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Myong-Hee Sung, Ph.D.

Portait Photo of Myong-Hee Sung
Laboratory of Receptor Biology and Gene Expression
Hormone Action and Oncogenesis Section
Staff Scientist
Center for Cancer Research
National Cancer Institute
Building 41, Room B915
Bethesda, MD 20892
Phone:  
301-402-0364
Fax:  
301-496-4951
E-Mail:  
SUNGM@MAIL.NIH.GOV

Biography

Mia Sung crossed over to biology in 2000 after Ph.D. training in geometry at SUNY Stony Brook and a postdoctoral fellowship in dynamical systems at the University of Maryland. Dr. Sung then developed an interest in cell signaling dynamics using NF-kappaB as a model system. In recent years, her expanded research topics include computational genomics and applications to chromatin biology and 3D genome organization.

Research

Systems Cell Biology: A Systems Biology Approach using Mathematical Modeling and Molecular Imaging Methods to Probe Intracellular Pathway Dynamics in Single Living Cells

Significance

Conventional methods of biological investigation have relied heavily upon biochemical assays that measure molecular material from a large population of cells. Some pioneering interdisciplinary studies have revealed the possibility of missing important dynamic patterns that exist in single cells. Today it has become possible to follow single cell activities over prolonged time intervals to detect these activities thanks to various technological advances in live cell imaging. However, imaging expertise alone is not sufficient to discern specific dynamic patterns expected from a given molecular network. By quantitatively analyzing the imaging data based on a mathematical model that captures the essence of the molecular pathway at hand, one can design rational experiments to study how the cell senses and sorts out the diversity of information that are encoded in the temporal patterns of signal transduction. Given the dynamic nature of the signaling at the single cell level, an important open question is, how the cell has evolved to respond to that diversity of input in making important downstream decisions relating to apoptosis, proliferation, and lineage differentiation. Pathway dynamics also has implications in molecularly targeted drug effects in that, in the presence of multiple feedbacks, the activity of the molecular network perturbed by a drug can exhibit unexpected behavior. Target selection in a therapeutic strategy must take this natural pathway dynamics into account for optimization of the intervention.

Background

The NF-κB pathway is an example of a molecular system that has been extensively studied and characterized, with a huge volume of literature. Yet, it is still not clear how the cell utilizes this same pathway in responding to widely different upstream signals and how the distinct signals are distinguished to produce appropriate functional outcome. Recently it was shown that in single cells this transcription factor is activated in cyclic 'waves' with a distinct period. The functional significance of these discrete activity peaks in target gene expression is yet to be established. Mathematical modeling predicts that a molecular network with a major negative feedback such as the NF-κB system will respond in a non-intuitive way to perturbations by specific molecularly targeted drugs (Sung & Simon, 2004). It has not been explored how the natural pathway dynamics is altered by such interventions and what the downstream consequences are for gene and phenotypic expression.

Goals

The general research interest is on quantitative analysis of molecular pathways that are implicated in cancer progression and immune system actions. Eventually, understanding of these pathways at the single cell level will be integrated into tissue-level modeling of tumor growth, invasion, and drug resistance. By relating the different levels of cancer-related mechanisms, we hope to establish a link between basic studies and clinical studies through systems biology. It is envisioned to result in the optimization of therapeutic strategies that aid the selection of appropriate molecular targets and minimize toxicity and maximizes efficacy.

This page was last updated on 7/14/2014.