Anisha Datta

Immunotherapy has shown great promise as a cancer therapeutic; however, its efficacy is limited to a subset of patients, necessitating further study of the mechanisms at play. One mechanism of particular interest is the recruitment and infiltration of T cells into the tumor, as studies have shown that tumors that have been infiltrated by these adaptive immune cells respond positively to immunotherapies as compared to tumors that do not have T cell infiltration. Given that there is cross-talk between innate immune cells and adaptive immune cells - in that the former help prime and activate the latter - there has been great interest in the factors that contribute to and determine whether innate immune cells carry out this vital function. My project focuses on a family of receptor tyrosine kinases known as the TAM receptors, and I am specifically studying Axl. Studies have demonstrated that targeting Axl and blocking it from carrying out its functions yields an immunostimulatory state in innate immune cells and decreases aggression and invasion in cancer cells. Thus, it is likely that anti-Axl therapies may synergize with immunotherapies. My work aims to understand how anti-Axl treatment alters the tumor immune landscape, thereby allowing for a better understanding of which immunotherapies it would synergize with as well as which patients would benefit most from such a combination.

Christine (Davis) Wiggins

My research interest is in using high-throughput data generation methods, large datasets, and computational models to investigate infectious disease impact, progression, and biomarkers. I am particularly interested in intrinsic variation in immune response within populations in addition to differences between disease severities. Currently, I am researching immune response and mechanisms of protection for tuberculosis.

Maddie Dery

IgA antibodies play a multifaceted role in the immune system. In the presence of pathogens, IgA immune complexes form which can trigger an inflammatory response. This response is desirable in the treatment of infectious diseases and cancer but has also been linked to many autoimmune diseases when it becomes overactive, potentially due to excessive recruitment of neutrophils. My research aims to better understand what factors control the nature of the IgA immune response using high-throughput assays to measure neutrophil-mediated effector functions with the goal of informing the development of therapies for autoimmune diseases and cancer.

Diana Gong

I am interested in understanding the heterogeneity of response of different cell types and different patients to inflammatory and infectious disease. Currently, I am analyzing how different mutations impact the immune environment in colorectal cancer using single-cell RNA sequencing. Moving forward, I will also be studying correlates of protection in tuberculosis by using a multi-omics approach and developing computational models.

Meelim Lee

Systems biology analysis of immune mechanisms in neurodegenerative disease

While amyloid and tau pathology are defining characteristics of Alzheimer's Disease (AD), they do not robustly correlate with disease severity. Nonetheless, amyloid and tau dysregulation are key features in a majority of AD mouse models used for preclinical evaluation of therapies. To address this limitation, I am applying a computational framework developed in the lab for inter-species translation of molecular/phenotype relationships, taking a consensus-driven approach across diverse mouse models that recapitulate different biochemical and cellular aspects of human AD. Specifically, this project aims to identify features of AD mouse model transcriptomic signatures predictive of human disease outcomes via a principal component regression. Identifying translatable signatures could provide a framework for rationally selecting multiple mouse models for preclinical testing.

Additionally, I am following up on a subset of computationally-identified translatable signatures related to the vascular endothelial growth factor (VEGF) signaling pathway and angiogenesis response. I am using a mechanistic modeling framework combined with quantitative experiments to understand subcellular changes related to predicted, translatable signatures with an initial focus on signaling and trafficking disruption in neurons.

Nikolaos Meimetis

I am broadly interested in understanding how different biological systems work and what causes them to differentiate, via computational modeling, in order to better understand diseases and their therapeutic solutions. Currently, I am exploring Artificial Intelligence approaches to identify important features that can explain several conditions across different biological systems, what connects them and how they can be used to make predictions in a therapeutic context.

Krista Pullen

Vaccines are one of the most cost-effective medical interventions for combating infectious disease. That being said, efficacious vaccines have yet to be developed for some of the deadliest of these diseases, including HIV/AIDS, Malaria, and Influenza. Currently vaccine development is limited by our knowledge of correlates of immunity against infection. I am interested in utilizing machine learning techniques to reveal antibody and immune cell effector functions predictive of disease protection. This information may lead to better understanding of disease mechanisms and allow us to rationally design more effective vaccines.