Robust gene networks in synthetic biology and development

Despite recent advances in synthetic biology, our ability to construct devices that are robust to changes in biochemical context and environmental fluctuations is still lacking. This is due, in part, to our inability to predict how devices will perform in the chassis cell using computer modelling. Our research in this area covers

  1. developing design methods based on Bayesian statistics that allow one to select devices based on their robustness to extrinsic noise
  2. constructing gene circuits with additional feedback mechanisms to increase robustness
  3. using flow cytometry and fluorescent time course microscopy to track individual cells, extract time course data and fit quantitative models

People: Luca Rosa
             Bez Karkaria

Collaborators: Vitor Bernardes Pinheiro, UCL
                     Darren Nesbeth, UCL
                     Andrew Phillips, Microsoft Research
                     Minres Technologies GmbH


Perez-Carrasco, R., Barnes, C.P., Schaerli, Y., Isalan, M., Briscoe, J., Page K.M. (2018)
Combining a Toggle Switch and a Repressilator within the AC-DC Circuit Generates Distinct Dynamical Behaviors
Cell Systems 6, 1–10

Leon, M., Woods, M.L., Fedorec, A.J.H., & Barnes, C.P. (2016)
A computational method for the investigation of multistable systems and its application to genetic switches
BMC Systems Biology 
2016 Dec 7;10(1):130.

Woods, M., Leon, M., Perez-Carrasco, R. & Barnes, C.P. (2016)
A statistical approach reveals designs for the most robust stochastic gene oscillators.
ACS Synthetic Biology 5 (6), pp 459–470

Filippi S, Barnes CP, Kirk PDW, Kudo T, Kunida K, McMahon SS, Tsuchiya T, Wada T, Kuroda S, Stumpf MPH (2016)
Robustness of MEK-ERK Dynamics and Origins of Cell-to-Cell Variability in MAPK Signaling
Cell Rep. 2016 Jun 14;15(11):2524-35. doi: 10.1016/j.celrep.2016.05.024

Cohen, M., Kicheva, A., Ribeiro, A., Blassberg, R., Page, K. M., Barnes, C. P. & Briscoe, J. (2015). 
Ptch1 and Gli regulate Shh signalling dynamics via multiple mechanisms. 
Nature Communications, 6. doi:10.1038/ncomms7709

Cohen, M., Page, K. M., Perez-Carrasco, R., Barnes, C. P., & Briscoe, J. (2014). 
A theoretical framework for the regulation of Shh morphogen-controlled gene expression.
Development, 141(20), 3868-3878. doi:10.1242/dev.112573

Silk, D., Kirk, P. D., Barnes, C. P., Toni, T., & Stumpf, M. P. (2014).
Model selection in systems biology depends on experimental design.
PLoS Comput Biol, 10(6), e1003650. doi:10.1371/journal.pcbi.1003650

Liepe, J., Kirk, P., Filippi, S., Toni, T., Barnes, C. P., & Stumpf, M. P. H. (2014).
A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation.
Nature Protocols, 9(2), 439-456. doi:10.1038/nprot.2014.025

Mechanistic modelling of mutational processes in the human genome and cancer

Over recent years it has become clear that structural variation (SV) accounts for a highly significant fraction of human polymorphisms. In addition cancer cells can exhibit large amounts of SV in addition to point mutations. These mutations arise from erroneous repair of double strand breaks (DSBs) which occur naturally by endogenous processes and replication error. Currently we are applying mechanistic modelling and statistical inference in order to try to elucidate these different mechanisms from existing data sets.

People:          Mae Woods
                     Marc Williams

Collaborators: Trevor Graham, Bart's Cancer Institute
                     Andrea Sottoriva, ICR


Quantification of subclonal selection in cancer from bulk sequencing data
Williams, M.J., Werner, B., Heide, T., Curtis, C., Barnes, C.P., Sottoriva, A., Graham, T.A. (2018)
Nature Genetics 2018 Jun;50(6):895-903. doi: 10.1038/s41588-018-0128-6. 

Woods M.L., Barnes C.P. (2016)
Mechanistic Modelling and Bayesian Inference Elucidates the Variable Dynamics of Double-Strand Break Repair.
PLoS Comput Biol. 2016 Oct 14;12(10):e1005131. doi: 10.1371/journal.pcbi.1005131

Williams, M.J., Werner, B, Barnes, C.P., Graham, T.A., & Sottoriva, A. (2016)
Identification of neutral tumor evolution across cancer types.
Nature Genetics, 2016 Jan 18. doi: 10.1038/ng.3489

Chaidos, A, Barnes, C.P. et. al. (2013)
Clinical drug resistance linked to interconvertible phenotypic and functional states of tumor-propagating cells in multiple myeloma.
Blood. 2013 Jan 10;121(2):318-28.

Synthetic biology approaches to microbiome engineering

The human intestine and the wide range of prokaryotic and eukaryotic organisms it supports form a mutualistic host-microbe symbiotic system crucial for many processes including the breakdown of plant polysaccharides and microbial fermentation. Variation and disruption of this natural ecosystem is linked to a diverse array of disorders including infectious disease, autoimmunity, obesity and cancer. We are engineering probiotic strains for therapeutic and sensing applications, plus understanding how to engineer microbial consortia.

People: Tanel Ozdemir
            Alex Fedorec
            Bethan Wolfenden (currently at Bento Lab)
            Bez Karkaria

            Elaine Allen, UCL
            Vitor Bernardes Pinheiro, UCL
            Filipe Cabreiro, UCL
            Rich Poole, UCL
            Tal Danino, Colombia University


Two new plasmid post-segregational killing mechanisms for the implementation of synthetic gene networks in E. coli
Fedorec, A.J.H, Ozdemir, T., Doshi, A. Rosa, L., Velazquez, O., Danino, T., Barnes, C.P. 
bioRxiv 350744

Towards an Aspect-Oriented Design and Modelling Framework for Synthetic Biology
Boeing, P., Leon, M., Nesbeth, D.N., Finkelstein, A., Barnes, C.P. (2018)
Processes 2018, 6(9), 167; 

Synthetic Biology and Engineered Live Biotherapeutics: Toward Increasing System Complexity.
Ozdemir, T., Fedorec, A.J.H., Danino, T., Barnes, C.P. (2018)
Cell Systems 2018 Jul 25;7(1):5-16. doi: 10.1016/j.cels.2018.06.008.

This page was last modified on 21 Sep 2018.