Functional　module　detection　in　Protein-Protein　Interaction　(PPI)　networks　is　essential　to　understanding　the　organization,　evolution　and　interaction　of　the　cellular　systems.　In　recent　years,　most　of　the　researches　have　focused　on　detecting　the　functional　modules　from　the　static　PPI　networks.　However,　sometimes　the　structure　of　the　PPI　networks　changes　in　response　to　stimuli　resulting　in　the　changes　of　both　the　composition　and　functionality　of　these　modules.　These　changes　occur　gradually　and　can　be　thought　of　as　an　evolution　of　the　functional　modules.　In　our　opinions　the　evolutionary　analysis　of　functional　modules　is　a　key　to　form　important　insights　of　the　functional　modules＇　underlying　behaviors,　particularly　when　targeting　complex　living　systems.　In　this　paper,　we　propose　a　novel　computational　framework　which　integrates　a　PPI　network　with　multiple　dynamic　gene　coexpression　networks　to　categorize　and　track　the　evolutionary　pattern　of　functional　modules　over　consecutive　timestamps.　We　first　propose　a　method　to　construct　dynamic　PPI　networks,　and　then　design　a　new　functional　influence　based　algorithm　to　detect　the　functional　modules　from　these　dynamic　PPI　networks.　Based　on　the　results　of　this　approach,　we　provide　a　simple　but　effective　method　to　characterize　and　track　the　evolutionary　patterns　of　dynamic　modules,　which　involves　detecting　evolutionary　events　between　modules　found　at　consecutive　timestamps.　Extensive　experiments　on　the　fermentation　process　dataset　of　S.　cerevisiae　show　that　the　proposed　framework　not　only　outperforms　previous　functional　module　detection　methods,　but　also　efficiently　tracks　the　evolutionary　patterns　of　functional　modules.　Copyright　©　2012　ACM.