Research
Research
Investigating Biological Systems through Computational and Quantitative Approaches
Our lab is dedicated to understanding the dynamic behavior of biological systems through mathematical modeling, computational simulations, and quantitative biology. We integrate experimental data with theoretical frameworks to uncover fundamental principles governing neural circuits, immune system dynamics, and cellular processes.
Research Areas
Neural Dynamics and Circuits
Understanding the fundamental mechanisms of neuronal excitability, synaptic transmission, and network activity in the brain and nervous system. Our research explores the computational principles underlying neural function and dysfunction.
Key Projects
- Neural excitability and sensory processing
- Neuromodulation and brain rhythms
- Computational modeling of neural disorders
- Electrophysiological and EEG modeling
Immunodynamics
Using computational tools to understand immune responses, autoimmune diseases, and immunotherapies with applications to disease treatment. We develop models that capture the complex dynamics of immune cell interactions and regulation.
Key Projects
- T-cell dynamics in autoimmune diseases
- Predictive modeling of immune responses
- Host-pathogen interactions
- Systems biology of immunotherapies
Quantitative Biology
Applying systems biology, mathematical modeling, and biophysical simulations to cellular and molecular processes in living systems. We integrate theoretical approaches with experimental data to understand biological functions at multiple scales.
Key Projects
- Cellular signaling and gene regulation
- Mechanobiology and cell motility
- Bioenergetics and metabolic modeling
- Computational biophysics of ion channels
Methods and Tools
Mathematical Modeling
Developing mathematical frameworks using differential equations, stochastic processes, and network theory to describe biological system dynamics.
Computational Simulations
Implementing models in MATLAB, Python, and other languages with high-performance computing for large-scale biological simulations.
Data Analysis & Machine Learning
Applying advanced signal processing, pattern recognition, and predictive modeling techniques to extract meaningful insights from biological datasets.