Complex Systems Science in Biomedicine

前表紙
Thomas Deisboeck, J. Yasha Kresh
Springer Science & Business Media, 2007/06/13 - 864 ページ

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.

 

レビュー - レビューを書く

レビューが見つかりませんでした。

ページのサンプル

目次

THE NERVOUS SYSTEM
460
NEUROBIOLOGY AND COMPLEX BIOSYSTEM MODELING
463
1 NEURONAL SYSTEMS DYNAMICS
464
2 FUTURE WORK AND RELEVANCE TO BIOMEDICINE
473
3 CONCLUSIONS
477
MODELING SPONTANEOUS EPISODIC ACTIVITY IN DEVELOPING NEURONAL NETWORKS
483
1 INTRODUCTION
484
3 MODEL OF SPONTANEOUS ACTIVITY IN THE EMBRYONIC CHICK SPINAL CORD
487

COMPLEX SYSTEMS SCIENCE THE BASICS
30
METHODS AND TECHNIQUES OF COMPLEX SYSTEMS SCIENCE AN OVERVIEW
33
2 STATISTICAL LEARNING AND DATAMINING
37
3 TIME SERIES ANALYSIS
46
4 CELLULAR AUTOMATA
63
5 AGENTBASED MODELS
65
6 EVALUATING MODELS OF COMPLEX SYSTEMS
70
7 INFORMATION THEORY
76
8 COMPLEXITY MEASURES
81
91 General
95
NONLINEAR DYNAMICAL SYSTEMS
115
2 DYNAMICAL SYSTEMS IN GENERAL
118
3 LINEAR SYSTEMS AND SOME BASIC VOCABULARY
119
4 NONLINEAR EFFECTS IN SIMPLE SYSTEMS
121
SPATIAL STRUCTURE AND NETWORK STRUCTURE
130
6 DISCUSSION AND CONCLUSIONS
136
BIOLOGICAL SCALING AND PHYSIOLOGICAL TIME BIOMEDICAL APPLICATIONS
141
1 INTRODUCTION
142
THEORY FOR THE ORIGIN OF SCALING RELATIONSHIPS
146
3 BIOMEDICAL APPLICATIONS
153
4 DISCUSSION AND CONCLUSIONS
158
THE ARCHITECTURE OF BIOLOGICAL NETWORKS
165
2 BASIC NETWORK FEATURES
166
3 NETWORKS MODELS
169
4 BIOLOGICAL NETWORKS
172
5 CONCLUSIONS
176
ROBUSTNESS IN BIOLOGICAL SYSTEMS A PROVISIONAL TAXONOMY
183
2 GENOTYPIC VERSUS ENVIRONMENTAL VERSUS FUNCTIONAL ROBUSTNESS
185
4 CASE STUDIES OF ROBUST PRINCIPLES
190
5 AWAITING A SYNTHESIS OF ROBUSTNESS IN BIOLOGICAL SYSTEMS
201
COMPLEX ADAPTIVE BIOSYSTEMS AMULTISCALED APPROACH
206
COMPLEXITY IN MOLECULAR NETWORKS
210
NOISE IN GENE REGULATORY NETWORKS
211
2 THE MASTER EQUATION APPROACH
212
3 THE LANGEVIN APPROACH
220
4 DISCUSSION AND CONCLUSIONS
224
MODELING RNA FOLDING
227
2 RNA SECONDARY STRUCTURES AND THEIR PREDICTION
230
3 NEUTRAL NETWORKS IN THE SEQUENCE SPACE
232
4 CONSERVED RNA STRUCTURES
235
5 DISCUSSION
236
PROTEIN NETWORKS
247
2 LARGESCALE APPROACHES TO IDENTIFY PROTEIN EXPRESSION
248
3 IDENTIFYING PROTEIN INTERACTIONS
253
4 MEDICAL APPLICATIONS
259
ELECTRONIC CELL ENVIRONMENTS COMBINING GENE PROTEIN AND METABOLIC NETWORKS
265
2 BIOMEDICAL BACKGROUND
266
3 MODELING AND SIMULATION
268
4 FUTURE WORK AND ITS RELEVANCE TO BIOMEDICINE
277
Section 2 THE CELL AS A COMPLEX SYSTEM
281
TENSEGRITY DYNAMIC NETWORKS AND COMPLEX SYSTEMS BIOLOGY EMERGENCE IN STRUCTURAL AND INFORMATION NETW...
283
MOLECULAR BIOLOGY AND COMPLEX SYSTEM SCIENCES
284
2 COMPLEXITY IN LIVING SYSTEMS
287
