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All computational methods of biological network reconstruction up to now aim only to find pairwise interactions. While metabolic networks composed mainly of reactions that often consist of from 2 to 6 substrates/products, the existing computational methods may not be appropriate to reconstruct interactions of more than two variables like reactions in the metabolic networks. In this paper, we develop a computational method for the metabolic network reconstruction that can uncover not only pairwise interactions but also interactions involving more than two substrates/products such as triple interactions, quartic interactions, etc. In the proposed method we use the ternary mutual information to capture high order interactions. The key idea is to propose a novel view on the ternary mutual information that can be appropriately used to reconstruct reactions involving more than two substrates/products. We have applied the proposed method to synthesized metabolome data; the reconstruction accuracy has been evaluated at the levels of pairwise and triple interactions. The performance of the method is promising.

Computational reconstruction of metabolic networks from high-throughput profiling data