A functional T4SS which includes the translocation of secreted effector proteins into the host cell or the vacuolar membrane is activated, thereby promoting Brucella’s survival and replication. The expression of the T4SS VirB is tightly regulated both in vitro and in vivo by several molecular systems. Direct interactions with the promoter region of VirB has been demonstrated for integration host factor, a DNAbinding and -bending protein with roles in local DNA structural organization and transcriptional regulation of a wide variety of bacterial genes, VjbR the quorum sensing-dependent regulator involved in surface modifications of Brucella, HutC, the transcriptional repressor of the histidine genes, and the two-component regulatory system BvrR/BvrS that participates in the homeostasis of the outer membrane controlling the Diatrizoic acid structure of the lipopolysaccharide, the expression of periplasmic and outer membrane proteins and in the expression of the transcriptional activator VjbR. The TCS BvrRS thereby controls the expression of Brucella’s T4SS VirB directly and indirectly. It appears that the co-regulation of Brucella genes encoding the synthesis or modification of cyclic-b-1,2-glucans with those encoding the T4SS secretion apparatus and/or secreted effectors could provide additional Brucella factors 
for cyclic-b-1,2-glucan’s mechanism in preventing phagosome maturation. External stimuli such as receptor activation by microbial toxins, pathogens and proteins induce the coalescence of the small DRMDs that are present in resting cells into larger ones. These clustered domains rapidly sequester proteins and lipids from detergent soluble membranes into DRMDs and serve as platforms for signal transduction, intracellular sorting, membrane transport, and possibly other functions. We reasoned that, as seen with other pathogens, pathogenic Brucella may rearrange proteins between DRMDs and detergent soluble membranes before it enters the host cell. In this study we analyzed the protein redistribution between DRMDs and non-DRMD membranes using a quantitative proteomic approach that compares protein profiles in DRMDs of monocytes exposed or not exposed to Brucella. We have developed a computational method to identify response networks in large biological networks based on expression data. This method and the corresponding computer program NetworkExpress is based on superimposing expression values upon the large network, identifying k-shortest paths between Nodakenin seed-nodes, scoring the sub-network spanned by the set of kshortest paths that are shorter than a pre-defined maximum weighted length l, and finding the best scored sub-network by optimization techniques. k-shortest paths refer to a set of paths between a start- and end-point in a network with shortest, second-shortest, third-shortest connections up to a given value k. Paths are weighted, thus connections between intermediate nodes along the path can be shorter or longer depending on the expression values and scoring function used. We have a variety of scoring functions available, from simple arithmetic or geometric means to different types of correlation functions for time-series correlations, optionally between same time-points or timeforward/backward. The best-scored sub-network refers to the response network of the system under the specific environmental condition measured by the corresponding expression experiment.