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Cell-to-Cell Communication Circuits: Quantitative Analysis of Synthetic Logic Gates

Overview of attention for article published in Frontiers in Physiology, January 2012
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (77th percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

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7 X users
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1 Facebook page

Citations

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10 Dimensions

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53 Mendeley
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2 CiteULike
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Title
Cell-to-Cell Communication Circuits: Quantitative Analysis of Synthetic Logic Gates
Published in
Frontiers in Physiology, January 2012
DOI 10.3389/fphys.2012.00287
Pubmed ID
Authors

Marta Hoffman-Sommer, Adriana Supady, Edda Klipp

Abstract

One of the goals in the field of synthetic biology is the construction of cellular computation devices that could function in a manner similar to electronic circuits. To this end, attempts are made to create biological systems that function as logic gates. In this work we present a theoretical quantitative analysis of a synthetic cellular logic-gates system, which has been implemented in cells of the yeast Saccharomyces cerevisiae (Regot et al., 2011). It exploits endogenous MAP kinase signaling pathways. The novelty of the system lies in the compartmentalization of the circuit where all basic logic gates are implemented in independent single cells that can then be cultured together to perform complex logic functions. We have constructed kinetic models of the multicellular IDENTITY, NOT, OR, and IMPLIES logic gates, using both deterministic and stochastic frameworks. All necessary model parameters are taken from literature or estimated based on published kinetic data, in such a way that the resulting models correctly capture important dynamic features of the included mitogen-activated protein kinase pathways. We analyze the models in terms of parameter sensitivity and we discuss possible ways of optimizing the system, e.g., by tuning the culture density. We apply a stochastic modeling approach, which simulates the behavior of whole populations of cells and allows us to investigate the noise generated in the system; we find that the gene expression units are the major sources of noise. Finally, the model is used for the design of system modifications: we show how the current system could be transformed to operate on three discrete values.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
Sweden 1 2%
United Kingdom 1 2%
Spain 1 2%
Luxembourg 1 2%
Unknown 48 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 28%
Researcher 9 17%
Student > Master 7 13%
Professor > Associate Professor 5 9%
Student > Bachelor 4 8%
Other 9 17%
Unknown 4 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 38%
Biochemistry, Genetics and Molecular Biology 9 17%
Mathematics 4 8%
Computer Science 3 6%
Physics and Astronomy 3 6%
Other 9 17%
Unknown 5 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 February 2013.
All research outputs
#5,959,225
of 22,675,759 outputs
Outputs from Frontiers in Physiology
#2,687
of 13,467 outputs
Outputs of similar age
#53,922
of 244,088 outputs
Outputs of similar age from Frontiers in Physiology
#62
of 309 outputs
Altmetric has tracked 22,675,759 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 13,467 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.5. This one has done well, scoring higher than 79% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 244,088 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 309 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.