Python 2’s end-of-life announcement meant that we had to move a very large codebase to Python 3. Here’s how we successfully did it in a matter of days, and when our approach may work for you for similarly large projects.
We love Python. We use it for a wide range of tasks, from automation control, to high-throughput biological design work, to our scripting and API libraries, to data science. Python 2’s end-of-life announcement meant that we had to move large codebases to Python 3. Pain and suffering inbound, right? Wrong! We got one of our biggest codebases up and running…
A followup to our previous discussion of running parallel tasks in Celery, with details on how we solved the largest problem with our initial implementation
Our Computational Biology software team develops several workflows for designing and executing large sets of genetic edits of microbes. These workflows are written in Python and executed asynchronously via an internal task-running service, ZWork, built using Celery.
A few months ago, we wrote about how to run parallel tasks with Celery, plus how and why we built an API on top of Celery’s task primitives. But one major problem remained: the manner in which Celery…
My job as a biologist in the lab used to consist of moving liquids back and forth by hand. At Zymergen, I’m training robots to automate the worst parts of that job, and I’m here to tell you why you should look forward to fully automated luxury science.
I’ve been working as a genetic engineer for about a decade. It’s not as glamorous as you might think.
No one is breeding velociraptors for a theme park, or making a fleet of android replicants. In fact, it’s pretty hard to make even minor changes to the DNA of the microbes I…
Our end-to-end approach for model selection for unsupervised outlier detection algorithms, with an emphasis on parameter tuning.
At Zymergen, we integrate robotics, software, and biology to predictably and reliably improve microbial strains through genetic engineering. We design large numbers of microbe variants, then test their performance in high throughput screens. We use data science to rapidly analyze data from these thousands of variants, predict performance in larger tanks, and identify statistically significant improvements.
Our robots allow us to run hundreds of experiments in parallel. Scientists need timely and reliable processing of this data to make the next round of designs…
No one would sell us a high-throughput system that could be reconfigured in minutes and would scale to fill a factory. So we built one.
In the past year, Zymergen has nearly doubled the size of our high-throughput life sciences facility, and our need for lab automation has grown even faster. Zymergen’s labs run experiments that last from weeks to months, use a remarkable diversity of organisms, and target dozens of end products. We onboard new protocols and processes every week.
In the past, we managed our scale by building integrated automation platforms, where a central robot arm was used…
An introduction to running parallel tasks with Celery, plus how and why we built an API on top of Celery’s Canvas task primitives
One of the technology goals of Zymergen is to empower biologists to explore genetic edits of microbes in a high throughput and highly automated manner. The Computational Biology team at Zymergen is responsible for building software to help scientists design and execute these genetic edits. (For a brief overview, see our Zymergen 101 tutorial).
Our team’s workflows are written in Python and run asynchronously using Celery. To improve the performance of these workflows, we have developed a…
How Solutions Engineering Goals Drive Our Architecture Design
While the Solutions Engineering team at Zymergen works on software, we do so in a fundamentally different way than the Software department. Our unique requirements call for us to architect a custom platform to best provide tools for our users. In this article, I’ll be talking about what makes Solutions Engineering so singular, what that means for our development practices, and how we address that with our software framework.
Scientists at Zymergen and the Solutions Engineering Team
The mandate of the Solutions Engineering team is to support our internal users (Zymergen scientists) by providing last-mile support, rapid prototyping, and one-off…
At Zymergen we are building a data science ecosystem where machine learning algorithms help predict which genetic edits will produce strains with enhanced chemical production. These strains can reduce our reliance on chemicals derived from fossil fuels, in line with Zymergen’s mission to build a sustainable future through biology. We’ve created data pipelines that update machine learning models as new data arrives, then generate new strain recommendations. These recommendations are presented to scientists who submit strain designs to the build team.
Strains are iteratively improved using miniaturized high-throughput experiments. We engineer and test microbe variants in parallel, gather data about…
We’re proud of our team’s culture. As we grew, we found we wanted to write down the values that have created that culture. In this post we introduce the Zymergen Tech Team values, share the method we used to develop them, and provide additional commentary which we hope captures our thoughts and intentions behind each value.
The Process
One day about two years ago, a few of us — Tessa, Kiyan, and Roxane — had a hallway conversation in which we lamented that the entire Technology Team could no longer have sprint planning in one room. With that small size, it also…
How to be the best dang code reviewers and reviewees possible
At Zymergen, we take our dedication to code quality seriously, so it should be no surprise that code reviews are an important part of our process — every line of code we write gets reviewed. But anyone who has been coding for a while knows that code reviews have a reputation for being a bit of a mixed bag. When done well, code reviews can be quick, good-natured, and a useful tool for catching issues early. When done poorly, however, they can be slow, useless, and a great way…
