435
List of contributors
Alexander Anderson, Department of Mathematics, University of Dundee, DD1 4HN, Scotland, UK; e-mail: [email protected]
Mats Akesson, Center for Process Biotechnology, BioCentrum-DTU, Technical University of Denmark, DK-2800 Lyngby, Denmark
Wolfgang Alt, Theoretical Biology, University of Bonn, Kirschallee 1,53115 Bonn, Germany; e-mail: [email protected]
Markus Bar, Max-Planck-Institut fUr Physik komplexer Systeme, Nothnitzer Str. 38, 01187 Dresden, Germany
Ulrich Behn, Institut fUr Theoretische Physik, Universitat Leipzig, Postfach 100920, 04009 Leipzig, Germany; e-mail: [email protected]
Uwe Borner, Max-Planck-Institut fUr Physik komplexer Systeme, Nothnitzer Str. 38,01187 Dresden, Germany; e-mail: [email protected]
Stefan Bornholdt, Interdisziplinares Zentrum fUr Bioinformatik, Universitat Leip• zig, Kreuzstr. 7b, 04103 Leipzig, Germany; e-mail: [email protected]
Debasish Bose, University of Delhi South Campus, Department of Biophysics, New Delhi 110021, India
Markus Brede, Institut fUr Theoretische Physik, Universitat Leipzig, Postfach 100920,04009 Leipzig, Germany
Till Bretschneider, Max-Planck Institute of Biochemistry, Am Klopferspitz I8A, 82152 Martinsried,Germany; e-mail: [email protected]
Markus A. Dahlem, Leibniz Institut fUr Neurobiologie, Brennecke-Str. 6, 39118 Magdeburg, Germany; e-mail: [email protected]
Sune Danp, University of Copenhagen, Department of Chemistry, H.C. 0rsted Institute, Universitetparken 5,2100 Copenhagen, Denmark; e-mail: [email protected] 436 List of contributors
Peter Dieterich, Technische Universitaet Dresden, Institut fUr Physiologie, Medizi• nische Fakultat Carl-Gustav-Carus, Fetscherstrasse 74, 01307 Dresden, Germany; e-mail: [email protected]
Sabine Dormann, University of Osnabruck, Dept. of Mathematics/ Computer Scien• ce, Applied Systems Science
Dirk Drasdo, Max Planck Institute for Mathematics in the Sciences, Inselstr. 22-26, 04103 Leipzig, Germany; e-mail: [email protected]
Frido Erler, Dresden University of Technology, Institute for Theoretical Physics, 01062 Dresden, Germany; e-mail: [email protected]
Katharina Flade, Technische UniversiHit Dresden, Institut fUr Werkstoffwissen• schaft, Helmholtzstr. 7, 01062 Dresden, Germany
Gabor Forgacs, University of Missouri, Department of Physics and Biology, 223 Physics Building UMC, Columbia, MO 65211, USA; e-mail: [email protected]
Jochen Forster, Center for Process Biotechnology, BioCentrum-DTU, Technical University of Denmark, DK-2800 Lyngby, Denmark
Ramsey A. Foty, Department of Surgery, UMDNJ-Robert Wood Johnson Medical School, New Brunswick, NJ 08903, USA; e-mail: [email protected]
Erwin Frey, Abt. Theoretische Physik, Hahn-Meitner-Institut, Glienicker Str. 100, 14109 Berlin, Germany; e-mail: [email protected]
Michael Gelinsky, Technische UniversiHit Dresden, Institut fUr Werkstoffwissen• schaft, Helmholtzstr. 7, 01062 Dresden, Germany; e-mail: [email protected]• dresden.de
Paramita Ghosh, University of Delhi South Campus, Department of Biophysics, New Delhi 110021, India; e-mail: [email protected]
Subhendu Ghosh, University of Delhi South Campus, Department of Biophysics, New Delhi 110021, India; e-mail: [email protected]
Ernst Dieter Gilles, Max-Planck-Institute for Dynamcis of Complex Technical Systems, Sandtorstrasse 1,39106 Magdeburg, Germany List of contributors 437
Martin Ginkel, Max-Planck-Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1,39106 Magdeburg, Germany
Hans Gruler, Ulm University, Department of Biophysics, Albert-Einstein-Allee 11, 89081 Ulm
Reinhart Heinrich, Institut fUr Biologie, Humboldt-UniversiUit Berlin, Invalidenstr. 42,10115 Berlin, Germany; e-mail: [email protected]
Andreas Herrmann, Institut fUr Biologie/Biophysik, Humboldt-Universitat zu Ber• lin, Invalidenstr. 43, 10115 Berlin, Germany; e-mail: [email protected]• lin.de
Benno Hesst, Max-Planck-Institut fUr Molekulare Physiologie, Otto-Hahn-Str . 11, 44227 Dortmund, Germany; and Max-Planck-Institut fur Medizinische Forschung, Jahnstr. 29, 69120 Heidelberg, Germany
Thomas HOfer, Institut fUr Biologie, Humboldt-Universitat zu Berlin, Invaliden• str. 42, 10115 Berlin, Germany; e-mail: [email protected]
Stefan Hbhme, University of Leipzig, Interdisciplinary Center for Bioinformatics, Kreuzstr. 7b, 04103 Leipzig, Germany
Bert Hobmayer, Molekulare Zellbiologie, Technische Universitat Darmstadt, Schnittspahnstr. 10, 64287 Darmstadt, Germany
Thomas W. Holstein, Molekulare Zellbiologie, Technische Universitat Darmstadt, Schnittspahnstr. 10, 64287 Darmstadt, Germany; e-mail: [email protected]• stadt.de
Hermann-Georg Holzhutter, Charite, Institut fUr Biochemie, Humboldt-Universitat zu Berlin, Monbijoustrasse 2, 10117 Berlin, Germany; e-mail: [email protected]
