Abstract

We are witnessing the emergence of the "data rich" era in biology. The myriad data in biology ranging from sequence strings to complex phenotypic and disease-relevant data pose a huge challenge to modern biology. The standard paradigm in biology that deals with hypothesis to experimentation (low throughput data) to models is being gradually replaced by data to hypothesis to models and experimentation to more data and models. And unlike data in physical sciences, that in biological sciences is almost guaranteed to be highly heterogeneous and incomplete. In order to make significant advances in this data rich era, it is essential that there be robust data repositories that allow interoperable navigation, query and analysis across diverse data, and a plug-and-play tools environment that will facilitate seamless interplay of tools and data. Further, the integrated data will enable the reconstruction and modeling of biological systems. This talk with address several of the challenges posed by enormous need for scientific data integration and modeling in biology with specific exemplars and possible strategies. The issues addressed will include--Architecture of Data and Knowledge Repositories--Databases Flat, Relational and Object-Oriented; what is most appropriate? The imminent need for Ontologies in biology--Reduction and Analysis of Data the largest challenge! How to integrate legacy knowledge with data? How can we carry out systems level modeling in biology?