Iowa State University

Iowa State University
Bioinformatics & Computational Biology Bioinformatics & Computational Biology

 

Faculty Research

 

Faculty in the BCB Graduate Program conduct research in all major research areas of computational molecular biology, including genomics, structural genomics, functional genomics, and computational systems biology, with access to some of the most modern experimental platforms. Many interdisciplinary research projects take place at Iowa State.

Below are brief descriptions of the research interests of the BCB faculty. Our faculty work together as core and associate members of the BCB graduate program, so students can be published as first author on their BCB-related research work in several publications over their PhD careers. Both core and associate members can be mentors (major professors) for BCB students. Associate members are identified below their names.

 

 

To View By Last Name: A B C D E F G H J L M N P R S T V W Y

 

To View by Research Area: Go here.

 

* Asterisked Faculty currently serve as major or co-major professors for BCB Students.

 

 

Dean Adams

Associate BCB Faculty Member

Education: Ph.D., SUNY at Stony Brook, 1999

Research Interests:

  • Mathematical Biology, Computational Modeling, and Metabolic and Developmental Networking--Theoretical and empirical studies of phenotypic change in ecology and evolution; statistical shape analysis (morphometrics); community organization and species interactions

Description: In the Adams lab our research is directed towards understanding how ecological and evolutionary forces generate and maintain phenotypic diversity. To do this we integrate computational, mathematical, statistical and quantitative morphological methods to examine ecological hypotheses from an evolutionary perspective. Our empirical work examines patterns of ecological and evolutionary morphology to understand species interactions, microevolution and community organization (largely in Plethodon salamanders). In our theoretical research, we develop new analytical tools for examining patterns of phenotypic variation. Current theoretical research focuses on developing analytical methods for assessing multivariate patterns of phenotypic change, such as are found in studies of plasticity, character divergence (and displacement) biomechanics, ontogenetics, local adaptation, and microevolution, and quantitative genetics.

Selected Publications:

  • Adams, D.C. 2008. Phylogenetic meta-analysis. Evolution. 62:567-572.
  • Adams, D.C., and J.O. Church. 2008. Amphibians do not follow Bergmann’s rule. Evolution. 62:413-420.
  • Adams, D.C. 2007. Organization of Plethodon salamander communities: guild-based community assembly. Ecology. 88:1292-1299.
  • Adams, D.C., and M.L. Collyer. 2007. Analysis of character divergence along environmental gradients and other covariates. Evolution.61:510-515.
  • Collyer, M.L., and D.C. Adams. 2007. Analysis of two-state multivariate phenotypic change in ecological studies. Ecology. 88:683-692.
  • Adams, D.C., M.E. West, and M.L. Collyer. 2007. Location-specific sympatric morphological divergence as a possible response to species interactions in West Virginia Plethodon salamander communities. Journal of Animal Ecology. 76:289-295.
  • Adams, D.C., and M.M. Cerney. 2007. Quantifying biomechanical motion using Procrustes motion analysis. Journal of Biomechanics. 40:437-444.
  • Maerz, J. C., E. M. Myers, and D. C. Adams. 2006. Trophic polymorphism in a terrestrial salamander. Evolutionary Ecology Research. 8:23-35.
  • Adams, D. C. 2004. Character displacement via aggressive interference in Appalachian salamanders. Ecology. 85:2664-2670.
  • Adams, D. C., and F. J. Rohlf. 2000. Ecological character displacement in Plethodon: biomechanical differences found from a geometric morphometric study. Proceedings of the National Academy of Sciences, U.S.A. 97:4106-4111.

Vita: Click here to download

Website: Dean Adams

*Srinivas Aluru

Education: Ph.D., Iowa State, 1994

Research Interests: Computational and comparative genomics, systems biology, parallel computational biology, and string algorithms.

  • Bioinformatics: Sequential/parallel algorithms and software systems for bioinformatics, with emphasis on EST clustering, sequence alignments, sequence databases and protein structure
Description:

Selected Publications:

  • B. Jackson, P.S. Schnable and S. Aluru, “Consensus genetic maps as median orders from
    inconsistent sources,” ACM/IEEE Transactions on Computational Biology and Bioinformatics, in
    press.
  • A. Kalyanaraman, S.J. Emrich, P.S. Schnable and S. Aluru, “Assembling genomes on large-scale
    parallel computers,” Journal of Parallel and Distributed Computing, Vol. 67, pp. 1240-1255, 2007.
  • S.J. Emrich, L. Li, T.-J. Wen, M.D. Yandeau-Nelson, Y. Fu, L. Guo, H.-H. Chou, S. Aluru, D.A.
    Ashlock and P.S. Schanble, “Nearly identical paralogs (NIPs): implications for maize (Zea mays L.) genome evolution," Genetics, Vol. 175, pp. 429-439, 2007. (featured in Science, Vol. 315, No.
    5810, pp. 302 in Editor's Choice: Highlights of the recent literature).
  • M. Ott, J. Zola, S. Aluru and A. Stamatakis, “Large-scale maximum-likelihood based phylogenetic
    analysis on the IBM Blue Gene/L,” ACM/IEEE Supercomputing Conference, 2007. (best paper
    finalist)
  • P. Ko and S. Aluru, “Optimal self-adjusting suffix tree layout for dynamic string data in secondary
    storage," Proc. 14th Symposium on String Processing and Information Retrieval (SPIRE),
    Springer Verlag Lecture Notes in Computer Science, Vol. 4726, pp. 184-194, 2007.
  • J. Zola, X. Yang, A. Rospondek and S. Aluru, “Parallel-TCoffee: A parallel multiple sequence
    aligner," Proc. ISCA Parallel and Distributed Computing Systems (PDCS), pp. 248-253, 2007.
  • S. Emrich, A. Kalyanaraman and S. Aluru, “Massively parallel expressed sequence tag
    clustering," Proc. ISCA Parallel and Distributed Computing Systems (PDCS), pp. 254-261,
    2007.
  • S. Aluru, N. Amato and D.A. Bader, “Editorial: Special section on high performance
    computational biology," IEEE Transactions on Parallel and Distributed Systems, Vol. 17, No. 8,
    pp. 737-740, 2006.
  • A. Kalyanaraman and S. Aluru, ``Efficient algorithms and software for detection of full-length LTR
    retrotransposons,'' Journal of Bioinformatics and Computational Biology, Vol. 4, No. 2, pp. 197-
    216, 2006.
  • S. Seal and S. Aluru, “Communication-aware parallel domain decomposition using space-filling
    curves," ISCA 19th International Conference on Parallel and Distributed Computing Systems
    (PDCS), pp. 159-164, 2006.
  • A. Kalyanaraman, S. Aluru and P.S. Schnable, “Turning repeats to advantage: scaffolding
    genomic contigs using LTR retrotransposons," Proc. Life Sciences Society Computational
    Systems Bioinformatics (CSB) Conference, pp. 167-178, 2006.

Vita: Click here to download

Website: Srinivas Aluru

*Amy Andreotti

Education: Ph.D., Princeton, 1994

Research Interests: Macromolecular structure-function, NMR spectroscopy

  • Macromolecular Structure and Function--Macromolecular structure/function relationships; protein structure determination using nuclear magnetic resonance; molecular recognition

Description:

Selected Publications:

  • R.E. Joseph, A.H. Andreotti , Bacterial expression and purification of interleukin-2 tyrosine kinase: single step separation of the chaperonin impurity. Protein Expr Purif., 60, 194-7 (2008).

  • A. Severin, D.B. Fulton, A.H. Andreotti, Murine Itk SH3 domain. J Biomol NMR.40, 285-90 (2008).

  • R.E. Joseph, D.B. Fulton, A.H. Andreotti. Mechanism and functional significance of Itk autophosphorylation. J Mol Biol ., 373, 1281-92 (2007).

  • R.E. Joseph, L. Min, A.H. Andreotti, The linker between SH2 and kinase domains positively regulates catalysis of the Tec family kinases, Biochemistry, 46, 5455-62 (2007).

  • R.E. Joseph, L. Min, R. Xu, E. Musselman, A.H. Andreotti, A remote substrate-docking mechanism for the Tec family tyrosine kinases, Biochemistry 46, 5595-603 (2007).

  • Huang, Y.H., Grasis, J.A., Miller, A.T., Xu, R., Soonthronvacharin, S., Andreotti, A.H., Tsoukas, C.D., Cooke, M.P. & Sauer, K. Positive regulation of Itk PH domain function by soluble IP4, Science, 316, 886-9 (2007).

  • E. Pletneva, M. Sundd, D.B. Fulton & A.H. Andreotti, Molecular details of Itk activation by prolyl isoemrization and phospholigand binding: the NMR structure of the Itk SH2 domain bound to a phosphopeptide, J. Mol. Biol., 357, 550-561 (2006).

  • A.H. Andreotti Opening the pore hinges on proline. Nat Chem Biol., 2, 13-14 (2006).

  • D.V. Venkitaramani, D.B. Fulton, A.H. Andreotti, K.M. Johansen, J. Johansen, Mapping the Ca2+ -dependent binding of an invertebrate homolog of protein phosphatase 4 regulatory subunit 2 to the small EF-hand protein, calsensin. Biochim Biophys Acta. 1763, 322-329 (2006).

Vita: Click here to download

Website: Amy Andreotti

Lyric Bartholomay

Associate BCB Faculty Member

Education: Ph.D., University of Wisconsin-Madison, 2004

Research Interests:

  • In describing and understanding the interactions between mosquitoes and the pathogens they transmit to prevent transmission of vector-borne diseases, my lab employs bioinformatics/computational biology - we use bioinformatics (EST sequencing, genome mining, etc.) and functional genomics tools (microarrays), in which I was trained in my Doctoral and Postdoctoral work.

