smgroves@virginia.edu

Charlottesville, VA, 22901

Welcome to my site! I am currently an assistant professor studying cancer systems biology and biological network science at the University of Virginia in the School of Data Science.

After receiving a B.S. in Physics and Mathematics from The College of William & Mary in 2016, I entered Vanderbilt via their Quantitative and Chemical Biology program and completed my PhD in the Quaranta Lab in March 2022, where I modeled regulatory networks and phenotypic transitions (cell state plasticity) in Small Cell Lung Cancer. More recently, I was a postdoctoral researcher in the Center for Systems Analysis of Stress-adapted Organelles (SASCO), which combines systems biology with mathematical modeling and data science to solve complex biological problems. As a post-doc, I focused on computational modeling of dysregulated of chromosomal segregation during mitosis in cancer. I consider myself a physicist by training and a biological network scientist by practice. My research experiences in undergrad, graduate school, and my post-doc allow me to combine these identities to approach complex biological problems through the analysis and modeling of high-dimensional data.

I am also passionate about science communication, particular in the visual form. I am excited to teach Communicating with Data in the Fall 2026. I’ve been learning motion design to make educational video content in my role as the SASCO Outreach Core Education Scholar.

If you are interested in working with me, please reach out by email!

funding awards

2022-2023: NIH F32 Post-doctoral Training Grant Awardee
2017-2021: NSF Graduate Research Fellowships Program Fellow
2016-2017: BIDS (Vanderbilt Training Program in Big Biomedical Data Science) Trainee

news

Jul 17, 2026	My paper with the Janes lab, “Cell-specific Cahn-Hilliard models predict condensed fates of the chromosomal passenger complex,” was accepted at PLOS Computational Biology!
Jul 10, 2026	I just started as an Assistant Professor in the School of Data Science at UVA! I’m excited to teach DS2003 and DS5030 in the Fall.
Jul 7, 2026	I presented a poster, “Cell-specific Cahn-Hilliard models predict condensed fates of the chromosomal passenger complex,” at the Annual SASCO Symposium, on physics-based modeling of the chromosomal passenger complex with Catalina Alvarez, Min-Jhe Liu, Montserrat Gerardo, John Lowengrub, Todd Stukenberg, and Kevin Janes.

selected publications

1. Cell Sys

   Archetype tasks link intratumoral heterogeneity to plasticity and cancer hallmarks in small cell lung cancer

   Sarah M. Groves, Geena V. Ildefonso, Caitlin O. McAtee, and 23 more authors

   Cell Systems Aug 2022

   Abs Bib HTML

   Small cell lung cancer (SCLC) tumors comprise heterogeneous mixtures of cell states, categorized into neuroendocrine (NE) and non-neuroendocrine (non-NE) transcriptional subtypes. NE to non-NE state transitions, fueled by plasticity, likely underlie adaptability to treatment and dismal survival rates. Here, we apply an archetypal analysis to model plasticity by recasting SCLC phenotypic heterogeneity through multi-task evolutionary theory. Cell line and tumor transcriptomics data fit well in a five-dimensional convex polytope whose vertices optimize tasks reminiscent of pulmonary NE cells, the SCLC normal counterparts. These tasks, supported by knowledge and experimental data, include proliferation, slithering, metabolism, secretion, and injury repair, reflecting cancer hallmarks. SCLC subtypes, either at the population or single-cell level, can be positioned in archetypal space by bulk or single-cell transcriptomics, respectively, and characterized as task specialists or multi-task generalists by the distance from archetype vertex signatures. In the archetype space, modeling single-cell plasticity as a Markovian process along an underlying state manifold indicates that task trade-offs, in response to microenvironmental perturbations or treatment, may drive cell plasticity. Stifling phenotypic transitions and plasticity may provide new targets for much-needed translational advances in SCLC. A record of this paper’s Transparent Peer Review process is included in the supplemental information.

   @article{10.1016/j.cels.2022.07.006,
     year = {2022},
     title = {{Archetype tasks link intratumoral heterogeneity to plasticity and cancer hallmarks in small cell lung cancer}},
     author = {Groves, Sarah M. and Ildefonso, Geena V. and McAtee, Caitlin O. and Ozawa, Patricia M.M. and Ireland, Abbie S. and Stauffer, Philip E. and Wasdin, Perry T. and Huang, Xiaomeng and Qiao, Yi and Lim, Jing Shan and Bader, Jackie and Liu, Qi and Simmons, Alan J. and Lau, Ken S. and Iams, Wade T. and Hardin, Doug P. and Saff, Edward B. and Holmes, William R. and Tyson, Darren R. and Lovly, Christine M. and Rathmell, Jeffrey C. and Marth, Gabor and Sage, Julien and Oliver, Trudy G. and Weaver, Alissa M. and Quaranta, Vito},
     journal = {Cell Systems},
     issn = {2405-4712},
     doi = {10.1016/j.cels.2022.07.006},
     url = {https://www.cell.com/cell-systems/fulltext/S2405-4712(22)00313-1?\_returnURL=https\%3A\%2F\%2Flinkinghub.elsevier.com\%2Fretrieve\%2Fpii\%2FS2405471222003131\%3Fshowall\%3Dtrue},
     keywords = {},
     month = aug
   }

2. PLOS Comp Bio

   Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers

   David J. Wooten, Sarah M. Groves, Darren R. Tyson, and 6 more authors

   PLOS Computational Biology Oct 2019

   Abs Bib HTML

   Adopting a systems approach, we devise a general workflow to define actionable subtypes in human cancers. Applied to small cell lung cancer (SCLC), the workflow identifies four subtypes based on global gene expression patterns and ontologies. Three correspond to known subtypes (SCLC-A, SCLC-N, and SCLC-Y), while the fourth is a previously undescribed ASCL1+ neuroendocrine variant (NEv2, or SCLC-A2). Tumor deconvolution with subtype gene signatures shows that all of the subtypes are detectable in varying proportions in human and mouse tumors. To understand how multiple stable subtypes can arise within a tumor, we infer a network of transcription factors and develop BooleaBayes, a minimally-constrained Boolean rule-fitting approach. In silico perturbations of the network identify master regulators and destabilizers of its attractors. Specific to NEv2, BooleaBayes predicts ELF3 and NR0B1 as master regulators of the subtype, and TCF3 as a master destabilizer. Since the four subtypes exhibit differential drug sensitivity, with NEv2 consistently least sensitive, these findings may lead to actionable therapeutic strategies that consider SCLC intratumoral heterogeneity. Our systems-level approach should generalize to other cancer types.

   @article{10.1371/journal.pcbi.1007343,
     year = {2019},
     title = {{Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers}},
     author = {Wooten, David J. and Groves, Sarah M. and Tyson, Darren R. and Liu, Qi and Lim, Jing S. and Albert, Réka and Lopez, Carlos F. and Sage, Julien and Quaranta, Vito},
     journal = {PLOS Computational Biology},
     issn = {1553-734X},
     doi = {10.1371/journal.pcbi.1007343},
     pmid = {31671086},
     url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007343},
     pages = {e1007343},
     number = {10},
     volume = {15},
     keywords = {},
     month = oct
   }

Email is the best way to contact me.