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 Rachel  Fogle, Ph.D.

Rachel Fogle, Ph.D. Associate Professor of Biological Sciences

Rachel specializes in Cell & Molecular Biology, Proteomics, Bioinformatics, Responsible Conduct of Research.

Research Interests

As a broadly trained physiology, my research interests are widespread. I am interested in biological systems under normal and pathological states, proteomics, and bioinformatics. My dissertation project involved the use of proteomics to detect sex-dependent differences in myocardial protein content following chronic alcohol abuse in a rat model. Specifically, I utilized chemical labeling technology (iTRAQ) to allow direct comparison between study groups. In conjunction with the proteomic studies, I utilized echocardiography to monitor changes in cardiac structure and function with increasing levels of alcohol consumption. This approach provided an excellent platform for correlating alterations in whole organ structure and function with alcohol-induced sub-cellular events.

One of my specific interests includes the field of quantitative proteomics and bioinformatics. Managing large datasets generated from proteome-based experiments has led to an appreciation for statistical tools that allow easier data analysis. As part of my graduate research, I developed a new statistical model using statistical software analysis packages, such as STATA and R, to allow combination of multiple datasets with related research hypotheses, thereby increasing the sample size for statistical analysis. Given the recent advances in the fields of genomics, proteomics, and bioinformatics, and the increasing number of laboratories performing “omic”-based research, such a statistical tool will fill a gap in the current state of knowledge.

Undergraduate students interested in the application of statistical models to answer biomedical questions have the opportunity to participate in an “Outcomes Research Toolbox” as an independent study experience. This course allows participants to gain a working knowledge of the most common statistical and modeling methods used in biomedical research. Upon successful completion of the course, undergraduate students are equipped to analyze large datasets – including performing statistical comparisons, interpreting results, and appropriately describing their data analyses in summarized tables and figures.

More recently, in addition to statistical modeling, I have broadened by research interests to include education research. Specifically, the implementation and validation of innovative team-based learning curriculum for responsible conduct of research (RCR). It is hypothesized that ethical decision-making abilities (both short-term and long-term) can be increased through the use of a team-based, interactive RCR curriculum that is malleable to the instructor, biomedical discipline, and targeted audience.

Courses Taught at HU:

BIOL 102/103 – General Biology

BIOL 281/282 – Cell Biology

BIOL 320 – Genetics

BIOL 370 – Molecular Biology

GEND 460 – The Ethical Mind: Research Ethics & Scientific Integrity

SEMR 100 – Cornerstone

INSC 298/498 – Project I & II

INSC 365 – Internship

Education

B.S. Chemistry / Mathematics, York College of Pennsylvania.

Ph.D. Physiology, The Pennsylvania State University College of Medicine.

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