Computation in Cells and Tissues: Perspectives and Tools of Thought

348 Pages·2004·10.655 MB·English

by  R. Paton (auth.), Ray Paton, Hamid Bolouri, Mike Holcombe, J. Howard Parish, Richard Tateson (eds.)

Natural Computing Series
Series Editors: G. Rozenberg
Th. Biick A.E. Eiben J.N. Kok H.P. Spaink
Leiden Center for Natural Computing
s.
Advisory Board: Amari G. Brassard K.A. De Jong
C.C.A.M. Gielen T. Head L. Kari L. Landweber T. Martinetz
Z. Michalewicz M.C. Mozer E. Oja G. Păun J. Reif H. Rubin
A. Salomaa M. Schoenauer H.-P. Schwefel C. Torras
D. Whitley E. Winfree J.M. Zurada
Ro Paton t oH o Bolouri oM o Holcombe
JoHo Parish oR o Tateson (Edso)
CoDtputation in
Cells and Tissues
Perspectives and Tools of Thought
With 134 Figures
~ Springer
Editors J. Howard Parish
Ray Paton' School of Biochemestry
and Molecular Biology
Hamid Bolouri
University of Leeds
Institute for Systems Biology
Leeds LS2 9JT, UK
Seattle, \VA 98103, USA
[email protected]
[email protected]
Series Editors
Mike Holcombe
G. Rozenberg (Managing Editor)
Department of Computer Science
[email protected]
University of Sheffield
Th. Băck, J.N. Kok, H.P. Spaink
Sheffield SI 4DP, UK
[email protected] Leiden Center for Natural Computing
Leiden University
Richard Tateson
Niels Bohrweg 1
Future Technologies Group 2333 CA Leiden, The Netherlands
Intelligent Systems Lab
A.E.Eiben
BTexact Technologies
Vrije Universiteit Amsterdam
Ipswich IPS 3RE, UK
The Netherlands
richard. [email protected]
Library of Congress Control N umber: 2004042949
ACM Computing Classification (1998): EO, 1.1-2, 1.6, J.3
ISBN 978-3-642-05569-0 ISBN 978-3-662-06369-9 (eBook)
DOI 10.1007/978-3-662-06369-9
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© Springer-Verlag Berlin Heidelberg 2004
Originally published by Springer-Verlag Berlin Heidelberg New York in 2004
Softcover reprint of the hardcover 1s t edition 2004
The use of general descriptive names, registered names, trademarks, etc. in this publication
does not imply, even in the absence of a specific statement, that such names are exempt from
the relevant protective laws and regulations and therefore free for general use.
Cover Design: KiinkelLopka, Werbeagentur, Heidelberg
Typesetting: by the Authors
Production: LE-TEX Jelonek, Schmidt & Vockler GbR, Leipzig
Printed on acid-free paper 4513142/YL - 543210
It is with great sadness that we have to report the sudden death of Dr. Ray Paton,
the principal editor of this volume, just before we went to press. Ray worked tire
lessly to bring this book to Jruition and it stands as a rich testament to his inspira
tionalleadership and vision in the field.
Alt of the other editors wish to record our great gratitude to Ray, who was not
only an outstanding scientist but also agreat friend and colleague.
We hope that this book will, in some way, be looked upon as a memorial to Ray's
pioneering work in biologically inspired computing and computational biology.
Preface
The field of biologically inspired computation has coexisted with mainstream
computing since the 1930s, and the pioneers in this area include Warren
McCulloch, Walter Pitts, Robert Rosen, Otto Schmitt, Alan Turing, John von
Neumann and Norbert Wiener.
Ideas arising out of studies of biology have permeated algorithmics, automata
theory, artificial intelligence, graphics, information systems and software design.
Within this context, the biomolecular, cellular and tissue levels of biological
organisation have had a considerable inspirational impact on the development of
computational ideas. Such innovations include neural computing, systolic arrays,
genetic and immune algorithms, cellular automata, artificial tissues, DNA
computing and protein memories. With the rapid growth in biological knowledge
there remains a vast source of ideas yet to be tapped. This includes developments
associated with biomolecular, genomic, enzymic, metabolic, signalling and
developmental systems and the various impacts on distributed, adaptive, hybrid
and emergent computation.