Selected Publications:

  • Beerntsen, BT, Bartholomay, LC, Lowery, RJ. 2007. Penetration of the mosquito midgut is not required for Brugia pahangi microfilariae to avoid the melanotic encapsulation response of Armigeres subalbatus. Veterinary Parasitology 144: 371-374.
  • Platt KB, Tucker BJ, Halbur PG, Tiawsirisup S, Blitvich BJ, Fabiosa FG, Bartholomay, LC, Rowley , WA . 2007. West Nile virus viremia in eastern chipmunks (Tamias striatus) sufficient for infecting different mosquitoes. Emerg Infect Dis. 13(6) 831-837.
  • Oliver, J, Holscher, K, Hutcheson, HJ, Bartholomay, LC. Ticks and tick-borne diseases in Iowa. 6 pp. Iowa State University Extension Publications PM2036.
  • Waterhouse, RM, Kriventseva, EV, Meister, S, Xi, ZY, Alvarez, KS, Bartholomay, LC, Barillas-Mury, C, Bian, G, Blandin, S, Christensen, BM, Dong, Y, Jiang, H, Kanost, M, Koutsos, AC, Levashina, E A, Li, J, Ligoxygakis, P, MacCallum, MR, Mayhew, GF, Mendes, A, Michel, K, Osta, MA, Paskewitz, S, Shin, SW, Vlachou, D, Wang, L. Wei, W, Zheng, L, Zou, Z, Severson, DW, Raikhel, A, Kafatos, FC, Dimopoulos, G, Zdobnov, EM, Christophides, GK. 2007. Evolutionary Dynamics of Immune-Related Genes and Pathways in Disease-Vector Mosquitoes. Science. [in press]

Vita: Click here to download

Website: New Faculty

*William Beavis

Education: Ph.D., 1986, Plant Breeding, Iowa State University, Ames, IA

Research Interests:

  • Theoretically, the impact of genetic bottlenecks is through selection, drift and disequilibrium which limit genetic potential of a crop to sub-optimal levels (Robertson, 1960; Hill and Robertson, 1968; Bulmer, 1971). Despite theoretical considerations of limits to genetic potential, we do not know: 1) whether desirable allelic variants have been eliminated during the adaptation and breeding processes, 2) how to distinguish desirable and undesirable allelic variants in unadapted germplasm, and 3) if desirable alleles can be identified, how do we recover them without sacrificing genetic gains made by previous generations of breeders? 
    Until the emergence of genomics and high throughput genotyping technologies, these types of questions could not be addressed on an experimental basis. Thus, theories developed over 50 years ago have not been tested and modified. We use the tools of genomics, bioinformatics, population genetics, quantitative genetics and modeling to address these questions.  Explicitly, we are developing: 1) experimental strategies for identifying desirable alleles in unadapted germplasm 2) methods to predict the value of desirable alleles, 3) optimal breeding strategies for migrating desirable alleles from unadapted germplasm into elite breeding populations without sacrificing established genetic gains and 4)computational tools to recognize genomic signatures of breeding populations with sub-optimal limits to selection. The impact of addressing these challenges experimentally and analytically will assure that the full genetic potential of maize can be realized using the most effective genetic resources and efficient breeding methods.  From an academic perspective this project also will begin the process of integrating discoveries from genomics with theoretical models of population and quantitative genetics.

Selected Publications:

  • Kingsmore, SF, IE Lindquist, J Mudge, DD Gesler, WD Beavis (2008) Genome-wide association studies: progress and potential for drug discovery and development. Nature Reviews (adv online publication) doi:10.1038/nrd2519

  • Kingsmore, SF, IE Lindquist, J Mudge WD Beavis (2007) Genome-Wide Association Studies: Progress in Identifying Genetic Biomarkers in Common, Complex Diseases. Biomarker Insights 2007:2 1–10.

  • Beavis, WD , FD Schilkey, SM Baxter (2007). Translational Bioinformatics: At the Interface of Genomics and Quantitative Genetics. Crop Science 47(s3): 32-43

  • Gonzales, MD, K Gajendran, AD Farmer, E Archuleta, WD Beavis (2007) Leveraging model legume information to find candidate genes for soybean Sudden Death Syndrome using the Legume Information System (LIS). In Ed. Edwards D. Methods in Molecular Biology , Humana Press ( USA), pp. 245-260

  • Stein, L, DD Gessler, D Rokshar, D Main, L Mueller, E Huala, C Lawrence, S Rhee, WD Beavis (2006) Save our Data. The Scientist April 24-25.

  • Beavis, WD . (2005) Architectures for Integration of Data and Applications: Lessons from Integration Projects. In Genome Exploration:Data Mining and the Genome. The Stadler Genetics Symposia XXII:.Springer (eds. P. Gustafson, R. Shoemaker and J.W. Snape) pp. 31-46

  • Gepts, P. WD Beavis, EC Brummer, RC Shoemaker, HT Stalker, NF Weeden, ND Young. (2005) Legumes as a Model Plant Family: Genomics for Food and Feed. Report of the Cross Legume Advances through Genomics Conference. Plant Physiology 137: 1228-1235.

Vita: Click here to download

Website: William Beavis

Madan Bhattacharyya

Education: Ph.D., Western Ontario, 1987

Research Interests: Functional genomic and proteomic approaches to disease resistance and susceptibility

  • Functional and Structural Genomics: Functional genomic and proteomic approaches to disease resistance and susceptibility
  • Genome Evolution: Evolution of disease resistance genes

Description: My current research program has been on understanding the molecular basis of host-pathogen interaction. The soybean-Phytophthora sojae interaction has been the main model system investigated extensively in our lab to understand the molecular basis of plant disease resistance. We are also investigating the soybean-Fusarium virguliformae interaction to understand the mechanism used by a phytotoxin to cause plant disease. In addressing the above biological questions we are applying both functional genomics and proteomics approaches that require bioinformatics tools.

Selected Publications:

  • Narayanan, N.N ., Tasma, I.M., Grant, D., Shoemaker, R., and Bhattacharyya, M.K. (2008) Identification of candidate signaling genes including regulators of chromosome condensation 1 proteins family differentially expressed in the soybean-Phytopthora sojae interaction.Theoretical and Applied Genetics, in press.
  • Tasma, I.M., Brendel, V., Whitham S.A., and Bhattacharyya, M.K. (2008) Expression and Evolution of the Phosphoinositide-specific Phospholipase C Gene Family in Arabidopsis thaliana. Plant Physiology and Biochemistry. 46:627-637.
  • Gao, H., and Bhattacharyya M.K. (2008) The soybean-Phytophthora resistance locus Rps1-k encompasses coiled coil-nucleotide binding-leucine rich repeat-like genes and repetitive sequences. BMC Plant Biol. 8: 29.

  • Ji, J., Scott, M.P., and Bhattacharyya, M.K. (2006) Light is essential for degradation of ribulose-1,5-biphosphate carboxylase-oxygenase large subunit during sudden death syndrome development in soybean. Plant Biology 8:597-605.

  • Gao, H., Narayanan, N., Ellison, L., and Bhattacharyya M.K. (2005) Two classes of highly similar coiled coil-nucleotide binding-leucine rich repeat genes isolated from the Rps1-k locus encode Phytophthora resistance in soybean. Mol. Plant-Microbe Interact. 18: 1035-1045.

  • Bhattacharyya, M.K. , Narayanan, N. N., Gao, H., Salimath, S.S., Santra, D., Ellison, L., Brar, H., Kasuga, T., Liu, Y., Espinosa, B., Marek, L.F., Shoemaker, R.C., Gijzen, M. and Buzzell, R.I. (2005) Identification of a large cluster of coiled coil-nucleotide binding site-leucine rich repeat-type genes from the Rps1 region containing Phytophthora resistance genes in soybean. Theor. Appl. Genet. 111:75 – 86.

     

Vita: Click here to download

Website: Madan Bhattacharyya

Adam Bogdanove

Associate BCB Faculty Member

Education: Ph.D., Cornell, 1997

Research Interests: Genomic and proteomic approaches to bacterial plant diseases and plant disease resistance

  • Functional and Structural Genomics-- Genomic and proteomic approaches to bacterial plant pathology and plant disease resistance mechanisms, including bacterial genomic sequence analysis and protein profiling, and plant microarray and mutational analyses
  • Information Integration and Data Mining

Description:

Selected Publications:

  • Meyer, D.F., and Bogdanove, A.J. (2008). Genomics-driven advances in Xanthomonas biology. In Plant Pathogenic Bacteria: Genomics and Molecular Biology, R.W. Jackson, ed. (Norwich, UK, Horizon Scientific Press), in press.
  • Patil, P.B., Bogdanove, A.J., and Sonti, R.V. (2007) The role of horizontal transfer in the evolution of a highly variable lipopolysaccharide biosynthesis locus in xanthomonads that infect rice, citrus and crucifers. BMC Evol. Biol. 7:243.
  • Wang, L. Makino, S., Subedee, A. and Bogdanove, A.J. (2007) Novel candidate virulence factors in rice pathogen Xanthomonas oryzae pv. oryzicola revealed by mutational analysis. Applied Env. Microbiol. 73:8023-8027.
  • Nissinen, R.M, Ytterberg, A.J., Bogdanove, A.J., van Wijk, K.J., and Beer, S.V. (2007) Analyses of the secretomes of Erwinia amylovora and selected hrp mutants reveal novel type III secreted proteins and an effect of HrpJ on extracellular harpin levels. Molecular Plant Pathol. 8:55-67.
  • Wise, R.P., Moscou, M.J., Bogdanove, A.J., and Whitham, S.A. (2007). Transcript profiling in host-pathogen interactions. Ann. Rev. Phytopathol. 45:329-369.
  • Nino-Liu, D. O., Ronald, P. C., and Bogdanove, A. J. (2006) Xanthomonas oryzae pathovars: model pathogens of a model crop. Molecular Plant Pathol. 7:303-324.
Vita: Click here to download

Website: Adam Bogdanove

*Volker Brendel

Education: Ph.D., Weizmann ( Israel), 1986

Research Interests: Gene identification, pre-mRNA splicing

Description: Plant genomics and molecular genetics

  • Bioinformatics-- Algorithms for gene identification in genomic sequences; sequence alignment methods; plant transposon molecular biology; molecular phylogeny

