@article{mahout_answer_2020,
	title = {Answer Set Programming for Computing Constraints-Based Elementary Flux Modes: Application to Escherichia coli Core Metabolism},
	volume = {8},
	issn = {2227-9717},
	url = {https://www.mdpi.com/2227-9717/8/12/1649},
	doi = {10.3390/pr8121649},
	abstract = {Elementary Flux Modes ({EFMs}) provide a rigorous basis to systematically characterize the steady state, cellular phenotypes, as well as metabolic network robustness and fragility. However, the number of {EFMs} typically grows exponentially with the size of the metabolic network, leading to excessive computational demands, and unfortunately, a large fraction of these {EFMs} are not biologically feasible due to system constraints. This combinatorial explosion often prevents the complete analysis of genome-scale metabolic models. Traditionally, {EFMs} are computed by the double description method, an efficient algorithm based on matrix calculation; however, only a few constraints can be integrated into this computation. They must be monotonic with regard to the set inclusion of the supports; otherwise, they must be treated in post-processing and thus do not save computational time. We present aspefm, a hybrid computational tool based on Answer Set Programming ({ASP}) and Linear Programming ({LP}) that permits the computation of {EFMs} while implementing many different types of constraints. We apply our methodology to the Escherichia coli core model, which contains 226×106 {EFMs}. In considering transcriptional and environmental regulation, thermodynamic constraints, and resource usage considerations, the solution space is reduced to 1118 {EFMs} that can be computed directly with aspefm. The solution set, for E. coli growth on O2 gradients spanning fully aerobic to anaerobic, can be further reduced to four optimal {EFMs} using post-processing and Pareto front analysis.},
	pages = {1649},
	number = {12},
	journal = {Processes},
	author = {Mahout, M. and Carlson, R. P. and Peres, S.},
	year = {2020},
}