Department of Systems Biology and Translational Medicine
Director, SBTM Computational Biology and Bioinformatics Laboratory
Education: B.S., Biology, 1994, Cedar Crest College
Ph.D., Molecular Biology, 2002, Lehigh University
Computational approaches to biology may be summarized in three categories: (1) interpretation, or the analysis of biological data for the purpose of uncovering hidden relationships using an accepted model, (2) prediction, or using models to formulate computational hypotheses that may be further explored with bench experiments, and (3) model generation, or producing a new model by fitting known constraints on an in silico system in order to generate known results and new hypotheses.
Our group has two main interests that span the above approaches: (1) reconstruction of biological networks from data-derived theoretical networks and (2) modeling the emergent behavior of stochastic biological systems. With regard to network reconstruction, we are currently focusing our attention on the problems associated with comparing theoretical networks, or so-called relevance networks derived from high-throughput screening or other data streams, to networks built using traditional reductionist methods. Our goal is to reconstruct transcriptional regulatory networks involved in murine cardiac development. With regard to modeling, we are interested in modeling the emergent behavior of our reconstructed networks, as well as the emergent behavior of self-assembling cellular structures such as microtubules.
Several projects feed in to these two main interests. To build reliable networks from high-throughput data, the data must be carefully annotated and curated, and for microarray data, it is highly desirable to obtain estimates of absolute transcript abundance. We are developing data-handling SOPs and transcript abundance estimation methods to address these important issues. Additionally, we are constructing a suitable systems biology database for murine cardiac development in order to aid our efforts in comparing our reconstructed networks to “known” networks. It is expected that our development of a public database for this purpose will be of value to the scientific community at large.
Dr. VanBuren is the Director of the SBTM Computational Biology and Bioinformatics Laboratory (CBBL).
Chen H, VanBuren V (2014) A provisional gene regulatory atlas for mouse heart development. PLoS One 9(1), e83364
Chen H, VanBuren V (2012) A review of integration strategies to support gene regulatory network construction. ScientificWorldJournal 2012, 435257
Vanburen V, Chen H (2012) Managing biological complexity across orthologs with a visual knowledgebase of documented biomolecular interactions. Sci Rep 2, 1011
VanBuren V (2012) Visual data mining of coexpression data to set research priorities in cardiac development research. Methods Mol Biol 843, 291—307
Sampson HW et al (2011) Alcohol induced epigenetic perturbations during the inflammatory stage of fracture healing. Exp Biol Med (Maywood) 236(12), 1389—1401
Shibuya N et al (2011) Prevalence of podiatric medical problems in veterans versus nonveterans. J Am Podiatr Med Assoc 101(4), 323—330
Shibuya N et al (2010) Characteristics of adult flatfoot in the United States. J Foot Ankle Surg 49(4), 363—368
Huang HC, Zheng S, VanBuren V, Zhao Z (2010) Discovering disease-specific biomarker genes for cancer diagnosis and prognosis. Technol Cancer Res Treat 9(3), 219—230
Huang HC, Jupiter D, VanBuren V (2010) Classification of genes and putative biomarker identification using distribution metrics on expression profiles. PLoS One 5(2), e9056
Jupiter D, Chen H, VanBuren V (2009) STARNET 2: a web-based tool for accelerating discovery of gene regulatory networks using microarray co-expression data. BMC Bioinformatics 10, 332
Jupiter DC, VanBuren V (2008) A visual data mining tool that facilitates reconstruction of transcription regulatory networks. PLoS One 3(3), e1717
Huang HC et al (2008) Cluster analysis of hydration waters around the active sites of bacterial alanine racemase using a 2-ns MD simulation. Biopolymers 89(3), 210—219
Carter MG et al (2005) Transcript copy number estimation using a mouse whole-genome oligonucleotide microarray. Genome Biol 6(7), R61
VanBuren V, Cassimeris L, Odde DJ (2005) Mechanochemical model of microtubule structure and self-assembly kinetics. Biophys J 89(5), 2911—2926
Hamatani T et al (2004) Age-associated alteration of gene expression patterns in mouse oocytes. Hum Mol Genet 13(19), 2263—2278
Carter MG et al (2003) The NIA cDNA project in mouse stem cells and early embryos. C R Biol 326(10-11), 931—940
Sharov AA et al (2003) Transcriptome analysis of mouse stem cells and early embryos. PLoS Biol 1(3), E74
VanBuren V et al (2002) Assembly, verification, and initial annotation of the NIA mouse 7.4K cDNA clone set. Genome Res 12(12), 1999—2003
VanBuren V, Odde DJ, Cassimeris L (2002) Estimates of lateral and longitudinal bond energies within the microtubule lattice. Proc Natl Acad Sci U S A 99(9), 6035—6040
VanBuren Lab, Lensinck, computational systems biology, systems biology, bioinformatics, microtubule, microtubule dynamics, microtubule simulation, microtubule model, mouse heart development, microarray, network biology, Health Science Center, College of Medicine, CVRI, Cardiovascular Research Institute, Texas A&M, Vincent Lensinck, van Buren Lensinck, Vincent, VanBuren, Texas, VanBuren Lensinck