Concentration variations determine shape of region of high probability. Using the Monte Carlo algorithm to analyze data from a single cell containing [D0] = [A0] = 1 μM produced an elongated region of highly probable values (A), indicated by the collection of dots showing the locations in (Kd, Efr) visited by the biased random walk. These single-cell results are shown again in (B-E) (blue dots), with the results from analyzing other data sets superimposed. Analyzing three identical cells instead of one produced a narrower but still elongated region (B). However, when analyzing data from two cells (C and C inset) and three cells (D) with different concentrations of fluorophores, the resulting regions were contracted. Analyzing cells individually showed that the elongated highly probable regions for each all intersected near the true value (E). More extreme variation in concentrations led to an even smaller optimal region (F). In (A-F), there were 10 measurements/cell/channel, 3% added Gaussian noise, and 31,000 steps are shown for each walk. For other parameter values, see Methods.
Lichten and Swain BMC Biophysics 2011 4:10 doi:10.1186/2046-1682-4-10