Multidimensional binary search trees used for associative searching
Communications of the ACM
Double Description Method Revisited
Selected papers from the 8th Franco-Japanese and 4th Franco-Chinese Conference on Combinatorics and Computer Science
Double iterative optimisation for metabolic network-based drug target identification
International Journal of Data Mining and Bioinformatics
Brief paper: Fast computation of minimal elementary decompositions of metabolic flux vectors
Automatica (Journal of IFAC)
Decomposing Biochemical Networks Into Elementary Flux Modes Using Graph Traversal
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Heterogeneous combinatorial candidate generation
Euro-Par'13 Proceedings of the 19th international conference on Parallel Processing
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Elementary flux modes (EFMs)—formalized metabolic pathways—are central and comprehensive tools for metabolic network analysis under steady state conditions. They act as a generating basis for all possible flux distributions and, thus, are a minimal (constructive) description of the solution space. Algorithms to compute EFMs descend from computational geometry; they are mostly synonymous to the enumeration of extreme rays of polyhedral cones. This problem is combinatorially complex, and algorithms do not scale well. Here, we introduce new concepts for the enumeration of adjacent rays, which is one of the critical and stubborn facets of the algorithms. They rely on variants of k-d-trees to store and analyze bit sets representing (intermediary) extreme rays. Bit set trees allow for speed-up of computations primarily for low-dimensional problems. Extensions to pattern trees to narrow candidate pairs for adjacency tests scale with problem size, yielding speed-ups on the order of one magnitude relative to current algorithms. Additionally, fast algebraic tests can easily be used in the framework. This constitutes one step towards EFM analysis at the whole-cell level.