Introduction | 375 |
3 | 376 |
Models of the Cardiac | 392 |
Discussion | 402 |
CARDIAC OSCILLATIONS AND ARRHYTHMIA ANALYSIS | 409 |
Future | 416 |
4 | 425 |
The Langevin | 440 |
4 | 451 |
5 | 463 |
Future Work and Relevance | 473 |
2 | 483 |
Properties and Applications of | 490 |
33 | 580 |
46 | 588 |
Discussion Conclusions and Future | 597 |
63 | 600 |
The Interaction of Complex Biosystems | 604 |
MULTIPLE ORGAN | 631 |
Summary | 638 |
Therapy as an InformationYielding Perturbation 3 Employing Information on Progress toward Multiple Goals | 639 |
Examples of Complexity Loss with | 646 |
65 | 649 |
Conclusion | 652 |
1 | 657 |
Experimental Approaches and Behavioral 3 Theoretical Approaches 4 Relevance for Patients and Therapy | 679 |
Combining Selection Methods Produces a Richer Set | 685 |
Discussion | 695 |
APPLICATION OF BIOMOLECULAR COMPUTING | 701 |
A Biomolecular Database System 4 Applying Our Biomolecular Database System to Execute | 709 |
4 | 737 |
70 | 739 |
81 | 757 |
Chapter 5 | 763 |
Theoretical Model of Motivation | 770 |
95 | 771 |
Neuroimaging of the General RewardAversion System Underlying | 776 |
115 | 789 |
Implications of RewardAversion Neuroimaging for Psychiatric 6 Linking the Distributed Neural Groups Processing RewardAversion | 791 |
224 | 807 |
A NEUROMORPHIC SYSTEM | 811 |
Simulated | 819 |
A BIOLOGICALLY INSPIRED APPROACH TOWARD | 827 |
Discussion | 834 |
8 | 837 |
Biomedical 3 | 841 |
856 | |
859 | |
861 | |