repressed in response to Astragalus polysaccharide endotoxin. Subsequently, ANOVA test was applied to filter significantly differentially expressed probesets, resulting in 3,269 selected probesets. Average expression profiles of probesets over replicates for each time-point were used as the final input data for further analyses. The data are publicly available through the GEO Omnibus Database under the accession number GSE3284. The data have been appropriately de-identified, and appropriate IRB approval and 24900801 informed, written consent were obtained by the glue grant investigators. Model implementation.. The in silico human endotoxemia model is 
implemented in Java language, using the Repast Simphony toolkit and Eclipse environment. Clustering Utilizing the concept of the agreement matrix 11741928 in consensus clustering, we recently proposed a novel method to identify the core set of probesets showing most agreeable that they belong to the same or different patterns of gene expression. In order to produce the agreement matrix, a number of different clustering methods along with different metrics were used to reduce the bias inherent in the assumption of any specific clustering method. After identifying the core set of probesets, the AM is reduced correspondingly to those selected probesets and Agent-Based Model of Human Endotoxemia then the hierarchical clustering is applied on the reduced AM to obtain significant patterns of gene expression. Four patterns of gene expression which characterize critical dynamics of acute human inflammation are obtained. The `early-up’ and `middle-up’ pattern consist of genes that are involved in critical pro-inflammatory signaling pathways including apoptosis, Toll-like receptor signaling, and cytokine-cytokine receptor interaction. The `late-up’ pattern characterizes for anti-inflammatory processes with enriched inflammatory relevant pathways e.g. TLR signalling, JAK-STAT cascade. Finally, the `down’ pattern is the most populated expression motif characterized by genes involved in cellular bio-energetic processes e.g. oxidative phosphorylation, ribosome, TCA cycle, purine and pyruvate metabolism. Parameter tuning Based on the trend of the dynamics of each particular molecule type X, we adjust the probability of the associated production parameter pX so that the total number of X in the system does not change significantly over time. For each simulated day, we sample the level of X each hour and determine whether there is a significant change based on the sample vector using ordinary least square regression and significant mean difference. Let xj be the number of molecules X in the system at hour j, j~1,… J, J~24. The regression model used in this approach is xj ~azbjzej where a is the intercept, b is the slope, and ej are random errors which are assumed to be independent and identically distributed. The estimates of the slope and intercept are given by P of an agent-based multi-scale modular architecture for dynamic knowledge representation of acute inflammation. Theor Biol Med Model 5: 11. 30. Chavali AK, Gianchandani EP, Tung KS, Lawrence MB, Peirce SM, et al. Characterizing emergent properties of immunological systems with multicellular rule-based computational modeling. Trends Immunol 29: 589599. 31. Catron DM, Itano AA, Pape KA, Mueller DL, Jenkins MK Visualizing the first 50 hr of the primary immune response to a soluble antigen. Immunity 21: 341347. 32. An G In silico experiments of existing and hypothetical cytokine-directed clini
