Although not empirically demonstrated, it seems unlikely that the

Although not empirically demonstrated, it seems unlikely that the timing of turning on either the p R or p R ‘ promoter would have a positive or negative effect on the assembly of lysis apparatus such that their effects would cancel each other

out, resulting in the observed COV(t 1, t 3) + COV(t 2, t 3) = 0. Most likely, time intervals are mutually independent, i.e., COV(t 1, t 3) = COV(t 2, t 3) = 0. The standard deviations (“”absolute noise”" in their terminology) for t pR’-tR’ and t lysis can be extracted from their figure six A using data determined from cells carrying the pR’-tR’-GFP plasmid. The estimated SDs for t pR’-tR’ and t lysis are ~10 min and ~18 min, respectively; therefore, VAR(t pR’-tR’) Selleck GW4869 = ~100 and VAR(t lysis) = ~324. The SD for t pR can be estimated by extrapolating the line connecting between lysis and p R ‘ onset to the 20 min mean time at the x-axis (based on the result from cells carrying the pR-GFP plasmid in their figure six A). The corresponding SD for t pR is ~7 min, thus VAR(t pR) = ~49. Taken together, VAR(t 1)

= 49, VAR(t 2) = 51 (= VAR(t 1 + t 2) – VAR(t 1) = 100 AMN-107 in vivo – 49 ), and VAR(t 3) = 224 (= VAR(t 1 + t 2 + t 3) – VAR(t 1 + t 2) = 324 – 100). That is, VAR(t 1), VAR(t 2), and VAR(t 3) contributed to 15%, 16%, and 69% of total lysis time variance, respectively. Appendix B Studies of molecular stochasticity typically use the coefficient of variation (CV) as the measurement Glycogen branching enzyme for the degree of stochasticity [15, 25, 48, 49]. Since CV is a composite statistic (defined as standard deviation/mean), it is sometimes difficult to discern whether an increase in the observed stochasticity (as

quantified by CV) is due to decrease in mean or increase in SD. In some cases, a different metric, such as phenotypic noise strength (defined as variance/mean) [17, 20], or a slight INCB28060 concentration variant of it (defined as variance/squared mean) [19], has been used as well. Many times, it is not clear why a particular metric is used, except in the instance where the phenotypic noise strength is used to test against an a priori expectation of a Poisson distribution, for which variance/mean = 1. It is understandable why the CV, or a variant, is used in certain situations. For example, if the means are drastically different from each other or a comparison is made between measurements using different units [56], pp. 57-59.]. In our study, however, the means were not very different and the same measuring unit (i.e., min) was used. Therefore, we presented our means and SDs separately and then jointly as CVs. Except in one instance where presenting stochasticity as SD or CV makes a difference (i.e., effect of genotype on SD or CV vs. MLT), all the other results showed that SD and CV followed the same trend. Since CV can be derived from SD and mean, no information is lost by presenting them separately.

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