Abstract
This work investigates the influence of environmental inducers on the organization of cell regulation networks, using a connectionist approach. Protein interactions are modeled by an asymmetrical recurrent network, the units of which take continuous values. In contrast to classical models, we explicitly introduce a genome to encode the architecture of the system. This feature enables us to introduce an evolution model, in which a genetic algorithm that mimics the effects of evolution on proteins mutual interactions is used. We assume an efficient system to respond to persistent environmental stimuli, independently of their amplitude. Results are presented that show a structuration of the network with the emergence of specialized hierarchical structures. These structures seem to drive the system at the edge of chaos, so that it can present adapted responses to significant environmental changes.
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References
Amit D (1992) Modeling brain function, 2nd edn. Cambridge University Press, Cambridge, UK
Ando A, Fisher FM (1962) Two theorems on Ceteris Paribus in the analysis of dynamic systems. Am Pol Sci Rev 61:103–113
Bagley RJ, Farmer JD, Kauffman SA, Packard NH, Perelson AS, Stadnyk IM (1989) Modeling adaptive biological systems. BioSystems 23:113–138
Belew RK, McInerney J, Schraudolph NN (1992) Evolving networks: using the genetic algorithm with connectionist learning. In: Artificial life II. Addison-Wesley, New York
Berridge MJ (1985) The molecular basis of communication with the cell. Sci Am 253:142–152
Calakos N, Bennett M, Peterson K, Scheller R (1994) Protein-protein interactions contributing to the specificity of intracellular vesicular trafficking. Science 263:1146–1149
Clark SC, Kamen R (1987) The human hematopoietic colony stimulating factors. Science 236:1229–1237
Cormier F, Dieterlen-Lièvre F (1988) The walls of the chick aorta harbours M-CFC, G-CFC, GM-CFC and BFU-E. Development 102:279–285
Cyert MS, Thorner J (1989) Putting it on and taking it off: phosphoprotein phosphatase involvement in cell cycle regulation. Cell 57:891–893
Derrida B, Stauffer D (1986) Phase-transitions in two-dimensional Kauffman cellular automata. Europhys Lett 2:739–745
Endo YG, Lee MA, Kopf GS (1987) Evidence for the role of a guanosine nucleotide-binding regulatory protein in the zona pellucida-induced mouse sperm acrosome reaction. Dev Biol 119:210–216
Farmer JD, Kauffman SA, Packard NH, Perelson AS (1989) Adaptive dynamic networks as models for the immune system and autocatalytic sets. Ann NY Acad Sci 118–130
Fothergill-Gilmore LA (1986) The evolution of the glycolytic pathway. Trends Biochem Sci 11:47–51
Gardner E (1988) The space of interactions in neural network models. J Phys A: Math Gen 21:257–270
Gardner E, Derrida D (1988) Optimal storage properties of neural network models. J Phys A: Math Gen 21:271–284
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading
Hassoun MH (1991) Dynamic associative memories. In: Sethi IK, Jain AK (eds) Artificial neural networks and statistical pattern recognition, old and new connections, pp 195–218
Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 79:2554–2558
Hopfield JJ (1984) Neurons with graded responses have collective computational properties like those of two-state neurons. Proc Natl Acad Sci USA 81:3088–3092
Jacobson H (1955) Information, reproduction and the origin of life. Am Sci 43:119–127
Kauffman SA (1989) Principles of adaptation in complex systems. In: Stein D (eds) Lectures in the science of complexity, vol 1, pp 619–712
Kauffman SA (1990) Requirement for evolvability in complex systems, orderly dynamics and frozen components. Physica D 42:135–152
Kauffman SA (1992) Origins of order: self-organization and selection in evolution. Oxford University Press, Oxford
Kauffman SA (1994) Whispers from Carnot. In: Cowan G, Pines D, Meltzer D (eds) Complexity: metaphors, models and reality. (SFI studies in the science of complexity, Proc Vol. XIX), Addison-Wesley, New York, pp 83–160
Kauffman SA, Levin S (1987) Towards a general theory of adaptive walks on rugged landscapes. J Theor Biol 128:11–45
Kohonen T (1984) Self-organization and associative memory. Springer, Berlin Heidelberg New York
Langton CG (1990) Computation at the edge of chaos, phase transitions and emergent computation. Physica D 42:12–37
Langton CG (1992) Life on the edge of chaos. In: Artificial life II. Addison-Wesley, New York pp 41–91
Le Douarin NM (1986) Cell line segregation during peripheral nervous system ontogeny. Science 231:1515–1522
Linsker R (1988) Self-organization in a perceptual network. Computer 21:105–117
Marchionni M, Gilbert W (1986) The triosephosphate isomerase gene from maize: introns antedate the plant-animal divergences. Cell 46:133–141
Minai A, Levy W (1993) The dynamics of sparse random networks. Biol. Cybern. 70:177–187
Mitchell M, Hraber PT, Crutchfield JP (1993) Revisiting the edge of chaos: evolving cellular automata to perform computations. Complex systems 7:89–130
Rabilloud T, Vincens P, Tarroux P (1985) A new tool to study genetic expression using 2-D electrophoresis data: the functional map concept. FEBS Lett 189:171–178
Rabilloud T, Pennetier JL, Hibner U, Vincens P, Tarroux P, Rougeon F (1991) Stage transitions in B-lymphocytes differentiation correlate with limited variations in nuclear proteins. Proc Natl Acad Sci USA 88:1830–1834
Rammal R, Toulouse G, Virasoro MA (1986) Ultrametricity for physicists. Rev Mod Phys 58:765–788
Simon HA (1962) Architecture of complexity. Proc Am Philos Soc 106:467–482
Sompolinsky H, Crisanti A (1988) Chaos in random neural networks. Phys Rev Lett 61:259–262
Spears WM, De Jong KA (1991) On the virtues of parameterized uniform crossover. In: Belew RK, Booker LB (eds) Proceeding of the Fourth International Conference on Genetic Algorithms. Morgan Kauffman, San Mateo, pp 230–236
Syswerda G (1989) Uniform crossover in genetic algorithms, In: Schaffer JD (ed) Proceeding of the Third International Conference on Genetic Algorithms. Morgan Kauffman, San Mateo, pp 2–9
Tarroux P (1983) Analysis of protein patterns during differentiation using 2-D electrophoresis and computer multidimensional classification. Electrophoresis 4:63–70
Tarroux P, Vincens P, Rabilloud T (1987) HERMeS: a second generation approach to the automatic analysis of two-dimensional electrophoresis gels. Part V. Data analysis. Electrophoresis 8:187–199
Wagner A (1994) Evolution of gene networks by gene duplications: a mathematical model and its implications on genome organization. Proc Natl Acad Sci USA 91:4387–4391
Weisbuch G, Stauffer D (1987) Phase transition in cellular random Booleannets. J Phys 48:11–18
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Chiva, E., Tarroux, P. Evolution of biological regulation networks under complex environmental constraints. Biol. Cybern. 73, 323–333 (1995). https://doi.org/10.1007/BF00199468
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DOI: https://doi.org/10.1007/BF00199468