Weixiang Fang

I am a postdoctoral fellow in the Department of Biomedical Engineering and Center for Epigenetics at Johns Hopkins University. Currently, I am a member of the Reza Kalhor lab. Previously, I did my PhD in Biostatistics in the Hongkai Ji Lab.

I develop statistical and machine learning methods for the new data that genomics technologies produce, beginning with genomic records, the edits an engineered recorder writes into a cell’s own DNA. I use them to infer the dynamics of cell fate determination, and to transfer what a model organism reveals about the regulatory control of fate into human.

Research program

Model-based and scalable lineage inference from genomic recorders. Likelihood-based inference for each class of recorder, evolving, static, sequential and conditional, with learned tree proposals that scale whole-tree optimization to millions of cells. The same treatment extends to somatic mutations, the recorder every human already carries.
Joint inference of the intrinsic and extrinsic control of cell fate. A branching process model of development in which a cell’s own state and the signals it receives decide its fate over generations, fitted jointly to lineage, time-course and spatial data, and used to map the plasticity of each cell state.
Cross-species transfer learning for the regulatory control of fate decisions. A model of regulatory element activity across species and developmental stage that names the human cell states and regulatory elements matching an animal fate decision, and flags where no human counterpart exists.

selected publications

A branching process model of cell lineage links positional signaling to the canalization of clonal fate

Weixiang Fang, Yi Yang, Jessica D Lin, and 1 more author

Manuscript in preparation 2026
Quantitative fate mapping: A general framework for analyzing progenitor state dynamics via retrospective lineage barcoding

Weixiang Fang, Claire M Bell, Abel Sapirstein, and 5 more authors

Cell 2022
Quantifying Functional Conservation of Human and Mouse Regulatory Elements via FUNCODE

Weixiang Fang, Chaoran Chen, Boyang Zhang, and 4 more authors

bioRxiv 2024

Abs

Evolutionary conservation is crucial for understanding genome functions and lays the foundation for using animal models in studying human diseases. However, conventional conservation scores based on DNA sequence evolution do not capture the dynamic biochemical activities of DNA elements, termed functional conservation. Quantifying functional conservation has been limited by the availability of functional genomic data matched across species. To address this, we developed FUNCODE, a framework for characterizing functional conservation through in silico sample matching. Applying FUNCODE to 2,595 uniformly processed datasets from the Encyclopedia of DNA Elements (ENCODE), we generated genome-wide FUNCODE scores for human and mouse regulatory elements, identifying 3.3 million functionally conserved human-mouse element pairs. We demonstrate FUNCODE’s diverse applications, including annotating 78,501 novel regulatory elements, transferring 37,968 high-resolution human ENCODE Hi-C loops in immune lineages to mice, identifying conserved functional signals for disease modeling, and enhancing cross-species integration of single-cell omics data.Competing Interest StatementThe authors have declared no competing interest.