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Student Achievements

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Student Publications

Hunter QuBi students are actively involved in multidiscipliary research in broad areas of bioinformatics and computational biology.  They are challenged to solve real-world problems in infectious diseases, cancer and personalized medicine and to pursue these problems with confidence.  They are encouraged and supported to participate in scientific conferences and to publish papers.  The following publications showcase their achievements.

QuBi student co-authors are shown in bold

Casjens SR, Mongodin EF, Qiu W-G, Luft BJ, Schutzer SE, Eddie B. Gilcrease, Wai Mun Huang, Marija Vujadinovic, John K. Aron, Levy C. Vargas, Sam Freeman, Diana Radune, Janice F. Weidman, George I. Dimitrov, Hoda M. Khouri, Julia E. Sosa, Rebecca A. Halpin, John J. Dunn, Claire M. Fraser. (2012) Genome Stability of Lyme Disease Spirochetes: Comparative Genomics of Borrelia burgdorferi Plasmids. PLoS ONE 7(3): e33280. doi:10.1371/journal.pone.0033280.

Dannenfelser, R., Lachmann, A., Szenk, M., & Ma'ayan, A. FNV: Light-weight Flash-based Network and Pathway Viewer. Oxford Bioinformatics (2011). first published online February 23, 2011 doi:10.1093/bioinformatics/btr098.

Haven J, Vargas LC, Mongodin EF, Xue V, Hernandez Y, Pagan P, Fraser-Liggett CM, Schutzer SE, Luft BJ, Casjens SR, Qiu WG. (2011). Pervasive Recombination and Sympatric  Genome Diversification Driven by Frequency-Dependent Selection in Borrelia burgdorferi, the Lyme disease Bacterium. Genetics. 189:951-966.

S. Nia, X. Gong, C. M. Drain, M. Jurow, W. Rizvi, M. Qureshy J. Porphyrins Phthalocyanines, 2010, 14, 621–629. “Solvent-free synthesis of meso tetraarylporphyrins in air: product diversity and yield optimization”.

Xie L, Ge X, Tan H, Xie L, Zhang YL, Hart T, Yang XW, Bourne PE (2014) Toward structural systems pharmacology to study complex diseases and personalized medicine. PLoS Comp Biol. Accepted

Ng C, Hauptman R, Zhang, Y.L., Bourne, P.E. Xie L (2014) Anti-infectious drug repurposing using an integrated chemical genomics and structural systems biology approach. Pacific Symposium of Biocomputing. pp136-147

Xie L, Ng C, Ali T, Valencia V, Ferreira BL, Xue V, Tanweer M, Zhou D, Haddad G, Bourne PE, Xie L. (2013) Multiscale modeling of the causal functional roles of nsSNPs in a genome-wide association study: application to hypoxia, BMC Genomics 14(S3):

QuBi Student Accepted to the Harvard/MIT Summer Program in Bioinformatics

A current Hunter QuBi BIO major was accepted into the Bioinformatics and Integrative Genomics Summer Institute of 2014 at Harvard/MIT.

QuBi Graduate Wins Prestigious NSF Scholarship To Do Graduate Studies at MIT

Vincent Xue, who was a Computer Science QuBi concentrator and Macauley Honors College student, won the prestigious National Science Fund's GRFP Fellowship.  The fellowship supports Vincent's graduate studies with $30,000 per year.  The Graduate Research Fellowship Program selected him because of his outstanding abilities and accomplishments as well as his potential to contribute to stengthen US science and engineering.  At the same time he was accepted to MIT's Broad Institute, where he attends graduate school as of the Fall of 2012.

Hunter scholarships for QuBi students

The first QuBi scholarship was awarded in Fall 2009 and Hunter may award up to five QuBi scholarships in each academic year

The QuBi Logo designed by Mariola Szenk, a Biophysics/Eco major:


Our participation in the MIT Winter Workshop in Quantitative Biology, January 2014

Prof. Mneimneh of Hunter's Computer Science Department and five QuBi students participated in the week long MIT Winter Workshop in Quantitative Biology in January 2014 in Boston, MA.

