arXiv subjects

Yuri Mansury

Publications and source records attributed to Yuri Mansury.

5 recordsLinked to original sources

arXiv2004

Simulating the Impact of a Molecular 'Decision-Process' on Cellular Phenotype and Multicellular Patterns in Brain Tumors

Experimental evidence indicates that human brain cancer cells proliferate or migrate, yet do not display both phenotypes at the same time. Here, we present a novel computational model simulating this cellular decision-process leading up to either phenotype based on a molecular interaction network of genes and proteins. The model's regulatory network consists of the epidermal growth factor receptor (EGFR), its ligand transforming growth factor-a (TGFa), the downstream enzyme phospholipaseC-gamma (PLCg) and a mitosis-associated response pathway. This network is activated by autocrine TGFa secretion, and the EGFR-dependent downstream signaling this step triggers, as well as modulated by an extrinsic nutritive glucose gradient. Employing a framework of mass action kinetics within a multiscale agent-based environment, we analyze both the emergent multicellular behavior of tumor growth and the single-cell molecular profiles that change over time and space. Our results show that one can indeed simulate the dichotomy between cell migration and proliferation based solely on an EGFR decision network. It turns out that these behavioral decisions on the single cell level impact the spatial dynamics of the entire cancerous system. Furthermore, the simulation results yield intriguing experimentally testable hypotheses also on the sub-cellular level such as spatial cytosolic polarization of PLCg towards an extrinsic chemotactic gradient. Implications of these results for future works, both on the modeling and experimental side are discussed.

q-bio.CB↗

arXiv2004

Scaling Laws in Cancer: The Dynamic Behaviour of the Power Exponent p

We have previously reported that a 'universal' growth law with fixed scaling exponent p, as proposed by West and collaborators for all living organisms, appears to be able to describe also the growth of tumors in vivo. Here, we investigate in more detail the dynamic behaviour of p using data from the literature. We show that p initially decreases before it again increases up to or even beyond 3/4. These results support the notion that p can vary over time and yet its dynamics are independent from the cancer type.

q-bio.TO↗

arXiv2003

Insights from a Novel Tumor Model: Indications for a Quantitative Link between Tumor Growth and Invasion

Using our previously developed model we demonstrate here, that (1) solid tumor growth and cell invasion are linked, not only qualitatively but also quantitatively, that (2) the onset of invasion marks the time point when the tumor cell density exceeds a compaction maximum, and that (3) tumor cell invasion, reduction of mechanical confinement and angiogenesis can act synergistically to increase the actual tumor mass towards the level predicted by West et al. universal growth curve.

physics.bio-ph↗

arXiv2003

Simulating the time series of a selected gene expression profile in an agent-based tumor model

To elucidate the role of environmental conditions in molecular-level dynamics and to study their impact on macroscopic brain tumor growth patterns, the expression of the genes Tenascin C and PCNA in a 2D agent-based model for the migratory trait is calibrated using experimental data from the literature, while the expression of these genes for the proliferative trait is obtained as the model output. Numerical results confirm that the gene expression of Tenascin C is consistently higher in the migratory glioma cell phenotype and show that the expression of PCNA is consistently higher among proliferating tumor cells. Furthermore, detrended fluctuation analysis (DFA) suggests that for prediction purposes, the simulated gene expression profiles of Tenascin C and PCNA that were determined separately for the migrating and proliferating phenotypes exhibit lesser predictability than those of the phenotypic mixture combining all viable tumor cells typically found in clinical biopsies.

nlin.AO↗

arXiv2003

A Growth Model for Multicellular Tumor Spheroids

Most organisms grow according to simple laws, which in principle can be derived from energy conservation and scaling arguments, critically dependent on the relation between the metabolic rate B of energy flow and the organism mass m. Although this relation is generally recognized to be of the form B(m) = mp, the specific value of the exponent p is the object of an ongoing debate, with many mechanisms being postulated to support different predictions. We propose that multicellular tumor spheroids provide an ideal experimental model system for testing these allometric growth theories, especially under controlled conditions of malnourishment and applied mechanical stress.

physics.bio-ph↗