Patterning by genetic networks

We consider here the morphogenesis (pattern formation) problem for some genetic network models. First, we show that any given spatio-temporal pattern can be generated by a genetic network involving a sufficiently large number of genes. Moreover, patterning process can be performed by an effective algorithm. We also show that Turing's or Meinhardt's type reaction–diffusion models can be approximated by genetic networks. These results exploit the fundamental fact that the genes form functional units and are organized in blocks. Due to this modular organization, the genes always are capable to construct any new patterns and even any time sequences of new patterns from old patterns. Computer simulations illustrate some analytical results. Copyright © 2005 John Wiley & Sons, Ltd.

[1]  Lewis Wolpert,et al.  Principles of Development , 1997 .

[2]  R. Solé,et al.  Gene networks capable of pattern formation: from induction to reaction-diffusion. , 2000, Journal of theoretical biology.

[3]  F. Takens,et al.  Occurrence of strange AxiomA attractors near quasi periodic flows onTm,m≧3 , 1978 .

[4]  Yuichi Nakamura,et al.  Approximation of dynamical systems by continuous time recurrent neural networks , 1993, Neural Networks.

[5]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[6]  R. Thomas,et al.  Dynamical behaviour of biological regulatory networks--II. Immunity control in bacteriophage lambda. , 1995, Bulletin of mathematical biology.

[7]  S. Vakulenko Dissipative systems generating any structurally stable chaos , 2000 .

[8]  J. Hopfield,et al.  From molecular to modular cell biology , 1999, Nature.

[9]  David H. Sharp,et al.  Mechanism of eve stripe formation , 1995, Mechanisms of Development.

[10]  H. Meinhardt Beyond Spots and Stripes: Generation of More Complex Patterns by Modifications and Additions of the Basic Reaction , 2001 .

[11]  Hans Meinhardt,et al.  The Algorithmic Beauty of Sea Shells , 2003, The Virtual Laboratory.

[12]  Stephen Smale,et al.  The Mathematics of Time: Essays on Dynamical Systems, Economic Processes, and Related Topics , 1980 .

[13]  Zbigniew Nitecki,et al.  Differentiable dynamics;: An introduction to the orbit structure of diffeomorphisms , 1971 .

[14]  David H. Sharp,et al.  Prediction of mutant expression patterns using gene circuits. , 1998, Bio Systems.

[15]  Andrew R. Barron,et al.  Universal approximation bounds for superpositions of a sigmoidal function , 1993, IEEE Trans. Inf. Theory.

[16]  J. Hale Asymptotic Behavior of Dissipative Systems , 1988 .

[17]  F. Takens,et al.  On the nature of turbulence , 1971 .

[18]  Serge Vakulenko Complexité dynamique des réseaux de Hopfield , 2002 .

[19]  M. Viana Multidimensional nonhyperbolic attractors , 1997 .

[20]  D. A. Baxter,et al.  Mathematical Modeling of Gene Networks , 2000, Neuron.

[21]  A. Turing The chemical basis of morphogenesis , 1952, Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences.

[22]  L. Glass,et al.  Symbolic dynamics and computation in model gene networks. , 2001, Chaos.

[23]  Dmitry Grigoriev,et al.  Complexity of gene circuits, Pfaffian functions and the morphogenesis problem , 2003 .

[24]  David H. Sharp,et al.  A connectionist model of development. , 1991, Journal of theoretical biology.

[25]  Peter V. Gordon,et al.  NEURAL NETWORKS WITH PRESCRIBED LARGE TIME BEHAVIOUR , 1998 .

[26]  Roderick Edwards,et al.  Approximation of Neural Network Dynamics by Reaction‐Diffusion Equations , 1996 .

[27]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[28]  L. Jones A Simple Lemma on Greedy Approximation in Hilbert Space and Convergence Rates for Projection Pursuit Regression and Neural Network Training , 1992 .

[29]  S. Vakulenko A system of coupled oscillators can have arbitrary prescribed attractors , 1994 .

[30]  L. Glass,et al.  The logical analysis of continuous, non-linear biochemical control networks. , 1973, Journal of theoretical biology.