Our participation in Lehman College's CMACS Winter Workshop, January 2014

One Hunter QuBi student participated in Lehman College's fifth CMACS (Computer Modeling and Analysis of Complex Systems) workshop in January 2014.  He worked on modeling cellular signaling pathways.

Our participation in the MIT Winter Workshop in Quantitative Biology, January 2013

Prof. Mneimneh of Hunter's Computer Science Department and five QuBi students participated in the week long MIT Winter Workshop in Quantitative Biology in January 2013 in Boston, MA.

Our participation in Lehman College's CMACS Winter Workshop, January 2013

Three Hunter QuBi students participated in Lehman College's fourth CMACS (Computer Modeling and Analysis of Complex Systems) workshop in January 2013.  They worked on modeling arrythmia in Atrial Fibrillation.

Our participation in the MIT Winter Workshop in Quantitative Biology, January 2012

Prof. Binkowski of the Department of Mathematics & Statistics, and five QuBi students participated in the week long MIT Winter Workshop in Quantitative Biology in January 2012 in Boston, MA.

Our participation in Lehman's CMACS Winter Workshop, January 2012

Three Hunter QuBi students participated in Lehman College's third CMACS (Computer Modeling and Analysis of Complex Systems) workshop in January 2012.  They were working on Cellular Signaling Pathways and Pancreatic Cancer.

QuBi student in research workshop at Harvard in Summer 2011

Joan Marc, a Hunter QuBi student majoring in Computer Science, did a research internship in Harvard University's FAS Center for Systems Biology during the Summer of 2011.  She worked in Dr. Lahav's lab examining the dynamics of the tetramerication of the tumor suppressor p53.  The project involved mathematical modeling and molecular biology.  Last winter, she participated in the winter workshop at Lehman College.

Our participation in the MIT Winter Workshop in Quantitative Biology, January 2011

Prof. Draghicescu, a QuBi adviser and faculty member in the Department of Mathematics & Statistics, and five QuBi students participated in the week long MIT Winter Workshop in Quantitative Biology in January 2011 in Boston, MA.  As a result of attending the MIT winter workshop, one of our students was chosen to work with MIT faculty in a Bioengineering program sponsored by EBICS (Emergent Behavior of Integrated Cellular Systems) in the Summer of 2011.

Our participation in Lehman's CMACS Winter Workshop, January 2011

Two prospective QuBi students, and four other Hunter science majors participated in Lehman College's second CMACS (Computer Modeling and Analysis of Complex Systems) workshop in January 2011.  This year they were working on modeling Atrial Fibrillation, a cardiac condition.

Hunter QuBi student participated in the Summer 2010 QuBi Internship at UPitt/CMU

During the summer of 2010, Ilya Korsunsky, a Hunter Computer Science major with the QuBi concentration, worked under the supervision of James Faeder PhD and other biological and computational scientists from Carnegie Mellon University and the University of Pittsburgh to develop and test computational models of experimentally derived signaling pathways in Pancreatic cancer. Some CUNY students were presented with this opportunity after attending the Winter 2010 workshop on Computational Modeling and Analysis of Complex Systems (CMACS) hosted by Nancy Griffeth of Lehman College, in which they learned to model simple pathways in BioNetGen, a rule based biochemical modeling program.  During the summer of 2010 they continued using this modeling language with cancer pathways and used the BioLab model checking algorithm to test and fine tune the models.  Ilya found this opportunity to learn the biology of oncology from systems biologists and the theory and implementation of model checking from computational scientists most valuable.  He presented a talk about his experience at our seminar on Septemer 28, 2010 when Nancy Griffeth presented about their upcoming workshop in January of 2011.

