Our research is at the nexus of biology, engineering, and medicine. We combine mathematical modeling and experiments to analyze dynamics of cell signaling processes, including cell cycle regulation, bacterial response to antibiotics, and cell-cell communication.
Synthetic gene circuits that can precisely program cellular behavior have great potential for applications in biotechnology, computation, environmental engineering and medicine. However, constructing synthetic gene circuits with reliable, non-trivial function is extremely difficult. A major challenge is to deal with cellular noise or the stochastic variability in gene expression, which is often due to small numbers of interacting molecules inside the cell. We are exploring general and scalable control strategies that will allow us to realize robust gene circuit function despite cellular noise and external perturbations. We approach this problem by using a combination of experimental and computational techniques.
Past efforts in engineering robust circuit dynamics have focused on the role of feedback regulation. Our work focuses on an alternative yet complementary strategy: cell-cell communication. We are particularly interested in quorum sensing - the cell-cell communication mechanism by which many bacteria sense and respond to changes in their population density. Using a synthetic population control circuit (You et al. Nature 2004;428:868), we recently demonstrated that quorum sensing could be coupled with cell killing to generate integrated, robust population dynamics, despite variability among cells in their phenotype. We are currently investigating whether and to what extent quorum sensing can indeed reduce variability in gene expression, and lead to more robust gene circuit dynamics. Furthermore, we are interested in exploring mechanisms of cell differentiation and developmental pattern formation by engineering gene circuits to program these phenomena in bacteria.
Complementing with experiment, we use mathematical models to analyze dynamics of cellular networks, including the synthetic circuits that we are building and natural cellular networks of medical relevance. Modeling will facilitate the experimental work by guiding experimental design and by identifying design principles employed in natural systems. For cellular networks that are involved in human diseases, modeling may also identify components key to the proper function of these systems. These components may then represent potential targets for drug development. To aid in this effort, we have developed and continue to improve a user-friendly simulation package (Dynetica, You et al. Bioinformatics 2003;19:435).
Lingchong You is an Associate Professor in the Department of Biomedical Engineering at Duke. He was trained in Chemical Engineering (BE and PhD) and Molecular Biology (MS, Postdoctoral). His PhD research was carried out at the University of Wisconsin-Madison (supervisor: John Yin), focusing on mathematical modeling of biological systems and development of modeling tools and methods. He then did postdoctoral research at Caltech (supervisor: Frances H. Arnold), focusing on the design, modeling and experimental implementation & characterization of synthetic gene circuits.