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DFBAlab: a fast and reliable MATLAB code for dynamic flux balance analysis

Overview of attention for article published in BMC Bioinformatics, December 2014
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Title
DFBAlab: a fast and reliable MATLAB code for dynamic flux balance analysis
Published in
BMC Bioinformatics, December 2014
DOI 10.1186/s12859-014-0409-8
Pubmed ID
Authors

Jose A Gomez, Kai Höffner, Paul I Barton

Abstract

BackgroundDynamic Flux Balance Analysis (DFBA) is a dynamic simulation framework for biochemical processes. DFBA can be performed using different approaches such as static optimization (SOA), dynamic optimization (DOA), and direct approaches (DA). Few existing simulators address the theoretical and practical challenges of nonunique exchange fluxes or infeasible linear programs (LPs). Both are common sources of failure and inefficiencies for these simulators.ResultsDFBAlab, a MATLAB-based simulator that uses the LP feasibility problem to obtain an extended system and lexicographic optimization to yield unique exchange fluxes, is presented. DFBAlab is able to simulate complex dynamic cultures with multiple species rapidly and reliably, including differential-algebraic equation (DAE) systems. In addition, DFBAlab¿s running time scales linearly with the number of species models. Three examples are presented where the performance of COBRA, DyMMM and DFBAlab are compared.ConclusionsLexicographic optimization is used to determine unique exchange fluxes which are necessary for a well-defined dynamic system. DFBAlab does not fail during numerical integration due to infeasible LPs. The extended system obtained through the LP feasibility problem in DFBAlab provides a penalty function that can be used in optimization algorithms.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 261 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
Germany 1 <1%
France 1 <1%
India 1 <1%
Sweden 1 <1%
Mexico 1 <1%
Singapore 1 <1%
Unknown 253 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 73 28%
Researcher 35 13%
Student > Master 34 13%
Student > Bachelor 21 8%
Student > Doctoral Student 15 6%
Other 38 15%
Unknown 45 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 55 21%
Chemical Engineering 42 16%
Engineering 39 15%
Biochemistry, Genetics and Molecular Biology 28 11%
Computer Science 12 5%
Other 23 9%
Unknown 62 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 19 December 2014.
All research outputs
#18,386,678
of 22,774,233 outputs
Outputs from BMC Bioinformatics
#6,306
of 7,276 outputs
Outputs of similar age
#255,983
of 353,309 outputs
Outputs of similar age from BMC Bioinformatics
#141
of 153 outputs
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