The Cahan Lab develops computational and experimental approaches to understand how cell identity is encoded in gene regulatory networks — and how to harness that knowledge to engineer cells for regenerative medicine. Based in the Department of Biomedical Engineering and the Institute for Cell Engineering at Johns Hopkins University, we focus on stem cell biology, cell fate engineering, and the development and diseases of the synovial joint.

Graduate students in the Biomedical Engineering, BCMB, and Pathobiology PhD programs interested in rotating should to discuss.

Gene Regulatory Networks

We build computational tools to map and model the networks of genes that control cell identity.

Gene regulatory networks (GRNs) define the complete set of regulatory relationships among genes and gene products in a cell. These networks govern a cell’s transcriptional output and act as the molecular blueprint of cell-type identity. We develop algorithms to reconstruct GRNs from genomic data, measure how they are established during development, infer their dynamics, and model regulatory interactions between cells — with a particular focus on synovial joint development.

Evaluating Engineered Cell Populations

We develop computational platforms to measure how closely engineered cells match their intended target cell types.

When stem cells are directed to become a specific cell type — for transplantation or disease modeling — how equivalent are the resulting cells to their natural counterparts? We build tools that leverage single-cell RNA sequencing and other data modalities to answer this question. Our work has revealed common patterns of divergence between engineered and native cell populations and identified potential targets for improving cell engineering protocols.

Improving Cell Fate Engineering

We use gene regulatory network analysis to predict how to make cell engineering protocols more effective.

Our assessment work has uncovered key lessons: directed differentiation more closely recapitulates target cell identity than direct conversion; starting-cell GRNs often persist in engineered cells; and transplanting engineered cells into their native niche substantially improves their fidelity. Building on these insights, we develop GRN-based algorithms that predict the optimal identity, timing, and combination of transcription factor, microRNA, and signaling pathway perturbations to improve cell fate engineering outcomes.