Publications

  1. Oliver, S., Williams, M., Jolly, M. K., Gonzalez, D., & Powathil, G. (2025). Exploring the role of EMT in ovarian cancer progression using a multiscale mathematical model. npj Systems Biology and Applications, 11(1), 36.

  2. Gravenor, M. B., Dawson, M., Bennett, E., Thorpe, B., White, C., Rahat, A., … & Lucini, B. (2025). Real-time epidemiological modelling during the COVID-19 emergency in Wales. In More UK Success Stories in Industrial Mathematics (pp. 97-104). Cham: Springer Nature Switzerland.

  3. Pugh, K., Jones, R. D., Powathil, G., & Hamis, S. (2025). Simulations probe the role of space in the interplay between drug-sensitive and drug-resistant cancer cells. Journal of Theoretical Biology, 602, 112048.

  4. Spinicci, K., Powathil, G., Stéphanou, A. (2024). Modelling the impact of HIF on metabolism and the extracellular matrix: consequences for tumour growth and invasion. Bulletin of Mathematical Biology, In press.

  5. Pugh, K., Davies, M., Powathil, G. (2023). A mathematical model to investigate the effects of ceralasertib and olaparib in targeting the cellular DNA damage response pathway, Journal of Pharmacology and Experimental Therapeutics 387 (1), 55-65.

  6. Gravenor, M.B., Dawson, M., Bennett, E., horpe, B., White, C., Rahat, A., Archambault D., Picco, N., Powathil, G., Lucini, B. (2024). Real-time epidemiological modelling during the COVID-19 emergency in Wales. medRxiv, 2023.08. 02.23293519

  7. Mohammadrezaei, D., Moghimi, N., Vandvajdi, S., Powathil, G., Hamis, S., & Kohandel, M. (2023). Predicting and elucidating the post-printing behavior of 3D printed cancer cells in hydrogel structures by integrating in-vitro and in-silico experiments. Scientific Reports, 13(1), doi: 10.1038/s41598-023-28286-9.

  8. SPINICCI, K., Jacquet, P., Powathil, G., & Stéphanou, A. (2022). Modeling the role of HIF in the regulation of metabolic key genes LDH and PDH: Emergence of Warburg phenotype. Computational and Systems Oncology, 2(3), doi:10.1002/cso2.1040.

  9. Brüningk, S. & Powathil, G. (2022). Modelling Direct and Indirect Effects of Radiation: Experimental, Clinical and Environmental Implications. NATO Science for Peace and Security Series A: Chemistry and Biology (pp. 69-87). Springer Netherlands. doi: 10.1007/978-94-024-2101-9_5.

  10. Hamis, S., Yates, J., Chaplain, M. A., & Powathil, G. G. (2021). Targeting cellular DNA damage responses in cancer: An in vitro-calibrated agent-based model simulating monolayer and spheroid treatment responses to ATR-inhibiting drugs. Bulletin of Mathematical Biology, 83(10). doi: 10.1007/s11538-021-00935-y.

  11. Liu, R., Higley, K. A., Swat, M. H., Chaplain, M. A., Powathil, G. G., & Glazier, J. A. (2021). Development of a coupled simulation toolkit for computational radiation biology based on Geant4 and CompuCell3D. Physics in Medicine & Biology, 66(4), 045026.doi:10.1088/1361-6560/abd4f9.

  12. Hamis, S., Stratiev, S., & Powathil, G. G. (2021). Uncertainty and sensitivity analyses methods for agent-based mathematical models: An introductory review. THE PHYSICS OF CANCER: Research Advances, 1-37. doi: 10.1142/9789811223495_0001.

