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Education
PhD: Theoretical Physics 1995
Dissertation: Gauge Theories of Extended 2-D Conformal Symmetries
Advisor: Chris N. Pope PhD
Postdoctral training: Biophysics/Medical engineering
Mentors: Frederick F. Becker MD, & Peter R.C. Gascoyne, PhD
Research Interest
For more details go to http://igbio.dom.uab.edu/index.php
Our general interest lies in complex systems. This includes the study of the
spatial temporal orders emerging from interactions, the multi-scale properties,
how structure determines the functions, etc.
Methods in theoretical physics including statistical mechanics,
nonlinear dynamics, percolation theory, network theory are utilzied. In collaboration with biologists we apply such
study to the following areas.
1. Network Biology
Presently,
my group focuses on the modeling of transcription regulatory networks from
(time course) gene expression data. The interest include the identification of network
module/motifs; study of network structure versus function, and the dynamics
under pathological/physiological changes; identify network-phenotype association
utilzing network topological measures; application to mechanistic investigation of disease pathogenesis, and
development of disease markers (expression signatures).
Most of our developments, though initially made
for transcription networks, can be extended to other types of networks
including the protein-protein interaction and metabolic networks. Further,
network modeling is not necessarily limited to one type of data, integration
of data from different biological scales will allow a systems study of
network biology. We are specifically interested in the integration of
phenotypic and genetic/genomic data, to discriminate the causal genetic
variation, primary phenotypic changes, from secondary genetic (expression) or
phenotypic changes.
Interest in networks has grown rapidly in the past 10 years, with much of the
fundamental research in the area being conducted by physicists (see an
article in the Nov issue of physics today: Newman, M., The physics of
networks. Phys Today, 2008. 61: p. 33-38.).
2. Integrative Genomics of Complex Disease
Complex human diseases typically result from the interplay of multiple,
interacting genetic factors. Therefore understanding the disease biology is
much needed to dissect the genetics risk. My group has been developing a
multi-level, integrative genomics approach, and applying it to diabetes. It
first investigates and identifies key quantitative traits and disease
pathways that are important to the disease initiation, through the dynamic
modeling of disease pathogenesis. It then studies the network structure of
genes involved in these pathways. Based on the results, it compiles a
comprehensive list of candidate genes, and uses a Bayesian classifier to
prioritize the candidate genes. When applied to T1D, it led to the
identification of many known disease genes, as well as prediction of new
candidates. With collaborators we have typed the new predictions in
a cohort that we have obtained from Finland, and a replicate cohort from the
Type 1 Diabetes Genetics Consortium (T1DGC). This project is currently funded
by NIDDK/NIH through Oct of 2011 (R01 DK080100-01).
Presently we incorporate the recent GWAS (Genome Wide
Association Study) data in the identification of disease pathway and
candidate gene prioritization. In the mean time, our approach can be directly
applied to GWAS analysis. At the moment, only markers with extremely low
p-value (usually <~10-7) are retained. Lowering the threshold
will be plagued with false positives, though it is believed that a region
immediate below the threshold p value likely also harbors many true disease
genes. We plan to develop analysis algorithms to discriminate between true
disease genes from false positives in this region, and to identify the
etiological variants among markers in LD. Our integrative genomics approach,
by design, is applicable to the other diseases. With collaborators we plan to
look into type 2 diabetes, cardiovascular diseases, and asthma.
3. Systems Biology of Glycemic Control
Glucose homeostasis is a fundamental physiological process that provides
energy to all cells in the body. To maintain the blood glucose concentration
within the narrow physiological ranges it takes multiple hormones and several
tissue organs to operate synchronously at multiple levels. we are developing
a multi-scale (include intracellular, interceullar, islet/pancreas,
and blood circulation), systems approach that incorporates both spatial
(tissue structural organization, etc) and temporal (insulin dynamic rhythms,
etc) considerations, to investigate insulin secretion regulation, its role in
glycemic control, and changes responding to pathological modifications such
as diabetes. One particular question we are interested is the nonlinear relationship
between β-cell function and
β-cell mass; and to develop predictive models of
β-cell mass from functional (insulin secretion) measurements. This will better evaluation of glucose tolerance
and early detection of the β-cell destruction during diabetes.
Open Positions
We currently have two research (RA) positions for
graduate students that are interested in network theory and modeling,
nonlinear dynamics of complex systems, systems biology, or bioinformatics.
We also have a position open for a postdoctoral fellow interested in complex
systems mathematical/systems biology. More
information
Teaching
PH432/532 Statistical Mechanics and Thermodynamics
Syllabus
PH610/710, Advanced Classifcal Mechanics
Syllabus
PH797 introduction to systems biology
Syllabus
PH797 mathematical modeling of glucose tolerance
Syllabus
PH201 College Physics
Syllabus
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