Once you have defined the factors and levels to investigate in your DOE, you are ready to decide and calculate the type of design you want to use. The design calculation will determine which combination of factors and levels to use for each run, to best satisfy the questions you have about your system. For example, which factors are important, and which aren’t, or are there two-factor interactions.
The following tutorials will introduce different types of designs and the scenarios to which they are best suited. You’ll also find details about each specific design, how to set them up in Synthace, or how to import designs from a third party stats tool.
A brief background document which summarizes the main concerns to bear in mind when choosing a design.
Different design types are best suited for distinct experimental objectives. Learn about the designs supported in Synthace and where they are best used.
When you have very little prior knowledge about your biological system, a space-filling design can be a great place to start to help identify what is important and where you might focus your next design iteration.
When you have some prior knowledge about your biological system, optimal designs are very powerful for screening or optimisation with fewer experiments needed.
Often you have the throughput and are not constrained by cost - in these cases, a full factorial can be a powerful approach.
Synthace offers a limited selection of design types, in the aim to keep things simple and to get you up and running. But should you need functionality or design types not offered in Synthace, you can use third-party tools to calculate your designs and import them into Synthace.
As with any biological experiment you might want to define positive and/or negative controls to be included as part of your DOE. Learn how you can manually define controls to include as a part of your experiment.
When replicating runs within your DOE design you might consider randomizing the order of the replicates within each block to minimize plate-based effects or bias. Learn about the different ways in which runs can be randomized within your DOE design.