NETWORKS AS THE GENERAL CONCEPTUAL FRAMEWORK
288
4 RESULTS
290
5 CONCLUSION
306
SPATIOTEMPORAL DYNAMICS OF EUKARYOTIC GRADIENT SENSING
311
1 INTRODUCTION
312
2 MODEL AND SIMULATION 21 Model
317
3 FUTURE WORK
327
PATTERNING BY EGF RECEPTOR MODELS FROM DROSOPHILA DEVELOPMENT
333
2 TWO EXAMPLES OF EGFR SIGNALING IN FRUIT FLY DEVELOPMENT
335
3 MODELING AND COMPUTATIONAL ANALYSIS OF AUTOCRINE AND PARACRINE NETWORKS
341
4 CONCLUSIONS AND OUTLOOK
349
DEVELOPMENTAL BIOLOGY AND THE CARDIAC SYSTEM
354
DEVELOPMENTAL BIOLOGY BRANCHING MORPHOGENESIS
357
2 PREVIOUS WORK
360
3 MODEL 31 Hypothesis
361
4 DISCUSSION AND CONCLUSIONS
368
MODELING CARDIAC FUNCTION
375
2 CELLULAR MODELS
376
3 MODELS OF THE CARDIAC VENTRICLES
392
4 DISCUSSION AND CONCLUSIONS
402
CARDIAC OSCILLATIONS AND ARRHYTHMIA ANALYSIS
409
2 TWO ARRHYTHMIAS WITH A SIMPLE MATHEMATICAL ANALYSIS
412
3 REENTRANT ARRHYTHMIAS
414
4 FUTURE PROSPECTS
416
THE IMMUNE SYSTEM
423
HOW DISTRIBUTED FEEDBACKS FROM MULTIPLE SENSORS CAN IMPROVE SYSTEM PERFORMANCE IMMUNOLOGY AND MULTIPL...
425
2 THERAPY AS AN INFORMATIONYIELDING PERTURBATION
426
MULTIPLE GOALS TO REGULATE THE IMMUNE RESPONSE
427
4 CYTOKINES
431
5 CONTENDING WITH MULTIPLE INDEPENDENT GOALS
432
6 RELEVANCE TO BIOMEDICINE
433
EQUATIONS FOR THE MATHEMATICAL MODEL
435
MICROSIMULATION OF INDUCIBLE REORGANIZATION IN IMMUNITY
437
2 MODEL
440
3 RESULTS
444
4 DISCUSSION AND CONCLUSION
447
THE COMPLEXITY OF THE IMMUNE SYSTEM SCALING LAWS
451
2 SCALING LAWS IN IMMUNOLOGY
453
3 CONCLUSIONS
457
4 PROPERTIES AND APPLICATIONS OF THE MODEL
490
5 DISCUSSION AND FUTURE WORK
500
CLINICAL NEUROCYBERNETICS MOTOR LEARNING IN NEURONAL SYSTEMS
507
2 EXPERIMENTAL APPROACHES AND BEHAVIORAL DATA 21 Tests for Associative Processes
512
3 THEORETICAL APPROACHES
522
4 RELEVANCE FOR PATIENTS AND THERAPY
529
CANCER AS YSTEMS APPROACH
534
MODELING CANCER AS A COMPLEX ADAPTIVE SYSTEM GENETIC INSTABILITY AND EVOLUTION
537
2 CANCER RISK IN THE CONTEXT OF AN EVOLUTIONARY PARADIGM
538
3 CANCER EVOLUTION IN THE CONTEXT OF RECENT HUMAN EVOLUTION
540
4 MODELING CANCER AS A COMPLEX ADAPTIVE SYSTEM AT THE LEVEL OF THE CELL
544
APPLYING COMPLEXITY THEORY TOWARD A CURE FOR CANCER
551
SPATIAL DYNAMICS IN CANCER
557
2 POPULATION DYNAMICS
559
3 COMPETITION IN TUMOR CELL POPULATIONS
560
4 COMPETITION WITH SPATIAL DYNAMICS
563
5 METAPOPULATION DYNAMICS AND CANCER HETEROGENEITY
565
6 DISCUSSION
569
MODELING TUMORS AS COMPLEX BIOSYSTEMS AN AGENTBASED APPROACH
573
2 PREVIOUS WORKS
576
3 MATHEMATICAL MODEL
579
4 SPECIFICATIONS OF THE MODEL
586
5 BASIC MODEL SETUP
589
6 RESULTS
592
7 DISCUSSION CONCLUSIONS AND FUTURE WORK
597
THE INTERACTION OF COMPLEX BIOSYSTEMS
604
THE COMPLEXITY OF DYNAMIC HOST NETWORKS
