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Mukul Bansal Receives NSF CAREER Award

The Faculty Early Career Development (CAREER) Program is a Foundation-wide activity that offers the National Science Foundation’s most prestigious awards in support of junior faculty who exemplify the role of teacher-scholars through outstanding research, excellent education and the integration of education and research within the context of the mission of their organizations.

Dr. Bansal was recently awarded the prestigious NSF CAREER award for research on protein domains.

Protein domains are well-characterized functional constituents of genes that can be independently lost or gained during evolution, and domain shuffling is one of the primary mechanisms through which genes evolve and gain new functions. Proper inference and accounting of domain-level evolutionary events is crucial to understanding how genes evolve and function, but existing approaches for studying gene evolution ignore domain-level events. The newly funded research will lay the methodological and algorithmic foundations for a novel computational framework that addresses this critical problem and will benefit almost all areas of biology.

Professor Mukul Bansal’s research interests are in computational biology and bioinformatics, with a focus on computational molecular evolution. He specializes in the development of sophisticated computational methods, efficient algorithms, and powerful software tools that can make use of large genomic datasets to understand the evolution of genes, genomes, and species. Evolution is fundamental to our modern understanding of biology and an improved understanding of evolution is crucial for deciphering how genes and genomes function.

In addition to the research on protein domains, the Computational biology research lab, which Bansal heads, is currently engaged in the development of new computational algorithms for inferring the evolution of microbial genomes and gene families to understand how microbes evolve and adapt. This research has important implications for downstream comparative and functional genomic analyses of microbes.