Biology subjects

Perez-Aliacar, M.

Publications and source records attributed to Perez-Aliacar, M..

3 recordsLinked to original sources

bioRxiv2024

On the equivalence between Agent-Based and Continuum models for cell population modeling. Application to glioblastoma evolution in microfluidic devices

AO_SCPLOWBSTRACTC_SCPLOWMathematical models are invaluable tools for understanding the mechanisms and interactions that control the behavior of complex systems. Modeling a problem as cancer evolution includes many coupled phenomena being therefore impossible to obtain sufficient experimental results to fully evaluate all possible conditions. In this work, we focus on Agent-Based Models (ABMs), as these models allow to obtain more complete and interpretable information at the individual level than other types of in silico models. However, ABMs, need many parameters, requiring more information at the cellular and environmental levels to be calibrated. To overcome this problem we propose a complementary approach to traditional calibration methods. We used existent continuum models able to reproduce experimental data, validated and with fitted parameters, to establish relationships between parameters of both, continuum and agent-based models, to simplify and improve the process of adjusting the parameters of the ABM. With this approach, it is possible to bridge the gap between both kinds of models, allowing to work with them simultaneously and take advantage of the benefits of each of them. To illustrate this methodology, the evolution of glioblastoma (GB) is modeled as an example of application. The resulting ABM obtains very similar results to those previously obtained with the continuum model, replicating the main histopathological features (the formation of necrotic cores and pseudopalisades) appearing in several different in vitro experiments in microfluidic devices, as we previously obtained with continuum models. However, ABMs have additional advantages: since they also incorporates the inherent random effects present in Biology, providing a more natural explanation and a deeper understanding of biological processes. Moreover, additional relevant phenomena can be easily incorporated, such as the mechanical interaction between cells or with the environment, angiogenic processes and cell concentrations far from the continuum requirement as happens, for intance, with immune cells.

cancer biology↗

bioRxiv2023

Modelling glioblastoma resistance to temozolomide. Combination of spheroid and mathematical models to simulate cellular adaptation in vitro.

AO_SCPLOWBSTRACTC_SCPLOWDrug resistance is one of the biggest challenges in the fight against cancer. In particular, in the case of glioblastoma, the most lethal brain tumour, resistance to temozolomide (the standard of care drug for chemotherapy in this tumour), is one of the main reasons behind treatment failure and hence responsible for the poor prognosis of patients diagnosed with this disease. In this paper, we combine the power of three-dimensional in vitro experiments of treated glioblastoma spheroids with mathematical models of tumour evolution and adaptation. We use a novel approach based on internal variables for modelling the acquisition of resistance to temozolomide that is observed in a group of treated spheroids in the experiments. These internal variables describe the cells phenotypic state, which depends on the history of drug exposure and affects cell behaviour. We use model selection to determine the most parsimonious model and calibrate it to reproduce the experimental data, obtaining a high level of agreement between the in vitro and in silico outcomes. A sensitivity analysis is carried out to investigate the impact of each model parameter in the predictions. More importantly, we show the utility of our model for answering biological questions, such as what is the intrinsic adaptation mechanism, or for separating the sensitive and resistant populations. We conclude that the proposed in silico framework, in combination with experiments, can be useful to improve our understanding of the mechanisms behind drug resistance in glioblastoma and to eventually set some guidelines for the design of new treatment schemes.

cancer biology↗

bioRxiv2023

Modelling cell adaptation using internal variables accounting for cell plasticity in continuum mathematical biology

AO_SCPLOWBSTRACTC_SCPLOWCellular adaptation is the ability of cells to change in response to different stimuli and environmental conditions. It occurs via phenotypic plasticity, that is, changes in gene expression derived from changes in the physiological environment. This phenomenon is important in many biological processes, in particular in cancer evolution and its treatment. Therefore, it is crucial to understand the mechanisms behind it. Specifically, the emergence of the cancer stem cell phenotype, showing enhanced proliferation and invasion rates, is an essential process in tumour progression. We present a mathematical framework to simulate phenotypic heterogeneity in different cell populations as a result of their interaction with chemical species in their microenvironment, through a continuum model using the well-known concept of internal variables to model cell phenotype. The resulting model, derived from conservation laws, incorporates the relationship between the phenotype and the history of the stimuli to which cells have been subjected, together with the inheritance of that phenotype. To illustrate the model capabilities, it is particularised for glioblastoma adaptation to hypoxia. A parametric analysis is carried out to investigate the impact of each model parameter regulating cellular adaptation, showing that it permits reproducing different trends reported in the scientific literature. The framework can be easily adapted to any particular problem of cell plasticity, with the main limitation of having enough cells to allow working with continuum variables. With appropriate calibration and validation, it could be useful for exploring the underlying processes of cellular adaptation, as well as for proposing favorable/unfavourable conditions or treatments.

cancer biology↗