Research in the Biosystems groupcan be broadly characterized as a blend of Bioengineering, Computational Biology (including Bioinformatics and Computational Therapeutics), Systems Biology, and Therapeutic Engineering. It is uniquely interdisciplinary. Our goal is to use and create computational and informatic approaches to discover useful new biological knowledge. We seek novel approaches that will accelerate drug development and enable therapeutic advances, optimization, and individualization.
No two members of the group have the same interests, expertise or research focus.Nevertheless, a goal for each of us is to generate new knowledge that can be translated into either real, tangible improvements in healthcare or into the drug discovery and development.
Our attention is currently drawn to aspects of the following interrelated problems and issues.
A goal of computational biology is to develop and apply modeling and computational simulation techniques to study biological organisms and their various organs and systems. Because biological organisms are uniquely adaptable, no one model will ever be able to represent more than a tiny subset of a biological systems full range of behaviors.To more realistically simulate these systems we need a new class of models. A new class of event-driven models currently under development in this lab meet this need.
Questions that drive the Hunt Lab
Fueled by technological innovation, the biomedical sciences are poised to benefit from a tidal wave of data.Massive datasets and warehouses of such data are increasingly available.How does one organize, visualize, and utilize massive heterogeneous datasets to make better scientific, engineering, and medical decisions?
At all levels of organization, from gene expression to groups of genetically diverse individuals, all biosystems exhibit variability.Why and how is it essential? How do biological pathways, networks and systems interact to account for and manage biological variability? What is the role of biological variability in health and in disease? How should we represent such variability within the above new class of models?
We all know that a drug treatment will not produce the same effect in all people.We believe that optimally individualized treatments are essential for improved, more cost-effective health care.How can basic knowledge of biological pathways, networks, and systems be harnessed within the above new class of models?How can we use such models to guide individual optimization of currently available and emerging treatments?
Currently, the drug development and discovery process seeks new drugs that will work reasonably well in most people. Is there a better, more cost effective approach, one thatfrom the startpreserves and amplifies legacy expertise while drawing upon available knowledge to develop new drugs that are optimized for, and targeted to, definable subsets of the population?
The premise of the agent paradigm, its related theory and methodologies together with advances in multilevel modeling of complex systems of interactions opened new frontiers for advancing the physical, natural, social, military, and information sciences and engineering...