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The next frontier: Systems biology, big data, and advanced analytics

Back to Vestigo Issue 3

Group photo of (left to right) Emily Hodges, Carlos F. Lopez, Jonathan Irish, Vito Quaranta, Ken Lau, and Gregor Neuert.
From left, Emily Hodges, Carlos F. Lopez, Jonathan Irish, Vito Quaranta, Ken Lau, and Gregor Neuert. Photo by John Russell.

By Aaron Conley

Traditionally, biological research was done in a wet laboratory and involved a series of individual experiments that each revealed a fragment of a larger picture. However, scientists are now at a crossroad. Technology has advanced to allow the collection of data of previously unimaginable size and complexity. Researchers can then harness new computational tools to analyze them, addressing complicated questions about unified biological systems. This burgeoning field at the intersection of big data, advanced analytics, physics, chemistry, and biology is called systems biology.

Systems biology research at Vanderbilt University benefits from world-class facilities and groups, including the Quantitative Systems Biology Center, the Vanderbilt Institute of Chemical Biology, various departments from the Vanderbilt University School of Medicine Basic Sciences, state-of-the-art imaging technologies, and a strong multidisciplinary and collaborative environment.

The QSBC, housed within the School of Medicine Basic Sciences, is a point of convergence for systems biology at Vanderbilt. “The QSBC’s objective,” said Dr. Vito Quaranta, director of the QSBC, “is to amplify systems biology into a sustainable program of excellence in basic and translational science.” The center designs mathematically rigorous yet flexible advanced analytics through partnerships with the Department of Biomedical Informatics and Vanderbilt’s Data Science Institute. Together, they create data analysis tools that facilitate novel and impactful research across fields. This article describes recent work by QSCB members.

Single-cell technologies

Jonathan Irish, associate professor of cell and developmental biology, uses single-cell mass cytometry and phospho-specific flow cytometry tools—techniques that allow researchers to label, visualize, and measure molecules of interest on cells—to search through billions of human cells to precisely find rare, abnormal cells or to identify beneficial immune cells. Irish’s current work includes collaborative projects on glioblastoma, lung cancer, viral infections—including with rhinovirus and SARS-CoV-2—and drug discovery. This research could help scientists program immune cells to become therapeutic agents or target malignant signaling events to specifically kill cancer cells.

The Irish lab has created several computational tools and machine learning algorithms. Irish previously developed cloud-based analysis software for single-cell cytometry data, co-founding and becoming the chief scientific officer of Cytobank until it was acquired by Beckman Coulter Life Sciences in 2019. Most recently, the Irish lab created two algorithms—RAPID and T-REX—that identified a new type of aggressive brain tumor cell and pinpointed the virus-specific T cells responding to SARS-CoV-2 RNA vaccines, respectively.

Ken Lau, associate professor of cell and developmental biology, studies the cell-microbiome ecosystem in inflammatory bowel disease and the origins of colon cancer stem cells. He uses big data modeling with state-of-the-art technologies to profile tissues at single-cell resolution with thousands of data points and dimensions. Lau has developed unique software and protocols for single-cell and spatial transcriptomics. Spatial transcriptomics allow scientists to visualize and conduct quantitative analysis on the collection of expressed mRNA molecules in a cell, with spatial resolution in individual tissue sections. Additionally, Lau has developed software and protocols for the removal of ambient RNAs in single-cell data (page 4) and contributes to the Human Biomolecular Atlas Program profiled on page 14.

Mechanistic models

The lab of Carlos F. Lopez, associate professor of biochemistry, employs biochemical concepts, physicochemical methods, computational or data science approaches, and applied mathematics to explain and predict cell behaviors in health and disease, focusing on basic cellular processes and their mechanisms of dysregulation in cancer. The lab also develops new tools for systems biology research.

“Just like when you’re building a house,” he said, “you are restricted by the available tools.” The same is true in biology—new or improved theories and tools can expand the possibilities of research.

One such tool recently developed by the Lopez lab and collaborators is Thunor, an open-source software that organizes, analyzes, and visualizes cell responses to drug treatment from large-scale screens. The ability to visualize and analyze cell proliferation—the balance between cell division and cell death—which increases in cancer, allows researchers to pinpoint drugs with the best reduction in cancer cell proliferation.

Gregor Neuert, assistant professor of molecular physiology and biophysics, studies how an individual cell perceives and responds to its environment. His work combines a variety of methods, including quantitative single-cell and single-molecule experiments, evaluations of cell behavior in vitro, genetics, and computational biology to explore the fundamental mechanisms that enable cells to perceive and respond to environmental changes.

Pioneering research led by Neuert and Alexander Thiemicke, a recent Ph.D. graduate and a former Neuert lab student, shows that cells respond differently to acute stress than to gradual stress (page 4). The findings established an entirely new way to look at cell-to-cell communication and may fundamentally change how biomedical researchers study cells.


Emily Hodges, assistant professor of biochemistry, studies the epigenetic programming of human genomes. Epigenetic markers on the DNA can change how genes are expressed without changing the sequence of the DNA itself. As such, the Hodges lab focuses on two main things: 1) how these epigenetic or chemical modifications—such as methylation—are established for the functional specialization of cells during development and 2) how variation in DNA methylation is connected to disease susceptibility.

Hodges recently developed a cutting-edge approach, called ATAC-Me, to profile multiple epigenetic features simultaneously from a single DNA source. Her recent work using this new method challenges the textbook role of DNA methylation in gene regulation and may fundamentally change how we view its function.

In addition, Hodges uses BioVU, Vanderbilt’s repository of patient blood samples that are connected to anonymized electronic medical records, to investigate specific functional relationships between genetic variation, the “epigenome,” and disease risk.

Lung cancer

Quaranta is a cancer systems biologist who focuses on the role of transcription factors—proteins that help regulate gene expression—in tumor heterogeneity and drug resistance. His work is part of the National Cancer Institute’s Cancer Systems Biology Consortium at Vanderbilt. He has developed systems-level modeling tools for studying small cell lung cancer, mathematical models to predict tumor aggressiveness, and single-cell techniques to quantify the rate of cell proliferation in response to drugs.

Drug combinations

In collaboration, Lopez and Quaranta have built a unique algorithm called MuSyC, named in homage to Vanderbilt’s Music City home, to simplify the analysis that determines how efficacious and potent certain drug combinations are against cancers.

Christian Meyer and David Wooten, two talented graduate students, imagined the MuSyC project and, in an initial publication in Cell Systems, passionately explained that MuSyC has the “potential to transform the enterprise of drug-combination screens.” MuSyC acts by identifying combinations of drugs that can be prescribed to patients at lower doses, with improved efficacy, or both. Further, in their most recent publication in Nature Communications, Lopez and Quaranta provide additional metrics for “a consistent, unbiased interpretation of drug synergy” to apply the results of synergy studies to therapeutics faster.

The software, tools, and expertise at Vanderbilt are accessible to researchers all over the world and enable scientists to rigorously sift through huge, complicated datasets to help reduce bias and drive discoveries.