Can you tell us about your career journey and what led you to become the Chief Scientific Officer at Synthace ? What initially sparked your interest in the life sciences sector ?

To me , biology is the most complex , fascinating and powerful phenomenon known to humankind . It can solve the biggest problems facing us as a species , but for it to do so , we must learn how to work with biological systems as effectively as possible . This has been my drive throughout my career and was the main driving force when we started Synthace . extremely diverse and often unpredictable ways . But the problem is that most bioscience is being done with experiments that are too simplistic , experiments are blind to these fundamental interactions . Maybe with this context , it ’ s not so surprising that biological R & D is tough going !

How is Synthace set to transform the landscape of life science R & D , particularly in terms of automating high-throughput experiments and structuring complex datasets ? What excites you most about these advancements ?

What are some of the most pressing challenges currently facing the life sciences industry ?

The biggest challenge is the same one that the industry has always faced , and that is the complexity of biology . Except these days , we must reach further into that complexity as we look to solve increasingly challenging problems . This has resulted in an unrelenting increase in R & D costs and a corresponding decrease in return for all of that investment over the decades . We must find more effective ways of working so that the life sciences industry can continue to function effectively .

When we talk about biological complexity , we ’ re not just talking about the sheer number or diversity of the components that make up a cell or an organism . We ’ re also talking about how these components interact in

The mission of Synthace is to enable more powerful experiments that can shine a light on the complexities of biology . This means using software and automation to lift the burden of running complex experiments . The Synthace platform does a huge number of the tedious and involved planning and calculations that are needed for an experiment , then it can automatically convert the detailed plans that it generates into the scripts that are needed by automation . Once the automated experiment is run , it can automatically gather and structure the data that is produced .

The effect of all this is that scientists no longer have to worry about a lot of the more demanding and tedious parts of running an experiment , so they can run and analyse way more powerful experiments than they could have done before .

Can you elaborate on how Synthace integrates methodologies like Design of Experiments and what advantages this brings to scientific research ?

Design of Experiments is a bit of an odd term , in that it seems to refer to a very generic concept : after all , every scientist designs their experiments ! But what it actually refers to is multidimensional experimentation . What this means in practice is that a great many factors that might affect a biological system can be tested at once , instead of doing one at a time . But even more critically , it also enables us to identify the way these factors interact with each other . Put simply , it allows us to understand the interactions that are at the heart of biology . One of the key capabilities of Synthace is that it enables scientists to run this kind of multidimensional experiment a lot easier .

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