This multidisciplinary book brings together a collection of chapters by
biologists, computer scientists, engineers and mathematicians who were drawn
together to examine the ways in which the interdisciplinary displacement of
concepts and ideas could develop new insights into emerging computing
paradigms. Funded by the UK Engineering and Physical Sciences Research
Council (EPSRC), the CytoCom Network formally met on five occasions to
examine and discuss common issues in biology and computing that could be
exploited to develop emerging models of computation. Many issues were raised
concemed with modelling, robustness, emergence, adaptability, evolvability and
networks, and many tools of thinking and ways of addressing problems were
introduced and discussed. This book seeks to highlight many aspects of this
growing area of study and will allow the reader to explore a breadth of ideas.
Ray Paton'
Biocomputing and Computational Biology Group
Department of Computer Science
May 2004 The University of Liverpool, UK
Contents
CytoComputational Systems - Perspectives and Tools of Thought •••••••••••••••••• 1
R. C. Paton
1 Plan of the Book ....................................................................................... 2
2 History of the CytoComputational Systems Project ................................. 6
Cells in Telecommunications •...•..••.....•••••.•••••••...••••••..•••.••••••.••.•.••••••••••••.••••••••••••• 9
R. Tateson
1 Introduction ............................................................................................... 9
2 Telecommunication Problems ................................................................ 11
3 Features of Cells ..................................................................................... 12
3.1 Evolutionary History ....................................................................... 12
3.2 Division History .............................................................................. 12
3.3 'Life History' ................................................................................... 12
3.4 Dynamic, Metabolic ........................................................................ 13
3.5 Autonomous .................................................................................... 13
3.6 Emergent Control ............................................................................ 14
4 Cell-based Solutions for Telecommunications Problems ....................... 14
4.1 Fruitflies and Mobile Phones ........................................................... 15
4.2 Design by Morphogenesis ............................................................... 17
4.3 CellSim ............................................................................................ 20
5 Conclusion .............................................................................................. 25
References ........................................................................................................ 25
Symbiogenesis as a Machine Leaming Mechanism. ......................................... 27
L. Bull, A. Tomlinson
1 Introduction ............................................................................................. 27
2 Simulated Symbiogenesis ....................................................................... 28
2.1 The NKCS Model ............................................................................ 28
2.2 Genetic Algorithm Simulation ......................................................... 29
2.3 Results ............................................................................................. 31
2.4 Discussion. ....................................................................................... 33
3 Symbiogenesis in Machine Leaming ...................................................... 36
3.1 ZCS: A Simple Leaming Classifier System .................................... 36
3.2 Symbiogenesis in a Leaming Classifier System .............................. 38
3.3 Woods 1 ........................................................................................... 40
3.4 Symbiont Encapsulation .................................................................. 41
3.5 System Evaluation in Markov and non-Markov Environments ..... .44
4 Conclusion .............................................................................................. 48
References ........................................................................................................ 49
An Overview of Artificial Immune Systems ...................................................... 51
