In order to fulfill our main project goal of characterizing as many standardized chloroplast parts, we made use of automation in every aspect within our project. We successfully automated the process of generating our basic parts from chloroplast genome templates. Additionally we focussed on implementing a pipeline of setting up our cell-free reactions and analysing the data afterwards. We hope that our automation efforts can make a contribution to the future iGEM community by allowing other teams to have easier access via our workflows.


There was a time, when building cars relied on delivering each frame to the workers for continuing the manufacturing process by horse-drawn carriages. In order to make car production more efficient, Henry Ford invented the first moving assembly line in industry, which is a fundamental part of car manufacturing all over the world today. In synthetic biology engineering principles like standardization, decoupling and abstraction are used for the construction of highly standardized systems. However, several challenges, such as managing biological complexity and especially the unreliable construction of synthetic biological systems, have to be overcome to establish synthetic biology as an engineering discipline.

But how could we overcome these engineering challenges? One possible solution would be to learn from past lessons, when engineering disciplines, which have changed our world tremendously, emerged from natural science like physics and chemistry. But could we adapt these ideas from e.g. mechanical engineering to synthetic biology in a useful way? One example from past lessons is the manufacturing of cars.

Since the invention of the first moving assembly line in 1913, automation and utilizing robots found their way into car factory’s and have accelerated the process of cars production so that every 4 seconds a ford is built somewhere in the world.

What can we learn from building cars more efficiently for making biology easier to engineer? When we started our project, we tried to identify how we could accelerate the slow process of engineering plant chassis. Of course the slow growth of plants is the major challenge in this engineering process, but if you would transfer that to our car production analogy, it would be the same as exchanging all workers but sticking to all old and slow machines in the factory without all the automation, which has taken place. Only the combination with robots and an assembly line for production has led to the highly improved efficiency. Inspired by this idea, we set our goal to utilize state of the art automation in order to also speed up the other parts of our plant engineering pipeline:

The Cloning process

One of the cornerstones of our project was the creation of a vast amount of highly characterized parts for the use in chloroplasts. For that we used PCR amplification and Golden-Gates based Modular Cloning, which are both compatible for the usage of automation. In order to implement automation in our project, we have implemented it in every aspect of it, starting with the ordering of primers and DNA synthesis, which have directly been ordered in an high-throughput compatible 384-well format. Subsequently we successfully automated the process of dissolving the primers and setting up the PCR reactions for the domestications of our chloroplast parts on the Opentron liquid handler, which allowed us to clone a large amount of parts at the same time, that would be prone to errors and tedious to do by hand. The next protocol we were able to automate in this pipeline were there different Golden Gate assembly steps, where the Echo acoustic liquid handler was used. This method minimizes the costs of reagents and consumables, due to the noncontact, tipless, low-volume nature of this liquid handling approach. It allows for downscale cloning methods, such as Golden Gate to the nanoliter scale and at the same time increasing the assembly efficiency and decreasing the reagent cost by 20- to 100-fold. Writing the liquid handling instructions for the Echo can be quite tedious and time consuming. Therefore we wrote a small set of scripts, to directly create the transfer list from the genbank files, which are exported from the Geneious cloning software. This allows a non coding access to this automation method, which makes it easier to implement, also for future iGEM teams.

By combining the Opentron liquid handler robot with the nanoliter assembly via the Echo we were able to highly accelerate our part production pipeline and create our toolbox of 154 Parts, which would not have been possible without the utilization of automation.

The cell-free reaction setup

The next step in our part characterization pipeline is the chloroplast cell-free reaction set up. For this protocol we again used the power of the nanoliter liquid transfer of the Echo, which allows the setup of much smaller reaction volumes, saving our precious chloroplast extracts and enabling the characterization of even more parts. For the cell-free measurements 18 different components have to be combined in a highly accurate manner, as we have shown that even small changes in salt and DNA concentrations can make a big difference in the resulting expression. In order to test many parts with a good measuring practice , which means at least 5 replicates, automation becomes key in reducing human error and acquiring comparable data.

Data analysis automation

Due to the nature of our project, a vast quantity of data can be acquired in a short amount of time. Analysing the data by hand and plotting them via conventional tools is non feasible for a project like ours. Therefore we developed a data analysis pipeline which significantly helped to speed up the design build-test-learn cycle of part collection.

Our Partnership: The remote iGEM Team Foundry

Additionally to the automation effort within our own project we teamed up with the iGEM team Paris Bettencourt, to develop a workflow for decentralized and remote access to automation. This includes the sample logistics, the automation experiments and the subsequent quality control, in order to give as many future teams as possible access to expensive lab automation equipment and therefore so that they would have the same opportunities to implement high throughput experiments in their projects.

Read more about these efforts on our Partnership Page here.