Finn Hynne, Department of Chemistry, H.C. 0rsted Institute, Universitetsparken 5, 2100 Copenhagen, Denmark; e-mail: [email protected]
Mads Ipsen, Fritz-Haber-Institut der Max-Planck Gesellschaft, Dept. of Physical Chemistry, Faradayweg 4-6, 14195 Berlin, Germany; e-mail: [email protected] 438 List of contributors
Jeroen A.L. Jeneson, Department of Physiology, School of Veterinary Medicine, Utrecht University, Yalelaan 1, 3508 TD Utrecht, The Netherlands; e-mail: j.jene• [email protected]
Kunihiko Kaneko, Department of Basic Science, Graduate School of Arts and Sciences, University of Tokyo, Komaba, Meguro, Tokyo 153s-8902, Japan; e-mail: [email protected]
Dieter Kaufmann, Ulm University, Department of Human Genetics, Albert-Ein• stein-Allee 11,89081 Ulm
Ralf Kemkemer, Heidelberg University, Biophysical Chemistry, Institute for Physi• cal Chemistry, 1m Neuenheimerfeld 253,69120 Heidelberg, Germany
Jan T. Kim, Institut fUr Neuro- und Bioinformatik, Universitat zu Lubeck, Seeland• str. la, 23569 Lubeck, Germany; e-mail: [email protected]
Markus Kirkilionis, Mathematics Department & Centre for Scientific Computing, University of Warwick, Coventry, CV4 7AL, UK; e-mail: [email protected]
Birgit Knepper-Nicolai, Max-Planck-Institut fUr molekulare Zellbiologie und Genetik, Pfotenhauer Strasse 108,01307 Dresden, Germany; e-mail: knepper@mpi• cbg.de
Martin 1. Kushmerick, Departments of Bioengineering, Physiology and Biophysics, and Radiology, School of Medicine, University of Washington, Seattle WA, USA
LOic Le Goff, Physico-Chimie Curie UMR168, 26 rue d'Ulm, 75248 Paris cede x 05, France
Hans-Philipp Lerch, Complex Systems Group, Fritz-Haber-Institut der Max• Planck-Ges., Department of Physical Chemistry, Faradayweg 4-6,14195 Berlin, Ger• many; e-mail: [email protected]
Markus Loeffler, Universitat Leipzig, IMISE, Liebigstr. 27, 04103 Leipzig, Germany; e-mail: [email protected]
Kai Ludwig, Forschungszentrum fUr Elektronenmikroskopie, Freie Universitat Ber• lin, Fabeckstrasse 36a, 14195 Berlin, Germany; e-mail: [email protected] List of contributors 439
Thomas Mair, Otto-von-Guericke-Universitat, Institut fUr Experimentelle Physik, Universitatsplatz 2, 39106 Magdeburg, Germany; e-mail: [email protected]• magdeburg.de
Rengaswamy Maithreye, Centre for Cellular & Molecular Biology, Uppal Road, Hyderabad 500007, India; e-mail: [email protected]
Thomas Martinetz, Institut fUr Neuro- und Bioinformatik, Universitat zu Lubeck, Seelandstr. la, 23569 Lubeck, Germany; e-mail: [email protected]• beck.de
Hans Meinhardt, Max-Planck-Institut fUr Entwicklungsbiologie, Spemannstr. 35, 72076 Tubingen, Germany; e-mail: [email protected]
Michael Meyer-Hermann, Institut fUr Theoretische Physik, TU Dresden, 01062 Dresden, Germany; e-mail: [email protected]
Alexander S. Mikhailov, Abteilung Physikalische Chemie, Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6,14195 Berlin, Germany, e-mail: mikhai• [email protected]
Gertrud Muller, Institute for Cell Biology and Immunology, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
Stefan C. Muller, Otto-von-Guericke-Universitat, Institut fUr Experimentelle Phy• sik, Universitatsplatz 2,39106 Magdeburg, Germany; e-mail: stefan.mueller@phy• sik.uni-magdeburg.de lens Nielsen, Technical University of Denmark, Center for Process Biotechnology, BioCentrum-DTU, 2800 Lyngby, Denmark; e-mail: [email protected]
Lennart Olsson, Friedrich-Schiller-Universitat, Institut fUr spezielle Zoologie und Evolutionsbiologie, Erbertstr. 1,07743 lena, Germany; e-mail: [email protected]
Michal Or-Guil, Institut fur Theoretische Biologie, Humboldt-Universitat Berlin, Invalidenstr. 43,10115 Berlin, Germany; e-mail: [email protected]
Leiv 0yehaug, Norwegian Defence Research Establishment (FFI), P.o. Box 25, 2027 Kjeller, Norway 440 List of contributors
Andrea Parmeggiani, Hahn-Meitner-Institut, Theoretische Physik, Glienicker Str. 100, 14109 Berlin, Germany
Erik Plahte, Department of Chemistry, Biotechnology and Food Science, Agricultu• ral University of Norway, 1432 As, Norway; e-mail: [email protected]
Daniel Polani, Institut fur Neuro-und Bioinformatik, Seelandstr. la, 23569 Lubeck, Germany; current address: Department of Computer Science, University of Hert• fordshire, Hatfield,ALlO 9AB, UK; e-mail: [email protected]
Antonio Politi, Humboldt-UniversitiH zu Berlin, Institute of Biology, Theoretical Biophysics, Invalidenstr. 42, 10115 Berlin, Germany; e-mail: [email protected]• berlin.de
Wolfgang Pompe, Technische Universitat Dresden, Institut fUr Werkstoffwissen• schaft, Helmholtzstr. 7, 01062 Dresden, Germany; e-mail: [email protected]• dresden.de
