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Chaitanya A Athale

Publications and source records attributed to Chaitanya A Athale.

3 recordsLinked to original sources

Development of a three-dimensional multiscale agent-based tumor model: simulating gene-protein interaction profiles, cell phenotypes and multicellular patterns in brain cancer.

Experimental evidence suggests that epidermal growth factor receptor (EGFR)-mediated activation of the signaling protein phospholipase Cgamma plays a critical role in a cancer cell's phenotypic decision to either proliferate or to migrate at a given point in time. Here, we present a novel three-dimensional multiscale agent-based model to simulate this cellular decision process in the context of a virtual brain tumor. Each tumor cell is equipped with an EGFR gene-protein interaction network module that also connects to a simplified cell cycle description. The simulation results show that over time proliferative and migratory cell populations not only oscillate but also directly impact the spatio-temporal expansion patterns of the entire cancer system. The percentage change in the concentration of the sub-cellular interaction network's molecular components fluctuates, and, for the 'proliferation-to-migration' switch we find that the phenotype triggering molecular profile to some degree varies as the tumor system grows and the microenvironment changes. We discuss potential implications of these findings for experimental and clinical cancer research.

Apoptosis↗

The effects of EGF-receptor density on multiscale tumor growth patterns.

We studied the effects of epidermal growth factor receptor (EGFR) density on tumor growth dynamics, both on the sub- and the multi-cellular level using our previously developed model. This algorithm simulates the growth of a brain tumor using a multi-scale two-dimensional agent-based approach with an integrated transforming growth factor alpha (TGFalpha) induced EGFR-gene-protein interaction network. The results confirm that increasing cell receptor density correlates with an acceleration of the tumor system's spatio-temporal expansion dynamics. This multicellular behavior cannot be explained solely on the basis of spatial sub-cellular dynamics, which remain qualitatively similar amongst the three glioma cell lines investigated here in silico. Rather, we find that cells with higher EGFR density show an early increase in the phenotypic switching activity between proliferative and migratory traits, linked to a higher level of initial auto-stimulation by the PLCgamma-mediated TGFalpha-EGFR autocrine network. This indicates a more active protein level interaction in these chemotactically acting tumor systems and supports the role of post-translational regulation for the implemented EGFR pathway. Implications of these results for experimental cancer research are discussed.

Algorithms↗

Inferring a system model of subcellular topoisomerase II beta localization dynamics.

Measuring the mobility of proteins in living cells has become critical to many studies in cell biology and forms the basis for discussion on sub-cellular dynamics. Increasingly localization networks are being put together into compartment models to represent the exchange of molecules, represented mathematically as ordinary differential equations (ODE). The set-up is based on published literature, the "knowledge" of the investigator and 3D visualization of the data. Here we demonstrate this method on the example of a simple distribution model of the molecule Topoisomerase II beta (Topo II beta), nuclear protein that modifies DNA topology. It is found in high concentration in the nucleolus and diffuse in the nucleoplasm, demonstrating a non-membranous inhomogeneity in its distribution. We expand on the simple model by adding additional components to fit fluorescence recovery after photobleaching (FRAP) experiments for protein (GFP) labeled Topo II beta to measure its mobility. This model is then validated by comparing it with alternative scenarios and shown to have predictive power.

Animals↗