Comparative Analysis of Molecular Interaction Networks

Ananth Grama
Computer Science, Purdue University


Comparative analysis of molecular interaction data provides understanding of functional modularity in the cell by integrating cellular organization, functional hierarchy, and evolutionary conservation. In this talk, we address a number of analytical and algorithmic issues associated with comparative analysis of interaction networks. Specifically, we address the following problems: (i) identification of conserved sub-networks in a large corpus of networks (protein interaction networks from different species), (ii) alignment of interaction networks to identify functional aggregates, (iii) identification of modularity in networks, (iv) inference of domain interaction networks from protein interaction networks, and (v) derivation of high-level pathway maps from synthetic lethality data. In each case, we present formal models, quantify solution quality and algorithmic complexity, and validate results in the application context. Various parts of this talk involve collaborations with Prof. Mehmet Koyuturk (CWRU), and Prof. Shankar Subramaniam (UCSD). The work is sponsored by the National Science Foundation and National Institutes of Health.