Reinhard Laubenbacher,

Dr. Laubenbacher joined the University of Florida in May 2020 as a professor in the Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine. He is the director of the Laboratory for Systems Medicine. Prior to joining UF, he served as director of the Center for Quantitative Medicine and Professor in the Department of Cell Biology in the University of Connecticut School of Medicine. Concurrently, he held an appointment as Professor of Computational Biology at the Jackson Laboratory for Genomic Medicine. He is a fellow of AAAS, the Society for Mathematical Biology, and the American Mathematical Society. Since 2016, he serves as editor-in-chief of the Bulletin of Mathematical Biology, the flagship journal of the Society for Mathematical Biology. Dr. Laubenbacher is a mathematician by training, and his broad research interests lie in computational and mathematical systems biology, with applications to human health. Most of his research is in collaboration with a broad spectrum of scientists and clinicians.

The Laubenbacher Lab is part of the Laboratory for Systems Medicine. The overarching focus is the development and application of mathematical and computational technology for the improvement of human health. Interests include multi-scale modeling and control of disease processes, systems biology, and computational immunology. We are highly collaborative, maintaining and seeking partnerships with clinical, basic science, computational, and private sector labs and entities. Our expertise includes the development of mechanistic and data-driven multi-scale models of disease-relevant processes, model-driven control and optimization problems, and cutting-edge data science methods applied to high-dimensional data from the molecular to the patient scale. Currently funded projects include the following.

1. Multiscale modeling of the battle over iron in invasive lung infection (NIH 1 R011AI1351128-01). Invasive aspergillosis is among the most common fungal infection in immunocompromised hosts and carries a poor outcome. Current therapeutic approaches have been focused primarily on the pathogen, but a better understanding of the components of host defense in this infection may lead to the development of new treatments against this infection, possibly in combination with antifungal drugs. Iron is essential to all living organisms, and restricting iron availability is a critical mechanism of antimicrobial host defense against many microorganisms. This mechanism has the potential to be harnessed therapeutically, for example with drugs that enhance the host’s iron sequestration mechanisms. The overarching goal of this project is to develop a multi-scale mathematical model that can serve as a simulation tool of the role of iron in invasive aspergillosis.

2. Modular design of multiscale models, with an application to the innate immune response to fungal respiratory pathogens. (1U01EB024501-01, NSF CBET-1750183) Increased availability of biomedical data sets across spatial and temporal scales makes it possible to calibrate complex models that capture integrated processes from the molecular to the whole organism level. This complexity poses multiple challenges related to mathematical modeling, software design, validation, reproducibility, and extensibility. Visualization of model features and dynamics is a key factor in the usability of models by domain experts, such as experimental biologists and clinicians. This project addresses these challenges in the context of the immune response to an important respiratory fungal infection. Its goal is to develop a novel modular approach to model architecture. The overarching computational goal is to develop a novel approach to the modular design of multiscale models.

3. Control of heterogeneous microbial communities using model-based multi-objective optimization (NIH 1R01GM127909-01, NIH 3 R01 GM127909-01S1) The project addresses an important biomedical problem: how to control biofilms formed by Candida albicans, a dimorphic fungus that is an important cause of both topical and systemic fungal infection in humans, in particular immunocompromised patients. C. albicans biofilms also form on the surface of implantable medical devices, and are a major cause of nosocomial infections. In recent years, it has been recognized that interactions with bacterial species integrated into biofilms can affect C. albicans virulence and other properties, It is therefore important to understand the interactions of C. albicans with bacterial species, in particular metabolic interactions. The next step then is to understand and, ultimately, control how varying compositions of the different microbial species affect their metabolic state and their ability to form biofilms. This project approaches the problem through model-based design of optimal compositions of the bacterial species for control of fungal growth, accomplished through a combination of the construction of a novel computational model of a heterogeneous biofilm consisting of bacterial as well as fungal species, and novel mathematical tools for dimension reduction and optimization. The applicability of the results of this project extends far beyond biofilms, such as studies of the human microbiome.

0000-0002-9143-9451

• Disease Modeling
• Mechanism-based mathematical modeling
• Systems biology
• data science