A semi-autonomous robot synthetic biologist for industrial biodesign and manufacturing
Lead Research Organisation:
Imperial College London
Department Name: Life Sciences
Abstract
The last decade has seen significant advances in the fields of synthetic biology as well as robotics and artificial intelligence (AI). Synthetic biology is an emerging multidisciplinary field with potential to have step-change benefits in many fields from medicine through to industrial biotechnology. This advance is dependent on the ability to rationally engineer biological organisms in a more predictable and defined way than has previously been possible.
Bio-manufacturing is an increasingly important platform for a sustainable manufacturing future. Many natural products have potentially valuable nutraceutical or pharmaceutical applications, but cannot be chemically synthesised or harvested from nature without significant ecological disruption. The engineering of biology by design seeks to construct new biological entities that are optimised for specific functionality such as bio-production within a 'cellular factory'. Synthetic biology provides a method for optimising production of complex natural products using sustainable methods in a microbial production host, much like ethanol is produced in yeast. Advanced synthetic biology tools will enable us to tackle more complex targets. Here, by integrating synthetic biology tools with robotics and AI we aim to make a significant advance to reducing the cost and development time of new biologically derived products.
It is now evident that robotics is essential for synthetic biology to fulfil its potential and is of particular relevance to industrial biotechnology. In parallel, big data has become increasingly important in many areas of technology as well as the biological domain. This is leading to new and powerful applications of AI in everyday life. Here we seek to address the application of AI to synthetic biology, using AI approaches to direct automated synthetic biology experiments.
These advances will have the potential to create new products, companies and even industries that will ultimately benefit the economy, health, quality of life and security of the UK general public and beyond. It will also have far-reaching effects on policy and society.
Bio-manufacturing is an increasingly important platform for a sustainable manufacturing future. Many natural products have potentially valuable nutraceutical or pharmaceutical applications, but cannot be chemically synthesised or harvested from nature without significant ecological disruption. The engineering of biology by design seeks to construct new biological entities that are optimised for specific functionality such as bio-production within a 'cellular factory'. Synthetic biology provides a method for optimising production of complex natural products using sustainable methods in a microbial production host, much like ethanol is produced in yeast. Advanced synthetic biology tools will enable us to tackle more complex targets. Here, by integrating synthetic biology tools with robotics and AI we aim to make a significant advance to reducing the cost and development time of new biologically derived products.
It is now evident that robotics is essential for synthetic biology to fulfil its potential and is of particular relevance to industrial biotechnology. In parallel, big data has become increasingly important in many areas of technology as well as the biological domain. This is leading to new and powerful applications of AI in everyday life. Here we seek to address the application of AI to synthetic biology, using AI approaches to direct automated synthetic biology experiments.
These advances will have the potential to create new products, companies and even industries that will ultimately benefit the economy, health, quality of life and security of the UK general public and beyond. It will also have far-reaching effects on policy and society.
Planned Impact
The last decade has seen significant advances in the fields of synthetic biology as well as robotics and AI. Synthetic biology is an emerging multidisciplinary field with potential to have step-change benefits in many fields from medicine through to industrial biotechnology and defence/security. This advance is dependent on the ability to rationally engineer biological organisms in a more predictable and defined way than has previously been possible. It is now evident that robotics is essential for synthetic biology to fulfil its potential and is of particular relevance to industrial biotechnology. In parallel, big data has become increasingly important in many areas of technology, including the biological domain. This is leading to new and powerful applications of AI in everyday life. Here we seek to address the application of AI to synthetic biology, using machine learning approaches to direct automated synthetic biology experiments. This will have important and potentially far reaching applications in the industrialisation of synthetic biology tools and processes. These advances will have the potential to create new products, companies and even industries that will ultimately benefit the economy, health, quality of life and security of the UK general public and beyond. It will also have far-reaching effects on policy and society.
Publications
Beal J
(2020)
Robust estimation of bacterial cell count from optical density.
in Communications biology
Dai W.-Z.
(2021)
Abductive Knowledge Induction From Raw Data
in IJCAI International Joint Conference on Artificial Intelligence
Hérisson J
(2022)
The automated Galaxy-SynBioCAD pipeline for synthetic biology design and engineering.
in Nature communications
Dwijayanti A
(2022)
A modular RNA interference system for multiplexed gene regulation.
in Nucleic acids research
Beal J
(2018)
Quantification of bacterial fluorescence using independent calibrants.
in PloS one
Lawrence JM
(2022)
Synthetic biology and bioelectrochemical tools for electrogenetic system engineering.
in Science advances
Haines MC
(2022)
basicsynbio and the BASIC SEVA collection: software and vectors for an established DNA assembly method.
in Synthetic biology (Oxford, England)
Beal J
(2022)
Multicolor plate reader fluorescence calibration.
in Synthetic biology (Oxford, England)
Description | A platform for automated DNA assembly has been created. We are developing a novel Inductive Learning approach that aims to bridge between symbolic learning and and numerical models. This novel approach is required to provide an abductive learning framework that can be applied to noisy experimental data. |
Exploitation Route | AI is an important and growing area of research and the approaches we are developing may be applicable to many other fields. The practical developments in experimental synthetic biology through the building of auotmated approaches is key to the advancement of the field. The low cost approach that we have taken is enabling across a wide diversity of research and industrial applications. |
Sectors | Agriculture Food and Drink Chemicals Manufacturing including Industrial Biotechology |
Description | 21EBTA: EB-AI consortium for bioengineered cells and systems (AI-4-EB) |
Amount | £1,554,946 (GBP) |
Funding ID | BB/W013770/1 |
Organisation | Biotechnology and Biological Sciences Research Council (BBSRC) |
Sector | Public |
Country | United Kingdom |
Start | 01/2022 |
End | 01/2024 |
Description | Using AI based modelling to drive the engineering of biology |
Amount | £322,805 (GBP) |
Funding ID | BB/Y514056/1 |
Organisation | Biotechnology and Biological Sciences Research Council (BBSRC) |
Sector | Public |
Country | United Kingdom |
Start | 02/2024 |
End | 08/2025 |
Title | Automated DNA Assembly |
Description | We have automated BASIC DNA assembly to enable high throughput and scaleable assembly of new DNA constructs |
Type Of Material | Technology assay or reagent |
Year Produced | 2019 |
Provided To Others? | Yes |
Impact | Since putting a prepring of our paper on BioRxiv we have had more than 900 pdf downloads |
URL | https://www.biorxiv.org/content/10.1101/832139v1 |
Description | CCBio |
Organisation | CC Biotech |
Country | United Kingdom |
Sector | Private |
PI Contribution | Expertise in engineering of microbial organisms and automated DNA assembly |
Collaborator Contribution | Automated DNA assembly |
Impact | none |
Start Year | 2021 |
Description | Neobe Therapeutics |
Organisation | Neobe Therapeutics Ltd |
Country | United Kingdom |
Sector | Private |
PI Contribution | Expertise in microbial engineering Automated DNA assembly |
Collaborator Contribution | Therapeutic approaches to cancer treatment. |
Impact | none |
Start Year | 2021 |
Title | DNABOT Software |
Description | Software that drives our automated DNA assembly process |
Type Of Technology | Software |
Year Produced | 2019 |
Open Source License? | Yes |
Impact | Since we published this work in BioRxiv it has been downloaded more than 900 times. |
URL | https://github.com/BASIC-DNA-ASSEMBLY/DNA-BOT |