Engineering Fellowships For Growth: Designing Feedback Control in Biology for Robustness and Scalability
Lead Research Organisation:
University of Oxford
Department Name: Engineering Science
Abstract
Synthetic Biology is the "Engineering of Biology": it aspires to use the Engineering design cycle to produce bio-circuits that behave predictably and reliably, usually with specific applications in mind. Synthetic Biology has the potential to create new industries and technologies in several sectors, from agriculture to the environment, and from energy to healthcare. Some of these applications require Synthetic Biology designs to be scalable, so that small circuits can be composed to form larger systems. Currently, however, even small bio-circuits seldom function as expected because of the high level of uncertainty in the cellular environment, the way poorly-characterized parts are assembled together and the lack of a systematic framework for integrating parts to form systems. This is a major challenge that needs to be overcome in order for the potential of Synthetic Biology to be fulfilled and for industry and society to reap the rewards.
Natural systems use several mechanisms to overcome this major challenge. The most important one involves careful use of feedback control. This is done at all levels of organization - from the genetic, metabolic, cellular to the systems level. The regulation of biochemical processes inside a cell is key for ensuring robust functionality despite the high levels of environmental uncertainty and intrinsic and extrinsic noise.
This Fellowship application will use a systems and control engineering approach, based on modelling, abstraction, standardization and the development of new bio-feedback modules to target specific uncertainties in the cell. I will create an interdisciplinary research team which will demonstrate that through careful design and implementation of feedback control components, the functionality of the rest of the designed circuitry can be made robust and allow scalability. The feedback designs will be done at multiple organizational scales and interactions (genetic, signalling and cell-cell), which will be implemented in the laboratory, demonstrating the effectiveness of the approach.
Natural systems use several mechanisms to overcome this major challenge. The most important one involves careful use of feedback control. This is done at all levels of organization - from the genetic, metabolic, cellular to the systems level. The regulation of biochemical processes inside a cell is key for ensuring robust functionality despite the high levels of environmental uncertainty and intrinsic and extrinsic noise.
This Fellowship application will use a systems and control engineering approach, based on modelling, abstraction, standardization and the development of new bio-feedback modules to target specific uncertainties in the cell. I will create an interdisciplinary research team which will demonstrate that through careful design and implementation of feedback control components, the functionality of the rest of the designed circuitry can be made robust and allow scalability. The feedback designs will be done at multiple organizational scales and interactions (genetic, signalling and cell-cell), which will be implemented in the laboratory, demonstrating the effectiveness of the approach.
Planned Impact
Synthetic Biology is expected to have significant impact on the economy and society, as outlined in the recent UK Synthetic Biology Roadmap for the UK. Currently, Synthetic Biology constructs do not operate as intended, as they suffer from the uncertainty of the cellular environment in which they have to operate. Moreover, our ability to interconnect them to form larger systems is limited.
The aim of this research fellowship is to develop bio-feedback control modules that can be used by Synthetic Biologists to improve the robustness of their constructs, but also to improve their composability. Allowing scalability and robustness of these modules will undoubtedly have a large impact on the economy and society.
In particular, using feedback control at various scales will bring significant industrial and economic benefits. Industries in which this research has a large potential for economic impact include pharmaceuticals, energy, and agriculture. In the medium to long term, possible applications of synthetic biological systems have been proposed in biosynthesis (including biofuels), biosensors, environmental clean-up, smart materials, etc. Another major long-term potential application of Synthetic Biology will bring wider benefits to society through the development of personalised medicine, with therapies delivered through the application of functionally engineered cells.
In order to realize the full potential of Synthetic Biology to perform the variety of tasks specific to this large number of industrial applications, it is rapidly becoming critical for simple isolated systems to be able to be combined to allow the design of the increasingly sophisticated functions envisioned. For industrial relevance, the design of biological systems needs to be specified at a high, functional level, which is currently not attainable due to the unreliability of the synthetic components at our disposal in the presence of uncertainty when implemented in the cell. This research will be crucial to providing the standardized framework needed to allow the design of biological systems to be scaled to the required level.
