The Big Picture
Cells are highly sophisticated information-processing systems that operate in ever-changing environments. They sense environmental signals using receptor molecules, typically located on the cell membrane, process these signals through chemical reaction networks, and use the resulting information to modulate their behavior, thereby enhancing their survival in dynamic surroundings. Our research goal is to understand the principles underlying such adaptive cellular behaviors, using bacteria as model systems and combining various quantitative approaches.
A means to control and predict cellular behavior is currently lacking, which underlies many challenges facing our society, including the prevention of biofilm formation and the treatment of infectious diseases and cancer. In engineering contexts, it also remains difficult to develop autonomous and adaptive systems capable of operating robustly in real-world environments. The relative simplicity of bacterial behaviors provides unique opportunities to extract the design principles of adaptive systems in a quantitative manner—principles that, we believe, can offer key insights into solutions for these broader challenges.
Our Questions
The cellular “computers” that sense and process environmental information comprise intricate webs of chemical reactions. However, unlike human-engineered digital computers, cellular information processing is inherently probabilistic, making it difficult to characterize.
The central questions we are currently pursuing include:
– What kinds of information processing do cells perform?
– How “good” are cellular computers—or more precisely, how can we quantify their proficiency?
– How do functional behavioral dynamics emerge from noisy and seemingly unreliable chemical reactions?
– Why (and how) has a specific reaction network evolved to perform a particular biological task?
Approaches
We address these fundamental questions by combining experimental and theoretical approaches. In addition to standard microbiology and cell biology techniques, our lab focuses on developing and integrating the following methods.
Fluorescence microscopy: To understand how cellular “computers” operate, it is essential to observe the dynamics of chemical reactions inside cells. We use fluorescence microscopy, particularly a single-cell Fluorescence Resonance Energy Transfer (FRET) system, for this purpose. FRET is a quantum-mechanical phenomenon that allows us to convert invisible molecular interactions within living cells into detectable fluorescence signals. We perform measurements at the single-cell level because the performance of cellular information processing critically depends on cell-to-cell and temporal variations, which are lost in population-averaged data.
Tracking single-cell motility: To understand microbial behavior, direct visualization is crucial. However, this is not as simple as it sounds, because bacterial cells are tiny yet move very fast. We are developing a custom long-term 3D tracking system that enables us to follow individual bacterial cells in bulk fluid and characterize their behaviors with unprecedented precision.
Microfluidics: The key to successful quantitative live-cell experiments is precise environmental control, as cells respond differently under varying conditions. Microfluidics allows for the manipulation of minute liquid volumes with high precision. We design and fabricate microfluidic devices tailored for our single-cell FRET measurements, enabling us to control the temporal dynamics of chemical signals delivered to cells.
Data modeling: We use a range of theoretical frameworks to analyze and interpret our data. Bayesian statistics and machine-learning techniques are employed to process raw microscopy data, which are then further analyzed using dynamical-systems theory and information theory. The insights gained from these analyses are fed back into experimental design, creating an iterative cycle between theory and experiment akin to approaches in physics.
Training
Our research approach is usually highly interdisciplinary, but nobody can be an expert in all those relevant fields simultaneously, especially when they are new to the field. Don’t worry! We develop tailored training programs in the lab, and all of our lab members with diverse academic backgrounds help each other to acquire new skills.