Richmond, Virginia, United States
Oncology & Bioinformatics Scientist with *14+ years of research experience. I’m a Senior Bioinformatics Scientist at Monoceros Biosystems, where I lead oncology-focused projects spanning early-stage target and indication identification through late-stage clinical trial analysis and FDA documentation. I love helping biotech and pharmaceutical partners turn complex biological data into insights that shape the future of cancer treatment. My passion for cancer research is deeply personal. I was drawn to science after losing my father to lung cancer while a junior in high school, and that drive grew even stronger through my husband’s experience with melanoma. These experiences inspire me every day to apply data science toward improving patient outcomes and transforming how we understand and treat cancer. Before joining Monoceros, I worked extensively with multi-omics, clinical, and systems biology data, earning recognition for my work with awards from organizations such as ASPET, KAS, and the NIH. I also founded R-Ladies Louisville, creating a space for women and gender minorities in data science to connect and share knowledge. Outside of work, I volunteer as an EMT—something that reminds me daily of why this work matters. Whether analyzing clinical trial data or reading the latest cancer research, I’m always looking for new ways to connect data, discovery, and compassion to improve human health. *Resume available below.
We provide top quality bioinformatics support to small and mid size biotech companies at all stages of drug development from target development, compound optimization, in vitro lead compound testing, to clinical trial analyses.
Our research group at New Mexico State University focuses on utilizing bioinformatics, systems biology, and multi-omics (genomics, transcriptomics, proteomics, and metabolomics) to study human disease, more specifically perturbations in metabolism and the role of the immune system. It is our goal to better understand how perturbations in metabolism, whether due to differences in DNA, gene expression, protein abundance, or metabolite abundance, contribute to disease incidence and progression. We believe with a better understanding of the molecular underpinnings of human disease and the role of the immune system in disease, more effective prevention and treatment strategies can be developed. We have 5 funded projects we are actively working on within this realm (from 1. Cowboys for Cancer Research, 2. NM-INBRE, 3. Partnership for the Advancement of Cancer Research, 4. NSF, and 5. UAB) and more grant submissions in the works. Our lab also collaborates with researchers from multiple universities on bioinformatics projects.
I worked closely with collaborators in many different fields of science including immunology, neurology, pediatrics, and basic science to analyze and visualize computationally complex & large datasets or problems, frequently utilizing high performance computing (HPC) resources. I also built bioinformatic analysis pipelines and worked with publicly available data and open-source software. I worked on separate projects with multiple collaborators simultaneously.
I provided bioinformatics and computational biology support for immunology scientists across the scientific spectrum (design of experiments, data analysis, interpretation of results). I have worked with in-house bulk RNA-seq datasets as well as publicly available single-cell RNA-seq (scRNA-seq) datasets to help interpret/strengthen internally generated data.
My funding was provided by NIH & NIEHS pre-doctoral fellowship--T32-ES011564. My dissertation research focused on using a multi-omics & systems biology approach to decipher the role the xenobiotic metabolizing enzyme NAT1 has in breast cancer and more generally cellular metabolism through bioenergetics, metabolomics, and transcriptomics (RNA-seq) experiments on genetically modified breast cancer cell lines. As a result of working with the metabolomics and transcriptomics (RNA-seq) data sets, I have become an expert R (The R project for statistical computing) user. I have also utilized publicly available datasets to annotate the previously undefined relationships between NAT1, NAT2, and ESR1 in breast cancer cell lines, primary breast tumors, and normal breast tissues. Through my research project I have developed a strong understanding of statistical learning methods and bioinformatics tools. I have expanded the scope of my lab’s research strategy to include omics, systems biology, and bioinformatics approaches to solve our research question.
My funding was provided by a graduate research assistantship. My master's thesis project was a pilot untargeted metabolomics study in which differences in polar metabolites were measured between MDA-MB-231 breast cancer cells, constructed in our lab through utilization of siRNA, that expressed parental, increased, and decreased levels of our enzyme of interest (NAT1) to determine if any cellular pathways alterations are associated with varying levels of our enzyme of interest. See publication here: https://www.ncbi.nlm.nih.gov/pubmed/27872580. The results of this pilot metabolomics study led to a more comprehensive metabolomics study, with greater coverage across the metabolome, being performed in our lab.
My funding for the first two years of graduate school was provided by a fellowship from the Integrated Programs in Biomedical Science (IPIBS) at the University of Louisville. My project during this time involved evaluating an inhibitor of our enzyme of interest (NAT1) in combination with the naturally occurring compounds of curcumin and resveratrol. As a result of working on this project I learned about the drug discovery process and how to conduct small molecule inhibitor studies and analyze resulting data.