At the societal level, it is important that researchers and users of Synthetic Biology are engaged with the wider public as research progresses. There are widely-held concerns over the ethical and safety implications of many aspects of the manipulation and creation of biological systems. This research is intended to apply the principles of Control Engineering to Synthetic Biology. Robust control theory is expressly concerned with ensuring the reliability and safety of engineered systems in the presence of uncertainty. Therefore a key impact of this research will be to help to transform the public perception of Synthetic Biology from a collection of ad hoc methods into a formal engineering discipline.
The aim of this research fellowship is to develop bio-feedback control modules that can be used by Synthetic Biologists to improve the robustness of their constructs, but also to improve their composability. Allowing scalability and robustness of these modules will undoubtedly have a large impact on the economy and society.
In particular, using feedback control at various scales will bring significant industrial and economic benefits. Industries in which this research has a large potential for economic impact include pharmaceuticals, energy, and agriculture. In the medium to long term, possible applications of synthetic biological systems have been proposed in biosynthesis (including biofuels), biosensors, environmental clean-up, smart materials, etc. Another major long-term potential application of Synthetic Biology will bring wider benefits to society through the development of personalised medicine, with therapies delivered through the application of functionally engineered cells.
In order to realize the full potential of Synthetic Biology to perform the variety of tasks specific to this large number of industrial applications, it is rapidly becoming critical for simple isolated systems to be able to be combined to allow the design of the increasingly sophisticated functions envisioned. For industrial relevance, the design of biological systems needs to be specified at a high, functional level, which is currently not attainable due to the unreliability of the synthetic components at our disposal in the presence of uncertainty when implemented in the cell. This research will be crucial to providing the standardized framework needed to allow the design of biological systems to be scaled to the required level.
At the societal level, it is important that researchers and users of Synthetic Biology are engaged with the wider public as research progresses. There are widely-held concerns over the ethical and safety implications of many aspects of the manipulation and creation of biological systems. This research is intended to apply the principles of Control Engineering to Synthetic Biology. Robust control theory is expressly concerned with ensuring the reliability and safety of engineered systems in the presence of uncertainty. Therefore a key impact of this research will be to help to transform the public perception of Synthetic Biology from a collection of ad hoc methods into a formal engineering discipline.
Organisations
- University of Oxford (Fellow, Lead Research Organisation)
- California Institute of Technology (Collaboration)
- ETH Zurich (Collaboration, Project Partner)
- Korea Advanced Institute of Science and Technology (KAIST) (Collaboration)
- Microsoft Research (Collaboration)
- Massachusetts Institute of Technology (Collaboration, Project Partner)
- Korea Advanced Institute of Science and Technology (Project Partner)
- California Institute of Technology (Project Partner)
- Microsoft Research (United Kingdom) (Project Partner)
Publications
Ahmadi M
(2017)
Safety verification for distributed parameter systems using barrier functionals
in Systems & Control Letters
Ahmadi M
(2016)
Dissipation inequalities for the analysis of a class of PDEs
in Automatica
Ahmadi M
(2019)
A framework for input-output analysis of wall-bounded shear flows
in Journal of Fluid Mechanics
Alexis E
(2023)
Regulation strategies for two-output biomolecular networks.
in Journal of the Royal Society, Interface
Alexis E
(2022)
On the Design of a PID Bio-Controller With Set Point Weighting and Filtered Derivative Action
in IEEE Control Systems Letters
Alexis E
(2021)
Biomolecular mechanisms for signal differentiation.
in iScience
Alexis E
(2022)
Regulation strategies for two-output biomolecular networks
Alexis E
(2021)
Biomolecular mechanisms for signal differentiation
Cazimoglu I
(2019)
Developing a graduate training program in Synthetic Biology: SynBioCDT.
in Synthetic biology (Oxford, England)
Chen JX
(2019)
Development of Aspirin-Inducible Biosensors in Escherichia coli and SimCells.
in Applied and environmental microbiology
Fantuzzi G
(2017)
Optimization With Affine Homogeneous Quadratic Integral Inequality Constraints
in IEEE Transactions on Automatic Control
Folliard T
(2017)
Ribo-attenuators: novel elements for reliable and modular riboswitch engineering.
