Here’s Why You Should Stop Doing Science Like You Were Taught in High School: Part 3
Check out Part 1 and Part 2 of “Here’s Why You Should Stop Doing Science Like You Were Taught in High School”.
In this series of blog posts, we challenge the traditional one-factor-at-a-time (OFAT) approach to doing science, which most of us were taught in school and continue to use until now. This approach studies the effect of a single component (factor) on a biological system, ignoring other potential components and any interactions between them. We argue this method is reductionist and often results in the loss of information and insights.
Design of Experiments (DoE) is a powerful alternative to OFAT as it studies the effects of a multitude of factors and their interactions simultaneously, allowing scientists to explore a wider assay design space and to optimise the conditions for the system under investigation more effectively.
However, sophisticated DoE assays cannot easily be executed manually and hence rely on lab automation, which comes with its own complexity and challenges. To overcome them, we have developed a flexible and user-friendly software platform, Antha, that can seamlessly program a range of automation devices, such as liquid handling robots, to execute sophisticated experiments, without the need for any programming input from the user. This makes Antha an intuitive platform for biologists with no programming background.
We touched upon Antha’s benefits in Part 1 and Part 2 of the series, but how exactly does it work? Here we explore its capabilities in more detail. For a full demonstration of Antha in action and to see how it enabled us to perform sophisticated 486-run DoE for buffer optimisation, please watch our online demo here.
Codeless Interface for Simple Execution of Sophisticated DoE Assays
Antha interprets simple user- or third party software-generated csv files describing the DoE conditions (different factors, their levels and combinations). It then propagates that information through a digital workflow encoding different experimental steps. Finally, Antha translates the workflow into a set of liquid handling instructions for a selected liquid handling robot, programming it to execute the experiment. Modifications can be easily brought to the design file and/or the workflow, and Antha updates the liquid handling instructions accordingly.
Even the hard-to-encode factors, such as incubation temperature, can simply be included into the input design file. Similarly, sophisticated DoE operations, such as staging and factor fixing, can also be easily inputted into Antha by the user. Antha then plans and calculates the most optimal way to set up DoE assays, including splitting the reactions into different plates when needed, and programmes the liquid handler to execute the experiments without user programming inputs.
Ultimately, Antha allows for considerable time and resource savings by simulating the designed experimental workflow in silico before the physical execution in the lab. In this way, calculation errors, missing experimental parameters, or even physical liquid handler constraints are taken into account, preventing the errors in the physical execution that would fail the whole experiment. Users can therefore correct and optimise their workflow as required to ensure experimental success and avoid wasting time and resources on trial-and-error runs.
Moreover, each liquid handling step of the experiment can be previewed as a result of this simulation, providing complete traceability of each sample and its components. This means that the same workflow can be used by multiple scientists/labs and on multiple liquid handling robots, while retaining its robustness and ensuring data reproducibility. If needed, the workflow can be easily modified for a different use case due to the built-in flexibility of Antha’s interface.
How Can Antha Help You Optimise Your Assays?
Overall, Antha leverages the strengths of lab automation to execute sophisticated experiments, such as DoE assays, rapidly and seamlessly, without requiring programming knowledge from the user. With Antha, DoE-driven optimisations are made simple, design modifications are easy to incorporate, and the resulting workflows are straightforward to share and re-use.
Ultimately, Antha empowers biologists to perform more sophisticated experiments, explore a greater design space, and gain insights that cannot be achieved otherwise, without compromising on time and resources. All this improves the quality of science: for instance, faster and more effective buffer optimisation for construct assembly assays can dramatically increase their success rate, facilitating downstream bioprocessing.
To see Antha in action and learn how it can empower your DoEs for buffer optimisation in construct assembly assays, watch the full demo. To learn more about the benefits of DoE, read Part 1 and Part 2 of “Here’s Why You Should Stop Doing Science Like You Were Taught in High School”. Finally, here you can check out how Antha leverages DoE for assay development and media optimisation.
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