I am a statistician with an interest in bioinformatics. In particular, I am interested in the analysis and interpretation of microarray data. DNA Microarrays (or gene chips) exist for many organisms - animals, plants, and humans. They are used to measure the

extent to which many genes (humans have

about 30,000 protein coding genes) are

“turned on” at any given time. Researchers

are interested in measuring gene expression

to study which genes are associated with a

particular condition such as cancer.

 

Genes are influenced by many things: external factors that an organism is subjected to (e.g., drought for plants or disease for humans) but genes and proteins also influence each other in a complicated network. Is it possible to identify genes that are up- or down regulated by other genes or by proteins in time course microarray data? This question is investigated using a state space model for gene regulation in my thesis.

 

Publications

 

· M. Bremer & R.W. Doerge, The KM-Algorithm identifies regulated genes in time series expression data, Advances in Bioinformatics (in press)

· M. Bremer & R.W. Doerge, Statistics at the Bench: A Step-by-Step Handbook for Biologists, Cold Spring Harbor Press, 2010 (in press)

· M. Bremer, E. Himelblau, A. Madlung, Introduction to the Statistical Analysis of Two-Color Microarray Data (book chapter) in Bang, H, Zhou, XK, Van Epps, H, Mazumdar, M. (Editors) Statistical Methods in Molecular Biology. Humana Press. To be published in 2010.