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Simulation of E. coli Gene Regulation including Overlapping Cell Cycles, Growth, Division, Time Delays and Noise

Overview of attention for article published in PLOS ONE, April 2013
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Title
Simulation of E. coli Gene Regulation including Overlapping Cell Cycles, Growth, Division, Time Delays and Noise
Published in
PLOS ONE, April 2013
DOI 10.1371/journal.pone.0062380
Pubmed ID
Authors

Ruoyu Luo, Lin Ye, Chenyang Tao, Kankan Wang

Abstract

Due to the complexity of biological systems, simulation of biological networks is necessary but sometimes complicated. The classic stochastic simulation algorithm (SSA) by Gillespie and its modified versions are widely used to simulate the stochastic dynamics of biochemical reaction systems. However, it has remained a challenge to implement accurate and efficient simulation algorithms for general reaction schemes in growing cells. Here, we present a modeling and simulation tool, called 'GeneCircuits', which is specifically developed to simulate gene-regulation in exponentially growing bacterial cells (such as E. coli) with overlapping cell cycles. Our tool integrates three specific features of these cells that are not generally included in SSA tools: 1) the time delay between the regulation and synthesis of proteins that is due to transcription and translation processes; 2) cell cycle-dependent periodic changes of gene dosage; and 3) variations in the propensities of chemical reactions that have time-dependent reaction rates as a consequence of volume expansion and cell division. We give three biologically relevant examples to illustrate the use of our simulation tool in quantitative studies of systems biology and synthetic biology.

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Geographical breakdown

Country Count As %
Spain 2 3%
United Kingdom 2 3%
Germany 1 2%
Denmark 1 2%
United States 1 2%
Unknown 56 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 29%
Researcher 16 25%
Student > Master 5 8%
Student > Bachelor 4 6%
Professor 4 6%
Other 10 16%
Unknown 6 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 22 35%
Biochemistry, Genetics and Molecular Biology 13 21%
Engineering 9 14%
Physics and Astronomy 4 6%
Computer Science 3 5%
Other 5 8%
Unknown 7 11%