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Synthace is an interdisciplinary and collaborative team of computer scientists, biologists, mathematicians, and engineers focusing on new ways of working digitally in biology. Its core technology platform is called Antha, software designed to automate bioscience R&D.
GEN recently interviewed Markus Gershater, CSO and co-founder of the company, to learn more about Synthace.
GEN: Why was Synthace founded?
Dr. Gershater: We knew there were better approaches for addressing the complexity of biology than the methods found in the industry. We used multifactorial optimization and synthetic biology to create more effective biological production systems for a variety of different products.
GEN: What drove the development of Antha?
Dr. Gershater: As we carried out more complex experiments, they were becoming increasingly difficult to run by hand. It was critical to find ways to make this work more tractable and that got us thinking about data, automation, and robots and how that could help us run experiments better. We developed in-house tools that would allow us to program and run the automation in a much more dynamic manner, which enabled the complex automated experiments we needed. These tools could convert an experimental design into specific steps that were needed for the robots to automate the running of that experiment. All these efforts ultimately led to the creation of Antha, which is the core software at the heart of our offering.
GEN: It sounds like you are talking about digital biology. What is your vision regarding digital biology?
Dr. Gershater: At the moment, digital tools are massively underutilized in biology. The most common tools used by a biologist are a handheld pipette and a copy of Excel. Given the complexities of what we are trying to address as biologists, we could do much better. We don’t think of this as pure digital biology as it’s when digital tools are tightly integrated with wet lab hardware and the experiments that are run on it that is the most powerful. We call this combination of sophisticated digital tools and lab automation computer-aided biology.
We envision a future where methods like machine and deep learning are used with much more regularity to give people significant assistance in gaining insights into the complexities of the biological systems with which they are working.
These AI terms are increasingly bandied around at biological conferences, but until sophisticated and structured data sets are being produced within the physical laboratory on a routine basis to common formats the powers of artificial intelligence or machine learning will not be truly unlocked. It is with lab automation, combined with automated data structuring, that these beautiful datasets can be produced with relative ease.
GEN: How are you currently implementing this computer-aided biology vision? What are some specific applications?
Dr. Gershater: We now have this platform for rapidly programming automation to do dynamic experiments in the lab. R&D is an environment that requires a lot of flexibility, so historically, automation has not been something that would be used there to any great degree. But now, with Antha, automation can be used to a much greater degree within R&D even with experiments that vary a lot from run to run, unlocking all the benefits of greater throughput, reproducibility, and complexity that automation offers.
We are putting digital integration of data at the back end of virtually every automated workflow in the lab. There are instances where we are doing high-dimensional optimizations based on designs of experiments of up to 30 factors simultaneously. Thousands of experimental runs can now be carried out in a day or two by a single scientist, so the data automation is vital to avoid a huge amount of manual data wrangling.
On a larger scale, we are applying our digital vision to up-stream bioprocessing where you might be running a large number of different fermentations or cell cultures in parallel, and each one of those produces a large amount and diversity of data, from real-time sensors that monitor how that culture is, to all of the assays that are run on samples taken out of the reactors and subjected to a variety of offline analytics. We automatically combine and structure these data to drive rapid insights into the biology in the bioreactors.
We are really excited about how Antha is starting to be used in companies that are running with it in a way that is changing how they go about doing their science in drug discovery and bioprocessing.
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