Selected Publications:

Vita: Click here to download

Website: Volker Brendel

Anne Bronikowski

Associate BCB Faculty Member

Education: Ph.D., University of Chicago, 1997

Research Interests: Evolution of life history variation with an emphasis on the evolution of senescence

  • Bioinformatics-- Our research focuses on the evolution of life history variation with an emphasis on the evolution of senescence (the functional decline in biochemical and physiological processes with age). We address fundamental questions in life history evolution using field studies, laboratory experiments (physiological and molecular), and mathematical modeling. Current research focuses on the evolution and ecology of senescence in 1) natural reptile populations; 2) laboratory populations of mice and 3) semi-natural populations of baboons
  • Mathematical Biology, Computational Modeling, and Metabolic and Developmental Networking—Same as above

Description: We address fundamental questions in life history evolution using field studies, laboratory experiments (physiological and molecular), and mathematical modeling

Selected Publications:

  • Promislow, D.E.L. and Bronikowski, A.M. in press. The Evolutionary Genetics of Senescence. In Evolutionary Genetics: Concepts and Case Studies. Eds. J. Wolf and C. Fox. Oxford University Press, U.K.
  • Bronikowski, A.M. and D.E.L. Promislow.   2005. Testing evolutionary theories of aging in wild populations. Trends in Ecology and Evolution 20(6): 271-273 .
  • Gammie, S.C., Hasen, N.S., Awad, T.A., Auger, A.P., Jessen, H.M., Panksepp, J.B., and Bronikowski, A.M.  2005. Gene array profiling of large hypothalamic CNS regions in lactating and randomly cycling virgin mice. Molecular Brain Research 139: 201-211.
  • Bronikowski, A.M., Carter, P.A., Morgan, T.J., Garland, T. Jr., Ung, N., Pugh, T.D., Weindruch, R. and Prolla, T.A. 2003. Lifelong voluntary exercise in the mouse prevents age-related alterations in gene expression in the heart. Physiological Genomics 12: 129–138.

Vita: Click here to download

Website: Anne Bronikowski

Steve Cannon

Associate BCB Faculty Member

Education: Ph.D., University of Minnesota, 2003

Research Interests: Plant genome and gene family evolution, comparative genomics, bioinformatic methods for gene and genome sequence analysis.

BCB Research Areas:

  • Bioinformatics-- methods for gene and genome sequence analysis, including genome comparisons, phylogenetics, and high-throughput transcript profiling.
  • Genome evolution-- patterns and rates of genome rearrangement; effects of polyploidy and transposon activity.

Description: I am a USDA-ARS Research Geneticist, and an adjunct assistant professor in the Agronomy Department.
My ARS research group focuses on bioinformatics for crop improvement, particularly in soybean and other crop legumes. We do this by developing software for analyzing and transferring information between crop and model species - including sequence, genetic, functional, and phenotype data. We study the evolution of disease resistance and nitrogen fixation, and the responses of genomes to polyploidy. We also provide support for two genome sequencing efforts: soybean, and a relative of alfalfa called Medicago truncatula. This support includes development of genetic markers, development of web-based tools for genomic data access and visualization.

Selected Publications:

  • Ameline-Torregrosa C, Wang B-B, O’Bleness M, Deshpande,S, Zhu H, Roe BA, Young ND, Cannon SB (2008) Identification and Characterization of NBS-LRR Encoded Genes in the Model Plant Medicago truncatula Plant Physiology, 146:5-21
  • Zhang X-C, Wu X, Findley S, Wan J, Libault M, Nguyen HT, Cannon SB, Stacey G (2007) Characterization of plant LysM domains and molecular evolution and comparative genomics of plant LysM type receptor-like kinases. Plant Physiology 144:623-636.
  • Cannon SB, Sterck L, Rombauts S, Sato S, Cheung F, Gouzy JP, Wang X, Mudge J, Vasdewani J, Scheix T, Spannagl M, Nicholson C, Humphray SJ, Schoof H, Mayer KFX, Rogers J, Quetier F, Oldroyd GE, Debelle F, Cook DR, Ernest F. Retzel, Roe BA, Town CD, Tabata S, Van de Peer Y, ND Young (2006) Legume genome evolution viewed through the Medicago truncatula and Lotus japonicus genomes. PNAS 103(40):14959-64.
  • Mudge J, Cannon SB, Kalo P, Oldroyd GED, Roe BA, Town CD, Young ND (2005) Hypersyntenic Regions in the Genomes of Soybean, Medicago truncatula, and Arabidopsis thaliana. BMC Plant Biology, 2005.

Websites: http://soybase.org/ http://medicago.org/

Vita: Click here to download

Website: Steven Cannon

Hui-Hsien Chou

Education: Ph.D., University of Maryland at College Park, 1996

Research Interests: Bioinformatics, computational and molecular biology

Description: My research interest has been the development of sophisticated computer science algorithms for efficiently solving large scale biological research problems, e.g., the automatic DNA sequence clean up programs (LUCY1 & 2), an efficient whole-genome oligo microarray design tool (PICKY), and an automatic Perl programming tool (VECT) for biologists. My software tools employ novel approaches to computing and generally provide better results than previous methods. For example, while most microarray design tools are still based on the sequence level comparison to screen out nontargets, my PICKY program has embedded a complete suite of thermodynamic equations in its calculations to infer the best oligo candidates based on the whole genome, and it does so orders of magnitude faster than any other comparable microarray design tools with the same computation depth. My recent focus is on the whole-genome siRNA design using a similar thermodynamic calculation approach and to verify the designs in my molecular biology lab using C. elegans as the model.
 
Ongoing projects: National Institute of General Medical Sciences, Visual Data Extraction and Conversion Programming Tool - The goal of this project is to develop an auto-programming tool for biomedical scientists to help them handle the large amount of data in their research; National Science Foundation Plant Genome Research Program - A Rice Oligonucleotide Array - Rice has become a model for grasses and cereal because of its small genome size, available genome sequence, and ease of transformation. The structural and functional analysis of rice genes has broad practical implications for the other economically important cereals such as corn and wheat. This project will design, manufacture and distribute whole genome rice microarrays to the public.

Selected Publications:

  • Young-Su Seo, Malinee Sriariyanun, Li Wang, Janice Pfeiff, Jirapa Phetsom, Ye Lin, Ki-Hong
    Jung, Hui-Hsien Chou, Adam J Bogdanove and Pamela C Ronald. A two-genome
    microarray for the rice pathogens Xanthomonas oryzae pv. oryzae and X. oryzae pv. oryzicola
    and its use in the discovery of a difference in their regulation of hrp genes. BMC
    Microbiology, 8(1):99, 2008.
  • Scott Emrich, Li Li, Tsui-Jung Wen, Marna Yandeau-Nelson, Yan Fu, Ling Guo, Hui-Hsien
    Chou, Srinivas Aluru, Daniel Ashlock, and Patrick Schnable. Nearly identical paralogs
    (NIPs): implications for maize (Zea mays L.) genome evolution. Genetics, 175(1): 429–439, 2007.
  • Philip M. Maher, Hui-Hsien Chou, Elizabeth Hahn, Tsui-Jung Wen, and Patrick S. Schnable. GRAMA:  Genetic Mapping Analysis of Temperature Gradient Capillary Electrophoresis (TGCE) Data. Theoretical and Applied Genetics, 113(1):156162, 2006.
  • Hui-Hsien Chou. Vect: an automatic visual Perl programming tool for nonprogrammers. BioTechniques, 38:615621, April 2005.
  • Song Li and Hui-Hsien Chou. UBViz: Explore Biochemical Pathways in 3-D Space. BioTechniques, 38:540542, April 2005.
  • Hui-Hsien Chou, An-Ping Hsia, Denise L. Mooney, and Patrick S. Schnable. Picky: Oligo Microarray Design for Large Genomes. Bioinformatics, 20:28932902, Nov. 2004.
  • Song Li and Hui-Hsien Chou. Lucy2: an interactive DNA sequence quality trimming and vector removal tool. Bioinformatics, 20:28652866, Nov. 2004.
Vita: Click here to download

Lab Website: Hui-Hsien Chou

Department Website: Hui-Hsien Chou

*Dianne Cook

Education: Ph.D., Rutgers, 1993

Research Interests: Visualization of high-dimensional data, linking data views with other visualization of biological phenomenon

  • Bioinformatics-- Visualization of multivariate, high-dimensional data, in particular gene expression data, and metabolic networks. Interested developing methods for linking data views with other informative visualization of biological phenomenon
  • Mathematical Biology, Computational Modeling, Metabolic and Developmental Networking—Same as above

Description: My research is in methods for visualizing high-dimensional data using interactive and dynamic methods. This includes data arising from biological experiments such as microarray data, metabolomics and proteomics. We're developing software to assist in this, called GeneGobi, which builds functionality in the data analysis language R and the data visualization software GeneGobi

Selected Publications:

  • Hobbs , J., Wickham, H., Hofmann, H. and Cook, D. (2008) Glaciers Melt as Mountains Warm: A Graphical Case Study Computational Statistics, To appear.
  • Lawrence, M., Cook, D., Lee, E.-K., Babka, H. and Wurtele, E. (2008) explorase: Multivariate Exploratory Analysis and Visualization for Systems Biology, Journal of Statistical Software, 25(9):http://www.jstatsoft.org/v25/i09.
  • Cook, D., Hofmann, H., Lee, E.-K., Yang, H., Nikolau, B., and Wurtele, E. (2007) Exploring Gene Expression Data, Using Plots, Journal of Data Science, 5(2):151{182.
  • Lee, E.-K., Cook, D., Klinke, S., and Lumley, T. (2005). Projection Pursuit for Exploratory Supervised Classification. Journal of Computational and Graphical Statistics, bf 14(4):831{846.
  • Swayne, D. F., Temple Lang, D., Buja, A. and Cook, D. (2003) GGobi: Evolving from XGobi into an Extensible Framework for Interactive Data Visualization, Journal of Computational Statistics and Data Analysis, 43(4):423{444.
  • Wurtele, E., Li, J., Diao, L., Zhang, H., Foster, C., Fatland, B., Dickerson, J., Brown, A., Cox, Z., Cook, D., Lee, E. K., Hofmann, H. (2003) MetNet: Software to Build and Model the Biogenetic Lattice of Arabidopsis, Comparative and Functional Genomics, 4:239{245.

