C-T.51: In Silico Prioritization of Combinatorial Regulatory Perturbations Using Gene Expression Models
Authors
Marcel H. Schulz
Goethe University
Keywords
gene expression prediction, in-silico perturbation
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Understanding how regulatory elements (CREs) control gene expression in disease remains a central challenge in epigenomics. While perturbation experiments can reveal causal relationships, systematically exploring combinatorial perturbations of CREs is experimentally infeasible due to the rapidly growing search space, which reaches billions of possible perturbation sets even for small combinations.
We propose a model-derived in silico perturbation framework to identify a small set of regulatory regions whose perturbation shifts a disease gene expression state towards a healthy profile. Our approach builds on gene-specific models that predict gene expression from epigenetic activity. As a representative implementation, we use random forest models trained on IHEC data, with candidate ENCODE CREs as input features. These models induce a bipartite graph linking regulatory regions to genes. Assuming approximate additivity of single-perturbation effects, we formulate the selection of k regions as a combinatorial optimization problem that minimizes distance (RMSE) between predicted disease and observed healthy expression profiles. This formulation enables efficient exploration of the search space using integer linear programming (ILP).
We assess the validity of the additivity assumption by comparing predicted combinatorial perturbation effects to a model-derived ground truth, and further validate prioritized regions using independent CRISPRi perturbation data.
Our framework enables systematic prioritization of regulatory regions for combinatorial perturbation and provides a scalable strategy for guiding targeted experimental interventions in disease.
Co-authors: Marcel Schulz
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