In our quest to make biology easier to engineer, one thing that continues to be of utmost importance is our ability to measure biology accurately. The Internet of Things (IoT) and advanced hardware platform systems are making the job easier every day — and organizations like the National Institute of Standards and Technology are facilitating collaborative research that will push our bioeconomy forward.
Recently we spoke with Darlene Solomon, CTO of Agilent, one of the leaders in raising the bar when it comes to measuring what matters in synthetic biology. Here are her thoughts on how hardware platforms, IoT, and government initiatives are enabling accurate and sensitive biological measurements — and where we have room to grow.
How do synthetic biologists identify what “matters” when it comes to measuring what matters? What challenges still remain?
Often the choice is made based not on what matters, but on what is possible. With new specific tools like Agilent’s Seahorse platform — which measure oxygen consumption and extracellular acidification in live cells — or highly flexible tools like mass spectrometry, Agilent constantly focuses on expanding what is possible for our customers, providing measurements that matter most for the biologist.
Reproducibility has been a critical challenge for synthetic biology. How are new novel hardware platforms like Seahorse and others addressing this challenge?
Biology is hard and there are many layers we don’t yet fully understand, especially when incorporating a foreign or novel biological circuit or pathway into the host cell’s native content. We must strive to eliminate all sources of variation to achieve reproducible results. This is a challenge because of the inherent variability of biological cells and specimens, and the dynamic nature of living things. At Agilent, we bring innovation which helps to ensure that the measurements don’t introduce new uncontrolled variation based on the instrument itself.
As leading providers of biological reagents and software, we also work hard to minimize any variation across the workflow. For example, in synthetic biology we want every enzyme, DNA oligo or guide RNA molecule to be precisely the one the customer wants. We continually refine our manufacturing processes to develop new characterization methods and improve the quality of our molecular products. We also provide validation tools to enable our customers to assess the quality of their materials prior to performing an experiment. Finally, we provide automation tools that not only enable higher throughput experiments, but also improve the reproducibility of measurements.
What other challenges in addition to reproducibility are novel hardware systems addressing?
The metabolic activity of cultured cells is of paramount importance to SynBio. Agilent’s Seahorse analyzers are novel in their ability to measure key metabolic cellular functions such as respiration and energetics of live cells.
What precision tools are still lacking?
Technologies that help understand cellular complexity. These include i) new and improved intracellular technologies for omic profiling to ‘see’ all the biochemistry – both the native and engineered circuits and pathways – working in concert inside the cell, and ii) intercellular technologies to begin to probe interactions and communication between cells. There is also much room for innovation in enabling researchers to identify and quantify biology in both space and time.
There are enough challenges to keep us innovating for the next 100 years, but that’s at the heart of what we do and the essence of Agilent – what motivates us the most is to build better and better tools for our customers.
Let’s talk about the internet of things (IoT). How much has IoT impacted reproducibility and scalability of synthetic biology research?
IoT has tremendously impacted both. Through massive collection of data, IoT and data science have enabled larger, integrated, automated studies that allow for more systematic control of known experimental variation and more rapid discovery of what is important to synthetic biology advancements. Readily deriving the most actionable information from this scaling data is a continuing challenge.
Can IoT help us with the “data deluge” that we are drowning in, helping pick the relevant from the noise?
Yes, IoT and AI technologies such as machine and deep learning are not only separating the relevant from irrelevant information, but also already enabling researchers to gain new insights and actionable information from large scale, highly complex data.
How can synthetic biologists from academia and industry work with government initiatives such as NIST to address the challenge of measuring what matters even more quickly?
NIST, a global leader in metrology, is part of the US Department of Commerce with an explicit charter to convene academia and industry in creating economic growth. Over the past decade, NIST has been increasingly expanding its profound capability in physical science metrology into the life sciences, and Agilent has had excellent partnerships with NIST in both scientific domains.
Together with academia and industry, NIST’s research programs will accelerate our understanding of biology and fuel growth of the bioeconomy. In the coming decades it will be possible to effectively model and predict biology, just as we are able to do with more mature disciplines such as physical sciences and engineering.1