Weixiang Fang

I am on the academic job market in 2026–27. My CV is here.

My research asks how cells decide their fates, and how what model organisms reveal about these decisions can be carried into human. I develop statistical and machine learning methods for data from emerging genomics technologies, focusing on genomic records, the edits an engineered recorder writes into a cell’s own DNA.

I am a postdoctoral fellow in the Kalhor Lab at Johns Hopkins, and earned my PhD in Biostatistics at Johns Hopkins with Dr. Hongkai Ji.

I developed Quantitative Fate Mapping (Cell, 2022), which recovers when progenitors commit and how many cells go to each fate, and FUNCODE for the ENCODE Consortium, which scores whether a regulatory element’s activity is conserved between human and mouse.

Research Areas

Reading the history of cells from their DNA

Engineered recorders now write each cell's history into its own DNA as it divides. I build statistical and machine learning models that read those records back into the tree of divisions that built a tissue, at the scale of whole embryos.

A lineage tree with colored recording events on its branches, and the character matrix of recorded sites in each sampled cell

What decides a cell's fate

A cell's future depends both on its own state and on the signals around it. Building on my branching process model of cell lineage, I am developing a virtual embryo model that asks how much of a cell's fate is driven by what signals, and how much can still be changed.

A simulation of clones growing across a tissue with a signal gradient, colored by clone and by fate

Across species, and into human

Transcription factors and gene networks are widely shared across animals, while the regulatory elements they act on turn over. I develop deep learning models of regulatory sequence that align these elements across species, to learn what evolution keeps and changes in fate decisions, and to carry that knowledge into human.

A phylogeny of zebrafish, mouse, macaque and human with functional genomics tracks, syntenic regions, and each organism's proposal onto the human track

selected publications

Cell

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

Weixiang Fang, Claire M Bell, Abel Sapirstein, Soichiro Asami, Kathleen Leeper, Donald J Zack, Hongkai Ji, and Reza Kalhor

Cell, 2022

DOI HTML Code Website
bioRxiv

Quantifying functional conservation of human and mouse regulatory elements via FUNCODE

Weixiang Fang, Chaoran Chen, Boyang Zhang, Yi Wang, Ruzhang Zhao, Weiqiang Zhou, and Hongkai Ji

bioRxiv (in revision at Genome Biology), 2024

Abs DOI HTML PDF Code

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.
In prep.

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

Weixiang Fang, Yi Yang, Jessica D Lin, and Reza Kalhor

2026

Manuscript in preparation