CLOSURE IN ARTIFICIAL CELL SIGNALLING NETWORKS - Investigating the Emergence of Cognition in Collectively Autocatalytic Reaction Networks

James Decraene

Abstract

Cell Signalling Networks (CSNs) are complex biochemical networks responsible for the coordination of cellular activities in response to internal and external stimuli. We hypothesize that CSNs are subsets of collectively autocatalytic reaction networks. The signal processing or cognitive abilities of CSNs would originate from the closure properties of these systems. We investigate how Artificial CSNs, regarded as minimal cognitive systems, could emerge and evolve under this condition where closure may interact with evolution. To assist this research, we employ a multi-level concurrent Artificial Chemistry based on the Molecular Classifier Systems and the Holland broadcast language. A critical issue for the evolvability of such undirected and autonomous evolutionary systems is to identify the conditions that would ensure evolutionary stability. In this paper we present some key features of our system which permitted stable cooperation to occur between the different molecular species through evolution. Following this, we present an experiment in which we evolved a simple closed reaction network to accomplish a pre-specified task. In this experiment we show that the signal-processing ability (signal amplification) directly resulted from the evolved systems closure properties.

References

  1. Cronhjort, M. and Blomberg, C. (1997). Cluster compartmentalization may provide resistance to parasites for catalytic networks. Physica D: Nonlinear Phenomena, 101(3-4):289-298.
  2. Decraene, J., Mitchell, G. G., and McMullin, B. (2007). Evolving artificial cell signaling networks: Perspectives and methods. In Advances in Biologically Inspired Information Systems, volume 69, pages 165- 184. Springer.
  3. Decraene, J., Mitchell, G. G., and McMullin, B. (2008). Unexpected Evolutionary Dynamics in a String-Based Artificial Chemistry. Proceedings of Artificial Life XI, MIT Press.
  4. Dittrich, P. and Speroni, P. (2007). Chemical Organisation Theory. Bulletin of Mathematical Biology, 69(4):1199-1231.
  5. Fontana, W. and Buss, L. (1994a). The arrival of the fittest: Toward a theory of biological organization. Bulletin of Mathematical Biology, 56(1):1-64.
  6. Fontana, W. and Buss, L. (1994b). What Would be Conserved if “the Tape were Played Twice”? Proc. of the National Academy of Sciences, 91(2):757-761.
  7. Gershenson, C. and Lenaerts, T. (2008). Evolution of Complexity. Artificial Life, 14(3):241-243.
  8. Grogono, P., Chen, G., Song, J., Yang, T., and Zhao, L. (2003). Laws and life. In Proceedings of the 7th IASTED Conference on Artificial Intelligence and Soft Computing (ASC 2003), pages 158-163.
  9. Hogeweg, P. and Takeuchi, N. (2003). Multilevel selection in models of prebiotic evolution: compartments and spatial self-organization. Origins of Life and Evolution of the Biosphere, 33(4-5):375-403.
  10. Holland, J. (1976). Studies of the spontaneous emergence of self-replicating systems using cellular automata and formal grammars. Automata, Languages, Development, pages 385-404.
  11. Holland, J. (1992). Adaptation in natural and artificial systems. MIT Press Cambridge, MA, USA.
  12. J.Decraene (2006). The Holland Broadcast Language. Technical Report ALL-06-01, RINCE, Dublin City University. http://elm.eeng.dcu.ie/˜alife/jd/ALL-06- 01/.
  13. Kauffman, S. (1997). At Home in the Universe. Mathematical Social Sciences, 33(1):94-95.
  14. Machné, R., Finney, A., Müller, S., Lu, J., Widder, S., and Flamm, C. (2006). The sbml ode solver library: a native api for symbolic and fast numerical analysis of reaction networks. Bioinformatics, 22(11):1406-1407.
  15. McMullin, B., Kelly, C., and O'Brien, D. Multi-level selectional stalemate in a simple artificial chemistry. In Proceedings of ECAL 2007, LNCS. Springer.
  16. Ray, T. (1991). An approach to the synthesis of life. Artificial Life II, 10:371-408.
  17. Ray, T. (1992). Evolution, ecology and optimization of digital organisms. Santa Fe.
  18. Stadler, P. F., Fontana, W., and Miller, J. H. (1993). Random catalytic reaction networks. Phys. D, 63(3-4):378- 392.
  19. van Duijn, M., Keijzer, F., and Franken, D. (2006). Principles of Minimal Cognition: Casting Cognition as Sensorimotor Coordination. Adaptive Behavior, 14(2):157.
Download


Paper Citation


in Harvard Style

Decraene J. (2009). CLOSURE IN ARTIFICIAL CELL SIGNALLING NETWORKS - Investigating the Emergence of Cognition in Collectively Autocatalytic Reaction Networks . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009) ISBN 978-989-8111-65-4, pages 107-114. DOI: 10.5220/0001545301070114


in Bibtex Style

@conference{biosignals09,
author={James Decraene},
title={CLOSURE IN ARTIFICIAL CELL SIGNALLING NETWORKS - Investigating the Emergence of Cognition in Collectively Autocatalytic Reaction Networks},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)},
year={2009},
pages={107-114},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001545301070114},
isbn={978-989-8111-65-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)
TI - CLOSURE IN ARTIFICIAL CELL SIGNALLING NETWORKS - Investigating the Emergence of Cognition in Collectively Autocatalytic Reaction Networks
SN - 978-989-8111-65-4
AU - Decraene J.
PY - 2009
SP - 107
EP - 114
DO - 10.5220/0001545301070114