  13. Hamis, S., Kohandel, M., Dubois, L. J., Yaromina, A., Lambin, P., & Powathil, G. G. (2020). Combining hypoxia-activated prodrugs and radiotherapy in silico: Impact of treatment scheduling and the intra-tumoural oxygen landscape. PLoS computational biology, 16(8), e1008041, doi: 10.1371/journal.pcbi.1008041

  14. Hamis, S., & Powathil, G. (2020). Can we Crack Cancer?. In The Art of Theoretical Biology (pp. 50-51). Springer, doi: 10.1007/978-3-030-33471-0_25

  15. Mothersill, C. E., Oughton, D. H., Schofield, P. N., Abend, M., Adam-Guillermin, C., Ariyoshi, K., … & Geras’ kin, S. A. (2020). From tangled banks to toxic bunnies; a reflection on the issues involved in developing an ecosystem approach for environmental radiation protection. International Journal of Radiation Biology, 1-16. doi: 10.1080/09553002.2020.1793022

  16. Stephanou, A., Ballet, P., Powathil, G. (2019). Hybrid Data-Based Modelling in Oncology: Successes, Challenges and Hopes, Mathematical Modelling of Natural Phenomena, 11 (1) 37-48, doi: 10.1051/mmnp/2019026

  17. Meaney, C., Powathil, G., Yaromina, A., J Dubois, L., Lambin, P., Kohande, M. 2019 Role of hypoxia-activated prodrugs in combination with radiation therapy: An in silico approach. Mathematical Biosciences and Engineering 16 (6) 6257-6273, doi: 10.3934/mbe.2019312

  18. Mothersill, C., Abend, M., Bréchignac, F., Copplestone, D., Geras’kin, S., Goodman, J., Horemans, N., Jeggo, P., McBride, W., Mousseau, T., O’Hare, A., Papineni, R., Powathil, G., Schofield, P., Seymour, C., Sutcliffe, J., Austin, B. 2019 The tubercular badger and the uncertain curve:- The need for a multiple stressor approach in environmental radiation protection. Environmental Research 168 130-140, doi:10.1016/j.envres.2018.09.031

  19. Hamis, S., Powathil, G., Chaplain, M. (2019) Blackboard to Bedside: A Mathematical Modeling Bottom-Up Approach Toward Personalized Cancer Treatments. JCO Clinical Cancer Informatics 3 1-11, doi: 10.1200/CCI.18.00068

  20. Hamis, S., Nithiarasu, P. & Powathil, GG. (2018). What does not kill a tumour may make it stronger: In silico insights into chemotherapeutic drug resistance. Journal of Theoretical Biology 454, 253, doi: 10.1016/j.jtbi.2018.06.014

  21. Bowness, R., Chaplain, M.A.J., Powathil, GG. & Gillespie, S.H. (2018). Modelling the effects of bacterial cell state and spatial location on tuberculosis treatment: Insights from a hybrid multiscale cellular automaton model. Journal of Theoretical Biology 446, 87-100. doi:10.1016/j.jtbi.2018.03.00

  22. Mothersill, C., Abend, M., Bréchignac, F., Iliakis, G., Impens, N., Kadhim, M., Møller, A.P., Oughton, D., Powathil, G., Saenen, E., Seymour, C., Sutcliffe, J., Tang, F. & Schofield, P.N. (2018). When a duck is not a duck; a new interdisciplinary synthesis for environmental radiation protection. Environmental Research 162, 318-324. doi:10.1016/j.envres.2018.01.022

  23. Brüningk, S., Powathil, G., Ziegenhein, P.., Ijaz, J., Rivens, I., Nill, S., Chaplain, M., Oelfke, U.. & ter Haar, G. (2018). Combining radiation with hyperthermia: a multiscale model informed by in vitro experiments. Journal of The Royal Society Interface 15(138), 20170681, doi:10.1098/rsif.2017.0681

  24. Borasi, G., Nahum, A., Paulides, M., Powathil, G., Russo, G., Fariselli, L., Lamia, D., Cirincion, R.,Forte, G., Borrazzo, C., Caccia, B., diCastro, E., Pozzi, S. & Gilardi, M. (2016). Fast and high temperature hyperthermia coupled with radiotherapy as a possible new treatment for glioblastoma. Journal of Therapeutic Ultrasound 4(1), doi:10.1186/s40349-016-0078-3

