UW Madison UW School of Medicine and Public Health

Computational Biology and Biostatistics
Summer Research Projects Index 2009 - 2004


Students in the Computational Biology and Biostatistics program have completed a wide variety of projects over the years. Examples of projects that were completed between 2001 and 2009 can be found in the tables below. In 2005, The CBB program became a sub-group of the larger Integrated Biological Sciences Summer Research Program (IBS-SRP). Thus, projects completed since then (including projects completed since 2009) can also be found under IBS-SRP Program History using the appropriate dates and headings.

To access these projects please visit the following link: IBS-RSP Program History

*Please note: Not all projects are posted on the website. Some have been withheld by request of the mentor so that the project can be published in a peer-review journal at a later time.

2003 - 2001


Student Mentor(s) Project Title
Ali Al-Hanooti Colin Dewey and Farzad Rastegar Breakage Models of Chromosomal Evolution
Mariangely Almenas-Santiago Ronald Gangnon Measuring County Level Excess Deaths
Brittney Bailey Paul R. Hutson Modeling Morphine Titration and Conversions in Palliative Care
Ashwin P. Devendiran David Page Warfarin Dose Model
Emily Lundt Bret Larget and Alex Richard Kreibich A Bayesian Approach to Estimating Phylogenies Using Both Gene Order and Sequence Data Analysis
Ritesh Ramchandani Amy Trentham-Dietz,
Ronald Gangnon and Brian Sprague
Socioeconomic Status and Breast Cancer Survival

Project Title: Breakage Models of Chromosomal Evolution
Student Ali Al-Hanooti
Mentor(s): Colin Dewey and Farzad Rastegar

Genomes are constantly undergoing mutations due to various types of events such as: deletion, duplication, inversion, insertion, and translocation. For the purpose of our research we decided to focus on comparing bacterial genome sequences. Theories have been proposed to how breakage happens in genome sequences. The common two theories are: Fragile breakage model, and Random breakage model. Up until 2003, the scientific community embraced the Random breakage model, which believed that genomic links break off at random locations. However, in 2003, Pevzner and Tesler challenged the scientific status quo by arguing that “hotspots” exist in the human genome. These “hotspots” allow links of the particular genome to detach from the main link. We will be using data made available by GenBank. The data is comprised of a number of bacterial genome sequences. We will perform comparative analysis on the sequences to find if they support the Fragile or Random breakage model.

Project Title: Measuring County Level Excess Deaths
Student Mariangely Almenas-Santiago
Mentor(s): Ronald Gangnon

Excess deaths are defined as deaths due to preventable or treatable causes that could be addressed with appropriately targeted strategies. For county level data, empirical percentiles of the observed mortality rates, particularly in the tails, are unstable and biased estimates of the corresponding true percentiles of the underlying mortality rates. Estimates of target percentiles for county-level mortality rates for United States mortality data, for years 1999 to 2005, were measured using an empirical Bayes procedure based on a posterior distribution. Results compared to two other estimation methods shows that empirical Bayes estimation produces less variable estimates for county mortality rates especially for low definitions of the target. Excess death rates are lower for women than for men, even though excess deaths counts for women become higher after the age of 75. Poorly performing states or counties are not always the principal source of excess deaths. Strategies to reduce the number of excess deaths should not be focused on reducing the excess mortality rates of the poorly performing counties or states, but in pushing for more stringent standards to those who contribute the higher amount of excess deaths.

Project Title: Modeling Morphine Titration and Conversions in Palliative Care
Student Brittney Bailey
Mentor(s): Paul R. Hutson

This study uses the R environment to simulate and evaluate two common morphine dosing scenarios. The first is a conversion from a basal intravenous (IV) infusion of morphine to an oral morphine dose of MS-Contin® (MSC); the second a basal IV infusion augmented by patient- and nurse-initiated IV boluses. Pharmacokinetic parameters were derived published clinical trials and plasma concentration models were based on a one-compartment model of the body. Results showed that 1) MSC should not be introduced before the discontinuation of the basal infusion; and 2) patient- and nurse-initiated IV boluses were effective in raising the overall plasma-morphine concentration levels but did not have a significant additive effect on concentration from one bolus to the next.

