How does a cell decide what it becomes? Transcription factors hold much of the answer, but until recently, systematically mapping their causal influence on cell identity has been out of reach.
In this webinar, Parse Biosciences and bit.bio share how a new alliance is combining massively parallel single cell perturbation with industry-leading cell programming to build a foundational map of transcription factor-driven cell fate. The result is a causal dataset linking specific genetic inputs to specific cellular outputs, designed to advance predictive drug discovery, human cell manufacturing, and the next generation of AI models of human biology.
What you'll learn by watching:
-The principles of massively parallel causal transcriptomics and how high-throughput perturbation experiments can resolve transcription factor function at single-cell resolution
-Experimental design considerations for systematically mapping transcription factor inputs to cell fate outputs
-How engineered, defined human cell models improve interpretability and reproducibility in large-scale perturbation studies
-The role of causal, perturbation-based datasets in training predictive models of cellular response
-Implications for drug discovery, disease modeling, and scalable production of human-relevant cell models
