Paul Rees

Paul Rees, PhD

Full Affiliate Member, Research Institute
Houston Methodist


Biography

Paul Rees received his Ph.D. in physics from Cardiff University. He calculated the optical properties of semiconductor lasers and the measured spontaneous emissions from laser diodes under operating conditions. He was a research fellow in the physics department at Trinity College, Dublin, Ireland, where he studied self-pulsation in laser diodes and the theory of many-body effects in wide band-gap semiconductors. He later joined the School of Informatics, University of Wales, Bangor, UK, where he was appointed senior lecturer.

In 2005, Dr. Rees was appointed as the chair of nanotechnology in the newly-formed Multidisciplinary Nanotechnology Centre at Swansea University. As an Full Affiliate Member of Houston Methodist Research Institute, Dr. Rees collaborates with the nanomedicine department to study the uptake of micro and nanoparticles by biological cells. Dr. Rees also participates as a mentor for the graduate exchange training program between the Houston Methodist Academy and Swansea University.

Description of Research

Dr. Rees’ research focuses on the uptake of micro and nanoparticles by biological cells using high throughput imaging techniques such as imaging flow cytometry and multi-field imaging microscopy, the biological process of particle uptake, and the study of dose responses for drug molecules deliv-ered by particulate delivery vectors.

Areas Of Expertise

Laser diodes Nonlinear systems and chaos Simulation of cell mitosis Colloidal quantum dot fluorophores
Education & Training

PhD, Cardiff University
Postdoctoral Fellowship, Trinity College Dublin
Publications

Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry
Barnes, CM, Power, AL, Barber, DG, Tennant, RK, Jones, RT, Lee, GR, Hatton, J, Elliott, A, Zaragoza-Castells, J, Haley, SM, Summers, HD, Doan, M, Carpenter, AE, Rees, P & Love, J 2023, , New Phytologist, vol. 240, no. 3, pp. 1305-1326. https://doi.org/10.1111/nph.19186

Label-free cell segmentation of diverse lymphoid tissues in 2D and 3D
Wills, JW, Robertson, J, Tourlomousis, P, Gillis, CMC, Barnes, CM, Miniter, M, Hewitt, RE, Bryant, CE, Summers, HD, Powell, JJ & Rees, P 2023, , Cell Reports Methods, vol. 3, no. 2, 100398. https://doi.org/10.1016/j.crmeth.2023.100398

OPTIMAL: An OPTimized Imaging Mass cytometry AnaLysis framework for benchmarking segmentation and data exploration
Hunter, B, Nicorescu, I, Foster, E, McDonald, D, Hulme, G, Fuller, A, Thomson, A, Goldsborough, T, Hilkens, CMU, Majo, J, Milross, L, Fisher, A, Bankhead, P, Wills, J, Rees, P, Filby, A & Merces, G 2024, , Cytometry Part A, vol. 105, no. 1, pp. 36-53. https://doi.org/10.1002/cyto.a.24803

Imaging flow cytometry
Rees, P, Summers, HD, Filby, A, Carpenter, AE & Doan, M 2022, , Nature Reviews Methods Primers, vol. 2, no. 1, 86. https://doi.org/10.1038/s43586-022-00167-x

Dinaciclib as an effective pan-cyclin dependent kinase inhibitor in platinum resistant ovarian cancer
Howard, D, James, D, Garcia-Parra, J, Pan-Castillo, B, Worthington, J, Williams, N, Coombes, Z, Rees, SC, Lutchman-Singh, K, Francis, LW, Rees, P, Margarit, L, Conlan, RS & Gonzalez, D 2022, , Frontiers in Oncology, vol. 12, 1014280, pp. 1014280. https://doi.org/10.3389/fonc.2022.1014280

Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis
Summers, HD, Wills, JW & Rees, P 2022, , Cell Reports Methods, vol. 2, no. 11, 100348, pp. 100348. https://doi.org/10.1016/j.crmeth.2022.100348

A quantitative and spatial analysis of cell cycle regulators during the fission yeast cycle
Curran, S, Dey, G, Rees, P & Nurse, P 2022, , Proceedings of the National Academy of Sciences of the United States of America, vol. 119, no. 36, e2206172119, pp. e2206172119. https://doi.org/10.1073/pnas.2206172119

The effects of curve registration on linear models of jump performance and classification based on vertical ground reaction forces
G. E. White, M, Neville, J, Rees, P, Summers, H & Bezodis, N 2022, , Journal of Biomechanics, vol. 140, 111167, pp. 111167. https://doi.org/10.1016/j.jbiomech.2022.111167

