We work at the interface between biophysical chemistry, the mathematical and engineering sciences, and pathophysiology. Our research departs from the premise that there is a continuum between health and disease. If we are capable of measuring this continuum, we will be in the position of detecting disease earlier and understanding it better to intervene more precisely.

Below you will find some of our current projects.

Principles of Enzyme Kinetics

We are deriving rate equations and developing standard based approaches to measure enzyme kinetic parameters.

Variance heatmap
Computed parameter variance for noisy progress curve experiments of the Michaelis-Menten reaction. Source: Wylie Stroberg and Santiago Schnell (2016) Biophysical Chemistry 219, 12-27.

We combine chemical kinetics, mathematical, computational and statistical methods to develop standard-based approaches to measure the rates of enzyme catalyzed reactions and distinguish their molecular mechanisms under diverse experimental conditions.

Most of our research focuses on deriving mathematical approximations of the governing rate law equations for progress curves of enzyme catalyzed reactions. This requires thed evelopment or implementation of sophisciated mathematical approaches for scaling and perturnation analysis. After deriving those approximation, we investigate the experimental conditions they can be effectively used to model the progress curves of enzyme catalyzed reactions and estimate their kinetic parameters.

In addition, we are also interested in developing and implementing algorithms for the accurate estimation of enzyme kinetic parameters from progress curve experiments.

Standards for Reporting Enzymology Data

We are developing standards for data reporting enzyme functional data with the aim to improve the quality of data published in the scientific literature.

Logo for the "Standards for Reporting Enzymology Data" Project

We are developing Standards for REporting ENzymology DAta (STRENDA) in collaboration with an international committee sponsored by the Beilstein-Institut.

The STRENDA Guidelines are developed through extensive interactions with the biochemistry community to define the minimum information that is needed to correctly describe assay conditions (List Level 1A) and enzyme activity data (List Level 1B). STRENDA aims to ensure that data sets are complete and validated, allowing scientists to review, reuse and verify them. The emphasis is on providing useful and reliable information.

STRENDA Dabatabase

Standards for REporting ENzymology DAta DataBase (STRENDA DB) is a validation and storage system for enzyme function data that incorporates the STRENDA Guidelines. It provides authors who are preparing a manuscript with a user-friendly, web-based service that checks automatically enzymology data sets entered in the submission form that they are complete and valid before they are submitted as part of a publication to a journal.



The EnzymeML language is an open standard based on the XML markup language.

EnzymeML code
Screenshot of EnzymeML XML markup

EnzymeML is a free and open standard based XML markup interchange format for a standardized monitoring and exchange of data. The purpose of EnzymeML is to store and exchange enzyme kinetic data between databases, instruments and software tools. EnzymeML will allow scientists to share their experimental protocols and results even if they are using different instruments, electronic laboratory notebooks, or databases.

EnzymeML follows the FAIR Principles. It is compatible with the Systems Biology Markup Language. It continues to be evolved and expanded by an international community and is supported by the STRENDA Commission.

The development is jointly led by Juergen Pleiss, Neil Swainston and Santiago Schnell.

EnzymeML GitHub

The Role of Crowding in Cell Physiology and Biochemistry

The study of macromolecular crowding is indispensable for understanding cellular physiology and biochemistry. We are investigating how crowding affects quantitatively the cellular biochemistry, and qualitatively cellular physiology.

Illustration of cross-section of a small portion of an Escherichia coli cell
Illustration of cross-section of a small portion of an Escherichia coli cell. Source: David S. Goodsell, The Scripps Research Institute

The intracellular environment is crowded. Macromolecules can occupy 30% of the cellular volume with their concentrations reaching 300 grams per Litre. In confined or crowded environments, the rates of biochemical reactions can differ markedly from their dilute, well-mixed counterparts. Crowding reduces the diffusion coefficient of reactants, slowing diffusion-limited reactions and giving rise to fractal-like reaction kinetics. Cellular crowded environments differ from concentrated environments in that crowded environments may have high molecular diversity. This means that although the volume fraction of solute molecules is high, the concentration of a specific species can be quite low.

