Research Focus

Who We Are

The Zhang Lab is dedicated to advancing quantitative systems pharmacology (QSP), physiologically based pharmacokinetic (PBPK) modeling, and digital-twin simulation frameworks that illuminate how complex biological systems behave under perturbation.

We integrate mathematical modeling, high-dimensional data analysis, machine learning, and mechanistic simulation tools to decode biological interactions spanning hematology, immunology, and drug disposition.

Ultimately, the Lab aims to build predictive, trustworthy computational models that improve therapeutic decision-making, guide experimental design, and accelerate translational research. Our work bridges cutting-edge computation with real-world clinical and experimental datasets to deliver actionable insight.

Our Questions

We work on problems that matter—scientifically, clinically, and mechanistically. Current questions include:

• How can we construct credible, physiologically grounded digital twins for blood, immune, or endocrine systems to help with clinical trials and the utilization of real-world observational data?
• What biological mechanisms drive variability in drug absorption, metabolism, inflammation, and cellular injury—particularly across stress, disease, or transfusion contexts?
• How can multi-omics and time-series data be connected mechanistically to mathematical models that predict patient-specific outcomes?
• How can computational modeling reduce experimental burden, improve study design, and reveal mechanisms not directly observable in the lab?

Our Goals

Our long-term vision is to:

• Build robust, transparent, and modular computational frameworks that can be shared, reused, and scaled within the scientific community.
• Develop representative virtual patients and digital twins to support precision medicine, regulatory decision-making, and therapeutic optimization.
• Advance model-informed experimental design, ensuring that each experiment yields maximal mechanistic insight.
• Serve as a technical hub for high-quality data curation, parameter estimation, uncertainty quantification, and simulation-based hypothesis testing.
• Train the next generation of interdisciplinary researchers fluent in biology, computation, and quantitative reasoning.

Interested in Collaborating or Need Help With Your Data?

We welcome collaborations with investigators who want to strengthen their quantitative approaches.

If you have datasets you aren’t sure how to analyze, complex questions that require mechanistic reasoning, or a modeling challenge you want to explore, we are happy to consult, brainstorm, or provide guidance.

Whether you have time-series experiments, pharmacology data, or clinical datasets, feel free to reach out—our toolbox and expertise can help you extract deeper, more rigorous insights.