Advanced Simulation in Biomedicine

205 Pages·1990·6.003 MB·English

by  E. J. H. Kerckhoffs, G. C. Vansteenkiste (auth.), Dietmar P. F. Möller (eds.)

Advances in Simulation
Volume 3
Series Editors:
Paul A. Luker
Bernd Schmidt
Dietmar P.F. Moller
Editor
Advanced Simulation
in Biomedicine
With 74 Illustrations
Springer Science+Business Media, LLC
Dietmar P.F. MolIer
Driigerwerk AG
D-2400 Liibeck 1
West Germany
Library of Congress Cataloging-in-Publication Data
Advanced simulation in biomedicine / Dietmar P.F. Miiller, editor.
p. cm.-(Advances in simulation ; v. 3)
Includes bibliographical references.
ISBN 978-0-387-97184-1
1. Medicine-Computer simulation. 2. Medical sciences-Computer
simulation. 3. Biological models-Computer simulation. 1. Miiller,
Dietmar. II. Series.
[DNLM: 1. Computer Simulation. 2. Medicine. 3. Models,
Biological. W 26.5 A244)
R859.7.C65A39 1989
610'.1' 13--dc20
DNLMlDLC
for Library of Congress 89-26082
Printed on acid-free paper.
© 1990 Springer Science+Business Media New York
Originally published by Springer-Verlag New York in 1990
Softcover reprint ofthe hardcover 1s t edition 1990
AII rights reserved. This work may not be translated or copied in whole or in part without the written
permission of the publisher (Springer Science+Business Media, LLC), except for brief excerpts in
connection with reviews or scholarly analysis. Use in connection with any form of information storage
and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now
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While the advice and information in this book is believed to be true and accurate at the date of going to
press, neither the authors nor the editors nor the publisher can accept any legal responsibility for any
errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect
to the material contained herein.
Camera-ready text prepared by the contributors.
9 8 7 6 5 4 3 2 1
ISBN 978-0-387-97184-1 ISBN 978-1-4419-8614-6 (eBook)
DOI 10.1007/978-1-4419-8614-6
Preface
This book presents a collection of invited contributions, each reflecting an area of
biomedicine in which simulation techniques have been successfully applied. Thus,
it provides a state-of-the-art survey of simulation techniques in a variety of
biomedical applications.
Chapter one presents the conceptual framework for advanced simulations
such as parallel processing in biological systems. Chapter two focuses on
structured biological modeling based on the bond graph method. This is followed
by an up-to-date account of advanced simulation of a variety of sophisticated
biomedical processes. The authors provide many insights into how computer
simulation techniques and tools can be applied to research problems in
biomedicine.
The idea for this book arose out of the daily work by experts in their field
and reflects developing areas. Therefore, I think the material is timely and hope
that the work described will be an encouragement for others. It is the objective of
this book to present advanced simulation techniques in biomedicine and outline
current research, as well as to point out open problems, in this dynamic field.
Finally, I wish to express my thanks to those colleagues who have made
this book possible with their contributions.
Dietmar P.F. Moller
Mainz/Liibeck, July 1989
Contents
Preface ................................................................................................................................. v
Contributors ..................................................................................................................... xi
Parallel Processing in Biological Systems
E.J.H. Kerckhoffs and G.c. Vansteenkiste .................................................................... 1
Multicomputers in simulation .......................................................................................... 1
Evolution in simulation oriented digital computers ...................................................... 1
Classification of multicomputers ..................................................................................... 2
Parallel simulation. ............................................................................................................ 4
Simulation of biological systems and its need for parallel
processing. .......................................................................................................................... 5
Simulation example on the ADlO multicomputer ......................................................... 6
Concluding notes ............................................................................................................... 8
References .......................................................................................................................... 8
An Elementary Bond Graph Approach to Structured Biological Modeling
J. Lefevre .......................................................................................................................... 11
The need for unified graphical structured modeling .................................................. ll
General lines of the presentation .................................................................................. 12
Bond graph representation of energetical systems .................................................... 13
Hypergraph representation of energeticallumped systems ....................................... 14
The set EP of elementary parts or nodes ..................................................................... 14
The two elementary connection branches .................................................................... 17
Bond graphs: A representation of a subclass of energetical hypergraphs ................ 19
The chemical energy domain ........................................................................................ .20
Chemical EBG variables and energy storage elements .............................................. 20
Chemical reaction and dissipation by R-elements based on mass action ................ 22
The pseudo bond graph approach to reaction kinetics and compartments ............. 24
Representation of kinetic and potential energies by dynamical bond graphs ......... 24
Algorithmic representation of idealized physical models by bond graphs ............... 25
The EBG representation of an electrical IPM ............................................................ 25
Representation algorithms for other systems .............................................................. 27
Coupling of bond graphs and informational block diagrams ..................................... 28
Representation of information transfer in BG's by active bonds. ............................. 29
Coupling of active bonds to BG nodes .........................................................................2 9
Formulation algorithms: Automatic obtention of simulation routines ..................... 29
The causal stroke: A graphical marker of causality .................................................... 30
Sequential assignment of causality for elements and junctions ................................. 32
Algorithmic derivation of the procedural simulation routine ....................................3 4
Conclusion ........................................................................................................................ 37
References. .......................................................................................................................3 9
Simulation of Typical Physiological Systems
T.G. Coleman and W.J. Gay. ........................................................................................ .41
Recent advances in digital computing. .......................................................................... 41
Success may cause trouble .............................................................................................. 43
Other issues ..................................................................................................................... .45
A typical physiological model. ........................................................................................ 47
Non-linear relationships ................................................................................................ .51
Algebraic relationships. .................................................................................................. 56
Integral relationships ....................................................................................................... 61
Summary. ..........................................................................................................................6 7
References. .......................................................................................................................6 8
Parameter Estimation: An Advanced Simulation Tool in Biomedicine
