Synchronization Between Inter and Intra-Cellular Simulation The Monte-Carlo simulation and the Gillespie simulation run at different time-scales and different length scales. The spatial interaction is explicitly manifested by assigning each cell to a unique lattice square. In order to account for the different time scales we must synchronize the constant time step of the Monte-Carlo with the variable (and often much faster) time step of the Gillespie simulations. To do this, we allow the Gillespie algorithm of each cell to run for a number of steps, until the total amount of time of these steps exceeds the Monte-Carlo time step. The last step is not performed, and the time is advanced to match the next Monte-Carlo time step. This procedure ensures the simulation of a real Markovian process when the external conditions change at pre-determined times. Another aspect of synchronizing the inter-cellular Monte-Carlo simulation with the intra-cellular Gillespie simulations involves the secretion of interferon molecules from each cell. At each Monte- Carlo step, each cell is queried for the number of IFNB1 messages in that cell. We assume that the number of new interferon proteins at each simulation time step is the number of IFNB1 transcripts multiplied by a factor of M = 0.1 (which corresponds to a translation rate of 901sec). We further assume that interferon secretion is a rapid process and that all the newly synthesized interferon is secreted at each time step. The number of secreted interferon molecules is added to the number of interferon molecules in the lattice square occupied by the cell. Simulations with Interferon-Blocking Antibodies Two kinds of antibodies were introduced in the experiments. One targets the interferon molecules, and the other targets the interferon cell surface receptors. To include this in the simulation we added two parameters to the Monte-Carlo simulation of the inter-cellular medium. First we allowed each interferon molecule to become inactive by binding to an antibody with a probability of 0.4 at each Monte-Carlo time step. This corresponds to an effective degradation rate of approximately251sec, and allows the interferon molecules to form a cloud around the emitting cells, but hardly any can reach a neighboring cell. The binding rate was reduced by multiplication of KON by a factor of 0.9. This means that even the few molecules that are able to reach neighboring cells have a harder time activating it, and is equivalent to reducing the number of active receptors by the same factor of 0.9. Supporting Information Figure S1 Measurement of NDV L gene, IFNB1, IFNA1, MXA, and DDX58 transcripts in human MDDCs at 0, 3, 6 and 10 hours following NDV infection. Total mRNA expression of these genes was quantified by real-time RT-PCR and induction fold increases were normalized to the initial value at 0 hrs post infection. A. IFNB1; B. DDX58; C. NDV L gene; D. MXA; E. IFNA1. (TIF) Figure S2 Time course for IFNB1 and DDX58 induction in MDDCs infected by NDV at different MOI. MDDCs were infected by NDV at MOI = 0.1, 0.5, 1.0, 2.0 and 5.0. Expression levels were measured at 1, 2, 3, 4, 5, 6 and 10 hours after infection. The relative induction level was normalized to ACTB and to an uninfected control. A. IFNB1 (upper panel early times, lower panel later times) B. DDX58. (TIF) Figure S3 Effect of DC heterogeneity in the DC response to NDV infection. The real-time RT-PCR cross point (Cp) value IFNB1-DDX58 Feedback Loop Cell-to-Cell Variability indicates the level of IFNB1 in each individual DC (high Cp value means low expression). 6 hour after NDV infection, single MDDCs sorted by DC differentiation marker CD14 (0 = CD14+ high, 1 = CD14+ low) showed high heterogeneity in both groups but no significant difference in IFNB1 production between the two groups (p = 0.17). (TIF) Figure S4 Schematic illustrating the hemi-nested PCR protocol used to measure IFNB1 mRNA levels in single DCs. DCs were directly sorted into 384-well PCR plates prepared with lysis buffer. Amplification of mRNAs was performed with the two-step hemi- nested real time RT-PCR. The first step was reverse transcription reaction followed by 8-cycle amplification. Internal control oligonucleotides containing mutations (AA/TT substitution and deletion of the Roche LNA probe binding site) and IFNB1 mRNA transcripts were both amplified by the primers in this first step reaction. The PCR products were split into two PCR wells and further amplified and detected by second real time PCR reactions. An IFNB1 cDNA-specific forward primer with AA at the 39end and the IFNB1 cDNA-specific Roche LNA Taqman probe were added into the second real time PCR reaction to discriminate the PCR products from internal control oligonucleotides. The final amplification signal of the second PCR originated only from the IFNB1 mRNAs. (TIF) Figure S5 Validation of the hemi-nested real time PCR method with serial dilutions of genomic DNA. 5A. Human genomic DNA was used as the PCR template and serial diluted to 4000 copies, 400copies, 40 copies, 4 copies in each well. The PCR amplification curves of 4 repeats of 4000 copies and 400 copies, 8 repeats of 40 copies, 10 repeats of 4 copies and negative controls are shown. A standard curve of qcPCR with diluted genomic DNA is presented as an inset. The linear fitting equation and r2 shown in the inset were given by KaleidaGraph. 5B. Real-time PCR amplification curve of human genomic DNA standard using IFNB1 control oligonucleotide specific primer. This oligonucleo- tide specific primer has been designed to anneal only with PCR amplicons originated from the IFNB1 control oligonucleotide (details see Materials and Methods section). Our data showed a very tight distribution at the different concentrations of the genomic standard (103–fold range). (TIF) Figure S6 Validation of multiplexed hemi-nested real time PCR detection with total RNA dilutions. Total RNAs were extracted from MDDCs and 10-fold serially diluted until the final concentration reached copy numbers similar to the low copy genomic DNA standards depicted in Supplementary Figure S5A. In order of RNA copy number from high to low, the results are for results from 6 repeats, 6 repeats, 12 repeats and 18 repeats of PCR reactions. 6A. PCR amplification curve of IFNB1 for total RNA dilutions. 6B. PCR amplification curve of DDX58 for total RNA dilutions. The final dilution did not show DDX58 amplification due to lower expression of DDX58 than IFNB1 (less than 1 copy/ cell). (TIF) Figure S7 Two dimensional agent-based model (ABM). The extracellular model is two dimensional, and the medium is represented by a square lattice, where each lattice square has the size of a single cell. Each cell is simulated as an independent agent, where the agents interface through the extracellular medium. (TIF) PLoS ONE | www.plosone.org 11 February 2011 | Volume 6 | Issue 2 | e16614