Hunter QuBi student participated in the Summer 2010 Research Program at HST (Harvard-MIT)

Kathleeen McGovern, a Hunter Mathematics major with the QuBi concentration, spent the summer of 2010 doing research at HST (Harvard-MIT) in biomedical optics.  The program runs for nine weeks from the beginning of June to the beginning of August.  The website for the program is:  The website of the lab Kathleen was working in is:

Hunter QuBi student participated in the Summer 2010 Research Program in Quantitative Biology at MIT

Vincent Xue, another Hunter Computer Science major with the QuBi concentration, participated in MIT's summer 2010 research program in Quantitative Biology.  He, along with two other QuBi students, participated in the one week winter workshop at MIT in January of 2010.

Three Hunter QuBi students participated in the MIT Winter 2010 Workshop in Quantitative Biology - view their presentation

Lecture Series

From MIT with evoL

Speakers:          Saymon Akther, Melanie Balmick, Anna Feitzinger, Linda Huang, Daniel Packer
                         Undergraduate Hunter QuBi students
                         Saad Mneimneh, Computer Science faculty
Date:                 Wednesday, April 24, 2013
Time:                 1:10 pm - 3:00 pm
Place:                HE 921

Hunter QuBi students and faculty witll report about their experience at the 2013 MIT Workshop in Quantitative Biology.  They will talk about what a great experice this is every year to learn about computational biology and network with students from other institutions and with researchers in the field.

Overview of Modeling Cell Signaling Pathways in Cancer at CMACS 2012

Speakers:      Daniel Packer, Melanie Balmick, Anna Feitzinger, Linda Miranda
                     Undergraduate Hunter Students
Date:             Wednesday, March 14, 2012
Time:             11 am - 12 noon
Room:            HE 922

Four Hunter College undergraduates learned the science and technology of simulating cell signaling pathways at CMACS 2012, the Computational Modeling and Analysis of Complex Systems winter workshop at Lehman College.

They will talk about the research they participated in cancer pathways and will give an overview of methods of modeling and simulating such pathways.

Development of Software Tools for Microarray Analysis at EBI

Speaker:        Vincent Xue
QuBi Concentrator majoring in Computer Science, Hunter College
Date:             Tuesday, October 25, 2011
Time:             12:30 PM - 1:30 PM
Room:            HE 920

Vincent, a QuBi student majoring in Computer Science, will talk about his recent internship at the European Bioinformatics Institute (EBI) in the United Kingdom.  EBI is Europe's largest bioinformatics research institute, which provides data and bioinformatics services to the international scientific community.

Vincent will talk about the services provided by the Functional Genomics group, and will talk about his work on the meta-analysis of large scale microarray data.  Topics will include web development, curation, high performance computing and data mining.

Cellular Signaling Pathways and Pancreatic Cancer

Speaker:        Nancy Griffeth
Department of Mathematics and Computer Science
                      Lehman College of CUNY
Date:             Tuesday, October 11, 2011
Time:             11:0 AM - 1:00PM
Room:            HE 920

Prof. Griffeth leads a winter workshop each year at Lehman College, founded by NSF, in which 15 undergraduate students from CUNY learn about applying techniques developed in computer science to biological problems.  This winter's workshop will explore cellular signaling pathways and pancreatic cancer.  Any undergraduate majoring in Biology, Computer Science or Mathematics can apply.  The workshop is ungraded and work on the project is collaborative, so each student brings his or her own skills to benefit the project.  whether from the biological or from the computational side.  The workshop will be held at Lehman College from January 4 to January 24, and each participating student will receive a $1,000.00 stipend.

This is an NSF funded collaborative project to apply computational methods from computer science and mathematics to improve our understanding of cancer, heart disease and other complex systems.