Antje Reinstorf, Technische Universitat Dresden, Institut fUr Werkstoffwissen• schaft, Helmholtzstr. 7, 01062 Dresden, Germany; e-mail: [email protected]• dresden.de
Fabian Rentzsch, Molekulare Zellbiologie, Technische Universitat Darmstadt, Schnittspahnstr. 10,64287 Darmstadt, Germany
Jan Richter, Institut fur Klinische Immunologie and Institut fUr Theoretische Phy• sik, Universitat Leipzig, Postfach 100920,04009 Leipzig, Germany
Ingo Roeder, Universitat Leipzig, IMISE, Liebigstr. 27, 04103 Leipzig, Germany
Thimo Rohlf, , Interdisziplinares Zentrum fUr Bioinformatik, Universitat Leipzig, Kreuzstr. 7b, 04103 Leipzig, Germany
Peter Ruoff, Stavanger University College, School of Science and Technology, P.O. Box 8002, Ullandhaug, 4068 Stavanger, Norway; e-mail: [email protected]
Christoph F. Schmidt, Vrije Universiteit Amsterdam, Faculty of Sciences, Division of Physics and Astronomy, Physics of Complex Systems, De Boelelaan 1081, 1081 HV Amsterdam, The Netherlands; e-mail: [email protected] List of contributors 441
Hans-I Schnittler, Technische Universitaet Dresden, Institut fUr Physiologie, Medi• zinische Fakultat Carl-Gustav-Carus, Fetscherstrasse 74,01307 Dresden, Germany; e-mail: [email protected]
Birgit Schoberl, Max-Planck-Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany; e-mail: schoeberl@mpi-magde• burg.mpg.de
Susanne Schreiber, Institut fur Biologie, Humboldt-Universitat zu Berlin, Invali• denstr. 43, 10115 Berlin, Germany; [email protected]
Jochen Seebach, Technische Universitaet Dresden, Institut fUr Physiologie, Medizi• nische Fakultat Carl-Gustav-Carus, Fetscherstrasse 74, 01307 Dresden, Germany; e-mail: [email protected]
Somdatta Sinha, Theoretical Biology Group, Centre for Cellular & Molecular Bio• logy, Uppal Road, Hyderabad 500007, India; e-mail: [email protected]
Gerhard Soff, Institut fUr Theoretische Physik, TU Dresden, 01062 Dresden, Ger• many; e-mail: [email protected]
Preben Graae S0rensen, University of Copenhagen, Department of Chemistry, H.C. 0rsted Institute, Universitetparken 5, 2100 Copenhagen, Denmark; e-mail: [email protected]
Joachim Spatz, Heidelberg University, Biophysical Chemistry, Institute for Physical Chemistry, 1m Neuenheimerfeld 253, 69120 Heidelberg, Germany; e-mail: Joa• [email protected]
Pedro Stange, Complex Systems Group, Fritz-Haber-Institut der Max-Planck-Ges., Department of Physical Chemisty, Faradayweg 4-6, 14195 Berlin, Germany
Angela Stevens, Max Planck Institute for Mathematics in the Science, Inselstr. 22- 26,04103 Leipzig, Germany; e-mail: [email protected]
Ulrich Technau, Molekulare Zellbiologie, Technische Universitat Darmstadt, Schnittspahnstr. 10, 64287 Darmstadt, Germany; e-mail: [email protected]• stadt.de
Eberhard O. Voit, Medical University of South Carolina, Biometry and Epidemio• logy, Charleston, SC 29425, USA; e-mail: [email protected] Peter Walden, Humboldt University, Department of Dermatology, Charite Medical School, 10089 Berlin, Germany
Hans V. Westerhoff, Biocentrum Amsterdam, Deptartment of Molecular Cell Phy• siology, Free University, Amsterdam, The Netherlands
Chris H. Wiggins, Columbia University, Department of Applied Physics and Applied Mathematics,200 S.w. Mudd Building, MC 4701, 500 W. 120th St., New York, NY 10027, USA; e-mail: [email protected] 443
Index ablation, experiments 338 Biochemical Systems Theory 125 actin filament 154 biological clock 19 action potential 67 biological rhythm 421 activation energy 21 biological systems, design principles 73 activation, in self-organization models 312 biomineralization 177 activation-range 313 bistability 253 activator 326 bit-string model 400,404 activator-inhibitor dynamics 115 B-lymphocyte 399 activator-inhibitor mechanism 327 BMP, signaling pathway 310 adaption, of cells 75 body axis 309,324 adjacency matrix 242 Bombina orientalis 338 allergy 399,400 bone marrow 288, 292 allosteric regulation 229 bone substitute 177 amebae 344 branchial stream 337 amphibian, head development 335, 337 Brownian particle 375,376 amplification 75 budhead 310 amplitude of oscillation 17 buffer 65 antibody-antigen complex 404 antigen, internal image 400 antigen presentation 392 cadherin 272 antigen presenting cell (APC) 401 Caenorhabditis elegans 304 Arrhenius equation 21 calcium (Ca2+), oscillations 99 ATP energy 31 calcium dynamics 63 ATP free energy 32, 43 calcium pump 64 ATP free energy homeostasis 45 calcium response 399 ATPase flux 33 calcium signal 403 attachment affinity 289 calcium, buffers 102 attractor 220 cAMP 346 autocatalysis 326 Carnot engine 168 auto-catalytic reaction 215 f3-catenin 310 auto-immunity 392 cell adhesion molecule 380 avascular tumor 367,375 cell cycle 289 cell differentiation 215,222 cell motion 279 bead assay 155 cell movement 344 bilaterality 324 cell sorting 269 bimodal relaxation 274 cell trajectories 200 binding site 259 cell-based model 349,367 biochemical networks 251 cell-cell adhesion 383,386-388 444 Index
