Daniel Baugh Institute
for Functional Genomics/Computational Biology
The Daniel Baugh Institute (DBI) for Functional Genomics/ Computational Biology has been established in the Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, to provide an interdisciplinary base for research and education in these rapidly evolving fields. Research interests of the group center on the development and use of quantitative system-wide "omic" datasets towards integrative modeling and computational analysis of the dynamics of biological systems.
Our focus is the development of mammalian systems biology to study the multi-scale regulatory networks in the context of adaptation in central autonomic control circuits, dysfunction of cardio-respiratory regulation, alcoholic liver disease and liver repair, and stem cell differentiation. In order to study intra- and inter-cellular networks, we employ genomic and other "omic" technologies to acquire datasets suitable for analyses to identify variables and relationships that provide a basis for modeling and simulation of multi-scale system dynamics. We develop bioinformatics tools to improve our ability to derive networks, pathways and relationships subserving cellular processes. We bring principles of control and systems theory as well as probabilistic/statistical techniques to bear on the analysis of biological processes.
We seek funding as an Institute for interdisciplinary team oriented projects. Ongoing projects are funded by National Institute of General Medical Sciences, National Heart Lung and Blood Institute, and National Institute on Alcohol Abuse and Alcoholism.
Daniel Baugh Institute
- Amygdalar neuronal plasticity and the interactions of alcohol, sex, and stress
- Pharmacological ceramide reduction alleviates alcohol-induced steatosis and hepatomegaly in adiponectin knockout mice
- Decorin induces mitophagy in breast carcinoma cells via peroxisome proliferator-activated receptor γ coactivator-1α (PGC-1α) and mitostatin
- Inputs drive cell phenotype variability
- Molecular modeling of ErbB4/HER4 kinase in the context of the HER4 signaling network helps rationalize the effects of clinically identified HER4 somatic mutations on the cell phenotype