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

Phone Number:

717-901-5100 x1632


B.S. Chemistry / Mathematics, York College of Pennsylvania
Ph.D. Physiology, The Pennsylvania State University College of Medicine

Areas of Specialization

Cell & Molecular Biology, Proteomics, Bioinformatics, Responsible Conduct of Research

Courses Taught (at HU)

BIOL 102/103 – General Biology Lecture & Laboratory
INSC 380 – Special Topics (Scientific Integrity)

Courses Taught (at previous institutions)

BIOL 100 – General Biology Lecture & Laboratory
BIOL 101 – Foundations of Biology Lecture & Laboratory
BIOL 110 – Biology: Basic Concepts & Biodiversity
BIOL 122 – Biological Science
BIOL 364 – Foundations of Genetics & Molecular Biology
BIOL 472 – Seminar Series (Scientific Integrity)
BISC 001 – Structure & Function of Organisms
MICRB 106/107 – Elementary Microbiology Lecture & Laboratory

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.

Selected Publications

Sinha I, Karagoz K, Fogle RL, Hollenbeak CS, Zea AH, Arga KY, Stanley AE, Hawkes WC, and Sinha R. ”Omics” of selenium biology: A prospective study of plasma proteome network before and after selenized-yeast supplementation in healthy men. OMICS. 20(4): 202-213, 2016.

Fogle RL, Hollenbeak CS, Stanley BA, Vary TC, Kimball SR, and Lynch CJ. Functional proteomic analysis reveals sex-dependent differences in structural and energy-producing myocardial proteins in rat model of alcoholic cardiomyopathy. Physiol Genomics. 43(7): 346-356, 2011.

Fogle RL, Lynch CJ, Palopoli M, Deiter G, Stanley BA, and Vary TC. Impact of chronic alcohol ingestion on cardiac muscle protein expression. Alcohol Clin Exp Res. 34(7): 1-9, 2010.