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Computing with Feedforward Networks of Artificial Biochemical Neurons

  • Huub M. M. ten Eikelder
  • Sjoerd P. M. Crijns
  • Marvin N. Steijaert
  • Anthony M. L. Liekens
  • Peter A. J. Hilbers
Conference paper
Part of the Proceedings in Information and Communications Technology book series (PICT, volume 1)

Abstract

Phosphorylation cycles are a common motif in biological intracellular signaling networks. A phosphorylaton cycle can be modeled as an artificial biochemical neuron, which can be considered as a variant of the artificial neurons used in neural networks. In this way the artificial neural network metaphor can be used to model and study intracellular signaling networks. The question what types of computations can occur in biological intracellular signaling networks leads to the study of the computational power of networks of artificial biochemical neurons. Here we consider the computational properties of artificial biochemical neurons, based on mass-action kinetics. We also study the computational power of feedforward networks of such neurons. As a result, we give an algebraic characterization of the functions computable by these networks.

Keywords

Cell signaling networks phosphorylation cycle artificial neurons 

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Copyright information

© Springer Tokyo 2009

Authors and Affiliations

  • Huub M. M. ten Eikelder
    • 1
  • Sjoerd P. M. Crijns
    • 1
  • Marvin N. Steijaert
    • 1
  • Anthony M. L. Liekens
    • 1
  • Peter A. J. Hilbers
    • 1
  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands

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