Noise-aided computation within a synthetic gene network through morphable and robust logic gates

Anna Dari, Behnam Kia, Xiao Wang, Adi R. Bulsara, William Ditto

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

An important goal for synthetic biology is to build robust and tunable genetic regulatory networks that are capable of performing assigned operations, usually in the presence of noise. In this work, a synthetic gene network derived from the bacteriophage λ underpins a reconfigurable logic gate wherein we exploit noise and nonlinearity through the application of the logical stochastic resonance paradigm. This biological logic gate can emulate or "morph" the AND and OR operations through varying internal system parameters in a noisy background. Such genetic circuits can afford intriguing possibilities in the realization of engineered genetic networks in which the actual function of the gate can be changed after the network has been built, via an external control parameter. In this article, the full system characterization is reported, with the logic gate performance studied in the presence of external and internal noise. The robustness of the gate, to noise, is studied and illustrated through numerical simulations.

Original languageEnglish (US)
Article number041909
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume83
Issue number4
DOIs
StatePublished - Apr 11 2011

Fingerprint

Gene Networks
genes
logic
Logic
Synthetic Biology
Internal
Genetic Regulatory Networks
Genetic Network
Stochastic Resonance
Control Parameter
bacteriophages
Paradigm
Nonlinearity
Robustness
Numerical Simulation
biology
nonlinearity
simulation

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Statistical and Nonlinear Physics
  • Statistics and Probability

Cite this

Noise-aided computation within a synthetic gene network through morphable and robust logic gates. / Dari, Anna; Kia, Behnam; Wang, Xiao; Bulsara, Adi R.; Ditto, William.

In: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, Vol. 83, No. 4, 041909, 11.04.2011.

Research output: Contribution to journalArticle

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