Dr Thomas Woolley
Senior Lecturer
- woolleyt1@cardiff.ac.uk
- +44 (0)29 2087 0618
- Room 2.47, 21-23 Senghennydd Road, Cathays, Cardiff, CF24 4AG
- Media commentator
Overview
Research Group
Applied Mathematics.
Research Interests
Mathematical biology, Morphogenesis, Reaction-diffusion theory, Cellular motion, Stochastic dynamics, Neurobiology, Oncology.
Biography
Dr Thomas Woolley studied mathematics at University of Oxford between 2004-2017. Through his education he ended up specializing in mathematical biology, where his doctorate focused on understanding the pattern formation behind fish spots and zebra stripes. Alongside this research he now investigates mathematical models of stem cell movement. The hope is that by understanding how stem cells move we can influence them and, thus, speed up the healing process.
When not doing mathematics he is a keen participant in mathematical outreach workshops and has given a variety of popular maths lectures nationally and internationally. He has previously worked for the BBC, illustrated Marcus du Sautoy’s book and worked on the popular maths show “Dara O’Briains school of hard sums”. Most recently he was the Fellow of Modern Mathematics at the London Science Museum and is helped redesign their mathematics gallery.
Publications
2020
- Krause, A.et al. 2020. Turing patterning in stratified domains. Bulletin of Mathematical Biology 82, article number: 136. (10.1007/s11538-020-00809-9)
- Mihai, L. A., Woolley, T. and Goriely, A. 2020. Likely cavitation and radial motion of stochastic elastic spheres. Nonlinearity 33(5), article number: 1987. (10.1088/1361-6544/ab7104)
- Krause, A.et al. 2020. From one pattern into another: analysis of Turing patterns in heterogeneous domains via WKBJ. Interface 17(162) (10.1098/rsif.2019.0621)
- Adamer, M. F.et al. 2020. Coloured noise from stochastic inflows in reaction-diffusion systems. Bulletin of Mathematical Biology 82, article number: 44. (10.1007/s11538-020-00719-w)
- Viglialoro, G. and Woolley, T. E. 2020. Solvability of a Keller-Segel system with signal-dependent sensitivity and essentially sublinear production. Applicable Analysis 99(14), pp. 2507-2525. (10.1080/00036811.2019.1569227)
2019
- Fitt, D.et al. 2019. Uncertainty quantification of elastic material responses: testing, stochastic calibration and Bayesian model selection. Mechanics of Soft Materials 1, article number: 13. (10.1007/s42558-019-0013-1)
- Budia, I.et al. 2019. Radiation protraction schedules for low-grade gliomas: A comparison between different mathematical models. Interface 16(161), article number: 20190665. (10.1098/rsif.2019.0665)
- Picco, N. and Woolley, T. 2019. Time to change your mind? Modelling transient properties of cortex formation highlights the importance of evolving cell. Journal of Theoretical Biology 481, pp. 110-118. (10.1016/j.jtbi.2018.08.019)
- Maini, P. K. and Woolley, T. 2019. The Turing model for biological pattern formation. In: Bianchi, A. et al. eds. The Dynamics of Biological Systems. Mathematics of Planet Earth Springer, pp. 189-204.
- Mihai, L. A.et al. 2019. Likely cavitation in stochastic elasticity. Journal of Elasticity 137(1), pp. 27-42. (10.1007/s10659-018-9706-1)
- Picco, N.et al. 2019. A mathematical insight into cell labelling experiments for clonal analysis. Journal of Anatomy 235(3), pp. 687-696. (10.1111/joa.13001)
- Mihai, L. A., Woolley, T. and Goriely, A. 2019. Likely chirality of stochastic anisotropic hyperelastic tubes. International Journal of Non-Linear Mechanics 114, pp. 9-20. (10.1016/j.ijnonlinmec.2019.04.004)
- Mihai, L. A.et al. 2019. Likely equilibria of stochastic hyperelastic spherical shells and tubes. Mathematics and Mechanics of Solids 24(7), pp. 2066-2082. (10.1177/1081286518811881)
- Mihai, L. A., Woolley, T. E. and Goriely, A. 2019. Likely equilibria of the stochastic Rivlin cube. Philosophical Transactions of the Royal Society of London. Series A: Mathematical and Physical Sciences 377(2144), article number: 20180068. (10.1098/rsta.2018.0068)
- Simonovic, J. and Woolley, T. 2019. Deterministic and stochastic parameter analysis of the bone cell population model. Presented at: 8th International Conference on Computational Bioengineering (ICCB2019), Belgrade, Serbia, 4-6 September 2019.
- Simonovic, J. and Woolley, T. 2019. Equation of osteocyte activity in the bone cell population model. Presented at: ACTC Advances in Cell and Tissue Cultures, Cardiff University School of Biosciences, Cardiff, UK, 4-5th June 2019.