Link: http://www.public.iastate.edu/~dicook/GeneGobi/MetNet_GeneGobi.htm

Vita: Click here to download

Website: Dianne Cook

*Jack Dekkers

Education: Ph.D., Wisconsin, Madison, 1989

Research Interests: Integrating molecular and quantitative genetics for animal breeding programs

  • Biological statistics -- Statistical methods for QTL detection and for estimating relationships between QTL and phenotype; identifying genes of economic importance in swine and poultry; systems analysis approaches to optimize the use of molecular genetic information in genetic improvement programs.

Description: Quantitative genetics and animal breeding with application to swine and poultry genetics, including design and optimization of breeding strategies, use of molecular genetic information, QTL detection, and economic aspects of breeding programs.

Selected Publications:

  • Li, Y., H.N. Kadarmideen, and J.C.M. Dekkers. 2008. Selection on Multiple QTL with Control of Gene Diversity and Inbreeding for Long-term Benefit. J. Anim. Breed. Genet. (Accepted)
  • Pyiasatian, N., R.L. Fernando, and J.C.M. Dekkers. 2008. Introgressing multiple QTL in breeding programs of limited size. J. Animal Breeding Genetics (In Press)
  • Dekkers , J.C.M., and J.H.J. van der Werf. 2007. Strategies, limitations, and opportunities for marker-assisted selection in livestock. In: E. Guimaraes, J. Ruane, B. Scherf, A. Sonnino, and J. Dargie (Eds): Marker-Assisted Selection. Current status and future perspectives in crops, livestock, forestry and fish: Current Status and the Way Forward. FAO, Rome, Italy.
  • Andreescu, C., S. Avendano, S. Brown, A. Hassen, S.J. Lamont, and J.C.M. Dekkers. 2007. Linkage disequilibrium in related breeding lines of chickens. Genetics 177: 2161–2169
  • Habier, D., R.L. Fernando, J.C.M. Dekkers. 2007. The impact of relationship information genome-assisted breeding values. Genetics 177: 2389–2397
  • Heifetz, E.M., J.E. Fulton, N.P. O'Sullivan, J.A. Arthur, J. Wang, J.C.M. Dekkers, and M. Soller. 2007. Mapping quantitative trait loci affecting susceptibility to Marek's disease virus in a backcross population of layer chickens. Genetics 177: 2417–2431.
  • Dekkers , J.C.M. 2007. Prediction of response to marker-assisted and genomic selection using selection index theory. J. Anim. Breed. Genet. 124: 331-341
  • Wang, J., K. Koehler, and J.C.M. Dekkers. 2007. Interval mapping of quantitative trait loci with selective DNA pooling data. Genetics Selection Evolution 39: 685-709.

Vita: Click here to download

Website: Jack Dekkers

*Julie Dickerson

Education: Ph.D., USC, 1993

Research Interests: Fuzzy expert systems, metabolic networks, macromolecular structure-function relationships. Systems biology and modeling of metabolic networks, analysis of microarray and
metabolomic data using pattern recognition methods, and data visualization in virtual reality

  • Bioinformatics--Systems biology and modeling of metabolic networks, analysis of microarray and metabolomic data using pattern recognition methods, and data visualization in virtual reality
  • Macromolecular Structure and Function-- Same as above

Description:

Selected Publications:

  • S.Y. Rhee, J.A. Dickerson, D. Xu, “ Bioinformatics and Its Applications in Plant Biology,” Annual Review of Plant Biology, 57, 335-359, 2006.
  • Y. Yang, L. Engin, E.S. Wurtele, Carolina Cruz-Neira, J.A. Dickerson, “Integration of metabolic networks and gene expression in virtual reality,” Bioinformatics, 21 : 3645-3650 2005.
  • L. Shen, J. Gong, R. A. Caldo, D. Nettleton, D. Cook, R.P. Wise, J. A. Dickerson, “Barleybase – An Expression Profiling Database For Plant Genomics,” Nucleic Acids Research , 33 (suppl_1): D614-618, 2004 .
  • P. Du, J. Gong, Eve S. Wurtele, and Julie A. Dickerson, “Modeling Gene Expression Networks using Fuzzy Logic,” Special issue of IEEE Transactions on Systems, Man and Cybernetics, Part B,35(6):1351-1359, 2005.
  • E. S. Wurtele, J. Li, L. Diao, H. Zhang, C. Foster, B. Fatland, J. A. Dickerson, A. Brown, Z. Cox, D. Cook, E.-K. Lee, and H. Hofmann, “MetNet: software to build and model the biogenetic lattice of Arabidopsis,” Comparative and Functional Genomics, 4, 239-245, 2003.

Vita: Click here to download

Website: Julie Dickerson

Philip Dixon

Associate BCB Faculty Member

Education: Ph.D., Cornell, 1986

Research Interests: Modeling virus sequence evolution and immune system response, ecological and environmental statistics

  • Mathematical Biology, Computational Modeling, and Metabolic and Developmental Networking-- Modeling virus sequence evolution and immune system response, ecological and environmental statistics

Description: develops and evaluates statistical methods to answer interesting biological questions.  A lot of this work is collaborative.  The themes are using likelihood inference in non-standard situations and using computer-intensive methods

Selected Publications:

  • Schleuter, J.A., Dixon, P., Granger, C., Grant, D., Clark, L., Doyle, J.J., and Shoemaker, R.C. 2004. Mining EST databases to resolve evolutionary events in major crop species. Genome 47:868-876
  • Picasso, V.D., Brummer, E.C., Liebman, M., Dixon, P.M. and Wilsey, B.J. 2008. Crop species diversity affects productivity and weed suppression in perennial polycultures under two management strategies. Crop Science 48:331-342.
    Prasifka, J.R., Hellmich, R.L. Dively, G.P., Higgins, L.S., Dixon, P.M., and Duan, J.J. 2008. Selection of nontarget arthropod taxa for field research on transgenic insecticidal crops: using empirical data and statistical power. Environmental Entomology 37:1-10
    Schmidt, N.P., O’Neal, M.E., and Dixon, P.M. 2008. Aphidophagous predators in Iowa soybean: a community comparison across multiple years and sampling methods. Annals Ent. Soc. Amer. 101:341-350.
    Xu, Z., Gleason, M.L, Mueller, D.S., Esker, P.D., Bradley, C.A., Buck, J.W., Dixon, P.M., and Monteiro, J.E.B.A. 2008. Overwintering of Sclerotium rolfsii and S. rolfsii var. delphinii in different latitudes of the United States. Plant Disease 92:719-724.
    Esker, P.D, Gibb KS, and Dixon, P.M., Survival Analysis and Space-Time Point Pattern Analysis to Improve the Epidemiological Understanding of the Papaya-Papaya Yellow Crinkle Pathosystem. Plant Health Progress, in press.
    Scott, E.M. and Dixon, P.M. Statistical Sampling Designs for Radionuclides. book chapter, in review
    Westerrnan, P.R., Dixon, P.M., and Liebman, M. Burial rates of surrogate seeds in arable fields. Weed Research, submitted.

Vita: Click here to download

Website: Philip Dixon

*Drena Dobbs

Education: Ph.D. Oregon, 1983

Research Interests: Computational biology; predicting protein and nucleic acid structure, function, and interactions

  • Macromolecular Structure and Function-- Analysis and prediction of macromolecular structure and function; protein-protein, protein-nucleic acid interactions, determinants of molecular recognition.

Description: Our long-term research goals are to understand how proteins and nucleic acids achieve their functional three-dimensional structures and to elucidate rules that dictate interactions between proteins, nucleic acids and other molecules in cells.  In collaboration with several groups on campus, we have begun to integrate computational and experimental approaches to explore sequence-structure-function relationships in macromolecular complexes.  Current areas of focus include: prediction  and validation of ligand binding residues in proteins (for  protein, DNA, RNA and small molecules), rational design of zinc finger DNA binding proteins, prediction of functional effects of SNPs in unannotated proteins.

Selected Publications:

  • An engineered zinc finger/target site design tool. Nucleic Acids Res., [Epub ahead of print]. In press.
  • Terribilini, M., Sander, J., Lee, J.H., Zaback, P., Jernigan, R., Honavar, V., Dobbs, D. (2007) RNABindR:  A server for analyzing and predicting RNA binding sites in proteins. Nucleic Acids Res., [Epub ahead of print]. In press.
  • Andorf, C., Dobbs, D. and Honavar, V. (2007) Exploring inconsistencies in genome-wide protein function annotations. BMC Bioinformatics. In press.
  • EL-Manzalawy, Y., Dobbs, D., Honavar, V. (2007) PepMIL: A novel method for predicting flexible length MHC-II binding peptides. BMC Bioinformatics. In press
  • Yan, C., Wu, F., Jernigan, R., Dobbs, D. and Honavar, V. (2007) Characterization of protein-protein interfaces. Protein J. In press.

Vita: Click here to download

Website: Drena Dobbs

*Karin Dorman

Education: Ph.D., UCLA, 2001

Research Interests: Mathematical modeling, virus evolution, phylogenetics

  • Genome Evolution-- I employ mathematical models and computational tools to capture the essential aspects of biological systems. I am interested in the role of pathogen diversity in diseases caused by rapidly evolving organisms (HIV-1, EIAV, HCV), statistical techniques for the detection of recombination or gene conversion, and stochastic models for explaining the uncertainty in biological outcomes
  • Mathematical Biology, Computational Modeling, and Metabolic and Developmental Networking-- Same as above

Description: I am interested in the genomic changes occurring in fast-evolving viruses and how these changes can be used to better understand how they cause disease. I have developed methods to detect recombination and selection in the hundreds and thousands of genomic sequences available for viruses like HIV, HBV, and EIAV. I am currently developing new methods to detect and quantitate selection in overlapping reading frames, a genomic structure common in viruses and heretofor largely unstudied. I am also developing computational models of virus/host interactions and the accompanying statistical methods for data analysis and tting. In the future, I will continue to develop new methods for genetic analysis, for example a model to estimate transposon birth and death rates, and explore extended models of host/pathogen interactions, for example to test the hypothesis that changes in EIAV during disease are helping the virus evade a maturing immune response.

