Our model, EVO, is designed to predict the next base in a DNA sequence, which can determine health outcomes, such as the presence of sickle cell anemia mutations.

The model can interpret genetic mutations and provide insights into their functional consequences, which is state-of-the-art in computational biology.

Biology is fundamentally digital; every organism is made up of sequences of DNA, and understanding how to program that DNA allows us to design biology to perform specific tasks.

The model learns from vast datasets of genomic sequences, allowing it to identify biologically meaningful patterns that can lead to new insights in biology.

EVO2 is a revolutionary biological foundation model that interprets and generates genomic sequences, identifying patterns in genetic code and predicting the effects of mutations that can impact health.

We're exploring directed evolution of bacteria to produce various products, including biomaterials and therapeutics, using computational methods in a lab setting.

The future may involve programming proteins as a new form of technology, bridging the gap between computers and human health, which is currently underexploited. With advancements in machine learning, like AlphaFold, we can now predict how DNA sequences fold into proteins in just minutes, potentially revolutionizing how we build machines that interact with the human body and our environment.

Evolutionary programming allows systems to explore new possibilities by mutating existing programs and comparing them against desired outcomes, effectively creating an autonomous AI research organization.

The next century will be about learning how to design biology and program DNA, leading to a new generation of designers focused on biological systems.