The goal within our lab is to automate all wet lab protocols. At this point, we are very close. There is a very small fraction of work that is still done manually to set up the robotics for the day. We have transformed our lab such that no researchers are doing any significant manual work.

Through a line function specialised in automation/robotics.

We are implementing cobots and automation platforms using the Open Standards SiLA2 and AnIML.

Digitize everywhere possible. The new challenge we face with the incredible increase in capacity gained from our automation efforts is in the digital space. How can we effectively triage the work in the lab through AI and digital efforts? Can we use AI to generate the multiple input files needed across our instruments? How can we automate downstream pipelines?

Blue sky vision: practically autonomous laboratories

To digitise all data capture at source, and automate many of the Ds (Dirty, Dangerous, Drudgery, Diversity)

Embracing change within the lab, and establishing a culture of playful exploration is critical to shifting towards an automated lab. Dedicating time towards standing these robotics systems up and understanding that there will be a learning curve. Also focusing on total inclusion of the entire lab team without prioritizing any one person as the lab automation expert.

Size and complexity of the organization. As well as the availability of deep and enough skills.

Point solutions are developing well, however, a global governance structure, both physical and organisational are still required to ensure automation remains safe, useable, sustainable and reliable around the world as deployments increase. The risk is the point solutions become silos that can't scale efficiently, or worse become dangerous through lack of safety awareness.

Don't wait, get started today! Think digitally before you do anything else. Where will the input data be captured? How will it be stored and accessed in the future? The wet lab portion will come much easier than the digital side.

Develop an end-to-end concept. Standardise capabilities.

Think about the end game, ensure your end users are prepared and that your organisation can support the long-term realisation of the potential that automation/robotics brings. For example, what does the scientist’s day look like? Are they in the same labs, or different ones, where is the data going and how will they interact with it in the future world?

Can you imagine seamless integration between platforms? You know that plate reader you have in one corner of the lab that pumps out excel sheets of data? It knows the plate is coming and how to format the output, and what database to store the data in, and how to fold that analysis in with your flow data, and your qPCR data, and it is all handled as a single stream allowing for automated insights across your entire platform.

We can move away from the liquid handler as center point and create more flexible arrangements. This will lead to smaller footprint and scaleable concepts.

Early days for us, but I think closed loop experimentation and autonomous formulation optimisation will be coming to labs soon.