Selected Publications:

  • J. A. Farfan-Ale, M. A. Loro~no-Pino, J. E. Garcia-Rejon, E. Hovav, A. M. Powers, M. Lin, K. S. Dorman, K. B. Platt, L. C. Bartholomay, V. Soto, B. J. Beaty, R. S. Lanciotti, B. J. Blitvich. (2008) Detection of RNA from a novel West Nile-like virus and high prevalence of an insect-specific avivirus in mosquitoes in the Yucatan Peninsula of Mexico. American Journal of Tropical Medicine & Hygiene. Accepted.
  • E. W. Bloomquist, K. S. Dorman, M. A. Suchard (2008) StepBrothers: inferring spatially shared ancestries among recombinant viral sequences. Biostatistics. In press.
  • G. M. Dancik, K. S. Dorman (2008) mlegp: statistical analysis for computer models of biological systems using R. Bioinformatics. Accepted.
  • B. Su, W. Zhou, K. S. Dorman, D. E. Jones. (2008) Mathematical modeling of immune response in tissues. Computational and Mathematical Methods in Medicine. Accepted.
  • W. O. Sparks, K. S. Dorman, S. Liu, S. Carpenter. (2008) Selection on Rev during persistent equine infectious anemia virus infection. Journal of General Virology. 89:1043-1048.
  • M. L. Rajaram, V. N. Minin, M. A. Suchard, K. S. Dorman. (2007) Hot and Cold: Spatial Fluctuation in HIV-1 Recombination Rates. Proceedings of the IEEE 7th Con- ference on Bioinformatics and Bioengineering (BIBE2007).
  • M. E. Sparks, V. Brendel, K. S. Dorman. (2007) Markov model variants for appraisal of coding potential in plant DNA. Lecture Notes in Computer Science. 4463:394{405.
  • V. N. Minin, K. S. Dorman, F. Fang, M. A. Suchard. (2007) Spatially smoothing change-point processes for phylogenetic mapping of recombination hot-spots. Genetics. 175(4):1773{1785.
  • F. Fang, J. Ding, V. N. Minin, M. A. Suchard, K. S. Dorman. (2007) cBrother: Relaxing parental tree assumptions for Bayesian recombination detection. Bioinfor- matics. 23(4):507{508.
  • K. S. Dorman. (2007) Identifying dramatic selection shifts in phylogenetic trees. BMC Evolutionary Biology. 7(suppl. 1):S10.

Vita: Click here to download

Website: Karin Dorman

Oliver Eulenstein

Education: Ph.D., Bonn, 1998

Research Interests: Design and analysis of algorithms for molecular biology

  • Bioinformatics-- Design and analysis of algorithms in computational biology: phylogenetic trees, protein folding, physical mapping, genome rearrangements, sequence alignment, fragment assembly
  • Genome Evolution-- Same as above

Description: Development of algorithms to solve problems in molecular biology

Selected Publications:

Vita: Click here to download

Website: Oliver Eulenstein

Rohan Fernando

Education: Ph.D., Illinois, Urbana-Champaign, 1984

Research Interests: Methods for mapping QTL and for marker-assisted selection

  • Functional and Structural Genomics-- Mapping and characterizing quantitative trait loci; use of genetic data in animal breeding

Description: Statistical methodology for mapping QTL; marker assisted selection; and genetic evaluation and parameter estimation in crossbred populations

Selected Publications:

  • Gianola, D., R. L. Fernando, and A. Stella. 2006. Genomic assisted prediction of genetic value with semi-parametric procedures. Genetics 173:1761-1776.
  • Minick, J.A., L. R. Totir, D. E. Wilson, R. L. Fernando. 2006. Conception rate in Angus heifers. J. Anim. Sci. 84:2022-2025.
  • Abraham, K. J., L. R. Totir, and R. L. Fernando. 2007. Improved techniques for sampling complex pedigrees with the Gibbs sampler. GSE 39: 27-38.
  • Zhao, H.H., R.L. Fernando, and J.C.M. Dekkers. 2007. Power and precision of alternate methods for linkage disequilibrium mapping of QTL. Genetics 175: 1975-1986
  • Habier, D., R.L. Feranndo And J.C.M. Dekkers. 2007. The Impact of genetic relationship information on genome-assisted breeding values. Genetics 177:2389-3397.
  • Fernando, R.L., D. Habier, C. Stricker, J.C.M. Dekkers, and L.R. Totir. 2007. Genomic selection. Acta Agricultrae Scandinavica, Section A, 57:192-195.
  • Fernando, R. L. Mapping QTL in Outbred Populations. In Biotechnology and Quantitative Genetics.
  • Pita, F. V. C., R. L. Fernando, L. R. Totir, M. Schelling, C. Stricker, S. A. Fernandez, J. C. M. Dekkers, P. S. Lopes. A comparison of public domain programs for computing identity by descent coefficients. Genetics Selection and Evolution.
  • Pita, F. V. C., R. L. Fernando, L. R. Totir, P. S. Lopes. An improved approximation of the gametic covariance matrix for marker assisted genetic evaluation by BLUP. Genetic Selection and Evolution.
  • Santos, N. T., A. E. Freeman, R. L. Fernando, J. C. M. Dekkers. Estimation of Adjustment Factors for Three Times a Day Milking Using a Random Regression Model. J. Dairy. Sci.
  • Totir, L. R., R. L. Fernando, and K. J. Abraham. An efficient algorithm to compute marginal posterior genotype probabilities for every member of a pedigree with loops. Genetics.

Vita: Click here to download

Website: Rohan Fernando

*M. H. West Greenlee

Education: Ph.D., Iowa State, 1999

Research Interests: The molecular basis of neural differentiation

Description:

My laboratory is focused on cell fate determination in the developing retina. Within this context we have generated protein expression datasets, and utilized previously reported gene expression data to identify genes and gene networks involved in this process. My intent is to develop the developing retina as a model for Systems Biology research. Bioinformatics and collaboration in Computational Biology are integral to my agenda.

Selected Publications:

  • Laura A. Hecker, Tim C. Alcon, Vasant G. Honavar, M. H. West Greenlee (2008) Using a seed-network to query large scale gene expression data from the developing retina. Bioinformatics and Biology Insights, 2008:2 91-102.
  • Samantha Van Hoffelen, M. Heather W Greenlee, Matthew M Harper, Daniel T Au. (2008) Cell birth and death in the developing retina of the Brazilian opossum, Monodelphis domestica. Brain Res. 1195:28-42
  • Tyra Dunn-Thomas, Drena L. Dobbs, Donald S. Sakaguchi, Michael J. Young, Vasant G. Honavar, M. Heather West Greenlee. (2008) Proteomic differentiation between murine retinal and brain derived progenitor cells. Stem Cells and Development, Jan 22 [Epub ahead of print]
  • J. Smith, J. Greenlee, A. Hamir, and M. H. West Greenlee (2008) Retinal cell types are differentially affected in sheep with scrapie. Comparative Pathology: 138:12-22.
  • J. Eucher, E. Uemura, D. Sakaguchi, M. H. West Greenlee (2006) Amyloid-beta peptide affects viability but not differentiation of embryonic and adult rat hippocampal progenitor cells. J. Exp. Neurol. 203:486-92

Vita: Click here to download

Website: M. Heather West Greenlee

*Xun Gu

Education: Ph.D., Texas, Houston, 1996

Research Interests: Computational biology, molecular evolution, comparative genomics

  • Genome Evolution-- Computational molecular biology; molecular evolution; comparative genomics

Description:

Selected Publications:

  • H Zhou, J Gu, S J. Lamont, Gu X * (2006) Evolutionary analysis for functional divergence of Toll-like receptor gene family and altered functional constraints. Journal of Molecular Evolution (in press)

  • Leebens-Mack, J, Vision T, Brenner E,…Gu X … (2006) Taking the first steps towards a standard for reporting on phylogenies: Minimal Information About a Phylogenetic Analysis (MIAPA). OMIC Integrated Biology (in press).

  • Guo, H, Weiss R. E., Gu, X, and Suchard, M. A. (2006) Time Squared: Repeated Measures on Phylogenies. Molecular Biology and Evolution (in press).

  • Gu X. (2006) A Simple Statistical Method for Estimating Type-II (Cluster-Specific) Functional Divergence of Protein Sequences. Mol Biol Evol. Jul 24; [Epub ahead of print]

  • Lin H, Zhu W, Silva JC, Gu X, Buell CR. (2006) Intron gain and loss in segmentally duplicated genes in rice. Genome Biol. 7(5):R41.

  • Su Z, Wang J, Yu J, Huang X and Gu X*(2006) Evolution of Alternative Splicing after Gene Duplications. Genome Res. 16(2):182-9.

  • Wu Q, Gu X, Wang Y, Li N, Liu X, Wu C, Yu L, Gu X. (2006) Neurotransmitter inactivation is important for the origin of nerve system in animal early evolution: a suggestion from genomic comparison. Prog Neurobiol. 78(6):390-5.

Vita: Click here to download

Website: Xun Gu

Kai-Ming Ho

Education: Ph.D., Berkeley, 1978

Research Interests: Protein structure and dynamics, membranes.

