The use of oscillatory signals in the study of genetic networks.

The structure of a genetic network is uncovered by studying its response to external stimuli (input signals). We present a theory of propagation of an input signal through a linear stochastic genetic network. We found that there are important advantages in using oscillatory signals over step or impulse signals and that the system may enter into a pure fluctuation resonance for a specific input frequency.

[1]  N. Kampen,et al.  Stochastic processes in physics and chemistry , 1981 .

[2]  M. Thattai,et al.  Intrinsic noise in gene regulatory networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[3]  U. Alon,et al.  Negative autoregulation speeds the response times of transcription networks. , 2002, Journal of molecular biology.

[4]  L. Serrano,et al.  Engineering stability in gene networks by autoregulation , 2000, Nature.

[5]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[6]  R. Fields,et al.  Specific regulation of immediate early genes by patterned neuronal activity , 1993, Journal of neuroscience research.

[7]  Farren J. Isaacs,et al.  Prediction and measurement of an autoregulatory genetic module , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[8]  J. Hasty,et al.  Synthetic gene network for entraining and amplifying cellular oscillations. , 2002, Physical review letters.

[9]  Carl Hirschie Johnson,et al.  Circadian gene expression in mammalian fibroblasts revealed by real-time luminescence reporting: Temperature compensation and damping , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[10]  H. Riezman,et al.  Transcription and translation initiation frequencies of the Escherichia coli lac operon. , 1977, Journal of molecular biology.

[11]  E. Huq,et al.  A light-switchable gene promoter system , 2002, Nature Biotechnology.

[12]  J. Collins,et al.  Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling , 2003, Science.

[13]  M. L. Simpson,et al.  Frequency domain analysis of noise in autoregulated gene circuits , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[14]  R. Fields,et al.  Regulated Expression of the Neural Cell Adhesion Molecule L1 by Specific Patterns of Neural Impulses , 1995, Science.

[15]  Nicola J. Rinaldi,et al.  Transcriptional Regulatory Networks in Saccharomyces cerevisiae , 2002, Science.

[16]  D. Slepian Some comments on Fourier analysis, uncertainty and modeling , 1983 .

[17]  Kai-Florian Storch,et al.  Extensive and divergent circadian gene expression in liver and heart , 2002, Nature.

[18]  D. V. Leenen,et al.  Mammalian Cry1 and Cry2 are essential for maintenance of circadian rhythms , 1999, Nature.

[19]  W. Ebeling Stochastic Processes in Physics and Chemistry , 1995 .

[20]  S. Reppert,et al.  Coordination of circadian timing in mammals , 2002, Nature.

[21]  J. Ross,et al.  Determination of causal connectivities of species in reaction networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[22]  B. H. Miller,et al.  Coordinated Transcription of Key Pathways in the Mouse by the Circadian Clock , 2002, Cell.

[23]  P. Swain,et al.  Intrinsic and extrinsic contributions to stochasticity in gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[24]  D. Sherrington Stochastic Processes in Physics and Chemistry , 1983 .

[25]  Mario Bertero,et al.  On the recovery and resolution of exponential relaxation rates from experimental data. - III. The effect of sampling and truncation of data on the Laplace transform inversion , 1985, Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences.

[26]  E. Hall,et al.  The nature of biotechnology. , 1988, Journal of biomedical engineering.