Reverse-engineering post-transcriptional regulation of gap genes in Drosophila melanogaster

The second strategy is based on a bootstrapping approach, where we sample a normal distribution (based on measured means and variances) for each of our protein expression data points. Data points for which no variance estimates were available were not randomised. Resulting sampled expression profiles were corrected by setting negative concentration values to zero. bootstrapping samples were generated for each gap gene in this way. These samples were then fitted by pLSA as described above. Bootstrapping runs were performed in parallel on a cluster provided by the Spanish Supercomputing Network (RES—Red Española de Supercomputación). From the resulting distribution of parameter values, we directly calculate confidence intervals, plus correlation coefficients indicating mutual dependence of model parameters. In the case of Kr, which shows a bimodal parameter distribution (Figure 6), we only considered a subset of the sampled estimates (see Results). For an analysis of the sensitivity of parameter values to changes in mRNA expression patterns, analogous to the protein bootstrap, please refer to Supplementary Text S4.