R script for Data Processing for Figure 2

Contains the R script we used for processing the data for Figure 2. Finds the maximum value, then performs linear regressions, and works out slopes. <br

Data for Figure 1b

Data set for Figure 1b showing velocity of LuxAB reaction for different concentrations of FMN<br

Data for Figure 2

Raw data used for creating the histogram in Figure 2; the model fits are shown in the Supplementary Information. Data processing and model fitting is as per the R script that is also included. <br

A Centered Gaussian Probability Distribution of Unit Variance (Black), Corresponding to the Random Distribution Obtained in the Null Models, and the Values Actually Observed in Our Clusters (Arrows)

<p>Values reported on the abscissae are <i>z</i>-scores, i.e., the deviations to the mean normalized by the standard deviation.</p> <p>Red solid and blue dashed arrows correspond to E. coli K12 and <i>B. subtilis,</i> respectively. Short arrows point to the values of the <i>z</i>-scores that we measure for the fraction of pairs of genes within a common operon and belonging to the same cluster.</p> <p>Long arrows refer to the same quantities for pairs of genes within a common metabolic pathway.</p> <p>Note that, as the Gaussian distribution is meant to show, our <i>z</i>-scores are highly significant, e.g., <i>z</i>_score, ≥ 8 ↦ probability = 6 <i>×</i> 10⁻¹⁶ to occur by chance. See also that values of the <i>z</i>-scores previously obtained, using general-purpose clustering methods, were much smaller: 5.30 and 3.29, for operons and metabolic pathways, respectively.</p

The Cluster Stability Curves, Quantified by the Difference Δ(<i>S</i>) = <i>B</i>(<i>S</i>) <i>− B_random</i>(<i>S</i>) of the Assignment Probabilities Defined in the Body of the Text, versus the Number of Clusters <i>S</i>

<p>The curves are for B. subtilis (dashed blue) and E. coli K12 (solid red). The retained number of clusters corresponds to the maximum of the stability curve.</p

The Histograms of Lengths of the Known Operons for B. subtilis and E. coli K12

<p>Blue boxes are for <i>B. subtilis,</i> and red boxes are for E. coli K12.</p

The Distribution of the Number of Genes on the Leading Strand for the Clusters of E. coli K12 and B. subtilis

<p>E. coli is shown on the top graph, and B. subtilis is shown on the lower graph. Clusters are identified by a roman number on the <i>x</i>-axis, and <i>z</i>-scores relative to null models are indicated on the <i>y</i>-axis.</p> <p>Note the depletion of leading strand genes in the third cluster of B. subtilis.</p

Steady-state endocytosis and degradative trafficking of occludin.

<p>A confluent monolayer of MDCK cells were serum starved in the presence of either DMSO or 250 nm BafA. Cell surface proteins were biotinylated and trafficking allowed to recommence for designated time-points in the presence of DMSO or BafA. Cell surface biotinylated proteins were reduced, thus only internalised proteins remain biotinylated. Biotinylated proteins were pulled down with NeutrAvidin beads. Protein abundance was quantified by western blot analysis. (A) Treatment with BafA (inhibitor of lysosomal degradation), attenuates the decrease in occludin signal following endocytosis observed in DMSO treated control cells. (B) Quantification of 3 repeats of the experiments shown in (A). (C) Quantification of the decrease in occludin signal between 30 and 120 minutes comparing control to BafA treatment. * p≤0.05.</p

Co-localisation between internalised occludin and late endosome/lysosomal compartments increases after BafA treatment.

<p>Incubation with BafA increases co-localisation between occludin and CD63-GFP in comparison to control treated cells. A confluent monolayer of MDCK cells transiently expressing CD63-GFP were incubated with DMSO or 250 nm BafA for 2 hours prior to fixation and immuno-localisation with mouse anti-occludin (1∶100). Cells were imaged by confocal microscopy. (A) Confocal image showing co-localisation between occludin and CD63-GFP in the presence of DMSO or BafA. (B) Quantification of 3 repeats of the experiments shown in (A) with a minimum of 19 cells analysed of each condition. *** p≤0.001.</p

Linear model (red lines) comparison with the experimental data (blue lines).

<p>The grey spreads illustrate the model output obtained using 50 random values of parameters from the posterior distribution. (A) and (B) use data taken from the steady-state endocytosis and degradative trafficking of occludin experiment (<i>M(0) = 100, I(0) = 0</i>), see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111176#pone-0111176-g001" target="_blank">Figure 1B</a>, both representing internal occludin with no degradation occurring in (B) (<i>γ = 0</i>). Data in (C) and (D) are both obtained from the endocytic occludin recycling experiment (<i>M(0) = 0, I(0) = 100</i>), see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111176#pone-0111176-g003" target="_blank">Figure 3B</a>, the first being the total occludin (<i>M(t)+I(t)</i>), and the second internal occludin. The model equations are given by <i>dM/dt = −ηM+ρI, dI/dt = ηM−(ρ+γ)I</i>, i.e. the only change from the model in the main text is that the rates of endocytosis and recycling are constant. For all high likelihood parameter values, the initial rise of internalized occludin in the trafficking experiments is considerably faster than experimentally observed. This suggests the necessity of a slower time scale, which is included in the main model as the rate of recovery from cooling.</p