Piccoli Lab

A Mathematical Biology Lab

At Rutgers University–Camden


Our research group uses computational biology and mathematical modeling in order to tackle questions in a host of different fields. Ongoing projects range from the study of metabolic pathways in order to better understand tuberculosis treatment to compartmental SIR model optimization in order to better understand the COVID–19 pandemic, to using control algorithms to further the capabilities of autonomous vehicles.

Modeling The Control Of Covid Via Non-Pharmeceutical Intervention

The motivation for this study was to forecast as early as possible the hospital bed shortfall in New Jersey from the COVID-19 pandemic. By quickly assembling a collaboration between the Center for Computational and Integrative Biology, the Senator Walter Rand Institute, and New Jersey Health Initiatives, we were able to release a briefing on March 16, 2020 which Governor Murphy cited in a letter to the president of the United States, which can be read Here.

Developing a Vaccination Schedule To Minimize Deaths

During the Covid-19 pandemic a key role is played by vaccination to combat the virus. There are many possible policies for prioritizing vaccines, and different criteria for optimization: minimize death, time to herd immunity, functioning of the health system. Using an age-structured population compartmental finite-dimensional optimal control model, our results suggest that the eldest to youngest vaccination policy is optimal to minimize deaths. Our model includes the possible infection of vaccinated populations. We apply our model to real-life data from the US Census for New Jersey and Florida, which have a significantly different population structure. We also provide various estimates of the number of lives saved by optimizing the vaccine schedule and compared to no vaccination.

Modeling Metabolic Systems Via Linear-In-Flux-Expressions

We have designed a framework for modeling systems of biochemical reactions. Our research addresses the foundation of modeling complex reactions (between three or more molecules) and the capability of a drug to inhibit or enhance fluxes in the system. We introduce the concept of metabolic graphs, a generalization of hypergraphs having specialized features common to metabolic networks; these features are visualizations of the framework that corresponds to complex reaction dynamics and drug inhibition or enhancement.