Determining jumping performance from a single body-worn accelerometer using machine learning
White, MGE, Bezodis, NE, Neville, J, Summers, H & Rees, P 2022, , PLoS ONE, vol. 17, no. 2, e0263846, pp. e0263846. https://doi.org/10.1371/journal.pone.0263846

Data-driven modeling of the cellular pharmacokinetics of degradable chitosan-based nanoparticles
Summers, HD, Gomes, CP, Varela-Moreira, A, Spencer, AP, Gomez-Lazaro, M, Pêgo, AP & Rees, P 2021, , Nanomaterials, vol. 11, no. 10, 2606. https://doi.org/10.3390/nano11102606

Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning
Wills, JW, Verma, JR, Rees, BJ, Harte, DSG, Haxhiraj, Q, Barnes, CM, Barnes, R, Rodrigues, MA, Doan, M, Filby, A, Hewitt, RE, Thornton, CA, Cronin, JG, Kenny, JD, Buckley, R, Lynch, AM, Carpenter, AE, Summers, HD, Johnson, GE & Rees, P 2021, , Archives of Toxicology, vol. 95, no. 9, pp. 3101-3115. https://doi.org/10.1007/s00204-021-03113-0

Developing ovine mammary terminal duct lobular units have a dynamic mucosal and stromal immune microenvironment
Nagy, D, Gillis, CMC, Davies, K, Fowden, AL, Rees, P, Wills, JW & Hughes, K 2021, , Communications Biology, vol. 4, no. 1, 993, pp. 993. https://doi.org/10.1038/s42003-021-02502-6

Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry
Doan, M, Barnes, C, McQuin, C, Caicedo, JC, Goodman, A, Carpenter, AE & Rees, P 2021, , Nature Protocols, vol. 16, no. 7, pp. 3572-3595. https://doi.org/10.1038/s41596-021-00549-7

Empirical comparison of genotoxic potency estimations: The in vitro DNA-damage ToxTracker endpoints versus the in vivo micronucleus assay
Wills, JW, Halkes-Wellstead, E, Summers, HD, Rees, P & Johnson, GE 2021, , Mutagenesis, vol. 36, no. 4, pp. 311-320. https://doi.org/10.1093/mutage/geab020

Cdk control pathways integrate cell size and ploidy information to control cell division
Patterson, JO, Basu, S, Rees, P & Nurse, P 2021, , eLife, vol. 10, e64592. https://doi.org/10.7554/eLife.64592

High content, quantitative AFM analysis of the scalable biomechanical properties of extracellular vesicles
Gazze, SA, Thomas, SJ, Garcia-Parra, J, James, DW, Rees, P, Marsh-Durban, V, Corteling, R, Gonzalez, D, Conlan, RS & Francis, LW 2021, , Nanoscale, vol. 13, no. 12, pp. 6129-6141. https://doi.org/10.1039/d0nr09235e

Dinaciclib, a bimodal agent effective against endometrial cancer
Howard, D, James, D, Murphy, K, Garcia-Parra, J, Pan-Castillo, B, Rex, S, Moul, A, Jones, E, Bilbao-Asensio, M, Michue-Seijas, S, Lutchman-Singh, K, Margarit, L, Francis, LW, Rees, P, Gonzalez, D & Conlan, RS 2021, , Cancers, vol. 13, no. 5, 1135, pp. 1-17. https://doi.org/10.3390/cancers13051135

Data Driven Cell Cycle Model to Quantify the Efficacy of Cancer Therapeutics Targeting Specific Cell-Cycle Phases From Flow Cytometry Results
James, DW, Filby, A, Brown, MR, Summers, HD, Francis, LW & Rees, P 2021, , Frontiers in Bioinformatics, vol. 1, 662210. https://doi.org/10.3389/fbinf.2021.662210

Synergistic CDK control pathways maintain cell size homeostasis
Patterson, JO, Basu, S, Rees, P & Nurse, P 2020, , bioRxiv. https://doi.org/10.1101/2020.11.25.397943

Application of automated electron microscopy imaging and machine learning to characterise and quantify nanoparticle dispersion in aqueous media
Ilett, M, Wills, J, Rees, P, Sharma, S, Micklethwaite, S, Brown, A, Brydson, R & Hondow, N 2020, , Journal of Microscopy, vol. 279, no. 3, pp. 177-184. https://doi.org/10.1111/jmi.12853