Computational reconstruction of metabolic networks from high-throughput profiling data

Abstract

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.