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Computational tools for phenotype classification and GRN inference

Many biological systems and processes rely on phenotypic heterogeneity– a mix of cell identities within a single system– such as disease states like cancer and organ systems like the pancreas. Over time, these cell identities may evolve in response to perturbations (such as cancer therapy) or intrinsic and extrinsic signaling (like external cues to differentiate into a new cell type). I am interested in developing computational tools that investigate these signals. Matrix factorization methods, like archetype analysis and NMF, play a large role in phenotype identification, and answer the question: what cell identity reference points are useful in my system of interest? Once reference clusters or archetypes are identified, dynamics of those groups can be analyzed. Predicting perturbation to cellular identity through mutations, epigenetic silencing, or overactivation relies on understanding the dynamics of an underlying gene regulatory network, so network inference can answer the question: what effect does a particular drug or chemical signal have on my system? If you are interested in building cell identity matrix factorization and GRN inference tools, please reach out via email!

Related papers

1. Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers. PLOS Computational Biology, 2019.

1. Archetype tasks link intratumoral heterogeneity to plasticity and cancer hallmarks in small cell lung cancer. Cell Systems, 2022.

1. Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics. Frontiers in Network Physiology, 2023.

Epigenetic heterogeneity and plasticity of Small Cell Lung Cancer

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Code on GitHub

Related papers

1. Transcriptional networks underlying tumour plasticity in small-cell lung cancer. bioRxiv, 2026.

1. Archetype tasks link intratumoral heterogeneity to plasticity and cancer hallmarks in small cell lung cancer. Cell Systems, 2022.

1. Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers. PLOS Computational Biology, 2019.

Physicochemical network control and condensate formation of chromosomal segregation proteins in breast cancer

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Code on GitHub for numerical solvers of condensate dynamics (Cahn Hilliard dynamics)

Related papers

1. Cell-specific Cahn-Hilliard models predict condensed fates of the chromosomal passenger complex. PLOS Computational Biology, 2026.

Public Transit and Transportation Infrastructure Networks

I am helping to develop a new public transit project. Stay tuned!

Code for Charlottesville

past

Data Science Essentials

Final project for the Data Science Essentials course: identifying Tennessee communities that most need expanded access to healthcare.

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Phantom Traffic Jams

Writing project for a Partial Differential Equations course on why traffic jams form with no bottleneck at all.

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