Automation and Robotics
Low-cost automation tools, enhanced with the power of artificial intelligence and data-sharing protocols, can revolutionize biological research and development.
The University has an active community of researchers interested in all aspects of automation and robotics. It has developed a roadmap for growth in this area and is working in close partnership with the academic and industrial research community to realise this.
New ways of working
We are developing new ways of working, deploying robotics and automation in our labs. This improves the reproducibility of experiments, reduces cost, increases speed and saves valuable time. Researchers can then focus on creative design and analysis (‘thinking’), rather than ‘doing.’
We are promoting the shift from doing individual experiments manually, varying one variable at a time, towards automating experiments in a high throughput manner to allow us to optimize several variables in a single experiment.
We are embracing the capacity of automation tools to obtain large, high quality datasets to enable the move towards data-driven research; we can take advantages of machine learning algorithms to elucidate new insights even if we initially could see not evident correlations.
We are incorporating artificial algorithms into automated platforms, which create an internal hypothesis in the design cycle, while allowing the researcher to generate bigger picture higher-level hypotheses.
New ways of thinking
In the future, automation and robotics will become an ever-increasing component of everyday research, indeed part of everyday life.
However, to derive greatest value from automation, we often need to rethink why and how we do things traditionally to transfer them efficiently to a more automated system. Therefore, training and education in the underpinning platforms – programming (scripting) of machines, developing and adapting protocols – is essential.
Many labs across Edinburgh are using automation routinely in their processes:
- The Edinburgh Genome Foundry – The Foundry is an automated platform that delivers high throughput DNA assembly at scale
- Edinburgh Genomics – Hamilton automated platforms for Next Generation Sequencing
- The Leo Rios Lab (School of Engineering) – Low cost open source automation, Opentrons and Digital Microfluidics (Digi.Bio)
- The Menolascina Lab (School of Engineering) – Microfluidics combined with Machine Learning algorithms
- The Edinburgh Centre for Robotics – a national centre of excellence for robotics and autonomous systems
- Dr Neil Carragher (Cancer Research UK Edinburgh Centre) – Automated cancer Drug Discover and Screening platforms
- Dr Eoghan O’Duibhir (Centre for Regenerative Medicine) – High throughput cell screening
- Prof Alistair Elfick, (School of Engineering) – Edwin platform: Beckman automated platform for dynamic promoter characterization
- Dr Maïwenn Kersaudy Kerhoas, (Edinburgh Medical School) – Microfluidics tools for high throughput biomarkers detection
- Prof Mark Bradley, (School of Chemistry) – Automated high throughput chemical biology
- Dr Guido Sanguinetti (School of Bioinformatics) – Machine Learning algorithms to model high-throughput biological data
- Dr Grant Mair (Centre for Clinical Brain Sciences) – Automated tools for precision medicine
- Dr Adam Stokes (School of Engineering) – Soft Robotic Systems
- Dr. Subramanian Ramamoorthy (School of informatics, Edinburgh Center for Robotics) – Robot learning and decision-making
Training in the following skills is essential for automation and robotics:
- Automation and coding skills to perform experiments in a high throughput way involving the optimization of several variables in a single experiment.
- Analysis of large datasets (i.e. Machine Learning) to elucidate new insights even if no evident correlations were observed initially.
- The design of automated experiments that carefully consider the different effects influencing the outputs.
- Artificial intelligence (AI) training to delegate the creation of internal hypothesis in the design cycle to the AI algorithm, while focusing on generating higher-level hypotheses.
The University offers training across all levels including:
- Edinburgh Genomics - a range of hands-on bioinformatics workshops
- Data Carpentries – These teach fundamental data skills needed for all those who deal with data and datasets over two day workshops.
- Open source automation:
- Opentrons hands on training courses
- Programming skills course
- Customization of automated platform using 3D printing
- Data standardization/Data sharing
- MSc course module - Automation and Industry
An undergraduate module is currently in development.
Throwing light on gene control
Optogenetics is the use of light to precisely control the timing and strength of gene expression. This control can advance both fundamental biological research and the optimisation of engineered metabolic pathways for the bioproduction of fuels, chemicals or pharmaceuticals. Dr James Gilman, a Postdoctoral Research Associate in Dr. Filippo Menolascina's (left) group at SynthSys, is working to design and optimise an optogenetic gene activation system in the yeast Saccharomyces cerevisiae.
An optogenetic gene expression systems consist of multiple parts; light-responsive photoreceptors are fused to transcription activation domains, and a genomic anchor must be used to accurately target the entire system to a gene of interest. Multiple variants of each of these parts are available, resulting in a complex design space in which the best combination of genetic parts is not obvious.
In his project, Dr. Gilman combines statistical Design of Experiments techniques, Machine Learning and laboratory automation to efficiently explore the complex design space of ontogenetic systems. The Edinburgh Genome Foundry’s automated platform facilitates the high-throughput, reliable assembly of DNA constructs, allowing optogenetic system variants to be rapidly designed and built. Construct testing can also be automated using the bioreactors developed by OGI Bio, a University of Edinburgh spin off that builds culturing systems in which microbial cultures can be exposed to arbitrary optical and chemical stimuli to achieve bio production objectives (e.g. maximise yield/minimise metabolic burden).
Bacterial flagellar motor as a multimodal biosensing chip
The Bacterial Flagellar Motor is one of nature’s rare rotary molecular machines. It enables bacterial swimming and is a key part of the bacterial chemotactic network that enables bacteria to direct their movement given the chemical environment. This network is one of the best-studied chemical signalling networks in biology, sensing down to nanomolar concentrations of specific chemicals on the time scale of seconds. The motor’s rotational speed is linearly proportional to the bacterial electrochemical gradients, most notably of proton or sodium ions, while its direction is regulated by the chemotactic network. Recently, it has been discovered that the motor is also a mechanosensor. Given these properties, the motor has the potential to serve as a multimodal biosensor with unprecedented speed and sensitivity, and thus a tool for characterizing and studying the external environment, but also bacterial physiology itself.
However, at the resolution needed, motor speed and rotational direction are currently detected optically, one motor at a time. A step-change in harnessing the unprecedented potential of this rotary molecular machine would be to detect each motor’s rotation electrically and with high throughput. To achieve this we have teamed with Prof. Michel Maharbiz from the University of California Berkley to specifically attach individual bacteria to a precise location on the surface and test two electrical means of detecting the motor’s rotation. If we are successful, we will enable portable biosensor-on-a-chip configuration of the motor speed and rotational direction detection, which we believe will be a game-changer in the biosensing field. For example, we are already teaming up with Dr. Baojun Wang to engineer his current biosensor to harness the novel capability of electrical motor rotation detection.
For further information about this strategic research theme, please contact