Cheminformatics has been defined as the science of examining the structure and function of chemicals through the use of computational analysis, statistics, and pattern recognition. A number of recent workforce studies have shown that there is a high current and unmet demand for people trained to various levels of expertise in informatics, from technicians and technical librarians to developers of new and improved methodologies and applications. Dr. Zhu’s lab is using the cheminformatics algorithms, workflows and other relevant computational tools to model chemical toxicity, ADME (Absorption, Distribution, Metabolism and Excretion) and other biological activities. The resulting models will be used in the regulatory chemical toxicity assessments and the CADD (Computer-Aided Drug Discovery) process. We welcome all potential collaborators and please view our major research projects.
Biological systems use complex macromolecular nanostructure networks to mediate a range of cellular functions, such as biomolecule synthesis, signal transduction, and gene expression and regulation, all with high efficiency and specificity. Many of these macromolecular systems have evolved through the spontaneous self-assembly of components into highly organized spatial structures. Mimicking these structures outside of the cell requires methods that offer nanoscale control over the organization of individual network components. We aim to apply the self-assembled molecular scaffolds to the organization of biomoleuclar networks with the intention of understanding fundamental mechanisms for biochemistry cascade reactions as well as developing novel regulatory biocircuits. Research themes in our lab include:
Spatially Interactive and Regulatory Biomolecular Network
The Brannigan Research Group is located in the Physics Department and Center for Computational and Integrative Biology (CCIB) at the Camden campus of Rutgers University.
We use high performance computing and Molecular Dynamics Simulation to investigate biophysical interactions of proteins, membranes, and small molecules. Many of our research topics involve using concepts and methods from physics to understand complex signaling mechanisms in the central nervous system.
The group includes members from a range of backgrounds, including biology, pharmacology, physical chemistry, and physics. Many are members of the graduate program in Computational and Integrative Biology at Rutgers-Camden.
Our research is guided by a critical question: how do the supply, storage, and flux of energy and matter constrain the structure and dynamics of ecological systems? We are particularly interested in how the theory of “ecological stoichiometry” (i.e., the balance of multiple chemical substances in ecological processes) and scaling principles (i.e., body size) can be applied to better understand the structure and function of food webs and ecosystems. The diversity of ways in which organisms uptake, store, and transfer energy and matter may have profound effects on the ability of ecological systems to respond to global environmental change.
Some current and future field projects in the lab include the study of: (1) Large-scale patterns of invertebrate stoichiometry; (2) Ecosystem consequences of human-driven nutrient inputs; (3) The role of habitat size on ecosystem function; (4) Energetic and matter budgets in web-building spiders; (5) Structure and dynamics of predator-prey networks; and (6) Modern and ancient food web structure and environmental change. See below if you are interested in reading more about our different projects.
The main interest of our laboratory is to understand mechanisms underlying cell fate determination by cell signaling. We focus on the two highly conserved signaling pathways, the bone morphogenetic protein (BMP) and epidermal growth factor receptor (EGFR), and use fruit fly egg formation, oogenesis, as a model system.
We are interested to understand the evolution of BMP signaling dynamics across Drosophila species, that is regulated by the pattern of the type I BMP receptor spatial expression. In addition, we aim to understand how spatiotemporal changes in EGFR activation are regulated by different distributions of Gurken, a TGF-alpha-like ligand..