It has been suggested that macromolecular crowding plays a role in cellular compartmentalization and phase separation. We are just beginning to understand the biophysical chemistry and kinetics of reactions inside cellular crowded environments. In our research group, we are interested in answering a number of interesting questions in this area. How does macromolecular crowding affects cellular physiology? Do we need to revise current theories for origin of cells and life taking into account the macromolecular organization of cells?

Molecular Control Mechanisms of the Unfolded Protein Response

We are deciphering the control mechanism of the unfolded protein response and protein homeostasis to open new therapeutic avenues for age-associated and protein folding diseases.

A schematic model of the unfolded protein response
A schematic model of the unfolded protein response. Source: Wylie Stroberg et al. (2018) Molecular Biology of the Cell 29, 2969-3062.

Roughly one third of all proteins produced in humans are folded in the endoplasmic reticulum (ER). Cellular protein homeostasis requires continuous monitoring of stress in the ER. Stress-detection networks control protein homeostasis by mitigating the deleterious effects of protein accumulation, such as aggregation and misfolding, with precise modulation of chaperone production. The unfolded protein response (UPR) is a multifaceted cellular response to excess unfolded or misfolded proteins within the ER. UPR activation is triggered by heightened protein concentration within the ER lumen which leads to accelerated protein folding and degradation within the ER along with decreased protein synthesis. If efforts to regain protein homeostasis are unsuccessful, the cell begins the process of cell death (apoptosis). Malfunction of the UPR has been implicated in numerous protein misfolding diseases. During aging, the function of the UPR is compromised causing a decline in the ability of the cell to handling protein folding and aggregation, and age-associated diseases.

Our long term goal is deciphering the control mechanism of the UPR to open new therapeutic avenues for the treatment of a range of age-associated and protein folding diseases. We are pursuing this goal in collaboration with Yonatan Savir at the Ruth and Bruce Rappaport Faculty of Medicine in the Technion Israel Institute of Technology, and Hesso Farhan at the Medical University of Innsbruck.

Mathematical Modeling of Biomedical Systems

We investigate complex biomedical systems comprising many interacting components, where modeling and theory may aid in the identification of the key mechanisms underlying the behavior of the system as a whole.

Schematic of possible regulation of the reproductive neuroendocrine system in adults
Schematic of possible regulation of the reproductive neuroendocrine system in adults. Source: SM Moenter (2018) Endocrinology 159, 199-205.

The Schnell lab works on collaborative projects investigating complex biomedical systems comprising many interacting components, where modeling and theory may aid in the identification of the key mechanisms underlying the behavior of the system as a whole. In these projects, we combine biophysical principles with mathematical, computational and statistical methods to develop models that provide novel insights about the mechanisms of biological processes, and refine current views of these processes.

Most of our projects involved collaborations with both theoretical and experimental researchers working at the University of Notre Dame, the USA and around the world. We are working on an extensive list of topics, such as the physiological feedback effects of estradiol on the underlying episodic gonadotropin-releasing hormone (GnRH) secretion, discovering the signalling crosstalk controlling the development and parterning of the small intestine, investigating the developmental mechanism controlling germ cell fate determination during mouse oogenesis, studying the molecular mechanism of RNA interference, identifying the developmental signals triggering neuroblastoma disease progression, and others.

You can learn more about current research through our Publications.

Scientometrics and Modeling of Research Universities

We are developing new metrics and models to measure and improve performance of scientists, research universities, and academic policies.

University of Notre Dame campus
University of Notre Dame campus at sunrise with the Golden Dome atop the Main Building and the Basilica of the Sacred Heart (Photo by Barbara Johnston/University of Notre Dame).

The modern research university is a complex enterprise. To make better policy and administrative decisions, we need to study scientometrics: the quantitative features and characteristics of scientific research. We also require to better understand university economics and consequences of any disruptions that occur in the research and business enterprise. By using large-data scientometrics and economic modeling, we are building models to enhance our understanding of how research universities work. Our goal is to investigate how to use limited resources available to research universities and their scientists to maximize their research output and economical impact in society.

We are interested in investigating the ongoing cost-benefits decisions made by academic institutions as they compete for resources and reputation. Also we are investigating how scientists respond to incentives and cost in research money, scientific quality and reputation.