↓ Skip to main content

A Whole-Cell Computational Model Predicts Phenotype from Genotype

Overview of attention for article published in Cell, July 2012
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Citations

dimensions_citation
769 Dimensions

Readers on

mendeley
2850 Mendeley
citeulike
50 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
A Whole-Cell Computational Model Predicts Phenotype from Genotype
Published in
Cell, July 2012
DOI 10.1016/j.cell.2012.05.044
Pubmed ID
Authors

Jonathan R. Karr, Jayodita C. Sanghvi, Derek N. Macklin, Miriam V. Gutschow, Jared M. Jacobs, Benjamin Bolival, Nacyra Assad-Garcia, John I. Glass, Markus W. Covert

Abstract

Understanding how complex phenotypes arise from individual molecules and their interactions is a primary challenge in biology that computational approaches are poised to tackle. We report a whole-cell computational model of the life cycle of the human pathogen Mycoplasma genitalium that includes all of its molecular components and their interactions. An integrative approach to modeling that combines diverse mathematics enabled the simultaneous inclusion of fundamentally different cellular processes and experimental measurements. Our whole-cell model accounts for all annotated gene functions and was validated against a broad range of data. The model provides insights into many previously unobserved cellular behaviors, including in vivo rates of protein-DNA association and an inverse relationship between the durations of DNA replication initiation and replication. In addition, experimental analysis directed by model predictions identified previously undetected kinetic parameters and biological functions. We conclude that comprehensive whole-cell models can be used to facilitate biological discovery.

Twitter Demographics

The data shown below were collected from the profiles of 293 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 2,850 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 131 5%
United Kingdom 30 1%
Germany 30 1%
Spain 19 <1%
France 17 <1%
Japan 16 <1%
Canada 14 <1%
Netherlands 13 <1%
Brazil 11 <1%
Other 107 4%
Unknown 2462 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 899 32%
Researcher 673 24%
Student > Master 282 10%
Student > Bachelor 246 9%
Professor > Associate Professor 156 5%
Other 473 17%
Unknown 121 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 1329 47%
Biochemistry, Genetics and Molecular Biology 423 15%
Computer Science 216 8%
Engineering 180 6%
Physics and Astronomy 110 4%
Other 406 14%
Unknown 186 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 497. 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 20 May 2020.
All research outputs
#21,976
of 15,444,557 outputs
Outputs from Cell
#162
of 15,145 outputs
Outputs of similar age
#91
of 127,387 outputs
Outputs of similar age from Cell
#1
of 141 outputs
Altmetric has tracked 15,444,557 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 15,145 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 35.4. This one has done particularly well, scoring higher than 98% 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 127,387 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% 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 has done particularly well, scoring higher than 99% of its contemporaries.