Laboratory - Research

We lead interdisciplinary research at the intersection of computational science, applied mathematics, and experimental radiation biology to investigate key mechanisms of tumor dynamics and treatment resistance. Our lab interrogates cancer across multiple biological length-scales using high-performance computing, multiscale modeling, advanced imaging technologies, and experimental systems biology. A defining strength of the lab is that we operate both an experimental research space (wet lab) and an in silico research space (dry lab), enabling seamless integration of experiment and theory.

Applied Mathematics in Oncology

Using differential equations, stochastic processes, and multiscale modeling, we create models of tumor dynamics, metabolic topology, and radiation effects. These models integrate imaging, molecular data, and experimental observations to characterize tumor appearance and behavior, and better understand cancer biology. Our vision is to establish unified mathematical frameworks that link cellular and tissue scale tumor dynamics with therapeutic response to transform mathematical oncology from a theoretical discipline into an experimental and translational engine.

Computational + Experimental Radiation Biology

We bridge computational techniques with bench experiments to explore mechanisms of resistance to radiation. Through integration of pre-clinical imaging and systems biology approaches, we study how radiation influences metabolic response, immune response, and tissue microenvironment. We aim to close the loop between computing and experiments in radiation biology via integrated bench experiments to unravel how radiation reshapes metabolic and immune landscapes. These insights fuel data-driven model calibration and discovery of targeted interventions to overcome resistance.

Multiscale Image Phenotyping

We leverage radiomics and pathomics techniques to extract high-throughput information from radiology and pathology images, enabling multi-scale phenotyping of tumors. This reveals spatial patterns linked to tumor biology, treatment resistance, and microenvironmental context, supporting both mechanistic modeling and translational biomarker discovery. We develop integrated pipelines that link radiomic, pathomic, and molecular data into a cohesive multi-scale representation of the tumor phenotype.

Tumor Immunology and Immune Dysregulation

We use next-generation digital spatial profiling and advanced computational techniques to analyze immune cell distributions within tumors, helping to map the complex interactions between tumor cells and immune cells. By interrogating tissue-specific data and mathematically modeling immune dysregulation as a dynamical system, we seek to understand the role of the immune microenvironment in tumor progression, therapy resistance, and immune evasion.

Digital Twins of Cancer

Digital twins represent the culmination of the four preceding research areas: a synthesis of mathematical modeling, advanced imaging, and molecular data into tumor-specific, mechanistic simulations of cancer. We are developing mechanistically-informed and data calibrated digital twins as virtual representations of tumors and their microenvironment. Grounded in applied mathematics and informed by imaging, pathology, and molecular data from clinical and pre-clinical sources, these models serve as an in silico framework for mechanistic research, treatment screening, and biomarker discovery.

radonc.duke.edu | radiology.duke.edu | ece.duke.edu | medicalphysics.duke.edu | math.duke.edu

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