Figure 3.

Attractor landscape by probability density distributions of correlations, transcriptomic elements and attractor genes. (A) Schematic trajectories for transcriptomic elements falling into attractor (red) and not falling into attractor (blue). (B) Distribution and (C) Standard deviation of R_v (top panel) and MI_v (bottom panel) with different transcriptomic element size (denoted as n) of replicate a at 0.5 minutes. Distribution of R_v and MI_v for ensembles of n randomly chosen genes (n = 25, 50, 100, 200, 400, 600, 800, 1000) were generated with 100 repeats. Standard deviation of R_v and MI_v decreases as n increases (except for when n = 25 for R_v), and follows $\alpha /\sqrt{n}$ + c law. See Fig. S3A,B for other time points. (D) 3D plot of the superimposition of the probability distribution (SPD) of R_v and MI_v over all time points for the whole transcriptome. SPDs were estimated by getting R_v and MI_v values of 100 randomly chosen genes for 100 times, using two-dimensional kernel density estimation. (E) Trajectory of whole genome (3389 genes) falling within attractor boundary (solid contour line) overlaid on SPD of whole transcriptome R_v and MI_v. The trajectory was generated by averaging 100 trajectories of 100 randomly chosen genes from the pool of 3389 genes. (F) Trajectories of cumulative attractor (1800), and non-attractor (1589) genes overlaid on SPD of R_v and MI_v for whole transcriptome. (G) Distribution of expression level for attractor (red), and non-attractor (blue) genes at representative t = 0. See Fig. S3D for other time points.