The goal of this post is to elucidate three things:
Important caveat: Many SaaS tools meant for broader applications (e.g. Excel, Dropbox, AWS) are used within bioinformatics. While this post does not include these, there’s good evidence that they account for a considerable chunk of SaaS spending in biology and life sciences.
Definition: “paid SaaS” in this article will refer to any paid software tool, platform, or infrastructure that is used bioinformatics, including both academic research and industry applications. …
Widespread access to cloud computing has definitely changed the world. From the comfort of an office chair, software engineers can now write ML training loops, fire up AWS instances, and train their models across multiple GPUs. Services like AWS and GCP have further simplified this transition.
Despite these advancements, it is still unreasonably difficult for someone without software engineering skills to interact with the cloud. At Berkeley, we’ve seen students struggle every semester through needlessly complex configurations to train their first machine learning model on the cloud.
We’ve also noticed this problem in biotechnology, in both research and industry. Many…
The convergence of laboratory automation, high-throughput assays, and machine learning is moving the medium of biological discovery to silicon.
Benchtop experimentation will be the machine code of the bioprogrammer, simple instructions that are executed by robotics just as bytes are fed through a compiler, essentially machine code engineered for organic primitives.
However, if the goal of creating arbitrary synthetic life is to be realized, we need to upgrade our tooling. We will need robust software that operates at the abstraction level of the organism. We will need a programming language that speaks fluently in DNA, RNA, and protein sequences.