Why Do We Need Antha?
Making biology simple, reproducible, and scalable
There is a huge problem of reproducibility in the biosciences. A report in Nature in 2012
revealed that scientists at Amgen managed to reproduce only 11% of 53 cancer-related studies which they had
attempted over the years. This is due largely to the fact that there are many technical differences when trying
to replicate the environment where the original discoveries were made. Despite a well-meaning investigator's
best efforts, it is staggeringly difficult to fully characterise cells and genes in a particular due
to the overwhelming complexity of the field.
Scientists at Amgen managed to reproduce only 11% of 53 cancer-related studies which they had
attempted over the years, as published in Nature, 2012
In order to ensure that our scientific research is reproducible, we must get better at making sure our
experiments are fully traceable, which means soliciting the aid of automation and using it in a way that is
flexible and robust.
Driven by our in-house needs and what we have learnt through statistical experimentation, automation, and
in the tech sphere, we have come to understand that the whole way in which s are designed and recorded
needs to fundamentally change in order to solve this problem. In response to this need, we have created
Scientists at Synthace explain how to use QbD, programming languages, and automation to
improve reproducibility in Biology, as published in Trends in Biotechnology, 2016
Redesigning the language of biology
Antha is a first-generation high-level
programming language for biology; designed to make simple, reproducible and scalable s by stacking
smart and reusable s.
An can be anything from genetic s (such as a promoter, gene, transcription
factor or the particular strain of yeast or bacteria used as host) to experimental procedures such as DNA assembly,
incubation, protein expression or enzyme
Antha will automatically track and log all associated data when the is executed. Essentially this
enables not just the standardization of the genetic parts but standardization and full tracking of the
procedure used to characterise a part. This will inherently allow for greater reproducibility, simplicity and
scalability when the s are wired together to form s. Once s and s are designed and
tested they can be shared or even embedded as a downloadable and executable material and methods section of a
Elements can be easily strung together in order to make logical workflows.
Experiments can then be generated from bundling s together. Potentially designed, scheduled, executed
s can then be generated from bundling s together. Potentially designed, scheduled, executed
processed in response to what the experimenter wants to find out, leaving the
how to be abstracted from the user if desired.
By using a high-level programming language, you can initiate a design, generate workflows,
schedule experiments on various machines, and interpret the results from one platform
Antha is an end-to-end fully integrated language with quality control built in to its DNA. As well as a
for experimental design, execution and data processing, it’s a LIMs system, data-management system and QA
yet simple and fun to use. The language is designed to support effortless incorporation of quality standards
as quality by design and GLP level quality
standards without the overhead for the user. This means enhanced traceability, reproducibility and simplicity
There are many factors which can affect an which typically wouldn’t be recorded. With the example of
simple step such as recovering a frozen cell stock and incubating there are many factors which might effect the
reproducibility and quality of the procedure which could have a knock on effect in subsequent processes.
Normally it would be very laborious to capture all of these variables but Antha aims to capture as many of them
as possible automatically.
Protocols have many sources of variability, some which are routinely captured, and some which
Because of the abstraction and modularity of Antha, it can potentially be the design and processing tool for
s carried out on any instrumentation; manually or automated.
The scheduler will figure out the most practical and robust strategy for executing the so the
experimenter can focus on what they want to do rather than how they do it.