in Scientific reports
Folliard T
(2017)
A Synthetic Recombinase-Based Feedback Loop Results in Robust Expression
in ACS Synthetic Biology
Furieri L
(2020)
Sparsity Invariance for Convex Design of Distributed Controllers
in IEEE Transactions on Control of Network Systems
Furieri L
(2019)
An Input-Output Parametrization of Stabilizing Controllers: Amidst Youla and System Level Synthesis
in IEEE Control Systems Letters
Gasparek M
(2023)
Deciphering mechanisms of production of natural compounds using inducer-producer microbial consortia.
in Biotechnology advances
Hancock EJ
(2017)
The Interplay between Feedback and Buffering in Cellular Homeostasis.
in Cell systems
Hancock EJ
(2015)
Simplified mechanistic models of gene regulation for analysis and design.
in Journal of the Royal Society, Interface
Harris AW
(2015)
Designing Genetic Feedback Controllers.
in IEEE transactions on biomedical circuits and systems
Kelly CL
(2018)
Synthetic negative feedback circuits using engineered small RNAs.
in Nucleic acids research
Lim B
(2022)
Reprogramming Synthetic Cells for Targeted Cancer Therapy.
in ACS synthetic biology
Miller J
(2022)
Decomposed structured subsets for semidefinite and sum-of-squares optimization
in Automatica
Newton M
(2020)
Network Lyapunov Functions for Epidemic Models
Newton M
(2023)
Rational Neural Network Controllers
Newton M
(2023)
Sparse polynomial optimisation for neural network verification
in Automatica
Newton M
(2022)
Sparse Polynomial Optimisation for Neural Network Verification
Newton M
(2021)
Neural Network Verification using Polynomial Optimisation
Nyström A
(2018)
A Dynamic Model of Resource Allocation in Response to the Presence of a Synthetic Construct.
in ACS synthetic biology
Papachristodoulou A
(2015)
Advances in computational Lyapunov analysis using sum-of-squares programming
in Discrete and Continuous Dynamical Systems - Series B
Prescott T
(2016)
Designing feedback control in biology for robustness and scalability
Prescott T
(2016)
Multi-scale design in layered synthetic biological systems
Prescott TP
(2015)
Quantification of Interactions between Dynamic Cellular Network Functionalities by Cascaded Layering.
in PLoS computational biology
Prescott TP
(2015)
Designing Conservation Relations in Layered Synthetic Biomolecular Networks.
in IEEE transactions on biomedical circuits and systems
Prescott, Thomas P.
(2015)
Quantification of Interactions between Dynamic Cellular Network Functionalities by Cascaded Layering
Raman D
(2016)
Delineating Parameter Unidentifiabilities in Complex Models
Description | The aim of this Fellowship is to use a systems and control engineering approach, based on modelling, abstraction and standardization to develop new bio-feedback modules to target specific uncertainties in the cell. The vision is to create an interdisciplinary research team which will demonstrate that through careful design and implementation of feedback control components, the functionality of the rest of the designed circuitry can be made robust and allow scalability. The feedback designs will be done at multiple organizational scales and interactions (genetic, signalling and cell-cell), which will be implemented in the laboratory, demonstrating the effectiveness of the approach. As part of this project we have developed feedback modules at the transcription and translation level and signalling pathways, to target uncertainties due to noise and cross-talk. We were the first group to use small RNA to design and implement feedback loops in cells, and we have introduced a new feedback mechanism to target cross-talk. At the same time we have developed Chi.Bio, an automated platform for characterisation of biological experiments, which has been taken up by a number of academic and industrial groups. |
Exploitation Route | The results of this fellowship can be used to design feedback controllers in Synthetic Biology and ensure that designs are robust and work as intended. Moreover, Chi.Bio is being used by tens of academic and industrial users to facilitate characterisation of their designs. |
Sectors | Energy Environment Healthcare Manufacturing including Industrial Biotechology Pharmaceuticals and Medical Biotechnology |
URL | http://sysos.eng.ox.ac.uk/wiki/index.php/Designing_feedback_control_in_Synthetic_Biology |
Description | The project has led to two major findings: one is the design of biofeedback controllers, which is currently being used by some industrial groups to improve the performance of their designs/yields and reduce cross-talk. The other finding has to do with Chi.Bio, an automated platform for characterisation of biological designs, which is being used by industry to characterise their designs. |
First Year Of Impact | 2000 |
