Jamie Inman

Divisions

Biological Systems and Engineering

• Organismal Systems & Bioresilience

Biography

Jamie Inman, PhD, is a scientist whose work focuses on understanding how genetically and environmentally complex biological systems respond to stress and perturbation. She received her PhD in Comparative Biochemistry from the University of California, Berkeley, where she trained under the guidance of Mina Bissell and Fenyong Liu.

Dr. Inman’s research integrates advanced experimental models, multi-omic data generation, and predictive computational frameworks to study biological responses across molecular, cellular, tissue, and organismal scales. Her work is driven by the goal of understanding, measuring, and ultimately controlling how complex systems respond to environmental, infectious, chemical, and radiation-associated stressors. By combining systems biology, engineered model systems, and data-driven analysis, she aims to identify mechanisms of resilience and susceptibility that can inform new strategies for prediction, intervention, and therapeutic development.

Research Interests

Systems Biology of Stress and Perturbation

Our research program focuses on understanding how complex biological systems respond to stressors such as radiation, chemical exposures, infection-associated signals, and immune challenges. We are particularly interested in how these responses are shaped by host genetics, environmental context, tissue organization, and prior biological state.

Genetic Diversity, Susceptibility, and Resilience

We leverage genetically diverse model systems to uncover variation in susceptibility, resilience, and disease outcomes. By studying biological responses across diverse genetic backgrounds, we aim to identify mechanisms that explain why individuals, tissues, or populations respond differently to the same stressor or therapeutic intervention.

Multi-Omics and Functional Phenotyping

Our work integrates transcriptomic, microbiome, cytokine, metabolomic, hematologic, imaging, and physiological datasets with functional readouts. This systems-level approach allows us to connect molecular signatures with measurable biological outcomes and to identify biomarkers of injury, adaptation, resistance, and recovery.

Radiation Biology and FLASH Radiotherapy

A major component of the program is radiation biology, with an emphasis on how dose, dose rate, oxygen dynamics, redox biology, and tissue architecture influence radiation responses across scales. We combine engineered 2D and 3D organotypic cultures with in vivo models to dissect mechanisms of radiation injury, normal tissue sparing, tumor response, and the biological basis of FLASH radiotherapy.

Non-Invasive Diagnostics, Sensing, and Microphysiological Systems

We develop diagnostic, biodosimetry, and sensing approaches aimed at real-world deployment, including blood-based, saliva-based, spectroscopic, hyperspectral, volatile organic compound, and machine learning-enabled assays. These efforts are designed to support rapid, minimally invasive assessment of exposure, injury, infection, and physiological state.

Our research also extends to the development and application of advanced instrumentation and microphysiological systems that enable high-resolution, multimodal measurement of dynamic biological processes. By integrating sensing technologies with human-relevant experimental models, we aim to capture biological responses in real time and translate complex signals into actionable indicators of health, injury, and recovery.

Predictive Modeling, Digital Twins, and AI-Enabled Biology

A central goal of the program is to make biological systems more predictable and controllable. We use machine learning, systems modeling, and physics-informed approaches to translate complex biological data into actionable predictions. These frameworks support applications ranging from medicine and radiation response prediction to engineered tissues, environmental health, and industrial bioprocessing.