LAUSR

Perhaps the most fundamental model in synthetic and systems biology for inferring pathways in metabolic reaction networks is a metabolic factory: a system of reactions that starts from a set of source compounds and produces a set of target molecules, while conserving or not depleting intermediate metabolites. Finding a shortest factory—that minimizes a sum of real-valued weights on its reactions to infer the most likely pathway—is NP-complete. The current state-of-the-art for shortest factories solves a mixed-integer linear program with a major drawback: it requires the user to set a critical parameter, where too large a value can make optimal solutions infeasible, while too small a value can yield degenerate solutions due to numerical error. We present the first robust algorithm for optimal factories that is both parameter-free (relieving the user from determining a parameter setting) and degeneracy-free (guaranteeing it finds an optimal nondegenerate solution). We also give for the first time a complete characterization of the graph-theoretic structure of shortest factories, that reveals an important class of degenerate solutions which was overlooked and potentially output by the prior state-of-the-art. We show degeneracy is precisely due to invalid stoichiometries in reactions, and provide an efficient algorithm for identifying all such misannotations in a metabolic network. In addition we settle the relationship between the two established pathway models of hyperpaths and factories by proving hyperpaths actually comprise a subclass of factories. Comprehensive experiments over all instances from the standard metabolic reaction databases in the literature demonstrate our parameter-free exact algorithm is fast in practice, quickly finding optimal factories in large real-world networks containing thousands of reactions. A preliminary implementation of our robust algorithm for shortest factories in a new tool called Freeia is available free for research use at http://freeia.cs.arizona.edu .