cell-cell contact 202 cooperativity, of enzyme subunits 230 cell-cell interaction 218,219,283 cospectral graphs 243 cell-matrix adhesion 381 coupled differential equation 252 cell-matrix interaction 380,386--388 coupled dynamical systems 213 cells, competition of 288 cranial musculature 336,337 cellular automaton 296,344,368,371, cranial neural crest stream 344 400,407 cross-suppression 401 channel-channel interaction 89 cryptic segmental boundary 337 channel collectivity 95 CSTR open flow reactor 8, 12 chaotic itinerancy 214,217 cytokine 400 chemical potential 161 cytoskeletal motor 152 chemical state 164 cytoskeleton 279 chemotaxis 371 chordin 310 circadian clock 19,24 Delta-Notch lateral inhibition 114 circadian period, general homeostasis Demand Theory 120 of 24 desensitization 403 clonality conversion 291 design of systems 119, 124 clone level 290 design principle 120, 128 cluster size distribution 406 development 341 cnidarians 309 developmental hierarchy 292 coherent molecular dynamics 230 differential cell adhesion 344 collective gating of channels 93 diffusion 11 collective interaction of ion channels diffusion-driven model 111 92 diffusion-controlled reaction 226 collective opening of channels 91 diffusive mixing 11 collective opening of gap junctions DiI 340,344 89 discrete dynamical networks 233,234 colon carcinoma 391 Dishevelled (HyDsh) 310 community effect 312 diversification 391 competition 290 dorsal lip 309 complex system 213 double-time mutant 25 concentration ratio 33 Drosophila 19,313 cone-plate rheometer system 199 dynamic clustering 216 confocal laser scanning microscopy dynamical networks 241 340 dynamical property 15, 16 conformational relaxation 227 dyne in 152 connective tissue 336 connective tissue attachment 337 connectivity 44,233,236 Escherichia coli 217 constructive approach 213 efficiency, definition 168 control design 31,32,42,44 EGF 77 convex analysis 52 elasticity 140 Index 445
Elementary Flux Mode Analysis 50 gap junctions, multi-channel ensembles embryonic development 309 of 89 endothelial cell 199 gastrulation 353 ephrin 344 gene expression 223,288 ES cell 219,220 gene regulatory networks 233 evolution of regulatory networks general homeostasis of the circadian 237 period 22 evolution, somatic 391 Generalized Mass Action (GMA) evolutionary algorithm 233,241 system 127 Evolving Network Topology 235 genetic circuit 256 excitable media 344,426 genetic network 127 experimental design 9 genome 128 external equivalence 128 Gierer-Meinhardt dynamics 115,317 extirpation, of neural crest 337 gill arch 337 extracellular matrix (ECM) 279,338, glycolysis 5,121 379,381 Goodwin model 23 extreme currents 14 Goodwin oscillator 19 Extreme Pathway Analysis 50 goosecoid 310 G-protein 153 gradient, stabilization 328 Fisher equation, discrete 113 green fluorescent protein (GFP) 344 fitting procedure 8 growth-environment 289 flexible substrate 283 GSK3 310 fluctuation 169,290 Flux Balance Analysis (FBA) 50 flux 33,44 "hand-over-hand" model 156 flux control 45 haptotaxis 381, 382, 386 flux, regulability 44 head 335,340 flux-split ratio 124 head activation gradient 312 "force clamp" 159 head inhibition gradient 312 force-velocity curve 159 heat shock 121,122 free energy 161 hemagglutinin 411 frequency (frq) gene 22 heterospecific graft 337 frequency of oscillation 15 hierarchy, developmental 291 frog, head development 340 Hill-constant 23 frog embryo 309 homeostasis 44 frq+-MRNA and FRQ half-lifes 26 homeostasis of the intracellular milieu FRQ-degradation 25 32 FRQ-protein 22 Hybrid Discrete-Continuum (HDC) functional dynamics approach 5 technique 380,382 functional unit 74 Hydra 303, 309,323 fusion pore 411 hyoid 337 fusion protein 411 hyomandibula 336 446 Index
idiotype 399 lattice model 368 idiotypic network 404 lattice-gas cellular automaton 368,371, idiotypic repertoire 405 375,376 image, internal, of antigen 400 lever arm 154 immune evasion 393,394 limb 335 immune suppression 393,394 limit cycle 253 immune surveillance 392 lineage commitment 292 immune system 392, 406 linear programming 52 immunological memory 406 living, functional cell 44 impedance matching 167 long-range inhibition 316 in vitro tumor growth 367,369,375 long-term-depression (LTD) 63 "inchworm" model 156 long-term-effects (LTE) 63 indicator 65 long-term-potentiation (LTP) 63 individual-ceIl-based approach 368, lungfish 335,337,340 376 lymphocyte, tumor-infiltrating 393 individual-ceIl-based model 367 influenza virus 411 input, impulse-like/sustained 75 macromolecules (MM) 379,380,385-387 insulin pathway 317 malignant development 393 intracellular calcium dynamics, models malignant transformation 391 of 64 mandibular stream 337 intracellular spatial homogeneity 11 MAPK 75 intracellular transport 152 mast cell 399,401 intra-inter dynamics 214 master equation 415 intramolecular synchronization 230 mathematical model 256 intrinsic parameter 14 mathematical modeling 73 Invar-pendulum 21 matrix degradative enzyme (MDE) in-vitro replication 221 379-381,384 irreversibility 219 maximum entropy 260 isologous diversification 214,215 membrane fusion 411 "isometric" conditions 158 memory, immunological 400 isothermal engine 162 mesoderm 337, 339 metabolic engineering 124 Jacobian matrix 15 Metabolic Flux Analysis (MFA) 50 juxtacrine signaling 115 Metabolic Network Analysis (MNA) 50 metabolic oscillation 5 metabolite profiling 47 kinesin 152 method of controlled mathematical kinetics 14 comparisons (MCMC) 127 kinetic order 126 Metropolis algorithm 241 Metropolis method 369 Mexican axolotl, embryo 337 Laplacian spectrum 242 Michaelis-Menten rate law 127 Index 447
microtubule 154 NO synthetase (Nos) 317 microvolume 226 node equation 126 migraine aura 431 non-equilibrium 161 minimal size 312 minority control 214 minority controlled 221 Ockham's razor 27 model organisms 341 off-lattice approach 368 model validation 15 off-lattice model 368,376 models of the intracellular calcium oncogene 391 dynamics 64 operating principle 121,128 modular network/pathway 32 opposing reactions 22 modular modeling concept 74,82 optical tweezer 156 molecular machine 226 optimization problems 241 molecular network 227, 233 order parameter 233, 235 Monte-Carlo approach 287 ordinary differential equation (ODE) morphogenesis 341 11,76 motility 202 organizer 309 motility assay 154 organizing center 329 motor protein 151 oscillation 290 multicellular structures 295 oscillation, phases of 17 multi-channel ensembles of gap oscillations, synchronous 103 junctions 89 osteoblast 177 multi potency 220,222 osteocaIcin 177 muscle contraction 152 osteoclast 177 muscle fiber 339 mutation 383,384 myosin 152 p53 383,384 myxobacteria 295 p53 gene 380 parameter combination 12, 15 parameter estimation 78 neck 336 parameter fitting 12 neck linker 154 parameter sensitivity 127 negative feedback 251 partial differential equation 344 negative feedback loop 22 pattern formation 111,295,323,324, nerve terminal 63 335 nested feedback 251 pattern of segmentation 339 network dynamics 237,241 percolation transition 400,405 network topology 236 perturbation 251 network, idiotypic 397 perturbation experiments 8, 15 network, randomly evolving 407 phase transition 235 neural crest 336 phases of oscillation 17 Neurospora crassa 19,20 phenotype 384,385,387 31P NMR spectroscopy 35 physiological rate control 44 448 Index
physiological rate control design 45 rhombomere 336 pigment cell, migration 338 rippling 295 pigment pattern 344 robustness 215,251 polygonal cone 13 rotating spirals 422 positional cue 309 positional information 221,222 potential energy landscape 162 Saccharomyces cerevisiae 16 power-law function 126 salamander, head development 340 principal node 124 scales, and modeling 347 processivity 152 Schnakenberg dynamics 115 protein-crystal interaction 180 selection 391 , 393 pseudo-elementary process 20 self-organization 225,236,312 pulse-like input 75 self-renewal 287, 291 pyramidal neurons 69 semiflexible biopolymers 140 sensitivity analysis 79 sensitivity coefficient 21 010 value 19 separatrix, dynamical 402 quenching 17 shear stress 199 quenching experiments 9 signal cascade 401 signal transduction 288 signaling driven model III Random Boolean Networks 234 simulation of the fortification illusion 431 random graph 244 single molecule experiment 154 Random Threshold Networks 234 single-cell fusion 416 rate constant 126 skeletal muscle 31 , 45 rate equation 14 skeletal, muscle and nerve elements 341 reaction network 215 skeleton 336,337 reaction-diffusion system 225 skull 336 reaction-diffusion coupling 420 slug, behaviour of amebae 344 reaction-diffusion model 311 sodium-calcium exchangers 64 reaggregation 312 somatic evolution 394 reconstruction of networks 241 somites 338 regeneration 328 spatio-temporal patterns 422 regulability 45 spectral density 242 regulation design 43 spectral distance 243 regulation of gap junction multi-chan- Spemann organizer 309 nel current 94 S-phase 291 regulatory gene network 259 spherical aggregate 271 reinforcement, of cytoskeleton 279 S-system 127 relaxation time t 23 stapes 336 remodeling, bone 177 stationary concentration 14 repertoire, idiotypic 400 stationary condition 15 repopulation ability 291 stationary states 13 Index 449
