Inferring Casual Gene Regulation from Interventional Molecular Measurements

Deciphering the molecular mechanisms that regulate gene expression would be transformative for biomedical research. With recent advances in biotechnology, such as CRISPR combined with single-cell sequencing, it is now possible to design high-throughput interventional experiments that perturb gene regulation in defined ways. However, the experiments are expensive, time-consuming, and produce noisy results. In addition, the true causal graph is large, cyclic, and context-dependent. I will share preliminary ideas about experimental design and causal inference methods for this problem. I would appreciate ideas and feedback from those with expertise in causal inference and/or experimental techniques.

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