In this project we seek 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.