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PROKARYO: an illustrative and interactive computational model of the lactose operon in the bacterium Escherichia coli

Overview of attention for article published in BMC Bioinformatics, September 2015
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  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Average Attention Score compared to outputs of the same age and source

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6 X users

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53 Mendeley
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Title
PROKARYO: an illustrative and interactive computational model of the lactose operon in the bacterium Escherichia coli
Published in
BMC Bioinformatics, September 2015
DOI 10.1186/s12859-015-0720-z
Pubmed ID
Authors

Afshin Esmaeili, Timothy Davison, Andrew Wu, Joenel Alcantara, Christian Jacob

Abstract

We are creating software for agent-based simulation and visualization of bio-molecular processes in bacterial and eukaryotic cells. As a first example, we have built a 3-dimensional, interactive computer model of an Escherichia coli bacterium and its associated biomolecular processes. Our illustrative model focuses on the gene regulatory processes that control the expression of genes involved in the lactose operon. Prokaryo, our agent-based cell simulator, incorporates cellular structures, such as plasma membranes and cytoplasm, as well as elements of the molecular machinery, including RNA polymerase, messenger RNA, lactose permease, and ribosomes. The dynamics of cellular 'agents' are defined by their rules of interaction, implemented as finite state machines. The agents are embedded within a 3-dimensional virtual environment with simulated physical and electrochemical properties. The hybrid model is driven by a combination of (1) mathematical equations (DEQs) to capture higher-scale phenomena and (2) agent-based rules to implement localized interactions among a small number of molecular elements. Consequently, our model is able to capture phenomena across multiple spatial scales, from changing concentration gradients to one-on-one molecular interactions. We use the classic gene regulatory mechanism of the lactose operon to demonstrate our model's resolution, visual presentation, and real-time interactivity. Our agent-based model expands on a sophisticated mathematical E. coli metabolism model, through which we highlight our model's scientific validity. We believe that through illustration and interactive exploratory learning a model system like Prokaryo can enhance the general understanding and perception of biomolecular processes. Our agent-DEQ hybrid modeling approach can also be of value to conceptualize, illustrate, and-eventually-validate cell experiments in the wet lab.

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X Demographics

The data shown below were collected from the profiles of 6 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 %
Singapore 1 2%
Unknown 52 98%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 12 23%
Student > Ph. D. Student 9 17%
Researcher 9 17%
Student > Master 5 9%
Student > Postgraduate 3 6%
Other 8 15%
Unknown 7 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 19%
Agricultural and Biological Sciences 8 15%
Immunology and Microbiology 5 9%
Computer Science 4 8%
Engineering 3 6%
Other 13 25%
Unknown 10 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 16 April 2016.
All research outputs
#13,373,453
of 22,829,083 outputs
Outputs from BMC Bioinformatics
#4,178
of 7,287 outputs
Outputs of similar age
#128,450
of 274,379 outputs
Outputs of similar age from BMC Bioinformatics
#72
of 141 outputs
Altmetric has tracked 22,829,083 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,287 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
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 274,379 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 141 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.