Inferring　causal　protein　signalling　networks　from　human　immune　system　cell　data　is　a　promising　approach　to　unravel　the　underlying　tissue　signalling　biology　and　dysfunction　in　diseased　cells,　which　has　attracted　considerable　attention　within　the　bioinformatics　field.　Recently,　Bayesian　network　(BN)　techniques　have　gained　significant　popularity　in　inferring　causal　protein　signalling　networks　from　multiparameter　single-cell　data.　However,　current　BN　methods　may　exhibit　high　computational　complexity　and　ignore　interactions　among　protein　signalling　molecules　from　different　single　cells.　A　novel　BN　method　is　presented　for　learning　causal　protein　signalling　networks　based　on　parallel　discrete　artificial　bee　colony　(PDABC),　named　PDABC.　Specifically,　PDABC　is　a　score-based　BN　method　that　utilises　the　parallel　artificial　bee　colony　to　search　for　the　global　optimal　causal　protein　signalling　networks　with　the　highest　discrete　K2　metric.　The　experimental　results　on　several　simulated　datasets,　as　well　as　a　previously　published　multi-parameter　fluorescence-activated　cell　sorter　dataset,　indicate　that　PDABC　surpasses　the　existing　state-of-the-art　methods　in　terms　of　performance　and　computational　efficiency.