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Modeling formalisms in Systems Biology

Overview of attention for article published in AMB Express, January 2011
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Mentioned by

4 tweeters


123 Dimensions

Readers on

332 Mendeley
5 CiteULike
Modeling formalisms in Systems Biology
Published in
AMB Express, January 2011
DOI 10.1186/2191-0855-1-45
Pubmed ID

Daniel Machado, Rafael S Costa, Miguel Rocha, Eugénio C Ferreira, Bruce Tidor, Isabel Rocha


Systems Biology has taken advantage of computational tools and high-throughput experimental data to model several biological processes. These include signaling, gene regulatory, and metabolic networks. However, most of these models are specific to each kind of network. Their interconnection demands a whole-cell modeling framework for a complete understanding of cellular systems. We describe the features required by an integrated framework for modeling, analyzing and simulating biological processes, and review several modeling formalisms that have been used in Systems Biology including Boolean networks, Bayesian networks, Petri nets, process algebras, constraint-based models, differential equations, rule-based models, interacting state machines, cellular automata, and agent-based models. We compare the features provided by different formalisms, and discuss recent approaches in the integration of these formalisms, as well as possible directions for the future.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 332 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 5 2%
Portugal 4 1%
United Kingdom 4 1%
United States 3 <1%
Luxembourg 2 <1%
Mexico 2 <1%
Brazil 1 <1%
Tunisia 1 <1%
Italy 1 <1%
Other 8 2%
Unknown 301 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 103 31%
Researcher 63 19%
Student > Master 54 16%
Student > Bachelor 34 10%
Student > Doctoral Student 17 5%
Other 41 12%
Unknown 20 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 125 38%
Biochemistry, Genetics and Molecular Biology 55 17%
Computer Science 46 14%
Engineering 29 9%
Medicine and Dentistry 8 2%
Other 40 12%
Unknown 29 9%

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 26 September 2020.
All research outputs
of 20,226,324 outputs
Outputs from AMB Express
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Outputs of similar age
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Outputs of similar age from AMB Express
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Altmetric has tracked 20,226,324 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,145 research outputs from this source. They receive a mean Attention Score of 2.9. This one has done well, scoring higher than 82% 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 170,907 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 50% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them