Complex Systems Science in Biomedicine

Ön Kapak
Thomas Deisboeck, J. Yasha Kresh
Springer Science & Business Media, 13 Haz 2007 - 864 sayfa

Complex Systems Science in Biomedicine
Thomas S. Deisboeck and J. Yasha Kresh

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.
In Parts I & II, Complex Systems Science in Biomedicine provides a general systems thinking perspective and presents some of the fundamental theoretical underpinnings of this rapidly emerging field. Part III then follows with a multi-scaled approach, spanning from the molecular to macroscopic level, exemplified by studying such diverse areas as molecular networks and developmental processes, the immune and nervous systems, the heart, cancer and multi-organ failure. The volume concludes with Part IV that addresses methods and techniques driven in design and development by this new understanding of biomedical science.


Key Topics Include:
• Historic Perspectives of General Systems Thinking
• Fundamental Methods and Techniques for Studying Complex Dynamical Systems
• Applications from Molecular Networks to Disease Processes
• Enabling Technologies for Exploration of Systems in the Life Sciences

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:
Thomas S. Deisboeck is currently Assistant Professor of Radiology at Massachusetts General Hospital and Harvard Medical School in Boston. An expert in interdisciplinary cancer modeling, Dr. Deisboeck is Director of the Complex Biosystems Modeling Laboratory which is part of the Harvard-MIT Martinos Center for Biomedical Imaging.

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.

 

İçindekiler

PERSPECTIVES
3
Chapter 1
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
426
856
811
859
621
861
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