As　protein-protein　interactions　always　change　with　time,　environments　and　different　stages　of　cell　cycle,　the　clustering　analysis　on　static　protein-protein　interaction　(PPI)　networks　can　not　reflect　this　dynamics　property　and　is　far　from　satisfactory.　To　solve　it,　this　paper　proposes　a　method　based　on　time-sequenced　association　and　Ant　Colony　Clustering　for　identifying　Protein　Complexes　in　Dynamic　PPI　networks　(called　ACC-DPC).　ACC-DPC　first　splits　a　PPI　network　into　a　series　of　dynamics　subnetworks　under　different　time　points　by　integrating　gene　expression　data,　and　then　makes　the　clustering　analysis　on　each　subnetwork　using　the　ant　colony　clustering　method.　For　each　subnetwork,　ACC-DPC　begins　with　constructing　initial　protein　clusters　by　introducing　the　time-sequenced　association　characteristic　of　protein　complexes　between　two　adjacent　time　points,　and　later　uses　the　picking　up　and　dropping　down　operators　of　ant　colony　clustering　to　accomplish　the　clustering　process　of　other　proteins.　The　experimental　results　on　two　PPI　datasets　demonstrate　that　ACC-DPC　has　competitive　performances　in　identifying　protein　complexes　of　dynamic　PPI　networks　compared　with　several　algorithms.