J. Timmis et al.
1 Introduction ............................................................................................. 51
X Contents
2 The Immune System: Metaphorically Speaking ..................................... 52
3 The Vertebrate Immune System ............................................................. 54
3.1 Primary and Secondary Immune Responses .................................... 55
3.2 B-cells and Antibodies .................................................................... 55
3.3 Immune Memory ............................................................................. 56
3.4 Repertoire and Shape Space ............................................................ 59
3.5 Learning within the Immune Network ............................................ 59
3.6 The Clonal Selection Principle ........................................................ 62
3.7 Self/Non-Self Discrimination .......................................................... 63
3.7.1 Negative Selection ........................................................................ 63
4 From Natural to Artificial Immune Systems .......................................... 64
4.1 Summary ......................................................................................... 65
5 The Immune System Metaphor ............................................................... 66
5.1 A Framework for AIS ...................................................................... 66
5.2 Machine Learning ............................................................................ 68
5.3 Robotics ........................................................................................... 76
5.4 Fault Diagnosis and Tolerance ........................................................ 78
5.5 Optimisation .................................................................................... 79
5.6 Scheduling ....................................................................................... 81
5.7 Computer Security ........................................................................... 82
6 Summary ................................................................................................. 83
7 Comments on the Future for AIS ............................................................ 84
References ....................................................................................................... 86
Embryonics and Immunotronics: Biologically Inspired
Computer Science Systems ................................................................................ 93
A. Tyrrell
1 Introduction ............................................................................................ 93
2 An Overview of Embryonics .................................................................. 95
2.1 Multicellular Organization .............................................................. 95
2.2 Cellu1ar Division ............................................................................. 95
2.3 Cellular differentiation .................................................................... 95
3 The Organism's Features: Multicellular Organization, Cellular
Differentiation, and Cellular Division .................................................... 97
4 Architecture of the Cell .......................................................................... 98
4.1 Memory ........................................................................................... 99
4.2 Address Generator ......................................................................... 100
4.3 Logic Block ................................................................................... 100
4.4 Inputloutput Router ....................................................................... 101
4.5 Error Detection and Error Handling .............................................. 102
5 Examples .............................................................................................. 103
6 Immunotronics ...................................................................................... 105
7 Reliability Engineering ......................................................................... 106
8 The Reliable Human Body ................................................................... 107
9 Bio-Inspired Fault Tolerance ................................................................ 107
10 Artificial Immune Systems ................................................................... 108
11 Domain Mapping .................................................................................. 109
Contents XI
12 Choice of Algorithm ............................................................................. 109
13 Architecture of the Hardware Immunisation Suite ............................... 109
14 Embryonics and Immunotronic Architecture ........................................ l12
15 Conclusion ............................................................................................ 112
References ...................................................................................................... 114
Biomedical Applications of Micro and Nano Technologies ........................... 117
C. J. McNeil, K. 1. Snowdon
1 Background ........................................................................................... 117
2 Biomedical Applications of Nanotechnology ....................................... 118
3 Developing a Multidiscip1inary Base - The NANOMED Network ..... 120
4 Initial Challenges to NANOMED Problems ......................................... 122
5 Concluding Remarks ............................................................................. 123
References ...................................................................................................... 124
Macromolecules, Genomes and Ourselves ...................................................... 125
S. Nagl et al.
1 Preamble ............................................................................................... 125
2 Macromolecules: Properties and Classification .................................... 126
2.1 Architecture, Form and Function ................................................... 126
2.2 Data Resources .............................................................................. 129
2.3 Protein Classification ..................................................................... 129
2.4 Protein Signatures .......................................................................... 130
3 Models and Metaphors .......................................................................... 131
3.1 Proteins as Machines ..................................................................... 131
3.2 lnformation Processing by Proteins ............................................... 132
4 Modelling of Complex Cellular Systems
for Post-genomic Biomedicine ............................................................. 134
4.1 Introduction: A Systems View of Life ........................................... 134
4.2 Complexity and Post-genomic Biomedicine ................................. 139
4.3 New Models for Biomedicine: Ethical Implications of Model
Choice ............................................................................................ 140
4.4 Models as Metaphoric Constructions .............................................. 142
5 Conclusions ........................................................................................... 144
References ...................................................................................................... 145
Models of Genetic Regulatory Networks ......................................................... 149
H. Bo10uri, M. Schilstra
1 What are Genetic Regulatory Networks? ............................................. 149
2 What is a Gene? .................................................................................... 150
3 Regulation of Single Genes .................................................................. 151
4 Differences in Gene Regulation Between Organisms ........................... 151
5 Modeling GRNs .................................................................................... 152
6 Some GRN Models to Date .................................................................. 153
7 GRN Simulators .................................................................................... 155
8 Uses ofGRNs Beyond Biology ............................................................ 156
References ...................................................................................................... 156
XII Contents
A Model of Bacterial Adaptability Based on Multiple Scales
of Interaction: COSMIC .................•...•••.••••••••••.•.•.•••.............................•.....•... 161
