We present a systems biology approach to study the global metabolic effects of the insertion of synthetic circuits in a cellular chassis. Our approach combines high-throughput proteomics with the MMG probabilistic tool, which integrates the data with the metabolic circuit's topology. We present a theoretical analysis of the foundations of our approach, as well as experimental results on a mutant strain of Escherichia coli where a light-receptor circuit was inserted and coupled with lactose metabolism. Our results show that the systems approach manages to extract meaningful information from the proteomic data that cannot be recovered by naive thresholding of the data. This tool can be used to characterise the relationship between new circuits and chassis in synthetic biology applications.