Computational systems biology  
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Research

 

Causal Pathways from Genotype to Phenotype

Elucidating causal pathways bridging genotype and phenotype is one of the fundamental challenges in genetic and genomic research. Gene variants that control phenotypes are typically discovered by combined linkage analysis and molecular validation. However, these genotype-phenotype associations do not expose the underlying causal pathways through which gene variants operate on phenotypes. We are exploring genomic and computational approaches to uncover the causal pathways through which genetic loci influence phenotypes and predict the effects of genetic and transcriptional variations/interventions on gene expression and physiological/behavioral phenotypes of individuals with different genetic backgrounds. We believe research towards this direction will ultimately lead to the development of the computational methods that enable personalized predictive genetics and medicine.

Ziebarth JD, Bhattacharya A, Cui Y (2013) Bayesian Network Webserver: a comprehensive tool for biological network modeling. Bioinformatics. 29(21): 2801-2803.

Miyairi I, Ziebarth J, Laxton JD, Wang X, van Rooijen N, Williams RW, Lu L, Byrne G and Cui Y. (2012) Host Genetics and Chlamydia Disease: Prediction and Validation of Disease Severity Mechanisms. PLoS One. 7(3): e33781.

Ziebarth JD, Cook MN, Wang X, Williams RW, Lu L and Cui Y (2012) Treatment- and population-dependent activity patterns of behavioral and expression QTLs. PLoS One. 7(2): e31805.

Bao L, Xia X and Cui Y (2010) Expression QTL modules as functional components underlying higher-order phenotypes. PLoS One 5: e14313.

Cui, Y. (2007) Bridging Genotype and Phenotype: Causal Pathways from DNA to Complex Traits. In Deng, H.W. (ed), Current Topics in Human Genetics: Studies in Complex Diseases. World Scientific, Singapore, Page 433-448.

Cui, Y. (2006) Elucidating gene regulatory networks underlying complex phenotypes: Genetical genomics and Bayesian network. In Shannon, F. (ed), Microarrays and Transcription Networks. Landes Bioscience, Georgetown, Page 114-126.

Bao L, Peirce JL, Zhou M, Li H, Goldowitz D, Williams RW, Lu L and Cui Y. (2007) An integrative genomics strategy for systematic characterization of genetic loci modulating phenotypes. Human Molecular Genetics 16:1381-1390

Bao L, Wei L, Peirce JL, Homayouni R, Li H, Zhou M, Chen H, Lu L, Williams RW, Pfeffer LM, Goldowitz D and Cui Y (2006) Combining gene expression QTL mapping and phenotypic spectrum analysis to uncover gene regulatory relations. Mammalian Genome 17:575-583.

Li H, Chen H, Bao L, Manly KF, Chesler EJ, Lu L, Wang J, Zhou M, Williams RW and Cui, Y (2006) Integrative Genetic Analysis of Transcription Modules: Towards Filling the Gap between Genetic Loci and Inherited Traits, Human Molecular Genetics 15: 481-492.

Li H, Lu L, Manly KF, Chesler EJ, Bao L, Wang J, Zhou M, Williams RW and Cui Y (2005) Inferring Gene Transcriptional Modulatory Relations: A Genetical Genomics Approach. Human Molecular Genetics 14:1119-1125.

 

Phenotypic Effects of Single Nucleotide Polymorphisms

There has been great expectation that knowledge of an individual’s genotype will provide a basis for assessing susceptibility to diseases and designing individualized therapy. Vast majority of sequence variants in humans are differences in single bases of DNA, called single nucleotide polymorphisms. Nonsynonymous single nucleotide polymorphisms (nsSNP) that lead to an amino acid change in the protein product are of particular interest because they account for nearly half of the known genetic variations related to human inherited diseases. We investigate the correlations between selective constraint, structural environments and functional impacts of non-synonymous single nucleotide polymorphisms (nsSNPs) and develope computational methods to predict the phenotypic effects of nsSNPs. We also study the phenotypic effects of SNPs in the non-coding regions such as insulators and microRNA target sites.

Bhattacharya A, Ziebarth JD, Cui Y (2014) PolymiRTS Database 3.0: linking polymorphisms in microRNAs and their target sites with human diseases and biological pathways. Nucleic Acids Research. 42(D1): D86-D91.

Bhattacharya A, Ziebarth JD, Cui Y (2012) Systematic Analysis of microRNA Targeting Impacted by Small Insertions and Deletions in Human Genome. PLoS One. 7(9):e46176.

Ziebarth JD, Bhattacharya A, Cui Y (2012) Integrative Analysis of Somatic Mutations Altering MicroRNA Targeting in Cancer Genomes. PLoS One. 7(10): e47137.

Ziebarth JD, Bhattacharya A, Chen A, Cui Y. (2012) PolymiRTS Database 2.0: linking polymorphisms in microRNA target sites with human diseases and complex traits. Nucleic Acids Research. 40 (D1):D216-D221.