Project Title: Warfarin Dose Model
Student Ashwin P. Devendiran
Mentor(s): David Page

Warfarin is used widely as an oral anticoagulant in many countries. The correct dose of warfarin depends on various factors such as genetic variability and geographic area of the patients1. The pharmacogenetic algorithm, which uses an ordinary least-squares regression model, has various limitations including high mean absolute error. As a result, a new model is required to minimize the adverse effects caused by incorrect dose of warfarin. In this research, the regressions namely least median square regression, pace regression, and support vector regression are used to create a new, better model. The warfarin dose data collected from the International Warfarin Pharmacogenetic Consortium (IWPC) was used to find a new model. The warfarin dose model created uses clinical and genetic data of the patients. Using this data the pharmacogenetic model was reproduced to make sure there were no experimental errors. In addition, various statistical models were tested to achieve a minimal mean absolute error. The absolute mean error of the two models, least median square regression and pace regression, was close to the mean absolute error of the pharmacogenetic algorithm; however, it was not more significant than the pharmacogenetic algorithm. On the other hand, the support vector regression has a higher mean absolute error than any other algorithm created. The pharmacogenetic algorithm is still the better model for predicting the initial dose of warfarin. In the future, a model with complex algorithm, which uses more attributes, might produce a predicted initial dose of warfarin with minimal mean absolute error.

Project Title: A Bayesian Approach to Estimating Phylogenies Using Both Gene Order and Sequence Data Analysis
Student Emily Lundt
Mentor(s): Bret Larget and Alex Richard Kreibich

Bryophytes are comprised of three classes that share some of the earliest ancestors of land plants. A long-standing open question is which of these classes (moss, liverworts, or hornworts) is the most recent ancestor to land plants. Determining which class shares the more recent ancestor is crucial to gaining insight about the characteristics leading to the widespread success of land plants. Chloroplast organelles, and thus chloroplast DNA, are common among bryophytes, land plants, and algae providing a means to study their relationships. Phylogenetic tree estimates for a subset of 19 species with sequenced chloroplasts was conducted. BADGER free software was used to analyze gene order and MrBayes free software was used to analyze the nucleotide sequence data of rbcL, atpA, and atpB genes. Analysis of gene order was inconclusive in that all possible phylogenetic trees were equally likely. Analysis of sequence data for rbcL and atpB supports hornworts as the closet relative of land plants, however atpA supports most strongly sister clade of hornworts and moss.

Project Title: Socioeconomic Status and Breast Cancer Survival
Student Ritesh Ramchandani
Mentor(s): Amy Trentham-Dietz, Ronald Gangnon and Brian Sprague

Some studies have observed lower breast cancer survival rates in women of lower socioeconomic status (SES). The primary aim of this study is to determine if there is an association between SES and breast cancer mortality in a cohort of Wisconsin women. We analyzed data from 5,865 Wisconsin women, ages 20-69, who were diagnosed with invasive breast cancer during 1995-2003. Vital status was determined up to December 31, 2006 from the National Death Index. We considered both individual and census tract SES variables as predictors of survival. Individual variables considered were education, marital status, and household income and size. Census tract variables were percent above age 25 without a high school diploma, percent in poverty, percent urbanicity, and percent in working class jobs. Cox proportional hazards models were used to measure the associations of SES and breast cancer-specific survival when controlling for other prognostic factors. When controlling for age at diagnosis, stage at diagnosis, tumor histologic type, year of interview, and mammography screening history, greater mortality was observed for women without a college education versus those with a college degree (Hazard Ratio: 1.36; 95% CI: 1.06-1.74), and for divorced, separated, or widowed women (1.33; 1.03-1.66) as compared with married women. Higher mortality was associated with women in census tracts with = 20 percent without a high school diploma (1.43; 1.07-1.91) and 10-19.9 percent without a high school diploma (1.30; 1.05-1.62) versus < 10 percent. Women in census tracts with high working class populations also had higher mortality rates (1.26; 1.00-1.57). Controlling for age, lower SES was also associated with lower odds of having received a mammogram 5 years before diagnosis. Lower SES was associated with reduced survival and lack of mammography screening. This suggests the need to improve knowledge of and access to breast cancer screening in women of lower SES and to achieve optimal treatment and care for all women after diagnosis.


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Please contact: Whitney Sweeney, Student Services Coordinator
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