Selected Publications:

  • H. B. Cao, Y. Ihm, C. Z. Wang, J. R. Morris, M. Su, D. Dobbs, and K. M. Ho. Three-dimensional
    threading approach to protein structure recognition, Polymer 45:687 (2004).
  • W. C. Lu, C. Z. Wang, and K. M. Ho, "Effect of Chain Connectivity on the Structure of Lennard-Jones Liquid and Its Implication on Statistical Potentials for Protein Folding",Phys. Rev. E 69, 061920 (2004).
  • D. M. Deaven and K. M. Ho. Molecular geometry optimization with a genetic algorithm, Phys. Rev. Lett. 75:288 (1995).
  • H. B. Cao, C. Z. Wang, D. Dobbs, Y. Ihm, and K. M. Ho " Codability criterion for picking proteinlike
    structures from random three-dimensional configurations", Phys. Rev. E 74, 031921 (2006).
  • W. C. Lu, C. Z. Wang, E. W. Yu, and K. M .Ho Dynamics of the trimeric AcrB transporter protein
    inferred from B-factor analysis of the crystal structure, Proteins 62, 152 (2006).
  • T. Z. Sen, A. Kloczkowski, R. L. Jernigan, C. H. Yan, V. Honavar, K. M. Ho, C. Z. Wang, Y. Ihm, H. B. Cao, X. Gu, and D. Dobbs Predicting binding sites of hydrolase-inhibitor complexes by combining several
    methods, BMC Bioinformatics 5, 205 (2004).
  • H. B. Cao, C. Z. Wang, and K. M. Ho Fast method for estimating the energy distribution of globular states
    of proteins", Phys. Rev. E 72, 021907 (2005).
  • Y. Ihm, W. O. Sparks, J.-H. Lee, H. B. Cao, C. Z. Wang, S. Carpenter, K. M. Ho, and D. Dobbs,
    Structural Model of the Rev Regulatory Protein from Equine Infectious Anemia Virus (EIAV) submitted.

Vita: Click here to download

Website: Kai-Ming Ho

*Vasant Honavar

Education: Ph.D., Wisconsin, Madison, 1990

Research Interests: Bioinformatics and computational systems biology, artificial intelligence, computational systems biology, data mining and machine learning, databases and knowledge bases, information integration, semantic web, e-science

  • Bioinformatics--Ontology-driven and probabilistic approaches to integrative and collaborative analysis of disparate biological data sets; description logics, ontology design, ontology tools, semantic web for life sciences including semantics based data integration, web services.
  • Macromolecular Structure and Function--Data-driven discovery of macromolecular sequence-structure-function-interaction-expression relationships, identification of sequence and structural correlates of protein-protein , protein-RNA, and protein-DNA interactions, protein sub-cellular localization, automated protein structure and function annotation
  • Computational Systems Biology-Qualitative, probabilistic, and dynamic modeling, simulation, and inference of protein-protein interaction networks, genetic regulatory networks, signal transduction networks and metabolic pathways; Biological Computation Evolutionary, Cellular and Neural Computation.
  • Data Mining and Machine Learning- Statistical, information theoretic, linguistic and structural approaches to machine learning, Learning and refinement of Bayesian networks, causal networks, Markov networks and Markov random fields, decision networks, neural networks, support vector machines, kernel classifiers, multi-relational models, language models (n-grams, grammars, automata), Learning classifiers from sequential and spatial data; Learning relationships from multi-modal data (e.g., text, images), Learning classifiers from distributed data, multi-relational data, and semantically heterogeneous data; Incremental learning, Ensemble methods, multi-agent learning, spectral clustering, selected topics in computational learning theory.
  • Semantic Web for e-science: Ontology-based user and query-centric approaches to information integration and acquisition of sufficient statistics for learning from data under different access and resource constraints from heterogeneous, distributed, autonomous, ubiquitous information sources, sensor networks, peer-to peer networks; description logics, ontology design, ontology tools, ontology-extended information sources, ontology-extended workflow components, ontology-extended agents and services, web service composition.

Description: My research interests cut across Computer Science, Information Science, Statistics, Cognitive Science, and Biological Sciences. This research is driven by fundamental scientific questions or important practical problems such as the following:

  • How can we query and use information from autonomous, heterogeneous, distributed, autonomous data and knowledge sources?
  • How can we build useful predictive models from large, distributed, semantically heterogeneous, autonomous data sources?
  • How can we develop software environments for collaborative development, sharing, and use of large, complex, knowledge bases?
  • How can we develop sophisticated machine learning algorithms for knowledge acquisition from richly structured data (sequences, images, graphs, text, etc.)
  • How can we support the design, assembly and execution of complex web services using autonomously developed components?
  • How can we represent and manipulate scientific knowledge in a form that lends itself to automated processing by the computer and at the same time, is comprehensible by, and communicable to humans?
  • How can we develop information processing models of perception, learning, inter-agent communication, multi-agent interaction, coordination, and organization?
  • How is information encoded, stored, retrieved, decoded, and used in macromolecular, neural, and cognitive systems?
  • How can we discover the relationships between macromolecular sequence, structure, expression, interaction and macromolecular function?
  • How can we construct accurate predictive models of signaling networks involved in cellular development, differentiation, and biological function?
  • How can we model complex systems at multiple levels of abstraction in space and time?
  • How can we automate scientific discovery?
Additional information about current projects can be found at www.cild.iastate.edu   

Selected Publications:

  • 1. Bao, J., Voutsadakis, G., Slutzki, G., and Honavar, V. (2008). On the Decidability of Role Mappings between Modular Ontologies. Proceedings of the 23nd Conference on Artificial Intelligence (AAAI-2008), AAAI, 404-409.
  • 2. Caragea, C. and Honavar, V. (2008). Machine Learning in Computational Biology. In: Encyclopedia of Database Systems. Raschid, L. (ed). Springer. To appear.
  • 3. Caragea, D. and Honavar, V. (2008). Learning Classifiers from Distributed Data. In: Encyclopedia of Database Technologies and Applications, Ferraggine, V.E., Doorn, J.H., and Rivero, L.C. (Ed). New York: Idea Group. In press.
  • 4. Caragea, D. and Honavar, V. (2008). Learning Classifiers from Semantically Heterogeneous Data. In: Encyclopedia of Data Warehousing and Mining. Wang, J. (Ed). In press.
  • 5. Dunn-Thomas, T., Dobbs, D.L., Sakaguchi, D. Young, M.J. Honavar, V. Greenlee, H. M. W. (2008). Proteomic Differentiation Between Murine Retinal and Brain Derived Progenitor Cells. Stem Cells and Development. 17: 191-131.
  • 6. El-Manzalawy, Y., Dobbs, D., and Honavar, V. (2008). Predicting linear B-cell epitopes using string kernels. Journal of Molecular Recognition, DOI:10.1002/jmr.893
  • 7. El-Manzalawy, Y., Dobbs, D., and Honavar, V. (2008). Predicting Flexible Length Linear B-cell Epitopes, 7th International Conference on Computational Systems Bioinformatics, Stanford, CA. Singapore: World Scientific. In press.
  • 8. Hecker, L., Alcon, T., Honavar, V., and Greenlee, H. (2008). Analysis and Interpretation of Large-Scale Gene Expression Data Sets Using a Seed Network. Journal of Bioinformatics and Biology Insights. Vol. 2. pp. 91-102.
  • 9. Honavar, V. and Caragea, D. (2008). Towards a Semantics-Enabled Infrastructure for Knowledge Acquisition from Distributed Data. In: Next Generation Data Mining. Kargupta, H. et. al., CRC Press. In press.
  • 10. Hughes, L., Bao. J., Honavar, V., and Reecy, J. (2008). Animal Trait Ontology (ATO): the importance and usefulness of a unified trait vocabulary for animal species. Journal of Animal Science. In press.

Vita: Click here to download

Website: Vasant Honavar

*Richard Honzatko

Associate Faculty Member

Education: Ph.D., Harvard, 1982

Research Interests: Structure-function studies of proteins, X-ray diffraction

  • Macromolecular Structure and Function--Structure-function studies of proteins by X-ray diffraction and biochemical techniques

Description: Structure determination of macromolecules of biological interest, crystallization of proteins and x-ray crystallography, energy-conformation analysis of protein ligand interactions

Selected Publications:

  • Skaff, D.A., Kim, C.S., Tsai, H.J., Honzatko, R.B. and Fromm, H.J. (2005) Glucose 6-phosphate release of wild-type and mutant human brain hexokinases from mitochondria. J. Biol. Chem. 280,38403-38409.
  • Iancu, C.V., Mukund, S., Fromm, H.J. and Honzatko, R.B. (2005) R-state AMP complex reveals initial steps of the quaternary transition of fructose-1,6-bisphosphatase. J. Biol. Chem. 280, 19737-19745.

Vita: Click here to download

Website: Richard Honzatko

Xiaoqiu Huang

Education: Ph.D., Penn State, 1990

Research Interests: Computational problems in genome sequencing and analysis

  • Bioinformatics-- Computational problems in genome sequencing and analysis

Description: Assembly of DNA fragments into longer sequences, identification of genes in genomic DNA sequences, comparison of genomic DNA sequences.Xiaoqiu Huang is an associate professor in computer science at Iowa State University. He received his Ph.D. in computer science from Pennsylvania State University in 1990. Xiaoqiu Huang is interested in computational problems in genome sequencing and analysis. He is the author of a widely used CAP3 assembly program. He and his colleagues have recently developed a whole-genome assembly program named PCAP. PCAP has been used by Washington University Genome Center in chimpanzee and chicken genome projects.

