Genetic sequences have had an enormous impact on our understanding of biology. The expectation is that biological network data will have a similar impact. However, progress is hindered by a lack of sophisticated graph theoretic tools that will mine these large networked datasets.In recent breakthrough work at the boundary of computer science and biology supported by my USA NSF CAREER award, I developed sensitive network analysis, comparison and embedding tools which demonstrated that protein-protein interaction networks of eukaryotes are best modeled by geometric graphs. Also, they established phenotypically validated, unprecedented link between network topology and biological function and disease. Now I propose to substantially extend these preliminary results and design sensitive and robust network alignment methods that will lead to uncovering unknown biology and evolutionary relationships. The potential ground-breaking impact of such network alignment tools could be parallel to the impact the BLAST family of sequence alignment tools that have revolutionized our understanding of biological systems and therapeutics. Furthermore, I propose to develop additional sophisticated graph theoretic techniques to mine network data and hence complement biological information that can be extracted from sequence. I propose to exploit these new techniques for biological applications in collaboration with experimentalists at Imperial College London: 1. aligning biological networks of species whose genomes are closely related, but that have very different phenotypes, in order to uncover systems-level factors that contribute to pronounced differences; 2. compare and contrast stress response pathways and metabolic pathways in bacteria in a unified systems-level framework and exploit the findings for: (a) bioengineering of micro-organisms for industrial applications (production of bio-fuels, bioremediation, production of biopolymers); (b) biomedical applications.