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Our lab integrates data from multiple in vitro and in vivo experimental systems to build computational models of multiple aspects of infection, spanning molecular to host scales.


Significant challenges to HIV cure include the existence of a viral reservoir in lungs, lymph nodes, brain and CNS; poor immunological recovery in some patients; incomplete understanding of drug distribution in different body compartments; and the optimal design of combination therapy. Host-directed therapies, ‘engineering’ the immune system to kill or reactivate latent viral reservoirs, are under active, clinical investigation as complements to ART, with mixed results. The complexity and chronicity of HIV and its treatment, as well as ethical and financial constraints, makes it incredibly challenging to study experimentally or in clinical trials.

We use computational models describing HIV, host and drug dynamics at cellular, organ and host scales. This model tracks dynamics in multiple relevant physiological compartments, e.g. lung, lymph nodes, blood. We calibrate the model to data from the literature, as well as from non-human primates (NHPs) infected with Simian Immunodeficiency Virus (SIV), a virus that produces similar symptoms in non-human primates as HIV does in humans.

Non-tuberculous Mycobacteria

Non-tuberculous mycobacteria (NTM) are bacteria that can cause pulmonary disease, affecting 86,000 people in the US, and cases are on the rise. Mycobacterium avium complex (MAC) and Mycobacterium abscessus (Mabs) are two of the most common NTMs causing human disease. MAC and Mabs treatment is currently 40-80% effective. Improving NTM treatment faces a number of challenges: NTMs have natural tolerance to some antibiotics; antibiotic susceptibility and disease manifestations vary between NTMs; and bacterial and drug dynamics at the site of infection are understudied. Designing new treatment regimens is therefore a complex and multi-dimensional problem that is difficult to address using experimental or clinical studies alone. We use computational systems pharmacology to complement and inform physical studies.

Our agent-based models recreate host-pathogen interactions in the lung airway, sputum and lung nodules. The model also contains treatment components including: plasma pharmacokinetics (concentration profiles in blood); tissue pharmacokinetics (spatial distribution via vascular permeation, diffusion, and binding); and pharmacodynamics (bacterial response to antibiotics).

Co-infections and co-morbidities

Infectious diseases often occur together. E.g. since HIV affects host immune function, it often pre-disposes patients to additional infections such as tuberculosis (TB) or NTMs. Understanding the complex interactions between multiple infections is crucial to optimizing treatment.

We use our computational models to: predict optimal treatment strategies, and identify key points of interactions between different infections that could provide new targets for immune-therapy.

Ebola Virus

Ebola virus (EBOV) infections continue to pose a global public health threat, with high mortality rates and sporadic outbreaks in Central and Western Africa. A quantitative understanding of the key processes driving EBOV assembly and budding could provide valuable insights to inform drug development. Thus, we are using a computational, ODE-based model to simulate the replication and assembly process of EBOV at a sub-cellular level. The model can elucidate the key mechanisms driving EBOV infection and help to evaluate the efficacy of new treatments for EBOV. 

Calcium signaling across kingdoms

As part of our work with the EMBRIO Institute, we are developing agent-based models that simulate calcium signaling spanning the sub-cellular to multi-cellular scales in diverse biological systems including zebrafish embryos and plants. These models will support translation of discoveries across a broad range of application areas and advance data integration techniques.


We are very interested in how undergraduate and graduate students use computation and computational tools to master complex concepts in Biomedical Engineering education. We work closely with Dr. David UmulisDr. Alejandra Magana and Dr. Monica Cardella and their teams to develop innovative pedagogical tools and strategies to support student learning across disciplines and cultural boundaries.