stationary velocity 14 transcription factor 259 stem cell 217,287,291,292 transcriptional regulatory pathway 251 stimulation, autocrine 401 trans endothelial resistance TER 200 stochastic approach 411 transformer element 78 stochastic effect 376 transplantation 337 stochastic evolution 241, 244 traveling wave 112 stochastic fluctuation 11 traveling wave, quasi translation stoichiometric coefficient 12,79 invariant 113 stoichiometry 12 traveling wave, translation invariant 113 storage element 78 trehalose cycle 122 stroma 288 tryptophan biosynthetic pathway 252 Stuart-Landau equation 8 tumor growth 367 subcritical Hopf bifurcation 253 tumor spheroid 371 supercritical Hopf bifurcation 8 tumor cell phenotype 380 surface gliding assay 155 tumor invasion 379,384 sustained input 75 tumor suppressor gene 391 synchronization 227 tumor-associated antigen 392 Systems Biology Workbench 77 Turing 326 Turing-type instability 303 two-state model 165 T-box gene Brachyury 313 Tcf 310 temperature-compensated mechanical upscaling 348 pendulum 21 temperature-compensation 19,21 temperature-compensation, modeling Van't Hoff's rule 19 of 26 velocity parameter 14,15 tensiotaxis 279 venom immunotherapy 400,402 TGF-~lBmp signaling 310 virtual cell 77 T-helper (Th) lymphocytes 399 visceral arch muscle 336 ThlrTh2 regulation 399,400 viscoelasticity of living tissues 274 thick filament 153 voltage-gated calcium channel 64 Thomas system 115 three-bead assay 157 time hierarchy 44,45 wave length problem 328 timescale separation 44,45 white-collar complex 23 tissue formation 283 within-tissue plasticity 287,291,292 tissue liquidity 269 Wnt-pathway 310 tissue plasticity 292 Wnt-Wg signaling 310 tissue surface tension 271 "worm-like chain" 140 T-lymphocyte 392 T-lymphocyte, cytolytic 393 yeast 5,121,122 Tolloid (TId) 318 Young modulus E 370 Color plates :1]: <'Omllm AT V _ A DI' I : .nOIe 11 Gk. 2-': A" ATV + AMV ------=- 2 ADP ( j l. T'.IIh
Gk AT. ~ J H" .\ AT. AD. ADP _ / \, G6. L 22: ~tORgI: • K;, [1 ."" ATP , 1'1 .... .B. _ ADP , NA I) ' ,. \1 L> NADH , • , NAD· OAI' 1) 1-1 ""- GI,." 7: T IM < lP(il." , b: ('.\PIlII 11 I d ifC;I).... NADH GI),<, .
AD' )~ 17. ootGI).;"" 9 ; IpPEP 1 ATP PEP AD'
10 · 1'"1\ NADII NAO ' II flOC !\ 12: ,\I)JI t ATP J --/"'. ACA " EoOH
III : d irACA I): di{EoOII 20: !.acto 11 11 • ACA. BOI-I •
"""'\ I'i; WIACA 14: .;,.. t~O Il C ..... -
2 1; inC"N 11 Color plate 2
A B
20 8
.:- 1.8 o
g 1.6 .,~
~" 1.4 2
1.2 L..JI...L.ILJL...... LJ LJl..LILL..L.ILJL...... LJ LL- o '=-'-:-:..lL-7'-:-:...u...-=-"'-:~~L....:---'--=' 5 10 15 20 1.3 1.4 1.5 1.6 1.7 1 .8 1.9 2 2 .1 cell no. Irequency (Hz x 10 ' )
c D
0,5
0,4
0,32:~
:1
0,2
0,1
,eli no 0111 no Figure 3 from page 107. Periodic intercellular calcium waves in an array of heterogeneous cells. (A) Intrinsic frequencies of the uncoupled cells. The frequency varies according to the activated agonist receptor in each cell, drawn from a normal distribution (R = 0.6 ± 0.05). Cell I is assigned a higher value (R = 0.8). (8) Frequency histogram. (C) Space time plot of the cytosolic calcium concentration if the cells are uncoupled (Pu = Pw = 0.6 Jlmls); (0) if the cells are coupled (Pu = 0.6 Jlm/s, Pw = 1.5 Jlmls). Du = 22.5 Jlm2/s, Vo = 0.05 JlM/s, the other parameters as in Table I (p. 103). Color plate 3
G;L S"'9 nm 5.5 nm ~+ I 37 nm I Actin filament
light chains
tail
motor domain
Myosin ...... • ...... • .. t • .- : ; : : : : : : : : :: '...... " -"+
Bnm Microtubule
Kinesin Figure I from page 154. Cartoons of the filaments and motor structures. Color plate 4
Figure 3 from page 163. Highly simplified two-dimensional energy landscape for a motor protein with a chemical and a spatial axis (arrows). An external load would tilt this landscape in the spatial direction, a chemical non-equilibrium in the chemical direction. Mechanochemical coupling is reflected in the diagonal path (red linc); a particle (red sphere) would advance along the spatial axis even with a purely "chemical tilt". Color plate 5
kllE'S 1 A
+
microtubule
w
a 1/2 x
Figure 4 from page 167. Schemes of the hand-over-hand mechanism (upper) and its translation in terms of a two-state model (lower). The different phases of the coordinated activity of the motor domains are reduced to the motion of a stochastic particle at position X and in chemical states 1 or 2 (see text). The phase A corresponds to the transition driven by the ATP hydrolysis (hydrolysis and energy release), while the phase B to the sliding of the particle over the potential profile (conformational change). Color plate 6
substrate . regulatory molecule enzyme • 10
product . feedback *y loop
Figure I from page 22g. Model of the enzyme as a protein machine.