Connecting genetic and transcriptional information to reveal cellular response pathways in pancreatic cancer

Speaker:        Oana Ursu
undergraduate researcher in the Department of Biological Engineering, MIT
Date:             Wednesday, April 6, 2011
Time:             1:30 PM - 2:30 PM
Room:            HN 310

In response to perturbations, cell signaling and regulatory pathways are rewired.  While these rewired response pathways can be interrogated at multiple levels, the biological interpretation is difficult due to experimental biases.  To integrate disjoint data into coherent pathways explaining the response, we use a computational method1 that relies on molecular interactions.  Our approach reconstructs a compact network, linking the genes found in multiple experiments through additional interactions with genes not detected experimentally, but likely to be involved in the pathways.

We use this approach to study mechanisms by which pancreatic cancer, PANC-1, cells display resistance to gemcitabine, the most frequently used chemotherapeutic agent for this cancer.  Despite its wide use, gemcitabine shows limited efficacy by mechanisms that are unclear.  Our approach provides testable hypotheses for the mechanisms of gemcitabine resistance, which potentially can be used to make this drug more effective, or to design complementary therapies for pancreatic cancer.

1E. Jaeger-Lotem et al. (2009) Nat. Genet. 41, 316-323

Highlights of the 2011 Quantitative Biology Workshop at MIT

Speaker:        Ilya Korsunsky (Computer Science), Kathleen McGovern (Mathematics), Pedro Pagan (Biology), Geoffrey Rice (Computer
                      Science) and Mariola Szenk (Biology)
Hunter Students in Quantitative Biology
Date:             Tuesday, March 29, 2011
Time:             3 PM - 4 PM
Room:            HN 310

Undergraduate students in Hunter’s Bioinformatics Program (QuBi) report about their experience at the MIT Quantitative Biology workshop in January, where they attended lectures on the foundations of and cutting edge research in five disciplines: genetics, biochemistry, systems biology, biostatistics and structural biology.  They will discuss the projects they presented at the end of the workshop, which highlight possible new directions for research and new ways to teach quantitative biology at the undergraduate level.

The participants' expenses were covered by Hunter's QuBi Project, which is supported by an NIH Quantitative Biology Training Grant.

Computational Modeling and Analysis of Complex Systems
NSF-CMACS Workshop in Atrial Fibrillation

Speaker:        Nancy Giffeth
Lehman College of CUNY
Date:             Tuesday, September 28, 2010
Time:             1 PM - 2 PM
Room:            The Chanin, B126 HW

Nancy Griffeth of Lehman College is part of an NSF-funded collaborative project to apply computational methods from computer science and mathematics to improve our understanding of cancer, heart disease, and other complex systems. Every winter, the project (Computational
Modeling and Analysis of Complex Systems, or CMACS) holds a workshop at Lehman College on a single topic of research. Last year, the topic was modeling cellular signaling pathways that have been implicated in the development of cancer. This coming winter, the topic is modeling the
behaviors of nerve and muscle cells in the heart to improve our understanding of atrial fibrillation.

Professor Griffeth will describe the activities and outcomes of the last workshop and discuss plans for the upcoming workshop. Ilya Korsunsky, an undergraduate at Hunter and a participant at last year's workshop, will describe some of the work he has done as part of the CMACS project.  The deadline to apply for the winter 2011 workshop has been extended till November 1, 2010.

Bioinformatics Algorithms and Data Mining: Sequence, Population and Network

Speaker:        Jie Zheng
                      National Center for Biotechnology Information, NIH
Date:             Friday, May 14, 2010
Time:             11:00 am - 12:00 noon
Room:            HN 310

Advances in biotechnology, such as next-generation sequencing, make it faster and cheaper to generate high throughput biological data.  The large volume and complexity of the new data pose algorighmic and statistical challenges.  The speaker will present computational approaches developed to meet these challenges.

He will speak of an algorighmic suite, called OligoSpawn, used to discover two types of short patterns, called "oligos" from large DNA sequence databases (unigenes).  One is called a "unique oligo", which matches one unigene, but does not match any other unigene exactly or approximately, and can be used as a probe for microarray gene expression profiling.  The other is called "popular oligo", which matches as many unigenes as possible, and can be used to identify gene-rich genomic fragments.  Using carefully engineered data structures, OligoSpawn can reduce the running time for unigene identification from weeks to a few hours on a PC.

