quantitative biology + synthetic biology + systems biology

Interdisciplinary and collaborative

We have backgrounds ranging across physics, computer science, microbiology, synthetic biology and genetics.

Coal Drops yard, 2021
Regents park, 2020
Rockefeller Building xmas party, 2019

Group retreat, Bath 2018

Current group members

Prof. Chris P Barnes
Dr Linda Dekker
Dr Alex Fedorec
Dr Bingxin Lu
Dr Ke Yan Wen
Dr Mohamed Ali Al-Badri
Dr Jack Rutter
Dr Neythen Treloar
Kimberley Owen
Dr Jurgen Riedel

Past group members

Dr Behzad Karkaria. PhD student 2017-2021. Now interning at Hummingbird Diagnostics
Dr Luca Rosa. PhD student 2017-2021. Now on placement at CC Bio
Clare Robinson. Research Assistant 2019-2020. Now doing a PhD with David Riglar, Imperial College
Dr Josh Russell-Buckland. PhD student 2016-2020. Now a software engineer at Guardian News and Media
Dr Marc Williams. PhD student 2014-2018. Now a PDRA with Sohrab Shah, Memorial Sloan Kettering
Dr Tanel Ozdemir. PhD student 2013-2017. Now an Investment Associate at AlbionVC
Dr Lourdes Sriraja. PhD student 2013-2017. Now a PDRA with Evangelia Petsalaki, EBI
Dr Mae Woods. PDRA 2013-2018. Now at Baylor College of Medicine
Dr Miriam Leon. PhD student 2012-2016. Now a data scientist at Stitch Fix

Mutational processes

Fig: Modelling chromosomal gain and loss within a stochastic branching process

Bollen, Y., Stelloo, E., van Leenen, P., van den Bos, M., Ponsioen, B., Lu, B., ...Snippert H.J.G. (2021). Reconstructing single-cell karyotype alterations in colorectal cancer identifies punctuated and gradual diversification patterns. Nature Genetics, doi:10.1038/s41588-021-00891-2

Williams, M.J., Zapata, L., Werner, B., Barnes, C.P., Sottoriva, A., Graham, T.A. (2020). Measuring the distribution of fitness effects in somatic evolution by combining clonal dynamics with dN/dS ratios. eLife, 9 doi:10.7554/eLife.48714

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

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. doi:10.1182/blood-2012-06-436220

Microbiome engineering

Fig: (Left) Fluorescent bacteria colonising the gut of a C. elegans nematode worm. (Right) An engineered GRN in E. coli enabling bacteriocin production control through quorum sensing (SPoCK system)

Rutter, J.W., Dekker, L., Fedorec, A.J.H., Gonzales, D.T., Wen, K.Y., Tanner, L.E.S., ...Barnes, C.P. (2021). Engineered acetoacetate-inducible whole-cell biosensors based on the AtoSC two-component system. Biotechnology and Bioengineering, doi:10.1002/bit.27897
Download the data here

Fedorec, A.J.H., Karkaria, B.D., Sulu, M., Barnes, C.P. (2021). Single strain control of microbial consortia. Nat Commun, 12, 1977. doi:10.1038/s41467-021-22240-x
Download the data here

Karkaria, B.D., Fedorec, A.J.H., Barnes, C.P. (2021). Automated design of synthetic microbial communities. Nat Commun, 12, 672. doi:10.1038/s41467-020-20756-2
Download the data here
Code available on GitHub

Fedorec, A.J.H., Robinson, C.M., Wen, K.Y., Barnes, C.P. (2020). FlopR: An Open Source Software Package for Calibration and Normalization of Plate Reader and FlowCytometry Data. ACS Synth Biol, doi:10.1021/acssynbio.0c00296
Download the data here
Code available on GitHub

Treloar, N.J., Fedorec, A.J.H., Ingalls, B., Barnes, C.P. (2020). Deep reinforcement learning for the control of microbial co-cultures in bioreactors. PLoS Comput Biol, 16 (4), e1007783. doi:10.1371/journal.pcbi.1007783
Download the data here
Code available on GitHub

Wen, K.Y., Rutter, J.W., Barnes, C.P., Dekker, L. (2019). Fundamental Building Blocks of Whole-Cell Biosensor Design. In Thouand, G. (Ed.), Handbook of Cell Biosensors. (pp. 1-23). Springer International Publishing.

Rutter, J.W., Ozdemir, T., Galimov, E.R., Quintaneiro, L.M., Rosa, L., Thomas, G.M., Cabreiro, F., Barnes, C.P. (2019). Detecting Changes in the Caenorhabditis elegans Intestinal Environment Using an Engineered Bacterial Biosensor. ACS Synth Biol, doi:10.1021/acssynbio.9b00166
Download the data and code here

Shaw, L., Bassam, H., Barnes, C.P., Walker, A., Klein, N., Balloux, F. (2019). Modelling microbiome recovery after antibiotics using a stability landscape framework. ISME Journal, doi:10.1038/s41396-019-0392-1
Article in The Telegraph

Fedorec, A.J.H., Ozdemir, T., Doshi, A., Ho, Y.-.K., Rosa, L., Rutter, J., ...Barnes, C.P. (2019). Two New Plasmid Post-segregational Killing Mechanisms for the Implementation of Synthetic Gene Networks in Escherichia coli. iScience, doi:10.1016/j.isci.2019.03.019
Download the data and code here
Plasmids available on addgene

Barnes, C.P., Fedorec, A.J.H. (2018). Five amazing ways redesigning biological cells could help us fight cancer. The Conversation

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

Gene regulatory networks

Fig: Post-translational coupling of an oscillator and a bistable switch

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. doi:10.1016/j.cels.2018.02.008

Boeing, P., Leon, M., Nesbeth, D.N., Finkelstein, A., Barnes, C. (2018). Towards an Aspect-Oriented Design and Modelling Framework for Synthetic Biology. Processes, 6 (9), doi:10.3390/pr6090167

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. doi:10.1186/s12918-016-0375-z

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. doi:10.1021/acssynbio.5b00179

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

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

Biological computing

Fig: Patterning as a spatial computation. Each panel represents a different interpretation of a morphogen gradient produced from two sources A and B

Treloar, N., Wen, K.Y., Fedorec, A., Barnes, C. (2021). SynBioBrain: building biological computers from bacterial populations. The Project Repository Journal, doi:doi.org/10.54050/PRJ1117751

Karkaria, B.D., Treloar, N.J., Barnes, C.P., Fedorec, A.J.H. (2020). From microbial communities to distributed computing systems Frontiers in Bioengineering and Biotechnology 8, 834, doi:doi.org/10.3389/fbioe.2020.00834

Dalchau, N., Szép, G., Hernansaiz-Ballesteros, R., Barnes, C.P., Cardelli, L., Phillips, A., Csikász-Nagy, A. (2018). Computing with biological switches and clocks. Natural Computing, 1-19. doi:10.1007/s11047-018-9686-x