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
(2018)
Quantification of bacterial fluorescence using independent calibrants
in PLOS ONE
Beal J
(2020)
Robust estimation of bacterial cell count from optical density
in Communications Biology
Beal J
(2020)
Author Correction: Robust estimation of bacterial cell count from optical density.
in Communications biology
Beal J
(2022)
Multicolor plate reader fluorescence calibration.
in Synthetic biology (Oxford, England)
Dai W
(2020)
Abductive Knowledge Induction From Raw Data
Dai W.-Z.
(2021)
Abductive Knowledge Induction From Raw Data
in IJCAI International Joint Conference on Artificial Intelligence
Dwijayanti A
(2019)
A modular RNA interference system for multiplexed gene regulation
| 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 |
| Title | Supplementary Code and Files for: J. M. Lawrence et. al. "An Expanded Toolset for Electrogenetics" (2022) |
| Description | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Within the "Code" folder can be found all data and the files required to process them 1) Raw data files for each figure are contained within .csv files. E.g. F2e_raw.csv contains the data displayed in figure 2e 2) Code for processing data and plotting figures are contained within .m files. E.g F2e.m processes F2e_raw.csv to create figure 2e 3) Code for fitting response functions are contained within the linear_response.m (for non-responsive contructs), dose_response.m (for responsive Uni-PsoxS & Activator constructs) & inverse_response.m (for responsive Inverter constructs) 4) Other .m files are functions created by others which were used to process and plot data: i) Rob Campbell (2021). raacampbell/shadedErrorBar (https://github.com/raacampbell/shadedErrorBar), GitHub. Retrieved August 21, 2021. ii) Ameya Deoras (2021). Customizable Heat Maps (https://www.mathworks.com/matlabcentral/fileexchange/24253-customizable-heat-maps), MATLAB Central File Exchange. Retrieved August 21, 2021. iii) Stephen Cobeldick (2021). ColorBrewer: Attractive and Distinctive Colormaps (https://github.com/DrosteEffect/BrewerMap), GitHub. Retrieved August 21, 2021. iv) Martina Callaghan (2021). barwitherr(errors,varargin) (https://www.mathworks.com/matlabcentral/fileexchange/30639-barwitherr-errors-varargin), MATLAB Central File Exchange. Retrieved August 21, 2021. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Within the "DNA" folder can be found all the annotated plasmid maps of plasmids used in the study %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% For any support please email jml203@cam.ac.uk %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://www.repository.cam.ac.uk/handle/1810/331363 |
| 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 |