Selected Publications:

  • Huang, X. and Madan, A. (1999) CAP3: A DNA Sequence Assembly Program, Genome Research, 9: 868-877.
  • Huang, X., Wang, J., Aluru, S., Yang, S.-P. and Hillier, L. (2003) PCAP: A Whole Genome Assembly Program, Genome Research, 13: 2164-2170.
  • Huang, X. and Chao, K.-M. (2003) A Generalized Global Alignment Algorithm, Bioinformatics, 19: 228-233.
  • Huang, X., Ye, L., Chou, H.-H., Yang, I-H. and Chao, K.-M. (2004) Efficient Combination of Multiple Word Models for Improved Sequence Comparison, Bioinformatics, 20: 2529-2533.
  • Ye, L. and Huang, X. (2005) MAP2: Multiple Alignment of Syntenic Genomic Sequences. Nucleic Acids Research, 33: 162-170.
  • Huang, X., Adams, M.D., Zhou, H. and Kerlavage, A.R. (1997) A Tool for Analyzing and Annotating Genomic Sequences. Genomics, 46: 37-45.
  • Huang, X., Yang, S.-P., Chinwalla, A., Hillier, L., Minx, P., Mardis, E. and Wilson, R.  (2006) Application of a Superword Array in Genome Assembly,
    Nucleic Acids Research, 34: 201-205.
  • Huang, X. and Brutlag, D.L. (2007) Dynamic Use of Multiple Parameter Sets in Sequence Alignment,
    Nucleic Acids Research, 35: 678-686.

Vita: Click here to download

Website: Xiaoqiu Huang

Fred Janzen

Associate BCB Faculty Member

Education: Ph.D., Chicago, 1992

Research Interests: Modeling phenotypic selection, demography, phylogenetics

  • Genome Evolution--Theoretical and empirical investigations of natural selection and phenotypic evolution; phylogenetic reconstruction and molecular evolution; modelling population demography and cycling environmental parameters

Description: Research interests in the Janzen Lab involve the study of ecology and evolution, including mechanistic work at the molecular and organismal levels, field studies that document the importance of phenotypic variation, and a comparative view of the long-term consequences of this variation. To do so, we often integrate molecular and quantitative genetic techniques with experimental laboratory and field studies, largely focusing on the impact of environmental and genetic factors in mediating the expression of physiological, behavioral, and life-history traits. Using these conceptual approaches in concert with comparative and computational approaches enables us to assess important biological issues, including

  1. the biological significance of diverse sex-determining mechanisms,
  2. the impacts of environmental and genetic factors on variation in early life-history traits, and
  3. the current and historical genetic and demographic structure of populations, with an emphasis on elucidating adaptive processes and solving conservation concerns.

Selected Publications:

  • Schwanz, L. E., and F. J. Janzen. 2008. Climate change and temperature-dependent sex determination: can individual plasticity in nesting phenology prevent extreme sex ratios? Physiological and Biochemical Zoology 81:in press.

  • St. Juliana, J. R., and F. J. Janzen. 2007. Can natural phenotypic variances be estimated reliably under homogeneous laboratory conditions? Journal of Evolutionary Biology 20:1406-1414.
  • Janzen, F. J., and P. C. Phillips. 2006. Exploring the evolution of environmental sex determination, especially in reptiles. Journal of Evolutionary Biology 19:1775-1784 .

  • Janzen, F. J., and J. G. Krenz. 2004. Phylogenetics: Which was first, TSD or GSD? Pp. 121-130 in N. Valenzuela and V. A. Lance (eds.), “ Temperature-Dependent Sex Determination in Vertebrates.” Smithsonian Books, Washington, DC.

  • Valenzuela, N., D. C. Adams, and F. J. Janzen. 2003. Pattern does not equal process: exactly when is sex environmentally determined? American Naturalist 161:676-683.

  • Janzen, F. J. 1995. Experimental evidence for the evolutionary significance of temperature-dependent sex determination. Evolution 49:864-873.

Vita: Click here to download

Website: Fred Janzen

*Robert Jernigan

Education: Ph.D., Stanford, 1967

Research Interests: Computational structural biology and bioinformatics

  • Macromolecular Structure and Function-- theoretical and computational studies of the structures of proteins, nucleic acids, and small molecules, and their interactions. Applications are made to develop molecular models and to select new drugs. more

Description: Elastic network models of bio-structures are used to develop mechanisms of processing, refine and improve structures, predict conformational transitions. Applications to learn about the mechanics of the ribosome are pursued. Sequence matching is being improved by including structural information and to improve comparative genomics efforts.

Selected Publications:
  • Cui, F, Jernigan, R, Wu, Z Knowledge-based versus experimentally acquired distance and angle
    constraints for NMR structure refinement. J Bioinform Comput Biol 2008;6:283-300.
  • Yang L, Song G, Jernigan RL How well can we understand large-scale protein motions using elastic
    normal modes? Biophys J 2007; 93:920-929.
  • Wu D, Cui F, Jernigan R, Wu, ZJ PIDD: Database for protein inter-atomic distance distributions. Nucl
    Acids Res 2007;35:D202-D207.
  • Miyazawa, S. and Jernigan, R.L. Estimation of effective inter-residue contact energies from protein
    crystal structures: quasi-chemical approximation. Macromolecules 1985; 18: 534-552.
  • Pokarowski, P., Kloczkowski, A., Jernigan, R.L., Kothari, N.S., Pokarowska, M. and Kolinski, A.
    Inferring ideal amino acid interaction forms from statistical protein contact potentials, Proteins: Struct.
    Funct. Bioinf. 2005; 59: 49-57.

Vita: Click here to download

Website: Robert Jernigan

Douglas Jones

Associate BCB Faculty Member

Education: Ph.D., University of Pennsylvania, 1993

Research Interests: Host factors that influence the development of resistance and susceptibility to infectious diseases caused by intracellular pathogens

  • Macromolecular Structures and Functions--Dr. Jones researches the host factors that influence the development of resistance and susceptibility to infectious diseases caused by intracellular pathogens. The laboratory studies the murine immune response to the protozoal parasite Leishmania and the bovine immune response to Mycobacterium avium subs paratuberculosis

Description:

Selected Publications:

Vita: Click here to download

Website: Doug Jones

Susan Lamont

Associate BCB Faculty Member

Education: Ph.D., Illinois Medical Center, 1980

Research Interests: Molecular markers, gene expression, quantitative trait loci, biodiversity. Structural and functional genomic associations with biological traits

  • Functional and Structural Genomics--Molecular genetic dissection of complex biological traits in poultry. Structural and functional genomics
  • Mathematical Biology, Computational Modeling, and Metabolic and Developmental Networking-- Estimation of genetic relationships by molecular analysis; characterization of gene regulatory regions; mathematical modelling and genetic dissection of complex biological traits; estimating epistatic effects of molecular markers

Description: Dr. Lamont's program investigates the associations of structural and functional genomic variation with complex biological traits in poultry.  Taking advantage of the complete draft genome sequence of the chicken genome, a 2.8-million SNP map, and unique genetic resource populations at Iowa State, specific projects seek to dissect the complex genetic architecture of traits such as disease resistance, growth and development.

Selected Publications:

  • Ye, X., Brown, S.R., Nones, K., Coutinho, L.L., Dekkers, J.C.M. and Lamont. S.J. 2007. Associations of myostatin gene polymorphisms with performance and mortality traits in broiler chickens. Genet. Sel. Evol. 39: 73-89.
  • Zhou, H., Deeb, N., Evock-Clover, C., Mitchell, A., Ashwell, C. and Lamont, S.J. 2007. Genome-wide Linkage Analysis to identify chromosomal regions affecting phenotypic traits in the chicken. III. Skeletal integrity. Poultry Sci. 86:255-266.
  • Zhou, H., Evock-Clover, C., McMurtry, J.P., Ashwell, C. and Lamont, S.J. 2007. Genome-wide Linkage Analysis to identify chromosomal regions affecting phenotypic traits in the chicken. IV. Metabolic traits. Poultry Sci. 86:267-276.
  • Abasht, B. Dekkers, J.C.M, and Lamont, S.J. 2006. Review of quantitative trait loci Identified in the chicken. Poultry Sci. 85:2079-2096.
  • Cheeseman, J.H., Kaiser, M.G., Ciraci, C., Kaiser, P. and Lamont, S.J. 2006. Breed effect on early cytokine mRNA expression in spleen and cecum of chickens with and without Salmonella enteritidis infection. Devel. Comp. Immunol. 31: 52-60.
  • Hangalapura, B. N., Kaiser, M. G., van der Poel, J.J., Parmentier, H. K., and Lamont, S. J. 2006. Cold stress equally enhances in vivo pro-inflammatory cytokine gene expression in chicken lines divergently selected for antibody responses. Develop. Comp. Immunol. 30:503-511.
  • Hasenstein, J.R., Zhang, G., and Lamont, S.J. 2006. Analyses of five gallinacin genes and the Salmonella enterica serovar enteritidis response in poultry. Infection & Immunity 74:3375-3380.
  • Kaiser, M.G., J.H. Cheeseman, Kaiser, P., and Lamont, S.J. 2006. Cytokine expression in chicken peripheral blood mononuclear cells after in vitro exposure to Salmonella enterica serovar Enteritidis. Poultry Sci 85:1907-1911. Lamont, S.J. 2006. Perspectives in chicken genetics and genomics. Poultry Sci. 85:2048-2049.
  • Wick, G., Andersson, L., Hala, K., Gershwin, M. E., Selmi, C.F., Erf, G. F., Lamont, S.J., and Sgonc, R. 2006. Avian models with spontaneous autoimmune diseases. Pp. 71-117. In: Advances in Immunology. F.W. Alt, Ed. Elsevier/Academic Press, San Diego, CA
  • McElroy, J.P., Kim, J.J., Harry, D.E., Brown, S.R., Dekkers, J.C., and Lamont, S.J. 2006. Identification of trait loci affecting white meat percentage and other growth and carcass traits in commercial broiler chickens. Poultry Sci. 85:593-605.
  • McElroy, J.P., Zhang, W., Koehler, K.J., Lamont, S.J., and Dekkers, J.C.M. 2006. Comparison of methods for analysis of selective genotyping survival data. Genetics Selec. Evol. 38:637-655.
  • Soller, M., Weigend, S., Romanov, M.N., Dekkers, J.C.M., and Lamont, S.J. 2006. Strategies to assess structural variation in the chicken genome and its associations with biodiversity and biological performance. Poultry Sci. 85:2061-2078.
  • Ye, X., Avendano, S., Dekkers, J.C.M. and Lamont, S.J. 2006. Association of twelve immune-telated genes with performance of three broiler lines in two different hygiene environments. Poultry Sci. 85:1555-1568.
  • Ye, X., McLeod, S., Elfick, D., Dekkers, J.C.M., and Lamont, S.J. 2006. Rapid identification of single nucleotide polymorphisms and estimation of allele frequencies using sequence traces from DNA pools. Poultry Science 85: 1165-1168.
  • Zhou, J., Deeb, N., Ashwell, C.M., and Lamont, S.J. 2006. Genome-wide linkage analysis to identify chromosomal regions affecting phenotypic traits in the chicken. I. Growth and average daily gain. Poultry Sci. 85:1700-1711.
  • Zhou, J., Deeb, N., Ashwell, C.M., and Lamont, S.J. 2006. Genome-wide linkage analysis to identify chromosomal regions affecting phenotypic traits in the chicken. II. Body composition. Poultry Sci. 85: 1712-1721.
Vita: Click here to download

Website: Susan Lamont

*Dennis Lavrov

Education: Ph.D., University of Michigan, Ann Arbor, 2000

Research Interests: My main research interests are the evolution of major groups of animals and their mitochondrial genomes.  My current research is focused on three groups of non-bilaterian animals: Cnidaria, Ctenophora, and Porifera.  Ongoing projects include: Comparative animal mitochondrial genomics; Parallel mitochondrial genome evolution; Phylogenetic analysis of basal animal relationships; and Cell-cell communication in sponges.

