Publicly-available data sets provide detailed and large-scale information on multiple types of molecular interaction networks in a number of model organisms. These multi-modal universal networks capture a static view of cellular state. An important challenge in systems biology is obtaining a dynamic perspective on these networks by integrating them with gene expression measurements taken under multiple conditions. We present a top-down computational approach to identify building blocks of molecular interaction networks by (i) integrating gene expression measurements for a particular disease state (e.g., leukaemia) or experimental condition (e.g., treatment with growth serum) with molecular interactions to reveal an active network, which is the network of interactions active in the cell in that disease state or condition and (ii) systematically combining active networks computed for different experimental conditions using set-theoretic formulae to reveal network legos, which are module...
T. M. Murali, Corban G. Rivera