- Weber, E.et al. 2019. Self-organizing hair peg-like structures from dissociated skin progenitor cells: New insights for human hair follicle organoid engineering and Turing patterning in an asymmetric morphogenetic field. Experimental Dermatology 28(4), pp. 355-366. (10.1111/exd.13891)
- Simonovic, J. and Woolley, T. 2019. Study of bone cell population models of s-system type. Presented at: Organ-on-a-Chip Technologies Network – Learning and Collaborative Event on 8 April 2019 at QMUL, London, UK, 8-9 April 2019.
- Mihai, L.et al. 2019. Likely oscillatory motions of stochastic hyperelastic solids. Transactions of Mathematics and Its Applications 3(1), article number: tnz003. (10.1093/imatrm/tnz003)
- Ho, W. K. W.et al. 2019. Feather arrays are patterned by interacting signalling and cell density waves. PLoS Biology 17(2), article number: e3000132. (10.1371/journal.pbio.3000132)
- Navajas Acedo, J.et al. 2019. Parallel control of mechanosensory hair cell orientation by the PCP and Wnt pathways. Nature Communications 10, article number: 3993. (10.1038/s41467-019-12005-y)
2018
- Viglialoro, G. and Woolley, T. E. 2018. Eventual smoothness and asymptotic behaviour of solutions to a chemotaxis system perturbed by a logistic growth. Discrete and Continuous Dynamical Systems - Series B 23(8), pp. 3023-3045. (10.3934/dcdsb.2017199)
- Picco, N.et al. 2018. Mathematical modelling of cortical neurogenesis reveals that the founder population does not necessarily scale with neurogenic output. Cerebral Cortex 28(7), pp. 2540-2550. (10.1093/cercor/bhy068)
- Kasemeier-Kulesa, J. C.et al. 2018. Predicting neuroblastoma using developmental signals and a logic-based model. Biophysical Chemistry 238, pp. 30-38. (10.1016/j.bpc.2018.04.004)
- Picco, N.et al. 2018. Mathematical modeling of cortical neurogenesis reveals that the founder population does not necessarily scale with neurogenic output. Cerebral Cortex 28(7), pp. 2540-2550. (10.1093/cercor/bhy068)
- Krause, A. L.et al. 2018. Heterogeneity induces spatiotemporal oscillations in reaction-diffusions systems. Physical Review E 97, article number: 52206. (10.1103/PhysRevE.97.052206)
- Woolley, T.et al. 2018. Changes in the retreatment radiation tolerance of the spinal cord with time after the initial treatment. International Journal of Radiation Biology 94, pp. 515-531. (10.1080/09553002.2018.1430911)
- Sanders, J.et al. 2018. PLCz induced Ca2+ oscillations in mouse eggs involve a positive feedback cycle of Ca2+ induced InsP3 formation from cytoplasmic PIP2. Frontiers in Cell and Developmental Biology 6, article number: 36. (10.3389/fcell.2018.00036)
- Viglialoro, G. and Woolley, T. 2018. Boundedness in a parabolic-elliptic chemotaxis system with nonlinear diffusion and sensitivity, and logistic source. Mathematical Methods in the Applied Sciences 41(5), pp. 1809-1824. (10.1002/mma.4707)
- Mihai, L. A., Woolley, T. and Goriely, A. 2018. Stochastic isotropic hyperelastic materials: constitutive calibration and model selection. Proceedings of the Royal Society A 474(2211), article number: 201708. (10.1098/rspa.2017.0858)
2017
- Adamer, M., Woolley, T. and Harrington, H. 2017. Graph-facilitated resonant mode counting in stochastic interaction networks. Journal of the Royal Society Interface 14(137), article number: 20170447. (10.1098/rsif.2017.0447)
- Woolley, T. E. 2017. Pattern production through a chiral chasing mechanism. Physical Review E 96, article number: 32401. (10.1103/PhysRevE.96.032401)
- Woolley, T., Gaffney, E. A. and Goriely, A. 2017. Random blebbing motion: a simple model linking cell structural properties to migration characteristics. Physical Review E 96, article number: 12409. (10.1103/PhysRevE.96.012409)
My research focuses on understanding emergent properties and producing rigorous limits, which allow us to scale between discrete elements and continuous systems. Specifically, my doctoral research considered the link between continuous reaction-diffusion equations and their agent-based analogues.
More recently, I have been working on newly discovered cellular protrusions, which are able to affect a variety of cellular phenomena, such as motion and division. In collaboration with the University of Reading I am researching muscle stem cells that navigate towards regions of muscular damage resulting in muscle healing and regeneration. In particular, I am deriving analytical links between the spatio-temporal discrete protrusions of an individual cell and the continuous population distribution and movement of the stem cells. Critically, we have been able to use this model to algebraically couple traits in the observable cellular motion to unobservable structural features of the cell membrane.
