Identifying　functional　modules　in　protein-protein　interaction　(PPI)　networks　is　fundamental　to　understand　cellular　organization,　processes,　and　functions.　As　an　emerging　evolutionary　computational　technology,　swarm　intelligence　approaches　are　now　becoming　a　new　research　hotspot　in　identifying　functional　modules.　This　paper　proposes　a　new　computational　approach　based　on　bacterial　biological　mechanisms　for　functional　module　detection　in　PPI　networks　(called　as　BBM-FMD).　In　BBM-FMD,　each　bacterium　is　first　initialized　to　a　candidate　module　partition　by　a　random　walk　behavior.　Then　four　biological　mechanisms　of　bacteria　including　chemotaxis,　conjugation,　reproduction,　and　elimination　and　dispersal　are　simulated　to　iteratively　search　for　better　protein　module　partitions.　At　last,　two　post-processing　steps　are　carried　out　to　refine　the　obtained　module　partition.　The　experimental　results　on　two　PPI　datasets　demonstrate　the　superior　performance　of　BBM-FMD　in　detecting　functional　modules　compared　with　several　other　algorithms.