MAY 12, 2016 10:30 AM PDT

Antha: An Operating System for Seamlessly Linking Experimental Design and Lab Automation

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  • Founder & Chief Technology Officer, Synthace Ltd
      Sean is a serial entrepreneur, first in the music industry with Relatable, and currently in synthetic biology with Synthace, where he is the architect and driving force behind Antha, a high level language and operating system for working with biology. Before founding Synthace, Sean was a research associate in bioinformatics at University College London, where he conducted research into protein folding, protein structure prediction, and gene coding. At Relatable, Sean invented audio fingerprinting which was used by major peer-to-peer companies such as Napster. Sean is also an adviser on synthetic biology to the BBSRC, and a SynBioLEAP Fellow.


    Working with biology currently takes too long, costs too much, and fails too often. At the core of this is the complexity of the systems we are trying to understand, compounded by a lack of reproducibility in our daily lab practices and poor traceability of what was actually done to perform an experiment. Antha is a high level, open source programming language for unambiguously defining lab protocols, which can then be combined into sophisticated workflows for biological investigations.  Antha operating system (AnthaOS) takes these workflows and compiles them into the computer code required to directly run the lab automation and analytical equipment needed for each experiment.  AnthaOS tracks the flow of information and physical samples through the workflows, so that every piece of data generated is linked to full provenance of the experimental procedure that generated it.  Antha relies on a range of drivers to talk to different lab equipment, meaning that it is interoperable between different makes and models of lab hardware.

    By linking diverse lab equipment and defined protocols in this way, Antha enables the rapid and flexible programming of lab automation, so large and sophisticated experiments can be easily designed, run and analysed.  These include high dimensional experimental designs, which are an exceptionally powerful way of addressing biological complexity and enabling the development of efficient and robust processes.

    This talk will present case studies on how high dimensional experimental designs can unpick high order interactions between genetics and environment in biological systems, and how these methods can also be used to optimize lab protocols.

    Learning objectives:

    • An overview of the power of using high dimensional experimental designs for characterization and optimization of biological processes and protocols.
    • How high level languages combined with lab automation can enable these complex experiments that unpick biological complexity.

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