Computing with Feedforward Networks of Artificial Biochemical Neurons

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.

[1]  D. Koshland,et al.  An amplified sensitivity arising from covalent modification in biological systems. , 1981, Proceedings of the National Academy of Sciences of the United States of America.

[2]  F. G. Young Enzymes , 1951 .

[3]  Upinder S Bhalla,et al.  Understanding complex signaling networks through models and metaphors. , 2003, Progress in biophysics and molecular biology.

[4]  C. Clevenger Signal transduction. , 2003, Breast disease.

[5]  Philip Ball,et al.  Chemistry meets computing , 2000, Nature.

[6]  J. Massagué TGF-beta signal transduction. , 1998, Annual review of biochemistry.

[7]  Dennis Bray,et al.  Bacterial chemotaxis and the question of gain , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[8]  H. Kitano Systems Biology: A Brief Overview , 2002, Science.

[9]  A Hjelmfelt,et al.  Chemical implementation of neural networks and Turing machines. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[10]  D. Bray Protein molecules as computational elements in living cells , 1995, Nature.

[11]  Huang Lin,et al.  Root locations of an entire polytope of polynomials: It suffices to check the edges , 1987, 1987 American Control Conference.

[12]  P. Cohen,et al.  The regulation of protein function by multisite phosphorylation--a 25 year update. , 2000, Trends in biochemical sciences.

[13]  H. Berg,et al.  Receptor sensitivity in bacterial chemotaxis , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[14]  C. Rao,et al.  Control motifs for intracellular regulatory networks. , 2001, Annual review of biomedical engineering.

[15]  J. Ross,et al.  Computational functions in biochemical reaction networks. , 1994, Biophysical journal.