The Laboratory of Biological Networks at Duke BME
PI: Lingchong You (CV)
Assistant Professor of Biomedical Engineering
Contact:
CIEMAS 2345
Phone: 919 660 8408 (Office), 919 684 3566 (Lab)
FAX: 919 668 0795
Email:
you (at) duke.edu
Lab Members
o Jun Ozaki (Postdoc)
o
Tae Jun Lee (graduate)
o
Peter Blais (undergraduate)
o
Jiwon Lee (undergraduate)
We are looking for motivated graduate and
undergraduate students and postdocs to participate in our exciting and
challenging research. Contact Dr. You for more information.
Teaching
o BME265-05 (Spring 2005). Modeling Gene Expression and
Cell Signaling
Research (Analysis, design and synthesis of biological networks)
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).
Selected publications (here is the
complete list)
o
L. You, R. S. Cox III, R. Weiss,
and F. H. Arnold. (2004). Programmed
population control by cell-cell communication and regulated killing. Nature. Published online April 4th
2004. Featured in ScienceNow
(405:3).
o
L. You, (2004) Towards computational systems biology. Cell
Biochemistry and Biophysics. 40: 167-184
o
L. You, A. Hoonlor, and J. Yin. (2003) Modeling biological systems using Dynetica –
a simulator of dynamic networks. Bioinformatics. 19: 435-436.
o
L. You and J. Yin (2002). Dependence
of epistasis on environment and mutation severity as revealed by in silico mutagenesis
of phage T7. Genetics. 160:
1273-1281
o L. You, P. F. Suthers, and J. Yin (2002). Effects of E. coli physiology on growth of phage
T7 in vivo and in silico. J. Bacteriology. 184: 1888-1894. (Highlighted in Editors’
Choice of Science (2002), 296:
219)
Software
o Dynetica: a simulator of dynamic
networks (written in Java; needs JDK1.3 or above from sun.com). Click here to see more...
o T7v2.5:
a simulation
package for the intracellular growth cycle of phage T7 (written in C++; needs
gcc2.95 or above). You can download the source code of this program by clicking
here
(217kb). Although this is label version 2.5, it is the latest version of T7
simulation. You can go to http://virus.molsci.org/t7/sim/index.html
to access via the web a previous version of the program.
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