The life sciences labs of the future will require skills in automation, open source technologies, artificial intelligence, and many other emerging areas. Here's what you need to know. In the early 21st century, wet laboratory skills in molecular biology, cell biology, and proteomics are key for anyone seeking a life sciences career. While expertise in these areas remains essential, future laboratories will require different skill sets based on emerging trends in research and technology.
Here are six skill areas we think it’s helpful to familiarize yourself with to advance your career and prepare you for the life sciences lab of the future.
Automation offers significant advantages to life science researchers, including improved data quality, increased cost efficiency, scalability, and more time to do other things in the lab other than repeat experiments. Automation also benefits new complex biotechnologies such as next-generation sequencing or mass spectrometry proteomic analysis, which require increasingly complex workflows that are sometimes too complex and time-consuming for humans to successfully execute. Automated innovation and capabilities are proliferating as the need for speed and throughput increases in drug discovery, diagnostics and even basic research.
Given the huge potential for fully automated workflows, many research labs are taking scientists away from lab work—pulling them off their benches and putting them in front of their computers to design experiments for lab robots to complete instead. Execute it yourself. This more effectively leverages scientists' understanding of biology, leaving repetitive lab work to robots so biologists can solve difficult scientific questions.
Ginkgo Bioworks designs custom organisms for customers and builds its own factories, leveraging software and hardware automation to scale the process. Synthego is the first and only company to offer full-stack genome engineering solutions, achieving this by leveraging cloud-based software automation. Synthace utilizes both hardware and software to aid large-scale automated laboratories. Of course, hundreds of biologists around the world use Opentrons software and hardware to automate protocols and workflows for a variety of experiments, from basic dilutions to PCR prep and NGS. Exploring how these companies are using automation will help you understand how automation is changing life science research.
Open source, collaboration and shareability skills
Open source refers to software whose source code is free for anyone to view, use, modify, and share. It allows users to build and learn from existing code, while promoting and encouraging collaboration and innovation among users around the world.
Open source applications in the life sciences are used to ingest the vast data sets produced by genomics and other related applications, such as the Ensembl Genome Browser database, which makes genomic and other related data accessible to any interested user. Open source computing technology is also used to model and simulate organisms: OpenWorm uses it to create virtual nematodes, while Virtual Cell uses it to model and simulate cells, and epidemiological researchers share genomic data to speed up pathogen analysis and identify the source of outbreaks. .
As the use of open source in life science disciplines continues to evolve, practical expertise in coding and data sharing methods will become a major asset for life scientists – so becoming familiar with some of these tools now will give you an advantage in the future. A good place to start is with open source protocol sharing platforms, such as Protocols.io and the Opentrons protocol library, which allow scientists to discover and co-develop protocols, and Plasmotron.org, which allows users to build on open source code.
The ability to effectively communicate the applications and impact of life science research to non-technical audiences creates important connections between scientists and society. Effective communication in this area involves using non-technical, plain language, making concepts easy to understand, and learning how to reach and engage the public. Practicing both disciplines encourages important discussion and debate and makes knowledge about scientific developments transparent and accessible to everyone. You can join thousands of biologists working to improve science communication and hone your science communication skills by participating in voluntary science outreach events, sharing your research activities on social media, or creating your own science blog or podcast.
In the life sciences, machine learning has revolutionized the speed of research and diagnosis through its ability to distinguish cells, analyze genomic data, perform image analysis and detect indicators of disease earlier and more sensitively than previous methods. One major growth area is using machine learning to design experiments. Asimov uses open source data to develop machine learning algorithms that connect large-scale data sets with biological mechanistic models to build biological circuit experiments. Cello uses machine learning to automatically design biological circuits in living cells. Another major growth area is bioinformatics. Deep Genomics uses machine learning to collect, analyze and process genomic data to develop better, more targeted medicines. Some companies, like Atomwise, are even using deep learning frameworks to try to screen drug candidates in software. Familiarity with these emerging applications will help you understand the future uses of this technology.
With the global CRISPR market expected to grow six-fold to $3 billion by 2023, opportunities to leverage gene-editing tools in future life science labs should be numerous.
Growth in the single-cell analysis field is driven by basic research and growing demand for early disease detection technologies, prenatal screening, biomarker discovery, liquid biopsies, and biopharmaceutical development.
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