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Designing feedback control in Synthetic Biology

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 page will host some of the results of project EP/M002454/1, whose aims is to 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.

Design of Biocontrollers

This project focuses on the design and implementation of biofeedback controllers to achieve different objectives. Our work to date has focused on theory as well as experimental implementations; please refer to the references below for more details.

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Chi.Bio - an automated experimental platform

In 2019 we launched Chi.Bio, invented by Dr Harrison Steel, which is an automated experimental platform that can have applications in biotechnology. More information can be found on Chi.Bio's website.

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Related Publications

  1. H. Steel and A. Papachristodoulou. The effect of spatiotemporal antibiotic inhomogeneities on the evolution of resistance. Journal of theoretical biology 486, 110077, 2019.
  2. H. Steel and A. Papachristodoulou. Low-burden biological feedback controllers for near-perfect adaptation. ACS synthetic biology 8 (10), 2212-2219, 2019.
  3. H. Steel, A. Sootla, N. Delalez and A Papachristodoulou. Mitigating Biological Signalling Cross-talk with Feedback Control. 18th European Control Conference (ECC), 2638-2643, 2019.
  4. J.X. Chen, H. Steel, Y.H. Wu, Y. Wang, J. Xu, C.P.N. Rampley, I.P. Thompson, A. Papachristodoulou and W. Huang. Development of aspirin-inducible biosensors in Escherichia coli and SimCells. Appl. Environ. Microbiol. 85 (6), e02959-18, 2019.
  5. H. Steel, R. Habgood, C. Kelly and A Papachristodoulou. Chi. Bio: An open-source automated experimental platform for biological science research. bioRxiv, 796516, 2019.
  6. H. Steel, A. Sootla, B. Smart, N. Delalez and A. Papachristodoulou. Improving Orthogonality in Two-Component Biological Signalling Systems Using Feedback Control. IEEE Control Systems Letters 3 (2), 326-331, 2018.
  7. C.L. Kelly, A.W.K.H. Harris, H. Steel, E.J. Hancock, J.T. Heap and A. Papachristodoulou. Synthetic negative feedback circuits using engineered small RNAs. Nucleic Acids Research 46 (18), 9875–9889, 2018.
  8. H. Steel and A. Papachristodoulou. Probing Intercell Variability Using Bulk Measurements. ACS synthetic biology 7 (6), 1528-1537, 2018.
  9. A. Nyström, A. Papachristodoulou and A. Angel. A dynamic model of resource allocation in response to the presence of a synthetic construct. ACS synthetic biology 7 (5), 1201-1210, 2018.
  10. H. Steel and A. Papachristodoulou. Design constraints for biological systems that achieve adaptation and disturbance rejection. IEEE Transactions on Control of Network Systems 5 (2), 807-817, 2018.
  11. N. Delalez, A. Sootla, G.H. Wadhams and A. Papachristodoulou. Design of a synthetic sRNA-based feedback filter module. BioRxiv, 504449, 2018.
  12. H. Steel, G. Lillacci, M. Khammash and A. Papachristodoulou. Challenges at the interface of control engineering and synthetic biology. 56th Annual Conference on Decision and Control (CDC), 1014-1023, 2017.
  13. E.J. Hancock, J. Ang, A. Papachristodoulou and G.-B. Stan. The interplay between feedback and buffering in cellular homeostasis. Cell systems 5 (5), 498-508. e23. 2017.
  14. T. Folliard, H. Steel, T. P. Prescott, G. Wadhams, L. J. Rothschild and A. Papachristodoulou. A synthetic recombinase-based feedback loop results in robust expression. ACS Synthetic Biology, 2017. PDF
  15. D.V. Raman, J. Anderson and A. Papachristodoulou. Delineating parameter unidentifiabilities in complex models. Physical Review E 95 (3), 032314, 2017. PDF
  16. T. Folliard, B. Mertins, H. Steel, T. P. Prescott, T. Newport, C. W. Jones, G. Wadhams, T. Bayer, J. P. Armitage, A. Papachristodoulou and L. J. Rothschild. Ribo-attenuators: novel elements for reliable and modular riboswitch engineering. Scientific Reports 7, 4599 (2017). doi:10.1038/s41598-017-04093-x 2017. PDF
  17. J. Scott-Brown and A. Papachristodoulou. sbml-diff: A tool for visually comparing SBML models in synthetic biology. ACS Synthetic Biology 6(7):1225–1229, 2017. PDF
  18. A.F. Villaverde, A. Barreiro and A. Papachristodoulou. Structural identifiability of dynamic systems biology models. PLOS Computational Biology 12 (10), e1005153, 2016. PDF
  19. T. P. Prescott and A. Papachristodoulou. Designing conservation relations in layered synthetic biomolecular networks. IEEE Transactions on Biomedical Circuits and Systems, 9(4):572-580, 2015 DOI PDF
  20. A. Harris, J.A. Dolan, C.L. Kelly, J. Anderson and A. Papachristodoulou. Designing Genetic Feedback Controllers, IEEE Transactions on Biomedical Circuits and Systems, 9(4):475--484, 2015.
  21. T. P. Prescott, M. Lang and A. Papachristodoulou. Quantification of interactions between dynamic cellular network functionalities by cascaded layering. PLoS Computational Biology, 11(5):e1004235, 2015. [1]
  22. T. P. Prescott and A. Papachristodoulou. Synthetic Biology: A Control Engineering perspective. In Proceedings of the European Control Conference, 2014. PDF
  23. T. P. Prescott and A. Papachristodoulou. Layered decomposition for the model order reduction of timescale separated biochemical reaction networks. Journal of Theoretical Biology, 356:113-122, 2014. [2]
  24. T. P. Prescott and A. Papachristodoulou. Signal propagation across layered biochemical networks. In Proceedings of the American Control Conference, 2014. [sysos.eng.ox.ac.uk/control/sysos/images/f/f0/Prescott2014_ACC.pdf]
  25. Y.-C. Chang, J. P. Armitage, A. Papachristodoulou, G. H. Wadhams. A single phosphatase can convert a robust step response into a graded, tuneable or adaptive response. Microbiology, 2013. [3]
  26. J. A. J. Arpino, E. J. Hancock, J. Anderson, M. Barahona, G.-B. Stan, A. Papachristodoulou and K. Polizzi. Tuning the Dials of Synthetic Biology. Microbiology, 2013. [4]