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Kristopher J. Irizarry, PhD

Kristopher J. Irizarry, PhD

Associate Professor, Bioinformatics, Genetics, Genomics

College of Veterinary Medicine

Phone: 909-469-5430

  • Education

    •2003 Ph.D., Biochemistry & Molecular Biology, UCLA - Los Angeles CA
    •1996 B.S., Biochemistry & Biophysics, Rensselaer Polytechnic Institute - Troy, NY

  • Education Experience

    •2003-2005, Postdoctoral Fellow Neuropsychiatric Institute, UCLA - Los Angeles CA

  • Teaching Experience

    •2006 - present, Assistant Professor, College of Veterinary Medicine, Western University of Health Sciences - Pomona CA

  • Philosophy

    Dr. Irizarry truly enjoys teaching and values the time he spends with each and every student. He believes strongly in the notion of student-centered learning, and is a life long student himself. Because he has pursued a multidisciplinary education in science, he views learning more as an art than as an end goal of the educational experience. Especially since the more he has learned, the more he realized how much he doesn't know! Accordingly, he views the role of teacher much like that of a guide who can accompany students on their journey to develop a personalized 'art of learning'. Throughout this journey, Dr. Irizarry places an important emphasis on searching for and identifying relevant resources. As a professor of bioinformatics he believes it is more valuable to be able to quickly locate an appropriate resource rather than recall a loosely related fact. As a professor in the College of Veterinary Medicine he provides content expertise in the areas of genetics, protein structure and function, cellular signaling, neuronal function, neuropsychopharmacology, pharmacogenomics as well as bioinformatics and genomics.

  • Research Interest

    Bioinformatics is a field of computational research that has emerged to manage the flood of biological information resulting from genome sequencing projects. Although genomic sequencing data has been collected and assembled for a number of different organisms, the interpretation of these molecular instructions for life's blueprint remains a challenge and is a central focus of research in the post-genomics era.
    Genomes encode both the structural building blocks of life as well as their spatial and temporal distribution patterns. Genetic variations may alter only the structural integrity of life's building blocks, just the distribution of building blocks, both or neither of these properties. Therefore deciphering which regions of the genome encode what molecular properties of cells, tissues and organs is of great importance in understanding normal and disease processes in animals and humans.

    Some biological properties are the result of only one gene residing within a single region of the genome, while other properties are under the control of hundreds of different genes, each of which has a unique location within the genome. Genetic traits controlled by a single gene, called Mendelian traits, have been well studied and many of the genes controlling these traits have been identified. Complex traits on the other hand, present a significant challenge to investigators due to the difficulty in identifying all of the genes involved and the complexity in separating out the individual contribution each gene contributes to the trait.

    Because living organisms share key features and properties with one another, comparative genomics –the field of comparing genomes from different organisms to identify evolutionarily conserved regions- has become a powerful approach to identify functionally important genomic regions involved in complex traits. The completion of multiple mammalian genomes has contributed to a situation where it is now possible to explore the genetic basis of hereditary traits, also called phenotypes, such as hair color, behavior, disease susceptibility and adverse drug effects in both animals and humans using bioinformatics and comparative genomics approaches.

    Such research employs bioinformatics methods to develop databases, algorithms and approaches for identifying genomic regions underlying complex phenotypes. Once genomic regions that are likely to be involved in the encoding of specific phenotypes are identified, subsequent effort is made to investigate how particular genetic variations within these genomic regions alter the phenotypes of interest.

    Specifically Dr. Irizarry focuses on integrating the cumulative effects of multiple genetic variations called single nucleotide polymorphisms onto the functional circuits encoded by the genome. These functional circuits, or signal transduction pathways, provide a template for assessing the phenotypic effects of genetic variation on cellular, tissue, organ and ultimately organism function. Because genomic data is very large and complex, his research employs high-throughput data mining techniques and parallel computing architectures to address genome-wide questions aimed at elucidating the genetic basis of disease susceptibility and progression.

    Dr. Irizarry's research interests can be classified into a number of different themes which all lie at the intersection of bioinformatics, genomics, evolution, signal transduction and protein function:
    (1) evolutionary models for use in reducing genomic complexity;
    (2) algorithms and approaches for mapping phenotypes onto genotypes;
    (3) single nucleotide polymorphisms across signal transduction pathways
    (4) high through-put data mining methods in comparative genomics
    (5) enhancing information exchange at the human / computer interface
    (6) parallel computing architectures for accelerated biological discovery

  • Publications

    1.) Irizarry KJ, Licinio J. "An explanation for the placebo effect?" Science. 2005 Mar 4;307(5714):1411-2.

    2.) Irizarry KJ, Merriman B, Bahamonde ME, Wong ML, Licinio J.
    "The evolution of signaling complexity suggests a mechanism for reducing the genomic search space in human association studies." Mol Psychiatry. 2005 Jan;10(1):14-26.

    3.) Licinio J, O'Kirwan F, Irizarry K, Merriman B, Thakur S, Jepson R, Lake S, Tantisira KG, Weiss ST, Wong ML. "Association of a corticotropin-releasing hormone receptor 1 haplotype and antidepressant treatment response in Mexican-Americans." Mol Psychiatry. 2004 Dec;9(12):1075-82.

    4.) Irizarry KJ, Galbraith SJ. "Complex disorders reloaded: causality, action, reaction, cause and effect." Mol Psychiatry. 2004 May;9(5):431-2, 430.

    5.) Wong ML, O'Kirwan F, Hannestad JP, Irizarry KJ, Elashoff D, Licinio J. "St. John's wort and imipramine-induced gene expression profiles identify cellular functions relevant to antidepressant action and novel pharmacogenetic candidates for the phenotype of antidepressant treatment response." Mol Psychiatry. 2004 Mar;9(3):237-51.

    6.) Lee CJ, Irizarry K. "Alternative splicing in the nervous system: an emerging source of diversity and regulation." Biol Psychiatry. 2003 Oct 15;54(8):771-6.

    7.) C. Lee and K. Irizarry "The GeneMine system for genome/proteome annotation and collaborative data mining" IBM Systems Journal. 2001 40(2):592-603.

    8.) Irizarry K, Hu G, Wong ML, Licinio J, Lee CJ. "Single nucleotide polymorphism identification in candidate gene systems of obesity." Pharmacogenomics J. 2001;1(3):193-203.

    9.) Irizarry K, Kustanovich V, Li C, Brown N, Nelson S, Wong W, Lee CJ.
    "Genome-wide analysis of single-nucleotide polymorphisms in human expressed sequences." Nat Genet. 2000 Oct;26(2):233-6.

    10.) Salerno JC, Harris DE, Irizarry K, Patel B, Morales AJ, Smith SM, Martasek
    P, Roman LJ, Masters BS, Jones CL, Weissman BA, Lane P, Liu Q, Gross SS. "An autoinhibitory control element defines calcium-regulated isoforms of nitric
    oxide synthase." J Biol Chem. 1997 Nov 21;272(47):29769-77.