Boland Lab

My interest in scientific research originally began with my high school AP chemistry class. After the course, I immediately decided I wanted to pursue a career as a scientist (whatever that meant). I attended Northern Illinois University where I joined the laboratory of Dr. James R. Horn, where I focused on engineering protein-protein interactions in various model systems. It was in this lab that I found my passion for biochemistry and biophysics.

In 2018, after receiving my B.S. in Chemistry, I pursued my Ph.D. in Biochemistry & Molecular Biophysics at Texas A&M University. I joined the plant biochemical research lab of Dr. Timothy P. Devarenne, where my thesis was originally supposed to focus on elucidating hydrocarbon biosynthetic pathways in the green microalga Botryococcus braunii. During the COVID-19 pandemic, my research shifted to include sequencing and assembling the genomes of the chemical races of B. braunii and performing a comparative genomic analysis of multiple Chlorophyta species. You could have likely quoted me throughout my early academic career as saying I would never need to learn topics such as computer science, programming, or bioinformatics, as I had mistakenly thought the research I wanted to do wouldn’t require it (how wrong I was!).

After graduating with my Ph.D. in 2023, I joined the Bioinformatics Core within the Texas A&M Institute for Genome Sciences & Society and the Vashisht College of Medicine, where I now serve as an Assistant Research Scientist. Drawing on over 12 years of research experience, a majority of my time is dedicated to developing new bioinformatic tools, collaborating with other faculty and the private sector on complex multi-omic analyses, and training the next generation of bioinformaticians. My teaching spans coursework covering comparative omics, single-cell/nucleus RNA-seq, and spatial transcriptomics, and I am currently authoring a textbook chapter on AI-driven protein structure prediction using tools like AlphaFold3. Beyond the lab, I am actively involved in the scientific community, serving as the President-Elect of the Texas Genetics Society and the Program Chair for the South Central Core Collective (SC3).

Bioinformatics is a large interdisciplinary field, and as such my research interests are wide-spread, spanning high-performance computing, advanced multi-omics, pangenomics, and machine learning.

Enablement of NGS-based Adventitious Agent Testing

Adventitious agent (AA) testing is the process of detecting contaminating agents—such as bacteria, viruses, and fungi—that are unintentionally introduced during the manufacturing of biotherapeutics and vaccines. While many of these agents are harmless, others can be dangerous or even deadly. Traditionally, AA testing has relied on a combination of in vitro and in vivo assays, which suffer from three major limitations:

1. They are targeted, requiring a priori knowledge of potential contaminants.
2. They require large sample inputs.
3. They have low throughput.

Next-generation sequencing (NGS) offers a powerful alternative. It enables multiplexing of hundreds of samples in a single sequencing run (600+ in some cases) and requires only 100 ng to 1 µg of DNA/RNA for library preparation. NGS addresses all the drawbacks of traditional AA testing and can even detect novel adventitious agents where conventional methods fall short.

Our lab develops new bioinformatics tools and Nextflow workflows that support robust and scalable NGS-based AA testing. Leveraging high-performance computing infrastructure like the VISION supercomputer and the eTRRAP initiative, we are building the scalable computational foundation required for modern biopharmaceutical safety testing.

Role of Eph Receptors in Host Immune Response to Pathogenic Infection

Erythropoietin-producing human hepatocellular (Eph) receptors are the largest family of receptor tyrosine kinases. They interact with membrane-bound ligands called ephrins, and their unique bi-directional signaling—along with their ubiquitous expression in mammals—makes them a compelling system for studying cellular migration, development, angiogenesis, and more.

Eph receptors are increasingly recognized for their roles in disease, including cancer and infectious diseases. Our lab focuses on elucidating the role of Eph receptors in the immune response to infection by Mycobacterium tuberculosis, using both human and murine model systems. This work is highly collaborative, bridging efforts with Dr. Jeffrey Cirillo at Texas A&M and our clinical and bioinformatics partners at Baylor College of Medicine.

Biomedical Artificial Intelligence & Knowledge Graphs

As biological data grows in complexity, there is a critical need to synthesize disconnected multi-omic and clinical insights. My research explores the application of machine learning and Artificial Intelligence—specifically Heterogeneous Graph Transformers (HGT) and Retrieval-Augmented Generation (RAG)—to construct and analyze large-scale biomedical knowledge graphs. These AI-driven architectures facilitate rapid hypothesis generation and help uncover novel therapeutic interventions from expansive datasets.

Single-Cell Multi-Omics & Reproductive Toxicology

I actively collaborate on interdisciplinary research leveraging advanced single-cell methodologies, integrating multi-omic modalities like ATAC-seq using computational workflows such as Seurat, scVI, and SAMap. A primary focus of this collaborative work includes investigating the molecular mechanisms and therapeutic interventions in Cr(VI)-induced premature ovarian failure.

Prospective students and researchers interested in joining the Boland Lab should email Devon at devonjboland@tamu.edu with a current CV and a brief statement of their research interests.