A Glycolysis Based in-silico Model for the Solid Tumor Growth

Abstract

Cancer-tumor growth is a complex process depending on several biological factors, such as the chemical microenvironment of the tumor, the cellular metabolic profile, and its proliferation rate. Several mathematical models have been developed for identifying the interactions between tumor cells and tissue microenvironment, since they play an important role in tumor formation and progression. Toward this direction we propose a new continuum model of avascular glioma-tumor growth, which incorporates a new factor, namely, the glycolytic potential of cancer cells, to express the interactions of three different tumor-cell populations (proliferative, hypoxic, and necrotic) with their tissue microenvironment. The glycolytic potential engages three vital nutrients, i.e., oxygen, glucose, and lactate, which provide cells with the necessary energy for their survival and proliferation. Extensive simulations are performed for different evolution times and various proliferation rates, in order to investigate how the tumor growth is affected. According to medical experts, the experimental observations indicate that the model predicts quite satisfactorily the overall tumor growth as well as the expansion of each region separately. Following extensive evaluation, the proposed model may provide an essential tool for patient-specific tumor simulation and reliable prediction of glioma spatiotemporal expansion.

Metabolic reprogramming dynamics in tumor spheroids: Insights from a multicellular, multiscale model

“…It has been known that cancer cells have increased flux through the glycolysis pathway in the absence of oxygen (the Pasteur effect [63]), which is also observed even in the presence of oxygen (the Warburg effect ). Thus, our simulations directly match experimental observations [17].…”

“…Thus, these models are essential tools to simulate heterogeneous biological behavior. While continuous models are capable of explaining the overall behavior of the tumor as a whole [17], a discrete model [18] can capture the stochasticity of the individual cell’s behavior within the tumor, leading to differential spatio-temporal evolution and inter-tumor heterogeneity. In this type of modeling, each cell’s decisions are simulated based on its current state, local environment, and interactions with its neighbors.…”

Metabolic reprogramming dynamics in tumor spheroids: Insights from a multicellular, multiscale model

“…It has been known that cancer cells have increased flux through the glycolysis pathway in the absence of oxygen (the Pasteur effect (51)), which is also observed even in the presence of oxygen (the Warburg effect). Thus, our simulations directly match experimental observations (16).…”

“…Thus, these models are essential tools to simulate heterogeneous biological behavior. While continuous models are capable of explaining the overall behavior of the tumor as a whole (16), a discrete model (17) can capture the stochasticity of the individual cell's behavior within the tumor, leading to differential spatio-temporal evolution and inter-tumor heterogeneity. In this type of modeling, each cell's decisions are simulated based on its current state, local environment, and interactions with its neighbors.…”

Advanced Non-linear Mathematical Model for the Prediction of the Activity of a Putative Anticancer Agent in Human-to-mouse Cancer Xenografts

“…Indeed, several models, based on nonlinear mathematical equations and on tumor growth physiology and biology have been proposed as tools to predict the growth of a tumor (4)(5)(6). Multiscale tumor growth modeling, dynamic differential equations models, delay differential mathematical models, etc., (7)(8)(9), are capable of modeling more efficiently the multiscale and the spatiotemporal evolution of tumors. Although these models are capable of simulating and computing tumor cells' growth accurately, some of them incorporate the inputs not explicitly and in a very complex way, i.e.…”