Biology subjects

Powathil, G.

Publications and source records attributed to Powathil, G..

5 recordsLinked to original sources

bioRxiv2025

Investigating the Impact of Omental Adipocytes on Ovarian Cancer: Insights from a 3D in-silico model

1Ovarian cancer is responsible for the most deaths of all gynaecological cancers in the Western world [1]. The symptoms of ovarian cancer are typically subtle and alike to less lethal diseases found more prevalently in the population, frequently resulting in late diagnoses and advanced tumour stages upon treatment initiation [2]. While surgery and platinum-based treatments can be curative, ovarian cancers found at the latter, metastasised stages are likely to be recurrent and more tailored towards palliative care [3]. Metastasised ovarian cancer spreads to surrounding organs and tissues such as the greater omentum [4], a large fat pad composed of adipose tissue stretching from the stomach and hanging over the intestines. The location of this is key in its role towards ovarian cancer and its progression [5]. In this study, we develop a mathematical model to investigate the role that adipocytes found in adipose tissue can have in ovarian cancer progression. Observations of biological experiments from two cell lines create foundations to build a multiscale agent-based model in a Physicell framework. The impact of the adipose derived media concentration, treatment dosage, and initial tumour size are explored to find how these conditions affect the spatio-temporal dynamics of cancer tumours.

cancer biology↗

bioRxiv2024

Exploring the role of EMT in Ovarian Cancer Progression: Insights from a multiscale mathematical model

Epithelial-to-mesenchymal transition (EMT) plays a key role in the progression of cancer tumours and can make treatment significantly less successful for patients. EMT occurs when a cell gains a different phenotype and possesses different behaviours to those previously exhibited. This may result in enhanced drug resistance, higher cell plasticity, and increased metastatic abilities. It has therefore has become essential to encapsulate this change and study tumour progression and its response to treatments. Here, we use a 3D agent-based multiscale modelling framework based on Physicell to investigate the role of EMT over time in two cell lines, OVCAR-3 and SKOV-3. The impact of conditions in the microenvironment are incorporated into the model by modifying cellular behaviours dependant on variables such as substrate concentrations and proximity to neighbouring cells. OVCAR-3 and SKOV-3 cell lines possess highly contrasting tumour layouts, allowing a vast array of different tumour dynamics and morphologies to be tested and studied. The model encapsulates the biological observations and trends seen in tumour growth and development, thus can help to obtain further insights into OVCAR-3 and SKOV-3 cell line dynamics. Sensitivity analysis was performed to investigate the impact of parameter sensitivity on model outcome. Sensitivity analysis showed that parameters used in generating the rate of EMT and cycle rates within the cells are relatively more sensitive than other parameters used.

cancer biology↗

bioRxiv2024

Simulations probe the role of space in the interplay between drug-sensitive and drug-resistant cancer cells

The interplay between drug-sensitive and drug-resistant cancer cells has been observed to impact cell-to-cell interactions in experimental settings. However, the role that space plays in these interactions remains unclear. In this study, we develop mathematical models to investigate how spatial factors affect cell-to-cell competition between drug-sensitive and drug-resistant cancer cells in silico. We develop two baseline models to study cells from the epithelial FaDu cell line subjected to two drugs, specifically the ATR inhibitor ceralasertib and the PARP inhibitor olaparib, that target DNA damage response pathways. Our baseline models are: (1) a temporally resolved ordinary differential equation (ODE) model, and (2) a spatio-temporally resolved agent-based model (ABM). The models simulate cells in well-mixed and spatially structured cell systems, respectively. The ODE model is calibrated against in vitro data and is thereafter mapped onto the baseline ABM which, in turn, is extended to enable a simulation-based investigation on how spatial factors impact cell-to-cell competition. Simulation results from the extended ABMs demonstrate that the in silico treatment responses are simultaneously affected by: (i) the initial spatial cell configurations, (ii) the initial fraction of drug-resistant cells, (iii) the drugs to which cells express resistance, (iv) drug combinations, (v) drug doses, and (vi) the doubling time of drug-resistant cells compared to the doubling time of drug-sensitive cells. These results reveal that spatial structures of the simulated cancer cells affect both cell-to-cell interactions, and the impact that these interactions have on the ensuing population dynamics. This leads us to suggest that the role that space plays in cell-to-cell interactions should be further investigated and quantified in experimental settings.

cancer biology↗

bioRxiv2020

Development of a Coupled Simulation Toolkit for Computational Radiation Biology Based on Geant4 and CompuCell3D

Understanding and designing clinical radiation therapy is one of the most important areas of state-of-the-art oncological treatment regimens. Decades of research have gone into developing sophisticated treatment devices and optimization protocols for schedules and dosages. In this paper, we presented a comprehensive computational platform that facilitates building of the sophisticated multi-cell-based model of how radiation affects the biology of living tissue. We designed and implemented a coupled simulation method, including a radiation transport model, and a cell biology model, to simulate the tumor response after irradiation. The radiation transport simulation was implemented through Geant4 which is an open-source Monte Carlo simulation platform that provides many flexibilities for users, as well as low energy DNA damage simulation physics, Geant4-DNA. The cell biology simulation was implemented using CompuCell3D (CC3D) which is a cell biology simulation platform. In order to couple Geant4 solver with CC3D, we developed a "bridging" module that extracts tumor cellular geometry of the CC3D simulation (including specification of the individual cells) and ported it to the Geant4 for radiation transport simulation. The cell dose and cell DNA damage distribution in multicellular system were obtained using Geant4. The tumor response was simulated using cell-based tissue models based on CC3D. By merging two powerful and widely used modeling platforms, CC3D and Geant4, we delivered a novel tool that can give us the ability to simulate the dynamics of biological tissue in the presence of ionizing radiation, which provides a powerful framework for quantifying the biological consequences of radiation therapy. The developed tool has an advantage on that it has strong extensibility due to the exploitability of two modeling platforms. In this introductory methods paper, we described our modeling platform in detail and showed how it can be applied to study the application of radiotherapy to a vascularized tumor.

biophysics↗

bioRxiv2017

What does not kill a tumour may make it stronger: in silico Insights on Chemotherapeutic Drug Resistance

Tumour recurrence post chemotherapy is an established clinical problem and many cancer types are often observed to be increasingly drug resistant subsequent to chemotherapy treatments. Drug resistance in cancer is a multipart phenomenon which can be derived from several origins and in many cases it has been observed that cancer cells have the ability to possess, acquire and communicate drug resistant traits.\n\nHere, an in silico framework is developed in order to study drug resistance and drug response in cancer cell populations exhibiting various drug resistant features. The framework is based on an on-lattice hybrid multiscale mathematical model and is equipped to simulate multiple mechanisms on different scales that contribute towards chemotherapeutic drug resistance in cancer. This study demonstrates how drug resistant tumour features may depend on the interplay amongst intracellular, extracelluar and intercellular factors. On a cellular level, drug resistant cell phenotypes are here derived from inheritance or mutations that are spontaneous, drug-induced or communicated via exosomes. Furthermore intratumoural heterogeneity and spatio-temporal drug dynamics heavily influences drug delivery and the development of drug resistant cancer cell subpopulations. Chemotherapy treatment strategies are here optimised for various in silico tumour scenarios and treatment objectives. We demonstrate that optimal chemotherapy treatment strategies drastically depend on which drug resistant mechanisms are activated, and that furthermore suboptimal chemotherapy administration may promote drug resistance.

cancer biology↗