For many biological scientists Design of Experiments (DOE) is often regarded as a daunting process with a lot of jargon, statistics, and often complicated statistical software that is geared towards the statistician, not the biologist. However, when broken down into the main steps and minimal tooling you need, it really isn’t as difficult or scary as it may seem.

In Synthace we have taken a biological scientist first approach to lower the barrier of entry to learning how to apply DOE to your biology and to get you up and running as quickly as possible. Statistical software such as JMP, Design Expert, and others, are extremely powerful statistical products. But the many different tools they offer can be bewildering to a biological scientist new to DOE. In Synthace, we have hand-picked the most important tools and streamlined the process, to have you running your first DOEs as quickly as possible.

Beyond getting in the lab and setting up your experiments there are several steps upstream and downstream that you need to go through in order to run a DOE:

However, when using common statistical software the majority of these steps are manual, and can be time consuming or error prone, with some requiring knowledge of coding. These statistical software are application-agnostic, therefore lacking the context of the experiment you are going to run, thus relying on the scientist to bring that context to the table when designing and planning their experiment.

We built the DOE capabilities on top of the our experimental user journey. This means that you can leverage the context of your experiment throughout your DOE. You can define your protocol first, and then map the DOE onto it. All the while, your entire journey is curated for you in a Synthace Experiment automatically, further relieving your burden as scientists to do all the data wrangling when you are ready to write a report (Figure 1).

Figure 1. The DOE journey in the Synthace platform. The various steps of the DOE journey are shown within the areas of the Synthace platform where you will perform those actions all curated within a Synthace Experiment.

This DOE documentation has all the information you will need to learn the basics of DOE, how to use Synthace to start your DOE journey, with some helpful statistical docs so you can learn, understand, and reference what is happening under the hood. You will find the following specific documents within.

Background documentation will provide more detailed background information to help you better understand the concepts of the topic at hand.

Statistical documentation, where applicable, will provide detailed information about the statistics for the topic at hand, including references to statistical packages used for citation purposes.

Instructional documentation will cover the “how to” aspects of DOE in the Synthace platform. These documents will demonstrate new user interfaces and how to interact with them for the topic at hand. These are step-by-step guides that do not have any specific scientific focus.

Goal Oriented documentation provides step by step guides on how to achieve a specific scientific goal. It does not go into the detail of how to use the user interfaces like the Instructional documents but how you would use those interfaces to achieve the specific scientific goal.

Get started with some fundamentals about what DOE is, how DOE is a set of iterative experiments organised into campaigns and the relationship between DOE design and data modelling.

Your DOE journey in Synthace starts with building a workflow that describes the biological protocol you intend to then optimise. This chapter covers how to best get started with DOE and how to build workflows ready for applying DOE.

Defining and calculating a DOE design in Synthace covers a number of steps from factor definition, design selection and calculation, and assessing the quality of a design. This chapter will cover topics that will help you create a DOE design for your specific biological protocol.

Executing DOEs often requires many hundreds of wells in microtiter plates. However, due to various logistical constraints in the lab it might not be possible to perform all of those runs on a single plate or in a single day. This chapter will cover how you are able to split your DOE design into multiple simulations to facilitate the logistical constraints you may have.

Once your DOE has been executed in the lab and you have generated experimental data it requires structuring to your DOE design in preparation for analysis. This chapter will cover how to structure, prepare and fit models to your data for analysis.