Parse Biosciences, a Qiagen unit, and bit.bio agreed to build a transcription factor-driven cell identity map by combining bit.bio’s cell programming and discovery platform with Parse’s Evercode single-cell assays. The collaboration aims to generate datasets linking thousands of genetic variables to cell states and cell fates. The partners said their goal is to enable AI-driven therapy design and support human cell manufacturing by moving toward causal, predictive models rather than correlation-only biology. bit.bio CEO Przemek Obloj and Parse chief technology officer Charlie Roco both emphasized the need for foundational maps that can feed predictive systems. Because cell identity and fate decisions remain a major bottleneck for next-generation therapeutic design, the alliance targets a practical infrastructure problem—how to scale the biology measurements needed for machine-learning feedback loops. Researchers will watch whether the first releases translate to better model performance and more reliable cell programming outcomes.