  • Bioinformatics--Molecular evolution, phylogenetics, comparative and functional genomics; use of gene order data for the analysis of ancient relationships; evolution of animal mitochondrial DNA with a special emphasis on arthopods and sponges; bioinformatics
  • Functional and Structural Genomics--Same as above
  • Genome Evolution--Same as above

Description:

Selected Publications:

  • Lavrov, D. V. 2007. Key transitions in animal evolution: a mitochondrial DNA perspective.  Integrative and Comparative Biology. 47:734-743.
  • Haen, K. M., Lang, B. F., Pomponi, S. A. and Lavrov D. V. 2007. Mitochondrial genomes of the glass sponges Iphiteon panicea and Sympagella nux (Hexactinellida, Plakinidae): an evidence of the bilaterian affinity or an example of parallel evolution? Molecular Biology and Evolution. 24:1518-1527.
  • Wang, X. and D. V. Lavrov 2007. Mitochondrial Genome of the Demosponge Oscarella carmela (Porifera, Demospongiae) Reveals Unexpected Complexity in the Common Ancestor of Sponges and Other Animals  Molecular Biology and Evolution.  24: 363-373.
  • Lavrov, D. V. and B. F. Lang, 2005.  Transfer RNA gene recruitment in mitochondrial DNA.  Trends in Genetics.  21:129-133.
  • Lavrov D.V. and Lang B.F. 2005 Poriferan mtDNA and animal phylogeny based on mitochondrial gene arrangements Systematic Biology 54:651-659.
  • Lavrov D.V., Forget L., Kelly, M., and Lang B.F. 2005 Mitochondrial genomes of two demosponges provide insights into an early stage of animal evolution Mol. Biol. Evol. 22:1231-1239.

Vita: Click here to download

Website: Dennis Lavrov

*Carolyn Lawrence

Education: Ph.D., The University of Georgia, 2003

Research Interests: My group manages the operation of the Maize Genetics & Genomics Database (MaizeGDB) and also investigates functional aspects of maize chromosomes during cell division. The work at MaizeGDB is focused on creating data storage, access, and analysis solutions for information generated by the community of maize geneticists. The maize chromosome research focuses on how chromosome move and how the physical structure of chromosomes relates to maps and DNA sequences. http://www.lawrencelab.org

Selected Publications:

  • L.D. Stein, W.D. Beavis, D.D. Gessler, E. Huala, C.J. Lawrence, D. Main, L.A. Mueller, S.Y. Rhee, and D.S. Rokhsar, Save our data!, The Scientist 20(4):24-25, 2006.
  • Lawrence, C.J. and Walbot, V. Maize as a model for bioenergy production from fuelstock grasses. The Plant Cell 19(7):2091-2094. 2007.
  • Duvick, J., Fu, A., Muppirala, U., Sabharwal, M., Wilkerson, M.D.,  Lawrence, C.J., Lushbough, C., and Brendel, V. PlantGDB: a resource for comparative plant genomics. Nucleic Acids Research 36(Database issue):D959-965. 2008.
  • Lawrence, C.J. MaizeGDB, the maize genetics and genomics database. In Plant bioinformatics, D. Edwards (Editor) for the series Methods in Molecular Biology. pp. 331-345. Humana Press, Totowa, New Jersey, USA. 2007.
  • Lawrence, C.J. and Walbot, V. Reply: specific reasons to favor maize in the U.S. Plant Cell 19(10):2973. 2007.
  • Lawrence, C.J. and Ware, D. Databases and data mining. In The maize handbook, S. Hake and J. Bennetzen (Editors), Springer. Accepted October 15, 2007.
  • Harper, L.C., Sen, T.Z., and Lawrence, C.J. Plant cytogenetics in genome databases. In: Plant cytogenetics: genome structure and chromosome function. J. Birchler and H. Bass (Editors), Springer.  Accepted October 30, 2007.
Vita: Click here to download

Website: Carolyn Lawrence

Howard Levine

Education: Ph.D., Cornell, 1969

Research Interests: Modeling of angiogenesis and tumor growth, chemotaxis in biological systems. Mathematical modeling of transport and branching processes in biological systems

  • Mathematical Biology, Computational Modeling, and Metabolic and Developmental Networking-- Mathematical modeling of biological branching processes including angiogenesis, vasculogenesis, neuronal growth, mammary duct development involving cell-cell and intra cellular signal transduction pathways.

Selected Publications:

  • Levine, H. A. and M. Nilsen-Hamilton A mathematical analysis of SELEX, Journal of Computational Biology and Chemistry , 31 (2007) 11-35 K.
  • Boushaba, Levine, H. A and Nilsen-Hamilton, M., A mathematical model for the regulation of metastatic tumor dormancy based on enzyme kinetics, Bull. Math. Biol., 68, 2006, 1-32.
  • Levine, H. A. and Smiley, M. W. , Tucker, A. and Nilsen-Hamilton, M) A mathematical model for the formation of avascular tumors based on the role of the p53 tumor suppressor gene, Cancer Informatics, 2, 2006 , 163-188.
  • Levine, H. A. and M. Nilsen-Hamilton, Angiogenesis-A Biochemical/Mathematical Perspective, Chapter 2., in Tutorials in mathematical biosciences: Cell cycle, proliferation, and cancer (Vol. 3). A. Friedman,ed., Springer-Verlag, Berlin, Heidelberg, New York , 2006.
  • Levine, H. A. , Renclawowicz, J., Singularity formation in chemotaxis - A conjecture of Nagai\jour( SIAM J. Appl. Math., ) 65(1) , 2004, 336-362 Hillen , T. and Levine, H. A. Blow up and pattern formation in hyperbolic models for chemotaxis, ZAMP 54, 2003 839-868.
  • Levine, H. A., Tucker, A. and Nilsen-Hamilton, M., A mathematical model for the role of cell signal transduction in the initiation and inhibition of angiogenesis, Growth Factors, 20(4), 2002, 155-175.
  • Levine, H. A. and Sleeman, B.,D. Modelling Tumour Induced Angiogenesis, Chapter 6, in: Cancer Modelling and Simulation, L. Preziozi, eds., Chapman and Hall/CRC Press, 2003, pp. 147-183. Levine, H. A., Pamuk, S., Sleeman, B. D. and Nilsen-Hamilton, M., Mathematical modeling of capillary formation and development in tumor angiogenesis: penetration into the stroma, Bull. Math. Biol., 63(5), 2001, 801-863.
  • Levine, H. A., S. Pamuk, B. D. Sleeman and Nilsen-Hamilton, M. Mathematical modelling of tumor angiogenesis and the action of angiostatin as a protease inhibitor, J. Theoret. Med. 4(2) 2002 133-145.
  • Levine, H. A., Sleeman, B. D. and Nilsen-Hamilton, M., Mathematical Modeling of the initiation of capillary formation initiating angiogenesis, J. Math. Biol., 42(3), 2001, 195-238.
  • Levine, H. A., Sleeman, B. D. and Nilsen-Hamilton, M., A mathematical model for the roles of pericytes and macrophages in the initiation of angiogenesis: I. The role of protease inhibitors in preventing angiogenesis., Mathematical Biosciences, 168, 2000, 77-115.

Vita: Click here to download

Website: Howard Levine

*Peng Liu

Associate BCB Faculty Member

Education: Ph.D., Cornell University, 2006

Research Interests: Functional and Structural Genomics-- Statistical design and analysis of microarray experiments, statistical methods for high-dimensional data including gene expression data and proteomic data.

Publications:

  • P. Liu and J. T. G. Hwang (2007), Quick estimation of sample size while controlling false discovery rate and application to microarray analysis, Bioinformatics, 23(6): 739-746
  • R. J. H. Sawers, P. Liu, K. Anufrikova, Q. Sun, G. Olsefski, J. T. G. Hwang, T. Brutnell (2007), Gene expression profiling of bundle sheath and mesophyll photosynthetic cell-types of maize, BMC genomics, 8:12
  • S. A. Jesch, P. Liu, X. Zhao, M.  T. Wells, and S. A. Henry (2006), Multiple endoplasmic reticulum-localized protein complexes respond to phospholipid metabolism and regulate gene expression by distinct mechanisms, Journal of Biological Chemistry, 281: 24070 - 24083

Vita: Click here to download

Website: Peng Liu

*Gustavo MacIntosh

Associate BCB Faculty Member

Education: Ph.D., University of Buenos Aires, 1997

Research Interests: My laboratory is engaged in understanding the interaction between plants and pests. We use different approaches to study this problem, including metabolomics and transcrip