Sector | Energy,Environment,Healthcare,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology |
Impact Types | Economic |
Description | Impact Acceleration Award - Oxford |
Amount | £63,272 (GBP) |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 03/2020 |
End | 09/2021 |
Description | Single-cell control of microbial selection and evolution |
Amount | £201,840 (GBP) |
Funding ID | EP/X017982/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 09/2022 |
End | 04/2024 |
Title | Chi.Bio: an automated platform for characterisation of biological designs. |
Description | Chi.Bio (https://chi.bio/) is an open-source robotic platform for experimental automation in biological science research and education. By combining heating, stirring, liquid handling, spectrometry, and optogenetics into a single easy-to-use platform, Chi.Bio can simplify laboratory protocols and drastically reduce equipment costs. |
Type Of Material | Technology assay or reagent |
Year Produced | 2019 |
Provided To Others? | Yes |
Impact | To date, the platform has been adopted by ~80 laboratories in academia and industry. |
URL | https://chi.bio/ |
Description | Collaboration in Synthetic Biology with Caltech |
Organisation | California Institute of Technology |
Country | United States |
Sector | Academic/University |
PI Contribution | Meetings and other exchanges of information, data and collaborations. |
Collaborator Contribution | Materials and knowledge. |
Impact | Multidisciplinary; outputs are still under development. |
Start Year | 2011 |
Description | Collaboration in Synthetic Biology with ETHZ |
Organisation | ETH Zurich |
Country | Switzerland |
Sector | Academic/University |
PI Contribution | Meetings, discussions, exchange of information and knowledge |
Collaborator Contribution | Meetings, discussions, exchange of information and knowledge |
Impact | A paper is currently under review. |
Start Year | 2011 |
Description | Collaboration in Synthetic Biology with KAIST |
Organisation | Korea Advanced Institute of Science and Technology (KAIST) |
Country | Korea, Republic of |
Sector | Academic/University |
PI Contribution | Discussions with KAIST staff. |
Collaborator Contribution | Discussions with my group. |
Impact | None to date. |
Start Year | 2015 |
Description | Collaboration in Synthetic Biology with MIT |
Organisation | Massachusetts Institute of Technology |
Country | United States |
Sector | Academic/University |
PI Contribution | Collaboration on Synthetic Biology, tools and methods. |
Collaborator Contribution | Collaboration on Synthetic Biology, tools and methods. |
Impact | A joint paper is currently under review; multidisciplinary. |
Start Year | 2011 |
Description | Collaboration in Synthetic Biology with Microsoft Research |
Organisation | Microsoft Research |
Department | Microsoft Research Cambridge |
Country | United Kingdom |
Sector | Private |
PI Contribution | Discussions with Microsoft Research staff. |
Collaborator Contribution | Discussions with our team on Synthetic Biology. |
Impact | Collaboration is ongoing. |
Start Year | 2015 |
Title | Chi.Bio: an automated platform for characterisation of biological designs. |
Description | Chi.Bio (https://chi.bio/) is an open-source robotic platform for experimental automation in biological science research and education. By combining heating, stirring, liquid handling, spectrometry, and optogenetics into a single easy-to-use platform, Chi.Bio can simplify laboratory protocols and drastically reduce equipment costs. |
Type Of Technology | New/Improved Technique/Technology |
Year Produced | 2019 |
Impact | To date, the platform has been adopted by ~20 laboratories in academia and industry. |
URL | https://chi.bio/ |
Description | School Visit |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Schools |
Results and Impact | Synthetic Biology outreach to Year 12 students |
Year(s) Of Engagement Activity | 2017 |
Description | Workshop in Control Engineering and Synthetic Biology |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | About 70 academics, industrialists, policymakers and postgraduate/undergraduate students took part in an international workshop at the interface of control engineering and synthetic biology, organised at Worcester College Oxford in September 2019. Discussions focused on the use of control engineering principles in Synthetic Biology but also how synthetic biology drives research in control engineering. |
Year(s) Of Engagement Activity | 2019 |
URL | http://sysos.eng.ox.ac.uk/wiki/index.php/SynBioControl2019 |
Description | Workshop organisation |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Workshop on control engineering and synthetic biology, "what next". |
Year(s) Of Engagement Activity | 2017 |
URL | http://sysos.eng.ox.ac.uk/wiki/index.php/SynBioControl2017 |