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Biological Signal Processing with a Genetic Toggle Switch

Overview of attention for article published in PLOS ONE, July 2013
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Title
Biological Signal Processing with a Genetic Toggle Switch
Published in
PLOS ONE, July 2013
DOI 10.1371/journal.pone.0068345
Pubmed ID
Authors

Patrick Hillenbrand, Georg Fritz, Ulrich Gerland

Abstract

Complex gene regulation requires responses that depend not only on the current levels of input signals but also on signals received in the past. In digital electronics, logic circuits with this property are referred to as sequential logic, in contrast to the simpler combinatorial logic without such internal memory. In molecular biology, memory is implemented in various forms such as biochemical modification of proteins or multistable gene circuits, but the design of the regulatory interface, which processes the input signals and the memory content, is often not well understood. Here, we explore design constraints for such regulatory interfaces using coarse-grained nonlinear models and stochastic simulations of detailed biochemical reaction networks. We test different designs for biological analogs of the most versatile memory element in digital electronics, the JK-latch. Our analysis shows that simple protein-protein interactions and protein-DNA binding are sufficient, in principle, to implement genetic circuits with the capabilities of a JK-latch. However, it also exposes fundamental limitations to its reliability, due to the fact that biological signal processing is asynchronous, in contrast to most digital electronics systems that feature a central clock to orchestrate the timing of all operations. We describe a seemingly natural way to improve the reliability by invoking the master-slave concept from digital electronics design. This concept could be useful to interpret the design of natural regulatory circuits, and for the design of synthetic biological systems.

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Geographical breakdown

Country Count As %
Netherlands 2 2%
Germany 1 1%
Brazil 1 1%
United Kingdom 1 1%
Argentina 1 1%
Spain 1 1%
United States 1 1%
Unknown 92 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 31%
Student > Master 14 14%
Researcher 13 13%
Student > Doctoral Student 11 11%
Student > Bachelor 10 10%
Other 13 13%
Unknown 8 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 32 32%
Biochemistry, Genetics and Molecular Biology 21 21%
Engineering 12 12%
Physics and Astronomy 9 9%
Computer Science 3 3%
Other 12 12%
Unknown 11 11%