Nalin Goonesekere

Nalin Goonesekere is a Professor in the Department of Chemistry & Biochemistry at the University of Northern Iowa within the College of Humanities Arts and Sciences. His research integrates computational bioinformatics with experimental molecular biology to address critical challenges in pancreatic cancer biomarker discovery, plant pathogen virulence mechanisms, and protein function annotation.

Education:

• B.Sc. from the University of Colombo, Sri Lanka
• Ph.D. from Princeton University
• Research Fellow at the National Cancer Institute, Bethesda

Dr. Goonesekere's research program focuses on three interconnected domains. His pancreatic cancer work targets early detection biomarkers through genomic meta-analyses, addressing the disease's devastating <5% survival rate. In plant pathology, he investigates virulence genes in Fusarium verticillioides using genome analysis and recombinant DNA techniques to combat crop destruction. His bioinformatics research pioneers methods for predicting functions of uncharacterized protein domains (DUFs) in Pfam, directly enhancing genome annotation efforts across species.

Analysis of his publication trends reveals a strategic evolution from wildlife conservation genetics (2002 elephant/bat studies) toward intensive pancreatic cancer biomarker discovery (2014-2021) and advanced bioinformatics tool development. His work consistently bridges computational prediction with laboratory validation, particularly in identifying tumor-suppressor genes and therapeutic targets through microarray meta-analyses. The recurring themes across his 20+ publications include genomic data interpretation for human health applications and agricultural disease mitigation.

Dr. Goonesekere has mentored numerous students evidenced by asterisked co-authors in publications (Ludwig, Andersen, Smith, Heinzman, Schmidt), though specific grant funding details aren't documented in available sources. His laboratory operates at the intersection of structural biology and computational genomics, utilizing both in silico modeling and wet-lab techniques to validate biomarker candidates and protein functions.