Kri-kri: A statically sound and accessible framework for causal regulatory inference from high-content CRISPR screens

• Applicant: Professorin Dr. Britta Velten
• Subject Area: Bioinformatics and Theoretical Biology
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 540147573

At the heart of research in organismal biology is the identification of regulatory mechanisms that underlie the development, function and plasticity of organisms. A comprehensive understanding of their molecular underpinnings can have substantial impacts on our society, e.g., allowing for the discovery of new drug targets, identification of diagnostic markers for diseases and developmental disorders or agricultural engineering of more robust and frugal plants. Recently, the combination of molecular profiling of individual cells with targeted genetic interventions in high-throughput based on the CRISPR technology (referred to as high-content CRISPR screens) has emerged as a new powerful toolset to study and probe those regulatory mechanisms in a diverse set of model systems. However, the reliable estimation of the molecular effects of a perturbation from the data and their comparison across different biological systems still requires tailored statistical methods to uncover the underlying causal mechanisms from the data. My Emmy Noether proposal aims to address this gap by developing a flexible, powerful and statistically sound analysis framework (Kri-kri) for the discovery of regulatory mechanisms from high-content CRISPR screens. In particular, I want to (1) improve the estimation of total causal effects of a perturbation on the molecular features, addressing shortcomings in calibration, power and flexibility of existing methods, (2) benchmark and develop methods for causal inference to learn directed causal networks from the data that can reveal the molecular mechanism underlying the observed changes, highlight novel roles of genes and guide the design of future experiments (3) dissect the interplay of cellular state and biological context with the effect of a perturbation, identifying invariant regulatory mechanisms in heterogeneous and dynamic biological systems.

• DFG Programme: Independent Junior Research Groups