Plasmodium knowlesi, a model malaria parasite, is responsible for a significant portion of zoonotic malaria cases in Southeast Asia and must be controlled to avoid disease severity and fatalities.

Optimization and control are important objectives across biology and biomedicine, and mathematical models are a key enabling technology. This paper reports a computational study of model-based multi-objective optimization in the setting of microbial ecology, using agent-based models. This modeling framework is well-suited to the field, but is not…

Aside from conducting research itself, communicating it to the broader mathematical sciences community—as well as the general public—is the most important task that a researcher must accomplish, for the sake of progress within both the mathematical sciences and one’s own career…

Helen Moore was Associate Director of Quantitative Clinical Pharmacology, Bristol-Myers Squibb in Princeton, New Jersey and recently joined UF as an Associate Professor in the Laboratory for Systems Medicine.

Modeling the Pancreatic Cancer Microenvironment in Search of Control Targets

Practical Recommendations for Supporting a Systems Biology Cyberinfrastructure

This project seeks to develop a novel approach to the modular design of multi-scale models using virtual machines and a user friendly open-source platform. 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.

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.

Multiscale modeling of disease requires the integration of data at various spatiotemporal scales. Extending these models is often challenged by complex computational implementations and interdependencies between components of the model. In this project, we are developing a computational blueprint so that computational models can be developed in a modular fashion, while minimizing interdependencies between model components.

Network analysis has gained popularity in systems medicine, though most applications have been “out of the box”. Cutting-edge network science draws from the rich history of graph theory and recent developments in statistical modeling and is aided by the speed, storage, and interoperability of computational resources. These distinctive research projects showcase the broader potential of network methods.