R. Gregory et al.
1 Introduction .......................................................................................... 162
2 Biology .................................................................................................. 163
2.1 DNA, RNA and Proteins .............................................................. 163
2.2 Transcription ................................................................................ 164
2.3 Protein Structure ........................................................................... 165
2.4 Optional Transcription .................................................................. 166
2.5 lac Operon .................................................................................... 166
2.6 trp Operon .................................................................................... 167
2.7 An E. ecoli Environment .............................................................. 168
3 The Genome and the Proteome ............................................................ 169
4 Model ................................................................................................... 170
5 Implementation ..................................................................................... 173
6 Results .................................................................................................. 173
6.1 Environmental Macroscopic View ............................................... 173
6.2 CeH Lineage ................................................................................. 175
6.3 Gene Expression ........................................................................... 176
6.4 Network Graphs ........................................................................... 178
6.5 CeH Statistics ................................................................................ 178
7 Discussion ............................................................................................ 182
References .................................................................................................... 183
Stochastic Computations in Neurons and Neural Networks ......................... 185
J. Feng
1 Abstract ................................................................................................ 185
2 The Integrate-and-File Model and Its Inputs ........................................ 189
3 Theoretical Results ............................................................................... 191
3.1 Behaviour of a.(A)"2c,r) ............................................................... 192
3.2 Input-Output Relationship ........................................................... 195
4 Informax Principle ................................................................................ 197
4.1 The IF Model Redefined .............................................................. 197
5 Leaming Rule ....................................................................................... 198
6 Numerical Results ................................................................................ 202
6.1 Supervised Leaming ..................................................................... 202
6.2 Unsupervised Leaming ................................................................. 203
6.3 Signal Separations ........................................................................ 204
7 Discussion ............................................................................................ 205
References .................................................................................................... 209
Spatial Patterning in Explicitly Cellular Environments:
Activity-Regulated Juxtacrine Signalling ••.•...................................................• 211
N. Monk
1 Introduction .......................................................................................... 211
2 Biological Setting ................................................................................. 212
3 Mathematical Models of Juxtacrine Signalling .................................... 213
Contents XIII
4 Pattern Fonnation ................................................................................. 215
4.1 Lateral Inhibition and Spacing Patterns ......................................... 215
4.2 Gradients and Travelling Fronts .................................................... 218
4.3 More Complex Spatial Patterns ..................................................... 220
5 Further Developments ........................................................................... 223
References ...................................................................................................... 223
Modelling the GH Release System ................................................................... 227
D. J. MacGregor et. al.
1 Introduction ........................................................................................... 227
2 Research Background ........................................................................... 227
3 GH Research ......................................................................................... 228
3.1 Experimental Approach ................................................................. 229
3.2 Anatomical Results ........................................................................ 230
3.3 Electrophysiological Results ......................................................... 231
3.4 Behavioural Results ....................................................................... 231
4 Creating the System .............................................................................. 233
4.1 Simplifications ............................................................................... 235
5 Making the Experimental Model .......................................................... 235
5.1 Storage V ariables ........................................................................... 236
5.2 Input Protocols ............................................................................... 236
6 The Model. ............................................................................................ 237
6.1 The Pituitary Model ....................................................................... 237
6.2 The GH System Model .................................................................. 238
7 Working with the Model ....................................................................... 240
7.1 The Model Parameters ................................................................... 241
7.2 Assessing Perfonnance .................................................................. 241
7.3 Initial Results ................................................................................. 242
7.4 Comparison with real GH release .................................................. 242
7.5 A GHRH-Somatostatin Connection ............................................... 244
7.6 GH-Somatostatin Stimulatory Connection .................................... 245
8 Conc1usions ........................................................................................... 246
References ...................................................................................................... 248
Hierarchies of Machines ..................................................................•.••..•••.••••.•• 251
M. Ho1combe
1 Introduction: Computational Models .................................................... 251
2 More Powerful Machines ..................................................................... 255
2.1 X-machines .................................................................................... 255
2.2 Communicating X-machines [6]. ................................................... 259
2.3 Hybrid Machines ........................................................................... 260
3 Agents and Agent Systems ................................................................... 262
4 Hierarchies of Machines ...................................................................... 264
4.1 Cellular Hierarchies ...................................................................... 264
4.2 Tissue Hierarchies ......................................................................... 266
5 Conc1usions and Further Work ............................................................. 267
References ...................................................................................................... 268