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Integrative Analysis of Metabolic Models – from Structure to Dynamics

Overview of attention for article published in Frontiers in Bioengineering and Biotechnology, January 2015
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Title
Integrative Analysis of Metabolic Models – from Structure to Dynamics
Published in
Frontiers in Bioengineering and Biotechnology, January 2015
DOI 10.3389/fbioe.2014.00091
Pubmed ID
Authors

Anja Hartmann, Falk Schreiber

Abstract

The characterization of biological systems with respect to their behavior and functionality based on versatile biochemical interactions is a major challenge. To understand these complex mechanisms at systems level modeling approaches are investigated. Different modeling formalisms allow metabolic models to be analyzed depending on the question to be solved, the biochemical knowledge and the availability of experimental data. Here, we describe a method for an integrative analysis of the structure and dynamics represented by qualitative and quantitative metabolic models. Using various formalisms, the metabolic model is analyzed from different perspectives. Determined structural and dynamic properties are visualized in the context of the metabolic model. Interaction techniques allow the exploration and visual analysis thereby leading to a broader understanding of the behavior and functionality of the underlying biological system. The System Biology Metabolic Model Framework (SBM (2) - Framework) implements the developed method and, as an example, is applied for the integrative analysis of the crop plant potato.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Spain 1 3%
South Africa 1 3%
Unknown 30 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 24%
Researcher 6 18%
Professor > Associate Professor 5 15%
Student > Master 4 12%
Student > Postgraduate 2 6%
Other 3 9%
Unknown 5 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 33%
Biochemistry, Genetics and Molecular Biology 5 15%
Computer Science 4 12%
Medicine and Dentistry 2 6%
Engineering 2 6%
Other 3 9%
Unknown 6 18%