He will also speak about a population genetics method, named LDsplit, to identify genomic loci associated with the regulation of meiotic recombination from HapMap single nucleotide polymorphisms (SNP) data.

Finally, to understand the signaling pathways at a system level in cancer cells, he will speak of a simulation and visualization program they developed, named SimBoolNet, based on probabilistic Boolean Networks.

Understanding Fungal Biology through Comparative Genomics

Speaker:         Li-Jun Ma
                       Research Scientist, The Broad Institute of MIT and Harvard
Date:              Wednesday, May 12, 2010
Time:              11:45 am - 1:00 pm
Room:             HN 926

The rapid development of new sequencing techniques made the field of genomics indispensible in virtually all areas of biomedical research.  This transformation of biological research requires that scientists combine theoretical, computational and experimental approaches.  In this presentation, I will use an example to illustrate the power of the combination of these three approaches in the understanding of genome dynamics, organism adaptation and evolutionary consequences.  Fungal species within genus Fusarium have been used as a model of such study because of their agricultural and ecological significance.  The comparative study revealed greatly expanded lineage-specific (LS) genomic regions in F. oxysporum, that include four entire chromosomes, and account for more than one quarter of the genome.  These regions are rich in transposons and genes involved in host-pathogen interactions, including known effectors, enzymes targeting plant substrata and processes and genes involved in lipid signaling and gene silencing.  The transfer of two LS chromosomes between strains of F. oxysporum was demonstrated experimentally, and resulted in the conversion of a non-pathogenic strain into a pathogen.  Transfer of LS chromosomes between otherwise genetically isolated strains explains the polyphyletic origin of host specificity and the emergence of new pathogenic lineages in the F. oxysporum species complex, putting the evolution of fungal pathogenicity in a new perspective.

Identification of Regulatory Elements and Biomarkers in Cancer

Speaker:        Mary Yang
                      National Human Genome Research Institute, NIH
Date:             Friday, April 30, 2010
Time:             11:00 am - 12:00 noon
Room:            HN 310

The advant of high-throughput sequencing technologies led to an increasing demand for high-performance computing and computational intelligence to handle the sheer volume of data.  Information retrieval, data mining and computational modeling are now essential in current cutting-edge biomedical research.  Dr. Yang's talk focuses on how the computational approach can be used effectively in studying the genome-wide regulatory regions that govern the transcription of genes, especially tumor related genes, and in identifying cancer biomarkers.

Dr. Yang investigated bidirectional promoters, the regulatory regions that fall between two genes that are oriented in opposite directions and transcribed away from one another.  These promoters are often associated with genes that function in DNA repair and metabolic pathways, with the potential to participate in the development of cancer.  So far no connection between these regolatory regions and cancer have been investigated.  Despite over 7 million spliced EST being available, previous studies of bidirectional promoters were limited to protein-coding genes because of the absensce of a systematic approach to handle EST data.  Dr. Yang developed a tree-based method to screen the spEST dataset that enabled the automated identification of a large number of novel bidirectional prromoters.  Based on the resulting comprehensive bidirectional promoter dataset from her work, Dr. Yang built a promoter database and used it to study gene regulation.  She discovered that bidirectional promoters regulate a significant amount of tumor suppressor genes, and genes involved in other disease-related pathways.

Dr. Yang also developed a systems biology method to incorporate a high throughput genome-wide expression profile with large scale protein-protein interaction data in cancer studies.  By overlaying gene expresson on top of the protein interactions, her network-based approach leads to the identification of novel cancer biomarkers, and to higher accuracy in classifying disease types.

