Biology is the world’s most advanced technology. It is precise, diverse, and operates on wide scales.
A cell can rearrange molecules with atomic precision, using enzymes that perform millions of chemical reactions each second. A redwood tree compiles 250 tons of carbon into roots, leaves, and other structures during its lifetime; the entire tree grows using the DNA instructions encoded in a seed. At a smaller scale, there are billions of microbes in a small test tube culture, and some microbes can divide in less than 10 minutes. A single microbe can become a billion in under five hours.
All this to say: Biology is capable of incredible feats. Organisms can be harnessed to do great things in the world, if we could reliably engineer them. Genetic engineering, though, is still in the dark ages compared to most other technical fields.
Today, cells are engineered mostly through trial-and-error. Scientists use robots, and rely on the natural multiplicity of organisms, to perform millions of experiments in a single vial. But as we try to design increasingly complex biology, this approach will likely fail. We cannot test even a small fraction of the combinatorial space within biochemistry. (A protein with four amino acids has 160,000 possible permutations. The average human protein has 430 amino acids.) If we truly want to harness living cells to “make just about anything,” then we need to make genetic engineering a quantitative, predictable field.
To do this, Asimov is building a suite of tools, cell lines, genetic parts, and CAD software. We use them to engineer cells to perform functions that would otherwise be impossible. Our CAD software is used to design DNA constructs made of individual genetic parts, and also to model how the DNA will work in cells. When we invent a new tool, improve a predictive modeling algorithm, or collect data on new DNA sequences, everything is added to the platform. We license everything to scientists.
A key goal here is democratization: We want anyone to be able to use engineering-grade tools to reliably design biology in sophisticated ways. If we’re successful, much of what we use in our everyday world — food, medicines, and clothes — could be a product of genetic engineering. Things that are already produced by genetic engineering — insulin, CAR-T cancer therapies, pest-resistant crops, and mosquitoes that curb disease — will become cheaper and more accessible.
We know that this vision will be difficult to achieve. And, like other audacious goals, it will require a veritable village to make it happen. That’s why we are launching a technical blog to document our ideas, progress, and impressions.
We will share our excitement, reveal our challenges, and explain what we are doing to move things forward. We also want to push beyond the hype and hyperbole that is so common in synthetic biology, and openly discuss “how the sausage gets made.”
This blog is not a place for press releases. It is inspired by the technical blogs published by Netflix, Square, and Stripe. We will do our best to build credibility over time, by sharing as many details as we possibly can.
Not all of the blogs will be about Asimov, either. Some will be deep explainers about emerging topics within genetic design, like deep learning for protein function or how to make more precise gene therapies. Other pieces will discuss our work in software design, automated bioreactor data processing, biophysical modeling, and therapeutics biomanufacturing. Each post will be co-written by scientists working on those projects.
We know that the key to credibility is specificity, so we’ll do our best to explain everything in an open and transparent way. When we don’t know the answer to a question, we’ll tell you. When we can’t talk about something because it’s proprietary, we’ll tell you. But we will always strive to be deeply technical in our effort to demystify biology.
- Virtual Private Network (VPN): Users connect to the cluster, provide some credentials and are then able to access internal tools.
- Single Sign-On: A tool like Kerberos allows you to use the same account across various components.
- Home-grown user accounts: You implement an authentication system and users have a separate username/password for your computing infrastructure.
Asimov, the synthetic biology company building a full-stack platform to program living cells, announced today it has been awarded a contract as part of the Defense Advanced Research Projects Agency (DARPA) Automating Scientific Knowledge Extraction (ASKE) opportunity.
Through ASKE, Asimov will work to develop a physics-based artificial intelligence (AI) design engine for biology. The goal of the initiative is to improve the reliability of programming complex cellular behaviors.
“To achieve truly predictive engineering of biology, we require dramatic advances in computer-aided design. Machine learning will be critical to bridge genome-scale experimental data with computational models that accurately capture the underlying biophysics. As genetically engineered systems grow in complexity, they become difficult for humans to design and understand. For simple genetic systems with only a couple of genes, synthetic biologists typically use high-throughput screening and basic optimization algorithms. But to engineer more complex applications in health, materials, and manufacturing, we need radically new algorithms to intelligently design the DNA and simulate cell behavior.”
Alec Nielsen, Phd, Asimov CEO
Asimov’s founders previously built a hybrid genetic engineering and computer-aided design platform called Cello to program logic circuit behaviors in cells. The ASKE opportunity will seek to support an ambitious expansion in the types of biological behaviors that can be engineered.
Asimov’s approach will leverage “multi-omics” cellular measurements, structured biological metadata, and novel AI architectures that combine deep learning, reinforcement learning, and mechanistic modeling. Over the past year, the company has ramped up hiring in experimental synthetic biology, machine learning, and data science to accelerate development of their genetic design platform.
DARPA recently announced a multi-year investment of $2B into innovative artificial intelligence research called the AI Next campaign. A part of this wide-ranging AI strategy is DARPA’s Artificial Intelligence Exploration program, which was developed to help expeditiously move pioneering AI research from idea to exploration in fewer than 90 days. DARPA’s ASKE opportunity is part of this program and is focused on developing AI technologies that can reason over rich models of complex systems.
“Over the past 50 years, DARPA has been a world leader in spurring innovation across the field of AI, including statistical-learning and rule-based approaches. We are proud to work with DARPA to advance the state-of-the-art in AI-assisted genetic engineering.”
Alec Nielsen, PhD, Asimov CEO