605
2 MODEL
606
3 RESULTS
607
4 DISCUSSION AND CONCLUSIONS
621
6 APPENDIX
622
PHYSIOLOGIC FAILURE MULTIPLE ORGAN DYSFUNCTION SYNDROME
631
2 PREVIOUS WORK
633
3 MODEL
635
4 RESULTS
636
5 IMPLICATIONS FOR TREATMENT
637
6 SUMMARY AND PERSPECTIVE
638
AGING AS A PROCESS OF COMPLEXITY LOSS
641
2 MEASURES OF COMPLEXITY LOSS
643
3 EXAMPLES OF COMPLEXITY LOSS WITH AGING
646
4 MECHANISMS OF PHYSIOLOGIC COMPLEXITY
648
5 LOSS OF COMPLEXITY AS A PATHWAY TO FRAILTY IN OLD AGE
649
6 INTERVENTIONS TO RESTORE COMPLEXITY IN PHYSIOLOGIC SYSTEMS
650
7 CONCLUSION
652
ENABLING TECHNOLOGIES
656
ELECTROKINETICS
657
1 INTRODUCTION
658
2 DC ELECTROKINETICS
659
3 AC ELECTROKINETICS
663
4 EXPERIMENTAL MEASUREMENTS OF ELECTROKINETICS
671
5 CONCLUSIONS
675
GENE SELECTION STRATEGIES IN MICROARRAY EXPRESSION DATA APPLICATIONS TO CASECONTROL STUDIES
679
GENE SELECTION METHODS IN MICROARRAY DATA
681
RICHER SET OF DIFFERENTIALLY EXPRESSED GENES
685
A CASE STUDY
690
5 DISCUSSION AND CONCLUSIONS
695
APPLICATION OF BIOMOLECULAR COMPUTING TO MEDICAL SCIENCE A BIOMOLECULAR DATABASE SYSTEM FOR STORAGE PROC...
701
1 INTRODUCTION
702
2 REVIEW OF BIOTECHNOLOGIES FOR GENOMICS AND THE BIOMOLECULAR COMPUTING FIELD
706
3 A BIOMOLECULAR DATABASE SYSTEM
709
4 APPLYING OUR BIOMOLECULAR DATABASE SYSTEM TO EXECUTE GENOMIC PROCESSING
725
5 DISCUSSION AND CONCLUSIONS
729
TISSUE ENGINEERING MULTISCALED REPRESENTATION OF TISSUE ARCHITECTURE AND FUNCTION
737
2 TISSUEENGINEERING INVESTIGATIONS AT VARIOUS LENGTH SCALES
741
3 CONTINUING EFFORTS IN TISSUE ENGINEERING
755
4 CONCLUSION
757
IMAGING THE NEURAL SYSTEMS FOR MOTIVATED BEHAVIOR AND THEIR DYSFUNCTION IN NEUROPSYCHIATRIC ILLNESS
763
1 INTRODUCTION
764
2 IN VIVO MEASUREMENT OF HUMAN BRAIN ACTIVITY USING fMRI
766
3 THEORETICAL MODEL OF MOTIVATION FUNCTION
770
4 NEUROIMAGING OF THE GENERAL REWARDAVERSION SYSTEM UNDERLYING MOTIVATED BEHAVIOR
776
5 IMPLICATIONS OF REWARDAVERSION NEUROIMAGING FOR PSYCHIATRIC ILLNESS
787
PROCESSING REWARDAVERSION INFORMATION TO THE GENE NETWORKS THAT ESTABLISH AND MODULATE THEIR FUNCTION
791
A NEUROMORPHIC SYSTEM
811
2 THE NEURON AND THE NEUROMORPH
812
3 HARDWARE SYSTEM
814
4 NEUROMORPHS IN A WINNERLESS COMPETITION NETWORK
816
5 SENSORIMOTOR DEVELOPMENT IN A NEUROMORPHIC NETWORK
818
6 SIMULATED NETWORK
819
7 NEUROMORPHS IN NEURAL PROSTHETICS
824
A BIOLOGICALLY INSPIRED APPROACH TOWARD AUTONOMOUS REALWORLD ROBOTS
827
2 MECHATRONICS
828
3 AMBULATION CONTROL
830
4 RESULTS
832
5 DISCUSSION AND OUTLOOOK
834
VIRTUAL REALITY INTRAOPERATIVE NAVIGATION AND TELEPRESENCE SURGERY
837
1 INTRODUCTION
838
3 THE FUTURE
843
4 DISCUSSION AND CONCLUSIONS
846
INDEX
849
著作権

他の版 - すべて表示

多く使われている語句

書誌情報