MOTIVATION: Elementary flux modes (EFMs) analysis constitutes a fundamental tool 
in systems biology. However, the efficient calculation of EFMs in genome-scale 
metabolic networks (GSMNs) is still a challenge. We present a novel algorithm 
that uses a linear programming-based tree search and efficiently enumerates a 
subset of EFMs in GSMNs.
RESULTS: Our approach is compared with the EFMEvolver approach, demonstrating a 
significant improvement in computation time. We also validate the usefulness of 
our new approach by studying the acetate overflow metabolism in the Escherichia 
coli bacteria. To do so, we computed 1 million EFMs for each energetic amino 
acid and then analysed the relevance of each energetic amino acid based on 
gene/protein expression data and the obtained EFMs. We found good agreement 
between previous experiments and the conclusions reached using EFMs. Finally, we 
also analysed the performance of our approach when applied to large GSMNs.
AVAILABILITY AND IMPLEMENTATION: The stand-alone software TreeEFM is implemented 
in C++ and interacts with the open-source linear solver COIN-OR Linear program 
Solver (CLP).