Demo Data from EDBT 2026 / reviewers in the wild / expert
Matthias Brock
dblp:34/9109
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2011
0000-0003-2774-1856ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › systems biology › metabolic network analysis
elementary flux mode computation |
0.1 | 1 | 2011 | Exploring metabolic pathways in genome-scale networks via generating flux modes · Bioinform. 2011 |
Bioinformatics and computational biology › systems biology
metabolic network analysis |
0.1 | 1 | 2011 | Exploring metabolic pathways in genome-scale networks via generating flux modes · Bioinform. 2011 |
Methods — techniques the papers use, named apart from their topics
optimization-based method · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2011 | Exploring metabolic pathways in genome-scale networks via generating flux modesabstractMOTIVATION: The reconstruction of metabolic networks at the genome scale has allowed the analysis of metabolic pathways at an unprecedented level of complexity. Elementary flux modes (EFMs) are an appropriate concept for such analysis. However, their number grows in a combinatorial fashion as the size of the metabolic network increases, which renders the application of EFMs approach to large metabolic networks difficult. Novel methods are expected to deal with such complexity. RESULTS: In this article, we present a novel optimization-based method for determining a minimal generating set of EFMs, i.e. a convex basis. We show that a subset of elements of this convex basis can be effectively computed even in large metabolic networks. Our method was applied to examine the structure of pathways producing lysine in Escherichia coli. We obtained a more varied and informative set of pathways in comparison with existing methods. In addition, an alternative pathway to produce lysine was identified using a detour via propionyl-CoA, which shows the predictive power of our novel approach. AVAILABILITY: The source code in C++ is available upon request. Alberto Rezola, Luis F. de Figueiredo, Matthias Brock, Jon Pey, Adam Podhorski, Christoph Wittmann, Stefan Schuster, Alexander Bockmayr, Francisco J. Planes |
Bioinform. | 3 |