Research
Our lab combines neuroscience, biophysics, and systems biology to understand how biological systems process information and maintain robust function. We are particularly interested in the intersection of structural connectivity and dynamic function.
Dynamics of Chemosensory Receptors
What is the principle of chemical sensing?
We investigate how sensory information is encoded at the molecular interface. Our research focuses on the dynamics of ligand-receptor binding to understand how olfactory neurons encode fluctuating odor signals. We analyze how the binding kinetics of olfactory receptors constrain the system's ability to encode context-dependent information. By modeling these initial interactions, we aim to determine the fundamental limits of sensory encoding and how these constraints shape downstream neural processing.
Olfactory Perception & Decision-Making 🪰👃
What forms our perception of a smell?
Navigation is an active process shaped by odor value and context. We investigate the algorithms that allow Drosophila to make informed decisions in complex environments.By integrating quantitative behavioral experiments with Artificial Intelligence, we model how the brain integrates various navigational cues to guide movement. We are particularly interested in how the brain updates the "value" of an odor based on changing environmental contexts to drive decision-making.
Information Processing in Biological Systems
What makes biological networks robust?
We use the olfactory system as a model to uncover universal principles of biological computation. Our goal is to understand how the structure of a network determines its function across different biological systems—from the large-scale connectomics of neural circuits to biochemical signaling pathways. We investigate the relationship between connectivity and neural dynamics, seeking to identify the topological motifs that ensure robust information processing and function.
Biological Algorithm Development
Software as a scientific output.
Biological processes are complex and demand advanced software tools. We develop novel computational methods to interpret high-dimensional biological data, focusing on network inference, optimization, and clustering. Our research addresses the challenge of recovering underlying system structures from sparse experimental data. Additionally, we focus on enhancing model reuse and reproducibility in biological modeling through the development of industry-standard simulation tools.
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