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.

Related Publications

  1. D.V. Raman, J. Anderson and A. Papachristodoulou. Delineating parameter unidentifiabilities in complex models. Physical Review E 95 (3), 032314, 2017 [1]
  2. 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. To appear, 2017.
  3. A.F. Villaverde, A. Barreiro and A. Papachristodoulou. Structural identifiability of dynamic systems biology models. PLOS Computational Biology 12 (10), e1005153, 2016. [2]
  4. J. Scott-Brown and A. Papachristodoulou. sbml-diff: A tool for visually comparing SBML models in synthetic biology. ACS Synthetic Biology [3]
  5. 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
  6. 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.
  7. 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. [4]
  8. T. P. Prescott and A. Papachristodoulou. Synthetic Biology: A Control Engineering perspective. In Proceedings of the European Control Conference, 2014. PDF
  9. 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. [5]
  10. T. P. Prescott and A. Papachristodoulou. Signal propagation across layered biochemical networks. In Proceedings of the American Control Conference, 2014. []
  11. 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. [6]
  12. 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. [7]