Department: Bioengineering
Research Institute Affiliation: Graduate Program in Bioinformatics
Faculty Advisor(s): Bernhard O. Palsson

Primary Student
Name: Nathan Da-Wei Mih
Email: nmih@ucsd.edu
Phone: 626-667-4197
Grad Year: 2017

Genome-scale models (GEMs) are high-quality, bottom-up reconstructions of metabolic, protein synthesis, and transcriptional regulatory networks for a specific organism. In recent years, GEMs have been extended to include protein structural information, which has opened up new vistas in systems biology research and empowered applications in structural systems biology and systems pharmacology. Translating genome-scale, protein-related information to structured data in the format of a GEM provides a direct mapping of gene to protein structure to biochemical reaction to biochemical network state to the characteristics of the entire organism. Integration of the molecular-level details of individual proteins, such as their physical, structural, and dynamical properties, further expands the description of biochemical network-level properties, and can ultimately influence how to model and predict whole cell phenotypes (e.g. cellular growth rate). Here, we present a multi-scale framework that integrates a systems biology model of metabolism for the human red blood cell, protein structure and molecular modeling tools, such as molecular dynamics and binding free energy calculations. By integrating these complementary, yet classically separate methods, we can understand how single nucleotide polymorphisms (SNPs) affect drug responses in human red blood cell metabolism. As a proof-of-principle case study, we present three metabolic enzymes that have clinically relevant associations with adverse drug reactions when considering population heterogeneity. This study represents one of the first pairings of genome-scale approaches and molecular dynamics and the resulting workflow provides an extensible framework for advancement of structural systems pharmacology applications for drug discovery and drug off-target predictions.

Industry Application Area(s)
Life Sciences/Medical Devices & Instruments | Bioengineering

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