Welcome to Pressé Lab!

Led by Dr. Steve Pressé, we are an interdisciplinary lab based at Arizona State University with dual appointments in both the Physics and Chemistry departments.

Our Mission

Our mission is to leverage the principles of physics, chemistry, and cutting-edge computational tools to understand the complex dynamics of biological systems. From exploring the life journey of a single protein to ecological-scale predator-prey interactions, our work strives to bridge the gap between theory and practice in biophysics.

Connect with Us

Interested in our work? We invite you to explore our latest projects and publications. For inquiries or potential collaborations, please feel free to get in touch with us through our contact page.

Research Focus

Our research revolves around two main axes. First, we employ methods derived from statistics, statistical physics, and stochastic processes to develop and adapt models that elucidate living systems across multiple time and length scales. Secondly, on the experimental front, we explore fascinating phenomena such as the role of hydrodynamics on the interaction of bacterial predators with their prey.

In particular, our theoretical work focuses on utilizing artificial intelligence solutions and Bayesian methods to derive insights from a variety of biophysical experiments. These range from superresolution imaging and single molecule tracking, to gene network analysis, and much more.

For more information check out our blog and publications.

Our Latest Projects

Concretely, we work in three focus areas: 1) we experimentally investigate bacterial population kinetics and predato-prey interactions; 2) we develop and adapt tools from statistics, particularly Bayesian nonparametrics and simulation-based inference, to infer models of dynamics from spectroscopy and imaging data (from the level of single molecules to cells); 3) we construct new models for the physics of diffusion and hydrodynamics.

1. Quantitative Microbial Ecology & Modeling

In our group, we ask: What fundamental principles govern how bacterial populations grow, interact, and persist—both inside living hosts and in controlled environments? We are especially interested in how the physics of population kinetics can explain the dramatic changes and patterns we observe in microbial communities.

To address these questions, we combine mechanistic mathematical modeling with experiments in two main systems: (1) tracking the rise and fall of bacterial populations inside the gut of C. elegans to understand why some hosts harbor thousands of bacteria while others clear their gut; and (2) studying predator-prey interactions in chemostats, where we model and quantify the feedbacks that drive coexistence or collapse. By applying rigorous Bayesian inference, we move beyond empirical fitting to distinguish real biological parameters from experimental noise.

These questions matter because they reveal how microbial communities establish, maintain, and disrupt themselves—insights with implications for health, disease, and ecosystem stability. Our collaborations in advanced imaging and expanding work in biochemical analysis help us build a more complete picture of these dynamic microbial worlds.

2. Data worth a thousand words

Current biophysical imaging and spectroscopy methods can probe time (< 10-6 s), length (10-9 m) and force (10-12 N) scales relevant to the life cycle of a cell. These methods have revealed that all steps involved in molecular biology’s central dogma (transcription, translation and DNA replication) are intrinsically stochastic. Despite the wealth of experimental data, the ability to gain meaningful insight from experiments on such small scales is severely limited by fundamental challenges common to all biological systems: current methods cannot capture complex processes in their full multi-dimensional detail. At best, current experiments provide a small slit through the curtains of the intricate cellular theatre on display by probing complex processes along just one or a few observable coordinates. Building models from such limited data is a central challenge in biophysics. Current modeling methods — which we call forward methods — are ill-equipped to tackle this challenge. Forward methods begin by positing models (such as reaction networks, coupled reaction-diffusion or multi-state models) and subsequently cross-check the model conclusions with experimental data. There are several disadvantages to this forward approach. First, forward models are rarely expressed using measurable quantities. Thus, model features and parameters must be fit to data. Second, the data’s rich structure does not inform the model because the model’s form is presupposed from the onset. Thus, in principle, other models could have worked equally well. To address these questions, we develop inverse modeling methods to directly infer stochastic models of complex biological systems from imaging and spectroscopy data with as few adjustable parameters as possible. Most recently our work has focused on developing the tools of Bayesian nonparametrics, an exciting rapidly evolving area of statistics, to tackle problems in biophysics. More in this soon! Through our analysis, we hope to learn about the following key issues that lie beyond the predictive ability of current models: How variable is protein structure and function inside living cells? What is the mechanism behind intracellular anomalous diffusion of proteins? How variable are protein assembly sizes? How do cells integrate noisy signal? Here is a concrete example of such a project:

2.1 Tackling the ‘single molecule counting problem’

Protein-protein interactions are the basis for most biological information processing and cellular control. A quantitative characterization of these interactions is an essential prerequisite for developing a mechanistic understanding of cell biology and the disease states associated with defective protein complexes. Nonetheless, characterizing protein complexes as they occur in their native cellular environment is a major challenge since complexes can involve up to many tens of proteins within approximately a 10nm range. Thus, there is currently no routine way to determine how many proteins of type X, say, are in a given complex in living cells. We have thus been focused on developing methods that can determine the stoichiometry of protein complexes in living cells using available data from PALM (a superresolution microscopy method) as well as photobleaching data. The methodology we propose here is closely inspired by our group’s work in inverse methods that we are simultaneously applying to other forms of spectroscopy (including fluorescence correlation and single particle tracking). For more details, see publications tab.

3. Detangling Single-Molecule Biophysics through Bayesian Methods

Our research focuses on developing statistical and computational methods to better understand how individual molecules behave inside cells. A key challenge in this area is that the experimental data are often extremely noisy, making it hard to extract reliable information about how molecules move and change shape. To address this, we use Bayesian approaches—tools that explicitly account for uncertainty—to analyze single-molecule fluorescence experiments (such as FRET). This allows us to more accurately track the conformational dynamics of important systems, including how protective protein complexes bind to the ends of chromosomes (telomeres) to safeguard the genome.

We also apply similar statistical ideas to imaging. In particular, we have created Bayesian-SIM, a method that improves the resolution of structured illumination microscopy by carefully accounting for noise, bringing image quality close to the physical resolution limits of the technique without needing large training datasets.

Taken together, this work connects advanced statistical inference with single-molecule spectroscopy and cutting-edge microscopy, providing new ways to reveal the physical principles that underlie how biomolecules function.