The basic underlying problem in reverse engineering of gene regulatory networks from gene expression data is that the expression of a gene encoding the regulator provides only limited information about its protein activity. The proteins, which result from translation, are subject to stringent posttranscriptional control and modification. Often, it is only the modified version of the protein that is capable of activating or repressing its regulatory targets. At present there exists no reliable high-throughput technology to measure the protein activity levels in real-time, and therefore they are, so-to-say, lost in translation. However, these activity levels can be recovered by studying the gene expression of their targets. Here, we describe a computational approach to predict temporal regulator activity levels from the gene expression of its transcriptional targets in a network motif with one regulator and many targets. We consider an example of an SOS repair system, and computationally infer the regulator activity of its master repressor, LexA. The reconstructed activity profile of LexA exhibits a behavior that is similar to the experimentally measured profile of this repressor: after UV irradiation, the amount of LexA substantially decreases within a few minutes, followed by a recovery to its normal level. Our approach can easily be applied to known single-input motifs in other organisms.