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Biological network alignment is currently in a state of disarray, with morethan two dozen network alignment tools having been introduced in the pastdecade, with no clear winner, and other new tools being published almostquarterly. Part of the problem is that almost every new tool proposes both anew objective function and a new search algorithm to optimize said objective.These two aspects of alignment are orthogonal, and confounding them makes itdifficult to evaluate them separately. A more systematic approach is needed. Tothis end, we bring these two orthogonal issues into sharp focus in twocompanion papers. In Part 1 (this paper) we show that simulated annealing, asimplemented by SANA, far outperforms all other existing search algorithmsacross a wide range of objectives. Part 2 (our companion paper) then uses SANAto compare over a dozen objectives in terms of the biology they recover,demonstrating that some objective functions recover useful biologicalinformation while others do not. We propose that further work should focus onimproving objective functions, with SANA the obvious choice as the searchalgorithm.

SANA: separating the search algorithm from the objective function in biological network alignment, Part 1: Search