Bao L, Zhou M, Wu L, Lu L, Goldowitz D, Williams RW and Cui Y. (2007) PolymiRTS Database: linking polymorphisms in microRNA target sites with complex traits. Nucleic Acids Research. 35: D51-D54.

Bao, L. and Cui, Y. (2006) Functional impacts of nonsynonymous single nucleotide polymorphisms: selective constraint and structural environments. FEBS Letters 580: 1231-1234.

Bao L, Zhou M and Cui Y (2005) nsSNPAnalyzer: identifying disease-associated nonsynonymous single nucleotide polymorphisms. Nucleic Acids Research 33: W480-W482.

Bao L and Cui Y (2005) Prediction of the phenotypic effects of non-synonymous single nucleotide polymorphisms using structural and evolutionary information Bioinformatics 21:2185-2190.

 

Protein Evolution

We are interested in the role of recombination in protein evolution. Crossovers and point mutations of lattice chains with a hydrophobic polar code are investigated in the context of a simple, polymer physics-based model mapping between sequence (genotype) and conformational (phenotype) spaces. Sequences encoding for a single ground-state conformation are considered viable and used as model proteins. Point mutations lead to diffusive walks on the evolutionary landscape, whereas crossovers can "tunnel" through barriers of diminished fitness. The degree to which crossovers allow for more efficient sequence and structural exploration depends on the relative rates of point mutations versus that of crossovers and the dispersion in fitness that characterizes the ruggedness of the evolutionary landscape. The probability that a crossover between a pair of viable sequences results in viable sequences is an order of magnitude higher than random, implying that a sequence's overall propensity to encode uniquely is embodied partially in local signals. Consistent with this observation, certain hydrophobicity patterns are significantly more favored than others among fragments (i.e., subsequences) of sequences that encode uniquely, and examples reminiscent of autonomous folding units in real proteins are found. The number of structures explored by both crossovers and point mutations is always substantially larger than that via point mutations alone, but the corresponding numbers of sequences explored can be comparable when the evolutionary landscape is rugged. Efficient structural exploration requires intermediate nonextreme ratios between point-mutation and crossover rates.

Cui Y, Wong WH, Bornberg-Bauer E, and Chan HS (2002) Recombinatoric exploration of novel folded structures: a heteropolymer-based model of protein evolutionary landscapes, Proceedings of the National Academy of Sciences USA 99:809-814.

 

Protein Structure Prediction

We endeavor to address two major challenges in protein structure prediction ─ to formulate (1) a potential energy function that distinguishes between native and non-native protein structures, (2) and an optimization algorithm that can find the global minima of the energy landscape, i.e. the native structure of the protein. The two problems are interconnected in the structure models of different levels of complexity and represent a grand challenge to biophysics and high performance computing.

Cui Y and Wong WH (2000) Multiple sequence information provides protection against mis-specified potential energy function. Physical Review Letters 85:5242-5245.

Cui Y, Chen RS and Wong WH (1998) Protein folding simulation using genetic algorithm and supersecondary structure constraints. Proteins: Structure, Function and Genetics 31:247-257.

Wong WH, Cui Y and Chen RS (1998) Torsional Relaxation for Biopolymers. Journal of Computational Biology 5:655-665.

 

Coevolution of Diploid Sexual Reproduction and Cell Senescence

We use probability analysis, computer simulation, and exact numerical computation to analyze the impacts of deleterious recessive mutations on sexual and asexual reproduction. Our study suggests that diploid sexual reproduction was unlikely to establish itself as a widespread reproduction mechanism without the complementary process of cell senescence.

Cui Y, Chen RS and Wong WH (2000) The coevolution of cell senescence and diploid sexual reproduction in unicellular organisms, Proceedings of the National Academy of Sciences USA 97:3330-3335.

 

 
 
 

 

Position Available

The computational systems biology lab at University of Tennessee Health Science Center is seeking highly motivated candidates for a postdoc position in bioinformatics. The applicant should have a Ph.D. degree in Bioinformatics or related fields. Research experience in Bayesian network, gene network modeling or quantitative genetics is a significant plus.

Located in Memphis, a dynamic Mid-South city rich in culture, history, diversity, music and cuisine, the University of Tennessee Health Science Center is one of the largest, most comprehensive academic health centers in the United States with a solid commitment to postdoctoral training. UTHSC is an Equal Employment/Affirmative Action Title VI/IX/ Section 504/ ADA/ADEA Employer.  

HOW TO APPLY: Send CV to Dr. Yan Cui.

 
 

Contact Information

Yan Cui, Ph.D.
Department of Microbiology, Immunology and Biochemistry

Center for Integrative and Translational Genomics
University of Tennessee Health Science Center

858 Madison Ave.
Memphis, TN 38163, USA
Tel: (901) 448-3240
Fax: (901) 448-7360
E-mail: ycui2@uthsc.edu