High-throughput profiling of gene expression has opened new avenues for the understanding of biological processes at the molecular level. However, the amount of information collected can be overwhelming, making interpretation of the data difficult and subsequent detailed biological understanding elusive. Reducing the complexity of such data by evaluating them in a relevant biological context is required to gain meaningful insight. We propose that “cause-and-effect'' network approaches to pharmacology and toxicology are valuable to quantify network perturbations caused by bio-active substances, and to identify mechanisms and biomarkers modulated in response to exposure. The underlying concept is that transcriptional changes are the consequences of the biological processes described in the network. We present a novel framework for the quantification of the amplitude of network perturbations that enables comparisons between different exposures and systems. Additionally, our approach enables quantification of each biological entity (nodes) in the network, among which key contributors can be identified to unravel biological mechanisms. A unique property of our methodology allows the mapping of transcriptomics data observed in individual samples onto the network nodes which enables the generation of diagnostic network signatures; these signatures are coherent with the overall quantification of the perturbation amplitude. The presented framework efficiently integrates transcriptomics data and ”cause and effect'' network models to enable a mathematically coherent framework from quantitative impact assessment, data interpretation and mechanistic hypothesis generation to patient stratification for diagnosis purposes.