  25. Kim, Y., Kang, H., Powathil, G., Kim, H., Trucu, D., Lee, W., Lawler, S., Chaplain, M. 2018 Role of extracellular matrix and microenvironment in regulation of tumor growth and LAR-mediated invasion in glioblastoma PLOS ONE 13 10 e0204865, doi: 0.1371/journal.pone.0204865

  26. Kim, Y., Powathil, G., Kang, H., Trucu, D., Kim, H., Lawler, S. & Chaplain, M. (2015). Strategies of Eradicating Glioma Cells: A Multi-Scale Mathematical Model with MiR-451-AMPK-mTOR Control. PLOS ONE 10(1), e0114370 doi:10.1371/journal.pone.0114370

  27. Powathil, G., Munro, A., Chaplain, M. & Swat, M. (2016). Bystander effects and their implications for clinical radiation therapy: Insights from multiscale in silico experiments. Journal of Theoretical Biology 401, 1-14,  doi:10.1016/j.jtbi.2016.04.010

  28. Chaplain, M. & Powathil, G. (2015). Multiscale modelling of cancer progression and treatment control: The role of intracellular heterogeneities in chemotherapy treatment. Biophysical Reviews and Letters, doi:10.1142/S1793048015500058

  29. Powathil, G., Swat, M. & Chaplain, M. (2014). Systems oncology: Towards patient-specific treatment regimes informed by multiscale mathematical modelling. Seminars in Cancer Biology, doi:10.1016/j.semcancer.2014.02.003

  30. Powathil, G. & Chaplain, M. (2014) A Hybrid Multiscale Approach in Cancer Modelling and Treatment Prediction. In A. D’onofrio and A. Gandolfi (Ed.), Mathematical Oncology 2013. (pp. 237-263). Birkhauser., doi: 10.1007/978-1-4939-0458-7_8

  31. Powathil, G., Chaplain, M. & Swat, M. (2014). Investigating the development of chemotherapeutic drug resistance in cancer: A multiscale computational study (preprint). arXiv.org, http://arxiv.org/abs/1407.0865

  32. Powathil, G., Adamson, D. & Chaplain, M. (2013). Towards Predicting the Response of a Solid Tumour to Chemotherapy and Radiotherapy Treatments: Clinical Insights from a Computational Model. PLoS Computational Biology 9(7), e100312, doi:10.1371/journal.pcbi.1003120
  33. Powathil, G., Gordon, K., Hill, L. & Chaplain, M. (2012). Modelling the effects of cell-cycle heterogeneity on the response of a solid tumour to chemotherapy: Biological insights from a hybrid multiscale cellular automaton model. Journal of Theoretical Biology 308, 1-19., doi:10.1016/j.jtbi.2012.05.015
  34. Powathil, G., Thompson, A. & Chaplain, M. (2012). The role of cellular heterogeneity on the therapeutic response of breast cancer: Clinical insights from a hybrid multiscale computational model. Cancer Research 72(24 Supplement), P5-05-02-P5-05-02., doi:10.1158/0008-5472.SABCS12-P5-05-02
  35. Powathil, G., Kohandel, M., Milosevic, M. & Sivaloganathan, S. (2012). Modeling the Spatial Distribution of Chronic Tumor Hypoxia: Implications for Experimental and Clinical Studies. Computational and Mathematical Methods in Medicine 2012, 1-11. doi:10.1155/2012/410602
  36. Powathil, G., Kohandel, M., Sivaloganathan, S., Oza, A. & Milosevic, M. (2007). Mathematical modeling of brain tumors: effects of radiotherapy and chemotherapy. Physics in Medicine and Biology 52(11), 3291-3306., doi:10.1088/0031-9155/52/11/023