Research

Area 1

Monoclonal antibodies (mAbs) have been used as therapeutic drugs for over 30 years. One of the outstanding issues of antibody drug is the poor stability of some drug candidates such as high aggregation, elevated viscosity and low solubility. This hinders the development of new antibodies. The production cost of antibodies is high, therefore developing computational tools that can predict antibody stability in the early-stage discovery is desired. 

Area 2

Metabolic fluxes are a fundamental descriptor of cellular state, representing the rates at which organisms operate metabolic pathways. Metabolic flux analysis (MFA) is a commonly used computational framework that identifies the set of fluxes that best simulate observed isotope labeling patterns. However, quantitative flux analysis remains an expert method, and the relationships between isotopic labeling patterns and fluxes remain elusive in complex metabolic environments. Our group in collaboration with Dr. Junyoung Park at UCLA innovated a two-stage machine learning framework that can directly predict metabolic fluxes from isotope labeling patterns at orders of magnitude faster speeds than traditional MFA. We aim to apply this approach for genome-scale 13C-MFA analysis.

Area 3

The emergence of antimicrobial resistance is a crucial public health problem due to the dissemination of bacterial strains that are resistant to multiple antibiotic drugs. AMPs are promising alternatives to traditional antibiotics. One of the most desirable advantages of AMPs is that bacterial resistance would evolve much more slowly than against antibiotics. However, AMPs can exhibit undesirable properties as drugs, including short circulating half-life, instability and toxicity to animals and humans. Therefore, novel approaches are needed to be developed to make AMPs less toxic for human while maintaining or improving their potency to eliminate bacteria and reduce the production cost.