D.P.F. Moller. .................................................................................................................. 71
Introduction ...................................................................................................................... 71
Model building of the renovascular system .................................................................. 74
Parameter estimation. ..................................................................................................... 76
Identification task ............................................................................................................ 78
Identification results. ....................................................................................................... 79
References ........................................................................................................................8 2
A Review of Respiratory System Applications of Computer Simulation and
Modelling Techniques
D.J. Murray-Smith. ..........................................................................................................8 3
Introduction. ..................................................................................................................... 83
Physiological background ............................................................................................... 84
The conceptual basis of modelling the system ............................................................. 85
Modelling and simulation as research tools .................................................................8 7
Simulation for teaching ...................................................................................................9 1
Simulation in respiratory gas exchange teaching ......................................................... 92
The MacPuf model. .........................................................................................................9 6
A computer simulation for training in anaesthesia ..................................................... 98
V111
Estimation techniques involving system identification methods ............................. 100
Applications involving gas exchange models ..............................................................1 00
Lung mechanics models ................................................................................................ 105
Models for on-line control. ........................................................................................... 106
Discussion ....................................................................................................................... 107
References. ..................................................................................................................... 109
Computer Simulation in Cancer Research
W. Diichting. .................................................................................................................. 117
The cancer problem ...................................................................................................... 117
Biological observations of cell growth ........................................................................ 118
Growth of normal cells ................................................................................................. 118
Growth of tumor cells ................................................................................................... 119
Possibilities of cancer treatment. ................................................................................. 120
Statement of the modeling problem ............................................................................ 121
Overview of previous work ........................................................................................... 123
Model design .................................................................................................................. 127
Fundamental components ............................................................................................ 127
Limitations ...................................................................................................................... 128
Computer-implementation of the model. ................................................................... 128
Simulation results and discussions .............................................................................. 130
Formation of capillaries. ............................................................................................... 130
Spread of tumor cells in the cortex. ............................................................................ 133
Tumor angiogenesis effect. ........................................................................................... 136
Outlook. .......................................................................................................................... 137
References ...................................................................................................................... 138
Mathematical Simulation of the Human Thermal System
J. Werner ........................................................................................................................ 141
Introduction. ................................................................................................................... 121
Distributed parameter model: Radial dependencies ................................................ 144
Distributed parameter model: Three dimensional dependencies ........................... 152
Data base ........................................................................................................................ 153
Physics and mathematics of the passive system ......................................................... 155
Metabolic heat production ........................................................................................... 155
Convective heat transport. ............................................................................................ 155
Controller equations ..................................................................................................... 157
Numerical solution ........................................................................................................ 158
Results of simulation. .................................................................................................... 159
Distribution of temperatures and profiles in neutral environment.. ...................... 159
Control strategy. ............................................................................................................ 163
Temperature profiles in the cold and in the warmth ................................................ 165
References ...................................................................................................................... 168
ix
Large-Scale Multiple Model for the Simulation of Anesthesia
R.O.Y. Tham, F.J. Sasse, and V.C. Rideout. ............................................................. 173
Introduction .................................................................................................................... 173
Concept of the large-scale multiple modeL ............................................................. 174
Model design .................................................................................................................. 175
Composition of the anesthesia model.. ....................................................................... 175
Hemodynamic submodel. ............................................................................................. 177
Compartmental transport model.. ............................................................................... 181
Baroreceptor control submodel. .................................................................................. 182
Pharmacodynamic submodel. ...................................................................................... 187
Simulation results .......................................................................................................... 188
Conclusions .................................................................................................................... 193
References ...................................................................................................................... 194
Index ................................................................................................................................ 197
x
Contributors
Thomas G. Coleman and David J. Murray-Smith
William J. Gay Department of Electronics and
Department of Physiology Electrical Engineering
and Biophysics University of Glasgow
University of Mississippi Glasgow G12 800
Medical Center United Kingdom
Jackson, MS 39216-4505
USA R.O.Y Tham, FJ. Sasse, and
v.c.
