What does Asimov do? A brief response. We create tools and software to more reliably engineer cells, thus bolstering humanity’s ability to design living systems and enabling biotechnologies with outsized societal benefit. Biological organisms are typically engineered, today, using iterative trial-and-error. Our goal is to push biotechnology into a true engineering discipline, where experimental outcomes are predictable ahead of time. Our work centers around a concept called genetic design. This as the process of intentionally modifying an organism's DNA using advanced techniques such as characterized parts, modeling, and multi-omics analysis. Genetic design is distinct from traditional genetic engineering in that it focuses on forward design driven by biophysical understanding and model-guided predictions. One of the ways we’re using genetic design is by engineering mammalian cells to make medicines. In other words, we’re making bio-tools, such as expression platforms and engineered cell lines, as well as software tools, including metabolic simulators, codon optimizers and signal peptide predictors, to help our customers engineer cells to make antibodies, AAV, and lentivirus. Let's use antibodies, a type of protein used to make many of the most popular medicines (including Humira for rheumatoid arthritis and Keytruda for cancer), as an example. Many pharmaceutical companies make antibodies using Chinese Hamster Ovary cells, or CHO, but the problem is that this process is unpredictable and has to be tweaked for every new antibody. We use our wet-lab and software tools to optimize and engineer these cells, thus coaxing them to make greater amounts of antibodies with fast timelines and good quality attributes. We also have product offerings for lentivirus and AAV manufacturing. Our basic “tech stack” is the same, regardless of application. We have teams working on optimizing cells and production processes for individual molecules and other teams building computational models — based on biophysical insights or transformer-based AI models — to predict aspects of how an engineered cell will behave before we make it. Another team collects large amounts of data to measure the function of myriad biological processes, and then works with computational biology teams to improve their models. We strive to make every experiment into a data point so that nothing goes to waste. Many of the models and tools we develop for various products are also bundled together and made available through Kernel, our browser-based software for genetic design. You can think of it as the “hub” or “vault” for all of the tools we’re building. So that’s the gist. We make wet-lab and computational tools to engineer cells in more predictable ways. While engineering those cells, we collect a large amount of data and build models to understand how they work. This research, in turn, bolsters Kernel and makes it easier for everyone to design biology. https://www.asimov.com/
Paul Trout
1w
This is great!
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4dTaking the complexity of biological engineering and turning it into a more predictable, data-driven discipline is fascinating!