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Summary. The basic building block of a gene regulatory network consists of a gene encoding a tran-scription factor and the gene(s) it regulates. Considerable efforts have been directed recently at devising experiments and algorithms to determine transcription factors and their corresponding target genes using gene expression and other types of data. The underlying problem is that the expression of a gene coding for the transcription factor provides only limited information about the activity of the transcription factor, which can also be controlled post-transcriptionally. In the absence of a reliable technology to routinely measure the activity of regulators, it is of great importance to understand whether this activity can be inferred from gene expression data. We here develop a statistical framework to reconstruct the activity of a transcription factor from gene expression data of the target genes in its regulatory module. The novelty of our approach is that we embed the deterministic Michaelis-Menten model of gene regulation in this statistical framework. The kinetic parameters of the gene regulation model are inferre