p(,.. ... l b) 1.2
0.8
0.4
4
Figure I from page 243. Map showing the problem of the Bridges of Konigsberg [20] which gave birth to the foundation of graph theory as a mathematical discipline (a). The same problem represented in terms of a graph G and its associated spectral density for y = 0.08 (b). Color plate 7
Limbbud ", 20.1 ':, :;; • ,<::'
Pigm. Epilh. 12.6
Heart 8.5
.J(~ 0 "
",,,< '" Liver 4.6
J • Retina
Figure 2 from page 272. Equilibrium sorting patterns in mixtures of embryonic chicken tissues. The surtllCC tcnsion of each tissue is indicated on the right in units of dyne/cm.
Figure 3 from page 273. Sorting in genetically transformed cell populations with controlled number of cell adhesion molecules. See text for details. The linear size of the aggregate is around 200 ,um. Color plate 8
Figure 2 from page 281. Pseudo-I D-triangulation with model c ell. Open circles denote binding sites, the rectangular box represents a model cell. In this configuration there are no fluxes in the y-direction.
F' A
pressure o tension B
Figure 3 from page 282. Force-stress relation. A: The force on a single binding site depends linearl y on the stress in the underlying matrix. B: Two neighbour cells pull stronger on the side where they face each other (black arrows: forces on each single bindig side, arrows below the cell: net force for the left and right side, color of the matrix: Vx). Color plate 9
A Frnl1l~ # I00. T:2 B Frmn..: tr iOO. T:2
0.35 0.025 '
0.01 ' 0.25003 0.015 • 0_2 1 , ~ .g 0.01 • 0.15 t 0.1 0_005 , ·0.05
O · (I
·0.05 '-"""'_~--:--:-__~ __--:--:-:---::'-~ ·(104 ·(j.3 ·0.2 ·0.1 0_1 0.1 0.3 0.-1
Figure 4 from page 283. Spontaneous cell motion in 10. A: Plot of the matrix density (p - pJ for the cell given below. To visualise the stochastic effect of the distribution of binding sites the cell is blocked in movement. As a result the forces are imbalanced and it pulls harder on the right site which causes an asymmetric density profile (Vertical lines in the density plot indicate the cell boundaries for the given cell below. arrows denote the net forces for the left and right side, coulor: matrix density). B: Inclusion of cell motion counter-balances the asymmetry.
- .,.. ..- '", ~-._...... ' 0 • . 1, • • __ ', "" . ,. . •
Figure 6 from page 285. Tensiotaxis mediated aggregation in 20. A: Attraction of two cells in 20. Cells now are represented by circles with a lamella region and traction forces (black arrows, only shown for the first time step) point to the cell centre. The colour of the matrix codes the stress. Maximum tension can be found in between cells and like in 10 cells move towards each other. B: Same situation as in A, but now with six cells. To exclude effects caused by cells coming to close to the domain boundary, a larger multi resolution grid is used. Black arrows here denote the matrix velocity. Color plate 10
HyTcf
domain restriction
HyWnt
point source HyBra1
24 hr 48 hr 96 hr
Figure 3 from page 315. Expression dynamics of HyTcf, HyWnt, and HyBral during aggregate development. In situ hybridization reveals patterning events during head organizer formation. HyWnt and HyBral appear simultaneously in small spots (24h) which enlarge during later stages (96h), and precede formation of morphological head structures by about 2-3 days. All spots eventually develop into heads. (From Proceedings of the National Academy of Sciences USA 97: 12127-12131 and Nature 407: 186-189; Nature Publishing Group, 2000; PNAS 2000). Color plate 11
Figure I from page 325. (A) the freshwater polyp hydra. (B) With the inner endodermal layer and the outer ecto• dermal layer, the basic body plan is close to a gastrula. (C) A drawing from Haeckel's paper Hydra-like [3], proposing that all higher organisms ancestor proceed through a similar-appearing gastru• lalike stage. (D-F) The oral-aboral axis of hydra and the proposed correspondence the anteroposterior axis in higher organisms. The foot of the hydra is proposed to be derived from the bottom of the cup-shaped ancient gastrula. This part of the ancestor gave rise to the most anterior part of verebrates, the for• brain and heart (Nkx2.x expression, pink). In contrast, the opening of the cup gave rise to the mouth opening in hydra and to the anus Brain of higher organisms (Wnt-expression). The region around the tentacles corresponds to the midbrain-hindbrain border (border of Otx-Csc expression, (blue/yellow). In verte• brates (F), Csc, (yellow) participates in head formation while Brachyurv (red) fonns the notochord and the tail bud. In hydra both genes are expressed next to each other. Thus, this is the zone that gave rise to the trunk (for details, see [6], for actual expression patterns in hydra [13-17]) .