Rideout
Werner Dtichting Department of Electrical
Fachbereich Elektrotechnik and Computer Engineering
Universitat Siegen and Department of
Holderlinstrasse 3 Anesthesiology
D-5900 Siegen University of Wisconsin
West Germany Madison, WI 53706
USA
EJ .H. Kerckhoffs
Faculty of Mathematics G.C. Vansteenkiste
and Informatics Department of Applied
Delft University of Technology Mathematics
Julianalaan 132 and Biometrics
2628 BL Delft University of Ghent
The Netherlands Coupure Links 653
9000 Ghent
J. Lefevre Belgium
Department of Automatics
and Physiology Jtirgen Werner
University of Louvain Ruhr-Universitat
Brussels Institut fUr Physiologie
Belgium Abteilung Biokybernetik, MA 4/59
D-4630 Bochum 1
Dietmar P.F. Moller West Germany
Physiologisches Institut
U niversitat Mainz
Saarstrasse 21
D-6500 Mainz
West Germany
PARALLEL PROCESSING IN BIOLOGICAL SYSTEMS
E.J.H. KERCKHOFFS
Delft University of Technology
Faculty of Mathematics and Informatics
Julianalaan 132, 2628 BL Delft, The Netherlands
G.C. VANSTEENKISTE
University of Ghent
Dept. of Applied Mathematics and Biometrics
Coupure Links 653, 9000 Ghent, Belgium
Abstract
Simulation of biological systems frequently results in models, that are very compli
cated and their solution on conventional computers may therefore be time-consuming.
There is a growing interest to use computers with internal parallelism and/or pipe
lining in this field. In this article we consider some aspects of the parallel simu
lation of biological systems. A general consideration on multicomputers in simulation
is presented. Simulation of biological systems and its needs for advanced computer
tools is analyzed. Finally, we give an example of biological systems simulation on
the multiprocessor ADI0. The emphasis is on models characterized by ordinary differen
tial equations.
1. MULTICOMPUTERS IN SIMULATION
1.1. SYQ1~!iQ~_Qf_~i~~12!iQ~:Qri~~!~~_~i9i!21_~Q~~~!~r~
The simulation of (complex) continuous systems has been particularly influential
in the evolution of special-purpose computer systems to attain high computing speeds
and performance. In this respect two major computer architecture innovations have
permitted the circumventing of the von Neumann bottleneck in order to attain high
speeds: parallelism and pipelining. The implementation of these techniques has re
sulted in some distinct families of high speed digital computers including supercom
puters, peri pheral array processors and multi processors [HOCK81] , [KARP84] , [KARP87] ,
[SPRI82] .
The first supercomputer to become operational (in 1975) was the ILLIAC-IV, which
consisted of an array of 64 processing elements, each containing an arithmetic/logic
unit and local memory. During the middle 1970's the focus in the development of super
computers shifted away from arrays of processing elements towards pipelining. Two major
pipeline-oriented supercomputers, known as vector processors, of the 1970's were the
Control Data Corporation STAR-I00 and the Texas Instruments ASC. The first pipeline
oriented supercomputer to win wide-spread acceptance was the CRAY-l, developed by
Cray Research Incorporated and first installed in 1976. This machine was the trend
setter for many other supercomputers with a similar approach to high-speed computations,
including the presently well-known products of Cray Research Incorporated and Control
Data Corporation in the USA, and Hitachi, Fujitsu and Nippon Electric Corporation in
Japan.
In the late 1970's a new class of pipeline-oriented computing devices began to