Recent advances in biotechnology have yielded an explosion of data describing biological systems, creating rich opportunities for new insights into cellular inner-workings and therapeutic discoveries. To keep up with this rapid growth and increase in data complexity, we need novel static, integrative, and dynamic methodologies to continue mining these networked systems. In this thesis we introduce new static, integrative, and dynamic computational frameworks for network analysis, and combine existing ones in new ways, to elucidate the biotechnological biases and functional principles governing molecular interactions and their implications in disease. We focus on mining new knowledge from the yeast and human interactomes, since these are currently the most complete data in biology. We perform three lines of experimental work: 1) the macro-scale study, where we model the yeast and human interactomes and show that their interactome data are growing in structurally and functionally principled ways, characterised by a non-random dual topological nature; 2) the micro-scale study, where we zoom into the specifics of wiring patterns around individual genes and uncover a unique core sub-structure within the human interactome, which contains driver genes dubbed to be the main triggers for disease onset; and 3) the data integration study, where we introduce a new computational framework for fusing multiple types of molecular interaction data and use it to construct the first unified model of the cell’s functional organisation and cross-communication lines. Similarly, a new field of systems economics has gained recent attention, with more financial and economic network data emerging at an increasing pace. Hence, we introduce a new computational methodology for tracking network dynamics and use it to quantify the micro- and macro-scale topological changes in the world trade network over the past 50 years, and to demonstrate the fundamental relationship between topological perturbations and indicators of countries’ political and economic stabilities.