•hypostome -t Figure 3 from page 330. 3' Generation of complex patterns by linkage of CD several pattern forming reactions: Simulation of hypos tome, tentacle and foot formation in hydra. Primary head activation (green) and foot-signal foot activation (pink) apppear at opposite ends of the field due to a coupling via the source density (or competence. blue). Ten• tacle activation (red) appears close to the hypostome since it requires a high source density but it is locally suppressed by head activation. Bottom right: In regeneration near-head frag• ments tentacle activation precedes head acti• vation. The new tentacle signal appears first at the tip, the region of the highest source density. Later, with the rising head signal, it is shifted to the appropriate position, in agreement with the experimental observa• tions [13]. Bottom left: In more basal fragments head activation occurs first. Tentacle activation takes place later at the final position after the source density has obtained a threshold value (from [11,18]). Color plate 12
_,.,.~~~~ mia mip C--;j..q..ll."C-::1i ~~: } ang mqa osh
BB
OC' CB
Mandibular stream Non-crest
Hyoid stream Mandibular arch muscles
Branchial stream Hyoid arch muscles
Figure 5 from page 342. Larval skull and cranial musculature of Bomhina orienlalis. depicted in dorsal (left) and ventral views. Neural crest-derived cartilages are shaded according to the migratory stream from which they originate (redrawn form Olsson and Hanken. 1996): light brown. mandibular stream; medium brown. hyoid stream; dark brown. bran• chial stream. The few non-crest-derived cartilages are light grey. Cranial muscles are depicted schematically. Mandibular (first) arch muscles are red. hyoid (second) arch muscles are blue. Paired muscles arc depicted on one side only. Cartilages: BB. basibranchial; BH basihyal; CB. ceratobranchials I-IV; CH. ceratohyal; CT. cornua trabecula (trabecular hom); IR. infrarostral; MC Meckel's; Oc. otic capsule; PQ. palatoquadrate; SR. suprarostral; TP. trabecular plate. Muscles: lev. levator mandibulae group-mlma. levator mandibulae anterior; mlmaa. levator mandibulae anterior articularis; mlmas. levator mandibulae anterior subexternus; mlmp. levator mandibulae posterior (comprising two parts; superficialis and profundus); ang. angularis group-mha. hyoan• gularis; mqa. quadratoangularis; msa. suspensorioangularis; hyoideus group-mih. interhyoideus; moho orbito• hyoideus; msh. suspensoriohyoideus; osh. orbito- and suspensoriohyoideus; others-mia. intermandibularis anterior; mip. intermandibularis posterior; mml. mandibulolabioalis. Anatomical nomenclature follows Cannatella [2]. Color plate 13
Figure 6 from 343. A larva of the Mexican axolotl stained with antibodies specifically recognising muscle (green) and cranial nerves (red). The eyes are autofluorescent in both green and red and therefore appear yellow. Following immunostaining, the larva was bleached and made transparent, and subsequently imaged using a confocal microscope. This allows optical sectioning through the larval head. The optical sections were then put together and all parts of the larva are in focus at the same time. The red dots (there are many of them ventrally) are cilia, not nerves. Both cranial nerves and cilia contain the molecule (acetylated alpha-tubulin) recognised by the antibody. Muscles were stained using a desmin antibody.
Figure I from page 385. Homogeneous tumour results, spatial distri• bution of tumour cells, MDE, MM and oxygen (clockwise) at time t = 200 units (i.e. 200 generations, approximately 133 days). Colouration of the tumour cells represents phenotype: orange (type I); yellow (type II); green (type 1II); blue (type IV) and brown represents dead cells. For the MDE, MM and oxygen concentration, the hot colour map is used, i.e. white = high concentration, black = low concentration and red is in between. Color plate 14
Figure 2 from page 386. Heterogeneous tumour results, spatial distribution of tumour cells, MDE, MM, and oxygen (clockwise) at time t = 200 units. Coloration as in Figure 2.
CELLS
OXYGENE
Figure 3 from page 387. Random tumour results, spatial distribution of tumour cells, MDE, MM and oxygen (clockwise) at time t = 200 units. Coloration as in Figure 2. Color plate 15
Figure 2 of page 413. Fusion oj" HA-expressing cell with douhle-Iaheled red h/ood cell. RBCs were double-labeled with a green fluorescent aqueous dye (cytoplasm) and a red fluorescent lipid-like dye (membrane). RBC was bound to an HA-expressing cells (A-C phase microscopy; 0-1 fluorescence microscopy). At time 0 min (A,O,G), fusion was initiated by lowering the pH from 7.4 to 5.0. Both fluorophores are still confined to the RBC. Images taken after I min 39 s (B,E,H), when the lipid fluorophore (H) but not the aqueous dye (E) starts moving into the fibroblasts (LP-signal). This signal is preceded by a sudden drop of the fluorescence of the lipid dye due to the formation of an early ion-permissive pore (lP-signal) (not visible, see text). Images taken after 5 min 2 s (C,F.I), when aqueous dye has redistributed to the cytoplasm (F), while the lipid fluorophore is spreading over the surface of the plasma membrane of the HA expressing cell (I). Experimental data in Figure 5 (p. 419) corresponds to intensities measured in region 3 (lP-signal) and region 4 (LP-signal). Reproduced from The Journal of Cell Biology [4] by copyright permission of the Rockefeller University Press.