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Xujing Wang, Ph.D.

Xujing Wang, PhD

Associate Professor
Department of Physics
310 Campbell Hall, 1300 University Blvd
University of Alabama at Birmingham
Birmingham, AL 35294-1170
Voice: 205-934-8186
FAX: 205-934-8042
Office: Campbell Hall 303; Shelby 1203
Website

 

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.