Postdoctoral Fellow

Daniel R. McFarlin Dept. of Biostatistics & Medical Informatics Faculty Supervisor: C. David Page and Michael Gould Email: drmcfarl@facstaff.wisc.edu 265-8612

Ras is the most frequently mutated inducer of human cancer. It is involved in every type of cancer. Ras was the first known oncogene, characterized before the word “oncogene” existed. Not surprisingly, the literature on Ras and Ras pathways is extensive. In addition to cancer, Ras is an important gene in development; hence, Ras inhibition is harmful, even in adults, and so may not be a viable approach to cancer treatment. Nevertheless, better understanding of the complex and interacting Ras pathways could reveal other targets for anti-cancer agents. This project proposes to integrate the known literature on Ras into a computer model of Ras pathways, and then to test and refine this model based on data. The response of cells to Ras activation is variable depending on cell type. Therefore, initially this project will focus on modeling mammary epithelial cells during gland development and carcinogenesis induced by Ras signal transduction.

In this project, the initial approach to modeling Ras pathways will be to construct a deterministic graphical model of the pathways. Nodes will represent genes, proteins or important small molecules. Edges in the graph will denote potential interactions. Each node will be associated with a simple quantitative model of the changes that can occur given changes in its neighbors in the graph. The form of these quantitative models is yet to be determined. These deterministic models will be extended to stochastic models if warranted by experimental data. Both the structural and quantitative parts of the model will be constructed based on the literature as well as results from experiments I performed in my doctoral research.

While the primary focus of my work will be construction of this computational model, testing the model will require further wet lab work. Using rats with different genotypes and with different Ras-mutations, we will measure protein (for key proteins) and mRNA levels (for all rat genes and ESTs), and compare these levels with the predictions of the model to assess accuracy. The test data will then be employed to further train the model for the next round of testing. I will compare the effectiveness of human training, where I modify the model based on the data, against training through machine learning. The learning algorithm will search through a space of possible model structures and parameters, scoring each by goodness of fit to the data as well as model complexity.

CV

CV 2003 (.pdf format, Download Reader)

http://www.cs.wisc.edu/~mcfarlin/SciWelcome.html

Publications

McFarlin DR and Gould MN. Rat Mammary Carcinogenesis Induced by In Situ Expression of Constitutive Raf Kinase Activity Is Prevented by Tethering Raf to the Plasma Membrane. Carcinogenesis, in press.

McFarlin DR, Lindstrom MJ, Gould MN. Affinity with Raf is Sufficient for Ras to Efficiently Induce Rat Mammary Carcinomas. Carcinogenesis, 24(1): 99-105, 2003.

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File last modified: August 13, 2003
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