--- license: cc-by-nc-nd-4.0 ---
1 University of Pennsylvania 2 Duke-NUS Medical School 3 Mila, Quebec AI Institute
4 Duke University
Figure 3: Application of BranchSBM on Learning Branched Paths on a LiDAR Manifold.
### 2. Modeling Differentiating Single-Cell Population Dynamics BranchSBM is uniquely positioned to model single-cell population dynamics where a homogeneous cell population (e.g., progenitor cells) differentiates into several distinct subpopulation branches, each of which independently undergoes growth dynamics. We demonstrate this capability on mouse hematopoiesis data.Figure 4: Application of BranchSBM on Modeling Differentiating Single-Cell Population Dynamics.
### 3. Modeling Drug-Induced Perturbation Responses Predicting the effects of perturbation on cell state dynamics is a crucial problem for therapeutic design. In this experiment, we leverage BranchSBM to model the trajectories of a single cell line from a single homogeneous state to multiple heterogeneous states after a drug-induced perturbation. We demonstrate that BranchSBM is capable of capturing the dynamics of high-dimensional gene expression data and learning branched trajectories that accurately reconstruct diverging perturbed cell populations. First, we modeled two branches to two divergent subpopulations in the Clonidine-perturbed cells from the initial control DMSO-treated cells with BranchSBM and compared with single-branch SBM.Figure 5: Results for Clonidine Perturbation Modeling with BranchSBM.
Finally, we used BranchSBM to model three branched trajectories in the Trametinib-perturbed cells from the initial control DMSO-treated cells.Figure 6: Results for Trametinib Perturbation Modeling with BranchSBM.
## Citation If you find this repository helpful for your publications, please consider citing our paper: ``` @article{tang2025branchsbm, title={Branched Schrödinger Bridge Matching}, author={Tang, Sophia and Zhang, Yinuo and Tong, Alexander and Chatterjee, Pranam}, journal={arXiv preprint arXiv:2506.09007}, year={2025} } ```