Computational systems immunology

Interdisciplinary research combining genomics, machine learning and immunology to understand host immune responses in human disease and therapy (e.g. cancer immunotherapy, viral infection/antibody response). We develop computational methods to address the analytic challenges of immunogenomic data..

Software: scimmunity, immunedynamics, tcrconnect

Virus and antibody evolution

Principles from evolutionary biology have been used to gain insights into the progression and clinical control of infectious diseases; selective pressures such as therapeutic intervention can lead to adaptation and expansion of resistant viral mutations. We develop mathematical and computational methods to predict virus fitness and to optimize antibodies against viral escape mutations.

1. Anti-SARS CoV2 antibody optimization

2. Evolutionary dynamics on computed fitness landscapes

Software: fitnesslandscape, satlasso, antitbodyopt

Treatment Optimization

This work focusses on designing treatment optimization algorithms to address therapeutic resistance using application of genomics, machine learning, optimization and control theory, in the following disease models:

1. HIV and broadly neutralizing antibody therapy

2. Non-small cell lung adenocarcinoma and small molecule inhibitors

3. CAR T cell combinations to address antigen escape

Software: treatmentopt