This paper defines and studies a new, interesting, and challenging benchmark problem that originates in systems biology. The minimal seed-set problem is defined as follows: given a description of the metabolic reactions of an organism, characterize the minimal set of nutrients with which it could synthesize all nutrients it is capable of synthesizing. Current methods used in systems biology yield only approximate solutions. And although it is natural to cast it as a planning problem, current optimal planners are unable to solve it, while non-optimal planners return plans that are very far from optimal. As a planning problem, it is inherently delete-free, has many zero-cost actions, all propositions are landmarks, and many legal permutations of the plan exist. We show how a simple uninformed search algorithm that exploits inherent independence between sub-goals can solve it optimally by reducing the branching factor drastically.