Predictive Modeling and Knowledge Discovery of Protein-Ligand Interactome

Speaker:         Lei Xie
                       Senior Scientist at the San Diego Supercomputer Center, UC at San Diego
Date:              Friday, April 23, 2010
Time:              11:00 am - 12:00 noon
Room:             HN 310

Biological function arises from the molecular interaction network under evolutionary constraints.  To untangle the complex network, dynamics and evolution of molecular interactions we have developed a chemical systems biology approach to predict genome-wide protein-ligand interactions on a multi-scale, and to infer phenotypic effects through both static and dynamic network analysis.  Our methodology is based on a sequence order independent profile-profile alignment (SOIPPA) algorithm that enables the detection of evolutionary and functional linkage of proteins across fold space.  A protein-ligand interaction modeling ontology (PLIMO) is developed to integrate SOIPPA with molecular modeling, text mining and systems biology simulation.  This integrated approach allows for the study of the functional effect of environmental perturbation in silico.  Besides details of the algorithm, applications of the methodology will be presented for anti-infectious drug discovery and investigation of the hypertensive isde effect of the cholesteryl ester transfer protein inhibitors.

Discovering the Biological Progression Underlying Microarray Samples

Speaker:          Peng Qiu
                        Integrative Cancer Biology Program, Department of Radiology, Stanford University
Date:                Friday, April 16, 2010
Time:                11:00 am - 12:00 noon
Room:               HN 310

 In many biological systems, a clear concept of progression exists.  When a microarray study takes samples at known points during a known
progression, there exist a variety of methods to identify genes that change in a manner consistent with the progression. However, consider the situation where microarray samples are generated but the progression is not known, that is, the correct order of the experimental samples is not known. In this talk, I will present a novel computational framework, Sample Progression Discovery (SPD), to discover the progression pattern and simultaneously identify the genes related to the progression. Different from most clustering/classification methods in the literature, which focus on
identifying between-class differences (normal vs. cancer, treatment vs. control), SPD discovers intrinsic progression patterns among individual
samples, both within and across sample classes. SPD can be applied in various biology contexts, such as time series analysis, developmental
biology, and cancer progression. Results showed that SPD is able to identify the correct sample progression pattern in cell cycle time series and B-cell differentiation. When applied to lymphoma, SPD identified signatures related to the progression from indolent to aggressive cancer stages.

Volumetric Dissection of Protein Functional Sites

Speaker:         Brian Y. Chen
                       Department of Biochemistry and Molecular Biophysics, Columbia University
Date:              Friday, April 9, 2010
Time:              11:00 am - 12:00 noon
Room:             HN 310

 Living things are composed of interacting and nested systems that exhibit symptoms of health and disease at all levels.  At the most fundamental level, understanding the correct behavior of healthy systems, and resolving the breakdowns that occur in disease, requires a precise understanding of how proteins function and malfunction.  Building such an understanding is much like the examination of a complex machine.  The analysis of protein shapes can yield insight especially at functional sites, where biochemical activity occurs.

Many algorithms have been designed to gather structural observations that point to functional hypotheses that can ultimately be tested.  These methods employ a "guilt by association" approach by assigning biological function based on site similarity or resemblance to establish norms.

This talk will demonstrate that more informative approaches are possible and presents VASP as a new method that dissects functional sites to isolate individual components that play influential functional roles.  VASP enables these new capabilities by exploiting a novel connection to concepts from computer graphics and computer aided design.

We will present a case study where VASP was applied to the analysis of the major serine proteases.  This points to applications in molecular bioengineering, and to applications in structure-based drug design to mitigate side effects and avoid drug resistance.

The View from MIT: New Bioinformatics Techniques

Speakers:       Hunter QuBi students, Evan Genest, Vincent Xue and Devin Ghamandy
Date:              Monday, February 8, 2010
Time:              11:00 am - 12:00 noon
Room:             HN 310

Hunter College students, enrolled in the multi-disciplinary Quantitative Biology program, attended a five day workshop in the same field of study at MIT this past January.  All their expenses were covered by the NIH grant that made this new academic program at Hunter possible.  They will speak about what they learned at MIT, such as ChipSeq, Lumina Sequencing, and other next generation DNA techniques.

