Mining　functional　modules　in　a　Protein-Protein　Interaction　(PPI)　network　contributes　greatly　to　the　understanding　of　biological　mechanism,　where　how　to　effectively　detect　functional　modules　in　a　PPI　network　has　a　significant　application.　As　a　meta-heuristic　and　stochastic　search　technology,　the　Ant　Colony　Optimization　(ACO)　algorithm　has　been　one　of　the　effective　tools　for　solving　discrete　optimization　problems.　In　this　paper,　we　propose　a　new　method　based　on　the　ACO　algorithm　for　detecting　functional　modules　in　a　PPI　network,　which　combines　topological　characteristics　with　functional　information.　First,　a　new　heuristic　function　is　introduced　to　conduct　ants　searching　effectively　in　constructing　solutions.　Second,　a　set　of　new　strategies　of　partitioning,　merging　and　filtering　are　adopted　to　form　the　final　functional　modules.　Finally,　we　present　experimental　results　on　the　benchmark　testing　set　of　yeast　networks.　Our　experiments　show　that　our　approach　is　more　effective　compared　to　several　other　existing　detection　techniques.