Computer simulation of glioma growth and morphology.

Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. Here, we address this multi-scalar problem by employing a novel predictive three-dimensional mathematical and computational model based on first-principle equations (conservation laws of physics) that describe mathematically the diffusion of cell substrates and other processes determining tumor mass growth and invasion. The model uses conserved variables to represent known determinants of glioma behavior, e.g., cell density and oxygen concentration, as well as biological functional relationships and parameters linking phenomena at different scales whose specific forms and values are hypothesized and calculated based on in vitro and in vivo experiments and from histopathology of tissue specimens from human gliomas. This model enables correlation of glioma morphology to tumor growth by quantifying interdependence of tumor mass on the microenvironment (e.g., hypoxia, tissue disruption) and on the cellular phenotypes (e.g., mitosis and apoptosis rates, cell adhesion strength). Once functional relationships between variables and associated parameter values have been informed, e.g., from histopathology or intra-operative analysis, this model can be used for disease diagnosis/prognosis, hypothesis testing, and to guide surgery and therapy. In particular, this tool identifies and quantifies the effects of vascularization and other cell-scale glioma morphological characteristics as predictors of tumor-scale growth and invasion.

Computer simulation of glioma growth and morphology.

Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. Here, we address this multi-scalar problem by employing a novel predictive three-dimensional mathematical and computational model based on first-principle equations (conservation laws of physics) that describe mathematically the diffusion of cell substrates and other processes determining tumor mass growth and invasion. The model uses conserved variables to represent known determinants of glioma behavior, e.g., cell density and oxygen concentration, as well as biological functional relationships and parameters linking phenomena at different scales whose specific forms and values are hypothesized and calculated based on in vitro and in vivo experiments and from histopathology of tissue specimens from human gliomas. This model enables correlation of glioma morphology to tumor growth by quantifying interdependence of tumor mass on the microenvironment (e.g., hypoxia, tissue disruption) and on the cellular phenotypes (e.g., mitosis and apoptosis rates, cell adhesion strength). Once functional relationships between variables and associated parameter values have been informed, e.g., from histopathology or intra-operative analysis, this model can be used for disease diagnosis/prognosis, hypothesis testing, and to guide surgery and therapy. In particular, this tool identifies and quantifies the effects of vascularization and other cell-scale glioma morphological characteristics as predictors of tumor-scale growth and invasion.

Screening of gap junction antagonists on dye coupling in the rabbit retina.

Many cell types in the retina are coupled via gap junctions and so there is a pressing need for a potent and reversible gap junction antagonist. We screened a series of potential gap junction antagonists by evaluating their effects on dye coupling in the network of A-type horizontal cells. We evaluated the following compounds: meclofenamic acid (MFA), mefloquine, 2-aminoethyldiphenyl borate (2-APB), 18-alpha-glycyrrhetinic acid, 18-beta-glycyrrhetinic acid (18-beta-GA), retinoic acid, flufenamic acid, niflumic acid, and carbenoxolone. The efficacy of each drug was determined by measuring the diffusion coefficient for Neurobiotin (Mills & Massey, 1998). MFA, 18-beta-GA, 2-APB and mefloquine were the most effective antagonists, completely eliminating A-type horizontal cell coupling at a concentration of 200 muM. Niflumic acid, flufenamic acid, and carbenoxolone were less potent. Additionally, carbenoxolone was difficult to wash out and also may be harmful, as the retina became opaque and swollen. MFA, 18-beta-GA, 2-APB and mefloquine also blocked coupling in B-type horizontal cells and AII amacrine cells. Because these cell types express different connexins, this suggests that the antagonists were relatively non-selective across several different types of gap junction. It should be emphasized that MFA was water-soluble and its effects on dye coupling were easily reversible. In contrast, the other gap junction antagonists, except carbenoxolone, required DMSO to make stock solutions and were difficult to wash out of the preparation at the doses required to block coupling in A-type HCs. The combination of potency, water solubility and reversibility suggest that MFA may be a useful compound to manipulate gap junction coupling.