The department of Biomedical Engineering at the University of Virginia has taken a number of steps to reflect the critical importance of biomedical data sciences and systems biology to the future of health care, medicine and biomedical research. Here is a round-up of recent news related to the department's vision and leadership for biomedical data sciences and systems biology at UVA and beyond.
Teaching and mentoring students in the areas of computational modeling of complex biological systems, molecular and cell biology assays, and medical device design.
Using multidisciplinary techniques to study the biological processes that go awry in brain development in neurodevelopmental disorders, like autism spectrum disorders and intellectual disability.
Leading a range of initiatives to encourage and facilitate the use of big data in large-scale research across the scientific and technological disciplines. Special emphasis on structural bioinformatics and systems pharmacology.
Using big data analytics to understand the molecular pathways of disease. Developing personalized medicine approaches to cardiovascular and metabolic disorders.
Developing new systems biology approaches to understand fundamental mechanisms of immune regulation and dysregulation, with an ultimate objective of identifying strategies to design and improve immunotherapies.
At the interface of systems biology, quantitative pharmacology and cancer biology, we link genetic, epigenetic and signaling mechanisms through which cells (in healthy or cancer states) respond to perturbations such as cytokines, stress and cancer drugs.
Designing and using new experimental and computational approaches for analyzing cell signaling and transcriptional networks in cancer.
Experimental and computational methods to study fundamental aspects of cell signaling regulation and applied aspects of cell signaling, including the efficacy of therapeutics that target particular signaling pathways in cancer.
Using data- and computational-driven approaches to predict, and experimental approaches to test, the regulation and function of tyrosine phosphorylation in complex networks.
Developing computational models of cellular networks and performing high-throughput experiments to characterize biological systems relevant to human disease, with a focus on infectious disease and cancer.
Combining experimental and computational models to design cell-based therapies for regenerating microvascular networks in diabetes, wound healing, and cardiovascular disease.
Developing computational predictive models using machine learning and signal and image processing with applications in pathology, radiology, systems biology and mobile sensing.
Combining computational models and high-throughput experiments to discover molecular networks that control cardiac remodeling and regeneration.
Developing data mining and machine learning approaches to modeling and analysis of structured and unstructured data with a variety of applications, especially biomedical applications.
Analysis of stem cell fate using single cell mass cytometry and high-dimensional modeling of cell lineage trajectories.
