Introducing Bits in Bio

A community for software enthusiasts and builders in biotech

Welcome to the first Bits in Bio blog post! I want to take a bit of time to introduce Bits in Bio and explain why this is an opportune time to bring together the people who will build the software to empower the biotech revolution.

What is Bits in Bio

Bits in Bio is a community devoted to people who build tools that help scientists unlock new insights. For the past few years, I have been searching for, but unable to find, a gathering place for people interested in the intersection of software and biotech. I hope that the Bits in Bio community can fill this gap. This is a space for anyone interested in the intersection of software and biotech to engage with other people working in this area. We welcome chemists, biologists, software engineers, informaticians, ML researchers – anyone with a willingness to learn!

If you are excited by discussing tradeoffs between different LIMS providers or where existing cheminformatics analysis solutions fall short – you’re in the right place! Breadth is important. This community is filled with people interested in a wide range of topics. I find that a lot of discussion surrounding “Software + Biology & Chemistry” typically focuses on AI and automation. These are important topics, but so are data pipelines, data storage, and visualizations!

Whether you’re just starting your PhD or have been a software engineer in industry for decades – Bits in Bio is for you. This is a place for industry practitioners, academics, builders, and hackers to mingle. We’ll be starting a number of initiatives (like this blog/newsletter) intended to kickstart the conversations, but the true value will be your participation.

The state of software in biotech

The last few decades have seen many examples of software changing the way companies operate and enabling new businesses across numerous industries. Amazon, Netflix, and Airbnb have revolutionized online shopping, media consumption, and travel. However, changes of this magnitude have yet to come to biotech. True, there have been huge advances in the space of ML + bio (looking at you, AlphaFold), but software in many parts of the biotech industry has not changed much in the last few decades. Data is often siloed and difficult to access, analytics is mostly bespoke, and spreadsheets dominate the industry.

Excel and Google Sheets are the most important pieces of software for many biotechs. Spreadsheets are incredibly flexible and can function as databases, experimental design interfaces, or visualization platforms. But that flexibility means that spreadsheets are inefficient for many common tasks in biotech. A spreadsheet doesn’t know what a negative control is or that plate-based assays have fixed size plates. For many companies, this may be an inconvenience that simply costs their scientists a little bit of extra time for each experiment. For others, this is a barrier that prevents them from scaling up effectively. In either case, software that is purpose built for the biotech industry can make scientists more effective and unlock otherwise inaccessible insights.

Biotech is well aware of the value of big data. Whether this is high throughput screening for small molecules, DNA encoded libraries, or multimodal -omics, large datasets are becoming ever more important to biotech companies. We need better software tools to design, perform, and analyze these experiments more effectively. We are in the middle of a major transition in the world of biotech where software solutions will no longer be “nice-to-have” – they’ll be *required* to operate effectively.

What comes next

If you haven’t already joined the Slack, please join us here. That will be our primary organizing tool – a place to ask questions, meet new people, organize events, and share new opportunities. We are in the process of planning some Slack Q&As with some really exciting people who have built popular open-source and commercial software in this space.

This blog/newsletter will also be a place to explore some of these topics in a longer form. Future posts include: 

• Business Models for SaaS in Biotech

• Building vs Buying LIMS

• SaaS in Biotech Landscape

If you have ideas for other topics you would like to see written about (or that you would like to write about), please reach out to me on Slack or Twitter (@nlarusstone).