Computational Modeling for Biological Systems

Speaker:        Nancy Griffeth
                      Department of Mathematics and Computer Science, Lehman College of CUNY
Date:              Friday, October 9, 2009
Time:              12:00 noon - 1 pm
Room:             HE 920
Intended for:  Students and Faculty

Model checking has been a powerful tool for ensuring that computer hardware and software work correctly.  Model checking provides for checking that all executions of the hardware or software of a system satisfy important properties - for example properties guaranteeing that the doses of radiation never exceed safe limits when using radiation therapy, or properties guaranteeing that automobiles never approach one another too closely.  However, the success of model checking has been limited to discrete systems so far.  In this talk I will describe a major new project to use model checking for hybrid systems, that is, systems including both discrete and continuous behaviors.  The goal of the project is to address problems in biological systems - such as the growth of cancer cells - and in embedded systems - such as those controlling airplanes and automobiles.

Analysis of Hidden Markov Models Applied to Gene Finding

Speaker:        Igor Balsim
                       Kingsborough Community College, Mathematics & Computer Science
Date:              Friday, May 15, 2009
Time:              12:00 noon - 1:00 pm
Room:             HN 310
Intended for:  Students and Faculty

A Hidden Markov Model (HMM) is a stochastic model that captures the statistical properties of computational biology: protein family profiling, protein binding site recognition, and gene finding in DNA, a foremost computational biology problem to explain the molecular interactions that occur in cells and to define important cellular pathways. Applications of Hidden Markov Models (HMM) will be described in general and specifically to classifying DNA bases according to which type of job they perform during transcription. Given a sequence of DNA identify for each nucleotides as belonging to coding regions in a gene, non-coding regions in a gene, or intergenic regions. Annotate the sets of genomic data with the specific areas such as promoter regions, introns, and exons. A description will be presented how HMM is used to find protein coding genes in E.coli DNA using E.coll genome DNA sequence from the EcoSeq6 database maintained by Kenn Rudd. This HMM Includes states that model the codons and their frequencies In E.coli genes, as well as the patterns found In the intergenic region. In addition I will present statistical method to annotate alternatively spliced exons using a single genome sequence, which us an important challenge in eukaryotic gene prediction.

Scientific and Statistical Computing in the Cloud; towards a Federative and Collaborative R-based Platform

Speaker:        Karim Chine
                      European Bioinformatics Institute, Imperial College of London, UK
Date:              Wednesday, April 1, 2009
Time:              2:30 pm- 3:30 pm
Room:             HE 920
Intended for:  Students and Faculty

We proposed to build on top of R an open platform for computing and data analysis.  Using a rich workbench within the browser, the statistician can now work with an R server running at any location as if it is on his local machine.  The platform hides the complexitiy of High Performance Computing or Cloud Computing infrastructures, and enables collaborative data analysis of large data sets.  This lecture will give an overview of the new platform.  Biocep's deployment on Amazon EC2 wll be demonstrated.

Oncology Biomarkers and Personalized Medicine

Speaker:       Hyerim Lee
                      Merck & Co.
Date:              Friday, March 27, 2009
Time:              3:00 pm- 4:15 pm
Room:             HN 310
Intended for:  Students and Faculty

Personalized medicine refers to medical care tailored to individuals based on their genetic makeup, gene expression, proteins and/or metabolites.  The development of personalized medicine becomes particularly important in cancer therapy, where only 20-30% of patients respond to drub treatment.  This sobering fact calls for the development of genetic markers (biomarkers) to predict sensitivity and resistance to chemotherapeutic agents.  This presentation focuses on the biomarker discovery effort for epothilone, a microtubule-stabilizing agent, and outlines how oncology biomarkers are identified and developed through preclinical and clinical studies.

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