Cancer cell growth simulation

An article in Cell reports a new simulation of the way cancer cells develop; MaxHealth website says the research: “represents a “sea change” in how biology is done.” It looks at the impact of the micro-environment on cell growth.

Sea change or not, the paper (“Tumor Morphology and Phenotypic Evolution Driven by Selective Pressure from the Microenvironment” by Alexander R.A. Anderson, Alissa M. Weaver, Peter T. Cummings, and Vito Quaranta”) suggest that their model “.. considers cellular and microenvironmental factors simultaneously and interactively” and adds:

“Unexpectedly, the model simulations predict that harsh tumor microenvironment conditions (e.g., hypoxia, heterogenous extracellular matrix) exert a dramatic selective force on the tumor, which grows as an invasive mass with fingering margins, dominated by a few clones with aggressive traits. In contrast, mild microenvironment conditions (e.g., normoxia, homogeneous matrix) allow clones with similar aggressive traits to coexist with less aggressive phenotypes in a heterogeneous tumor mass with smooth, noninvasive margins. ”

It concludes
“…. Eventually, mathematical representation of molecular networks will be introduced. At the moment, this is impractical for at least three reasons:
(1) There is no good agreement as to how even the best known molecular circuitry (e.g., cell cycle) should be represented;
(2) Computational power is still limiting to run these sorts of models feasibly in reasonable time; and
(3) The effect of a single molecular change on any or all of the model variables is difficult to predict and needs to be defined experimentally …
A long-term goal of the modeling effort is to be able to predict invasive behavior of individual tumors based on actual measurements of cellular and microenvironmental parameters. If successful, such a predictive tool could be useful in drug discovery, or in the management of cancer patients. ”

I cant see problem 2 lasting much longer. As for problem 1, simulation theorists might argue that you dont necessarily need to understand the process; the important point is to produce simulation results and test those against real life. The simulation then becomes a way of improving your understanding of the process, rather than the understanding being a pre-condition of the simulation..

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