CHE 598 Seminar: Bridging Genotype to Phenotype in Systems Biology
About the event
SPEAKER: Dr. Niaz Bahar Chowdhury, Staff Scientist, Environmental Molecular Sciences Laboratory, PNNL
BIOGRAPHY:
Niaz Bahar Chowdhury is a Staff Scientist at the Environmental Molecular Sciences Laboratory (EMSL), a DOE Office of Science user facility at Pacific Northwest National Laboratory. He earned his B.Sc. in Chemical Engineering from Bangladesh University of Engineering and Technology (Bangladesh) and an M.A.Sc. in Chemical Engineering from Queen’s University with a focus on process systems engineering (Canada). He then spent several years in industry across Canada as a process development engineer and earned his professional engineer (P.Eng.) designation before returning to academia to pursue his Ph.D. in Chemical and Biomolecular Engineering at the University of Nebraska-Lincoln. His research integrates multi-omics data with genome-scale metabolic models to connect genotype to phenotype, spanning constraint-based modeling, strain design, and AI and large language model driven approaches that make metabolic modeling more accessible and evidence-grounded. At EMSL, he continues to develop these methods to accelerate discovery in systems biology and metabolic engineering.
ABSTRACT:
Understanding how organisms translate their genetic potential into metabolic function is a central goal of systems biology. My research integrates multi-omics data with computational modeling to connect genotype to phenotype across diverse biological systems, from bacteria and archaea to crop plants. In this seminar, I will describe how genome-scale metabolic models, contextualized with transcriptomic, proteomic, and metabolomic data, reveal the metabolic strategies organisms use to adapt to environmental and genetic change. These approaches uncover hidden bottlenecks, predict engineering targets, and generate testable hypotheses about how metabolism reorganizes under stress. I will also discuss how artificial intelligence is reshaping this field, enabling metabolic reasoning that is grounded in experimental evidence and accessible to researchers without deep computational training. Together, these tools help transform complex molecular measurements into mechanistic understanding of biological function. I will highlight applications in crop resilience, microbial metabolism, and metabolic engineering, and reflect on how the convergence of constraint-based modeling and machine learning is accelerating discovery, particularly in non-model organisms where experimental resources are limited. Ultimately, this work points toward a more predictive, interpretable, and collaborative systems biology that bridges computational and experimental science.