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Dr. Colin Clarke

B1: The National Institute for Bioprocessing Research and Training, NIBRT

Position & Role in STACCATO

Dr. Clarke is a Principal Investigator at the NIBRT Systems Biology Research Group in Dublin, Ireland. He is the Coordinator of STACCATO and will be supervising ESR6 and co-supervise ESR2, ESR8 and ESR10.

Background

Dr. Clarke graduated with a PhD in Bioinformatics from Cranfield University, UK, and specialises in the application of multivariate statistics and machine learning algorithms to high dimensional data. Upon completion of his doctoral training Colin took a position at Dublin Institute of Technology (DIT) applying chemometric approaches to analyse Raman and Infrared spectroscopy data from cells and tissues. He moved to the National Institute for Cellular Biotechnology at Dublin City University in 2009 to work in Martin Clynes’ group investigating the biology of CHO cells during biopharmaceutical production.

A major component of his research in this time has centred on the application of statistical methods to study CHO transcriptomic and proteomic expression datasets. Examples of his work in the area include the use of partial least squares (PLS) to predict cell specific productivity from gene expression data and the elucidation of mRNA coexpression networks from a large scale CHO mRNA dataset. An area of particular interest was the integration of miRNA, mRNA, proteomic and genomic data to understand the biological processes regulating the growth rate of CHO cells.

In 2014 he moved to NIBRT following the award of SFI’s prestigious Starting Investigator Research Grant. Dr Clarke’s bioinformatics group is currently focussed on further understanding of the CHO cell biological system using next generation sequencing and advanced computational techniques.

Key Publications

Kelly, P.S., *Clarke, C., Costello, A., Monger, C., Meiller, J., Dhiman, H., Borth, N., Betenbaugh, M.J., Clynes, M. and Barron, N. (2017) Ultra-deep next generation mitochondrial genome sequencing reveals widespread heteroplasmy in Chinese hamster ovary cells. Metabolic Engineering. 41, 11-22. [DOI: 10.1016/j.ymben.2017.02.001]

Clarke, C., Henry, M., Doolan, P., Kelly, S., Aherne, S., Sanchez, N., Kelly, P., Kinsella, P., Breen, L., Madden, S.F., et al. (2012). Integrated miRNA, mRNA and protein expression analysis reveals the role of post-transcriptional regulation in controlling CHO cell growth rate. BMC Genomics 13, 656. [DOI: 10.1186/1471-2164-13-656]

Clarke, C., Doolan, P., Barron, N., Meleady, P., Madden, S.F., DiNino, D., Leonard, M., and Clynes, M. (2012). CGCDB: A web-based resource for the investigation of gene coexpression in CHO cell culture. Biotechnol. Bioeng. 109, 1368–1370. [10.1002/bit.24416]

Clarke, C., Doolan, P., Barron, N., Meleady, P., O’Sullivan, F., Gammell, P., Melville, M., Leonard, M., and Clynes, M. (2011). Predicting cell-specific productivity from CHO gene expression. J. Biotechnol. 151, 159–165. [DOI: 10.1016/j.jbiotec.2010.11.016]

Clarke, C., Doolan, P., Barron, N., Meleady, P., O’Sullivan, F., Gammell, P., Melville, M., Leonard, M., and Clynes, M. (2011). Large scale microarray profiling and coexpression network analysis of CHO cells identifies transcriptional modules associated with growth and productivity. J. Biotechnol. 155, 350–359. [DOI: 10.1016/j.jbiotec.2011.07.011]

Doolan, P., *Clarke, C., Kinsella, P., Breen, L., Meleady, P., Leonard, M., Zhang, L., Clynes, M., Aherne, S.T., and Barron, N. (2013). Transcriptomic analysis of clonal growth rate variation during CHO cell line development. J. Biotechnol. 166, 105–113. [DOI: 10.1016/j.jbiotec.2013.04.014]

Tzani, I., Monger, C., Kelly, P., Barron, N. Kelly, RM., *Clarke, C., (2018) Understanding biopharmaceutical production at single nucleotide resolution using ribosome footprint profiling. Current Opinion in Biotechnology. 53, 182-190. [DOI: 10.1016/j.copbio.2018.01.030]

Monger, C., Kelly, P.S., Gallagher, C., Clynes, M., Barron, N., and *Clarke, C. (2015). Towards next generation CHO cell biology: Bioinformatics methods for RNA-Seq-based expression profiling. Biotechnol. J. 10: pp.950-966 [DOI: 10.1002/biot.201500107]