In the early 00’s, wet lab skills in molecular biology, cell biology, and proteomics were key to anyone seeking a job in the life sciences. While know-how in these areas is still essential, the labs of the future will require a different skill set based on emerging trends in research and technology.
Here are six skill areas we think would be helpful to familiarize yourself with to move your career forward and prepare yourself for the life science lab of the future.
Research published at the 2018 World Economic Forum found that automated devices are expected to execute more than half of today’s tasks by 2025. Robots will also create about 60 million new jobs in a comparable time frame. The market for liquid handling robots alone is estimated to surpass $7 billion by 2026, and it’s hard to imagine a field where automation know-how won’t boost your resumé in the near future.
Automation offers significant advantages to life science researchers including improved data quality, cost efficiency, scalability — and more time to do things other than run repetitive experiments in the lab. Automation is also beneficial to new sophisticated biological techniques like next-gen sequencing or mass spec proteomic analysis, which demand increasingly complex workflows that are sometimes too complex and time-consuming for people to successfully execute. Paired with increased demand for speed and throughput from drug discovery, diagnostics, and even basic research, automation is seeing a surge in innovation and capabilities.
Given the huge potential of fully automated workflows, many research labs are abstracting scientists from lab work — pulling them off the bench and putting them in front of a computer to design experiments for lab robots to complete rather than executing them themselves. This makes more efficient use of scientists’ understanding of biology, leaving the repetitive lab work to robots so the biologists can solve difficult scientific problems.
Ginkgo Bioworks designs custom organisms for customers, building their foundries to scale that process using software and hardware automation. Synthego is the first and only provider of full stack genome engineering solutions, made possible by utilizing cloud-based software automation. Synthace utilizes both hardware and software to help large automated labs. And, of course, there are hundreds of biologists around the world using Opentrons software and hardware to automate protocols and workflows for many different kinds of experiments from basic dilutions to PCR prep and NGS. Exploring how these companies are using automation will be a helpful way to gain your footing in the ways automation is changing life science research.
Open source refers to software with source code that is freely available for anyone to view, use, modify, and share. It allows users to build upon and learn from existing code while facilitating and encouraging collaboration — and innovation — between users anywhere in the world.
Open source applications in life science are used to assimilate huge datasets yielded by genomics and other related applications, such as the Ensembl genome browser database which genomic and other related data accessible to any interested users. Open source computational technology has also been exploited to model and simulate living organisms: OpenWorm uses it to create a virtual nematode, while Virtual Cell uses it to model and simulate cells, and epidemiology researchers share genomic data to speed up pathogen analysis and identify outbreak sources.
As the use of open source in life science disciplines evolves, hands-on expertise in coding and data sharing methods will become major assets to life scientists — so getting familiar with some of these tools now will give you a leg up in the future. A good place to start are open source protocol sharing platforms such as Protocols.io and Opentrons Protocol Library, which allow scientists to discover and co-develop protocols, as well as Plasmotron.org that allows users to build on top of open-source code.
While all scientists communicate their work to each other through scientific presentations, research papers, literature reviews and attending conferences, they need to use different methods to communicate to the general public.
Being able to effectively communicate the applications and implications of life science research to a non-technical audience creates a critical link between scientists and society. Effective communication in this area involves utilizing non-technical, down-to-earth language that makes concepts accessible, as well as learning how to reach out to and engage the public. Practicing both of these disciplines encourages important discussions and debate and makes knowledge about scientific developments transparent and accessible to everyone. You can join thousands of biologists committed to better science communication and hone your science communication skills by taking part in voluntary science outreach activities, sharing your research activities on social media, or starting your own science blog or podcast.
Although it sounds futuristic, we are already surrounded by machine learning aided artificial intelligence applications every day: the voice assistants on our smartphones, live chat functions on websites, the targeted ads that adorn our social media feeds, and so on. These applications utilize complex algorithms and massive datasets to train computers to work and react like humans. This process refines computers’ learning process, making them more efficient in how they respond to conditions and yield results — rapidly augmenting the speed of future discoveries.
Within life sciences, machine learning is already revolutionizing the pace of research and diagnostics through its ability to differentiate cells, analyze genomic data, perform image analysis, and detect indicators of disease much earlier and with greater sensitivity than previous methods. One major growth area is in utilizing machine learning for designing experiments. Asimov uses open source data to develop machine learning algorithms that bridge large-scale datasets with mechanistic models of biology to build biocircuit experiments. Cello utilizes machine learning to automate the design of biological circuits in living cells. Another major growth area is in bioinformatics. Deep Genomics utilizes machine learning to collect, analyze and process genomic data to develop better, more targeted pharmaceuticals. Some companies like Atomwise are even using deep learning frameworks to attempt screening drug candidates in software. Familiarizing yourself with these emerging applications will help you get a grasp on the future use of the technology.
The discovery of CRISPR in the mid-2000s was a turning point in life science because of its applications for genetic engineering. And while we’re all familiar with the CRISPR platform now, the applications for gene editing platforms are far reaching — and often overlap with many of these other emerging areas. Oxford Genetics offers experiment design tools for gene editing that help streamline workflow. Synthego, again, utilizes machine learning to drive experiment design in genetic engineering. CRISPR Therapeutics utilizes the CRISPR gene editing platform to create medicines that target blood-based diseases like sickle cell anemia by their genetic makeup, while Caribou Biosciences, Editas Medicine and Cellectis utilize CRISPR and other gene editing techniques like TALENs to modify t-cells to target cancer cells.
The global CRISPR market is expected to grow 6-fold to $3 billion by 2023, so there should be no shortage of opportunities for utilizing gene editing tools in future life science labs.
Rapidly advancing technologies that allow the application of proteomics, genomics, transcriptomics, and epigenetics techniques to single cells provide novel and crucial insights into the complex biological processes that govern development, gene expression, tissue heterogeneity, and mechanisms of disease. These technologies are particularly helpful for analyzing biological phenomena like circulating tumor cells and rare stem cells that are challenging, if not impossible, for standard ‘omics’ applications. Parallel advances in genome editing, automation, and microfluidics have further facilitated the rapid and high throughput analysis of smaller samples that are common with single cell applications. 10x Genomics uses single cell cancer genomics assays to profile cancer cells. Metafluidics, a database of open source design and protocol files for reproducing or remixing microfluidic devices, is a good repository of information in the space.
Growth in the single cell analysis area is fuelled by basic research as well as increasing demands for early disease detection techniques, prenatal screening, biomarker discovery, liquid biopsies, and the development of biological drugs. Translating all of this into numbers, the global single cell analysis market was valued at USD 1.4 billion in 2016, and it is estimated to quadruple by 2025.
All of these emerging skill areas are not only increasingly important to life science researchers, but ones that Opentrons customers utilize every day. Learn more by visiting Opentrons.