While we are able to design and build incredibly complex mechanical engines, engineering biological systems, such as cells, remains a challenge. However, recent technological improvements, notably in biochemistry, recently enabled the construction of databases with millions of quantitative cell activity measures, covering multiple diseases.
The DeepLife team develops a SaaS platform combining state-of-the-art machine learning algorithms on single cell multi-omics data, public and private cell atlases, and a proprietary deep learning-based digital twin. Our goal is to supplement in vitro testing with in silico simulations and rapidly discover molecular triggers to efficiently engineer cell behavior.
We are working closely with biotech and pharmaceutical companies to accelerate their target identification processes. We are also collaborating actively with the academic community by publishing papers and providing open access to our platform’s features and data.
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