Computational Molecular Medicine

Single-cell multi-omics data offer powerful opportunities to study disease at unprecedented resolution, but they also present significant challenges. The data are often sparse, noisy, and extremely high-dimensional, with technical differences between batches or donors that can obscure true biological signals. Our lab combines advanced computational methods with high-dimensional biological data to exludes technical artifacts and uncover mechanisms of disease progresssion and therapy response, with a particular emphasis on cancer and metabolic disorder.

Single-Cell Multi-Omics for Clinical Cohorts

We specialize in the analysis of clinical single-cell multi-omics datasets, particularly from cancer and metabolic disease cohorts. Using state-of-the-art machine learning techniques, we analyze single-cell RNA sequencing and chromatin accessibility data to characterize cellular heterogeneity and regulatory dynamics. Our pipeline includes robust dimensionality reduction, clustering, and batch correction methods, allowing us to identify distinct cell populations and states across individuals. Through probabilistic graphical modeling and motif enrichment analysis, we reconstruct gene regulatory networks that govern disease-specific transcriptional programs. These approaches allow us to overcome the sparsity and noise inherent to single-cell data and extract biologically meaningful patterns that inform prognosis and therapeutic strategy.

Multi-Omics for Mouse Models of Cancer Progression

To explore tumor development and treatment effects in vivo, we analyze multi-omics data generated from mouse models, including xenografts and genetically induced cancers. These datasets are complex, encompassing multiple axes of variation such as treatment regimens, time points, and tumor subtypes. To disentangle these factors, we develop tailored probabilistic latent variable models (LVMs) that reveal how sources of variability interact and which molecular features are relevant to human disease. Recent projects:

• Multi-Omics combined with lineage tracing technology now allow us to quantify the clonal connectivity between different cell populations and infer the temporal dynamics of cell populations. Using mechanistic modeling, we can uncover the directionality of differentiation trajectories and the dynamical properties of the clones [pdf].

• Analyzing cancerous mouse models provides valuable insights for developing personalized oncology approaches. We will integrate patient data at an early stage in this process using a forward and reverse translation technique. This method ensures that the results are clinically relevant and enables us to identify patients who are eligible for a new treatment. For example, we are working on a TRR project that investigates how ubiquitination impacts DNA damage repair in AML in order to identify a new anticancer target [project].