Literature DB >> 18698117

A molecular noise generator.

Ting Lu1, Michael Ferry, Ron Weiss, Jeff Hasty.   

Abstract

Recent studies have demonstrated that intracellular variations in the rate of gene expression are of fundamental importance to cellular function and development. While such 'noise' is often considered detrimental in the context of perturbing genetic systems, it can be beneficial in processes such as species diversification and facilitation of evolution. A major difficulty in exploring such effects is that the magnitude and spectral properties of the induced variations arise from some intrinsic cellular process that is difficult to manipulate. Here, we present two designs of a molecular noise generator that allow for the flexible modulation of the noise profile of a target gene. The first design uses a dual-signal mechanism that enables independent tuning of the mean and variability of an output protein. This is achieved through the combinatorial control of two signals that regulate transcription and translation separately. We then extend the design to allow for DNA copy-number regulation, which leads to a wider tuning spectrum for the output molecule. To gain a deeper understanding of the circuit's functionality in a realistic environment, we introduce variability in the input signals in order to ascertain the degree of noise induced by the control process itself. We conclude by illustrating potential applications of the noise generator, demonstrating how it could be used to ascertain the robust or fragile properties of a genetic circuit.

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Year:  2008        PMID: 18698117     DOI: 10.1088/1478-3975/5/3/036006

Source DB:  PubMed          Journal:  Phys Biol        ISSN: 1478-3967            Impact factor:   2.583


  14 in total

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5.  Circuit-Host Coupling Induces Multifaceted Behavioral Modulations of a Gene Switch.

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Review 6.  Programming cells: towards an automated 'Genetic Compiler'.

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7.  Automatic compilation from high-level biologically-oriented programming language to genetic regulatory networks.

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8.  Computational design of synthetic regulatory networks from a genetic library to characterize the designability of dynamical behaviors.

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9.  Next-generation synthetic gene networks.

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Journal:  Nat Biotechnol       Date:  2009-12       Impact factor: 54.908

10.  Gene networks of fully connected triads with complete auto-activation enable multistability and stepwise stochastic transitions.

Authors:  Philippe C Faucon; Keith Pardee; Roshan M Kumar; Hu Li; Yuin-Han Loh; Xiao Wang
Journal:  PLoS One       Date:  2014-07-24       Impact factor: 3.240

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