Dear gabe:
Folding
I guess I know this as parameterization. Almost all models incorporate data to adjust some parameters (constants). It is generally thought that the fewer the number of adjustable parameters, the better the model. With biological systems, the number of needed parameters generally is several.
It is often said that, if you have enough adjustable parameters, anything can be suitably fitted to a model. In some models, the parameters have definite, assignable values. These parameters could be:
surface tension, temperature, viscosity, initial nucleus radius, micronuclei size-number distribution, modulus of tissue elasticity, solubility of gas in water, solubility of gas lipid, diffusivity [in several possible places], blood flow rate, surfactant concentration, etc.
There are methods to match, or fit, multiple constants in an equation to data sets. Without a computer to do the calculations, anything but the simplest is not really possible. However, if the computer is large enough and/or you are willing to have the machine invest the time, you can adjust the model parameters (constants) to give the best fit. If the constants/parameters are realistic, then you know that the model might be correct to a major degree. If the model fits are not really what you expected, then probably the computer is forcing the constants to fit the data. You will get numbers that are spooky. For example, you might get a surface tension that is 15 dynes/cm when you expect it to be somewhere are 60 dynes/cm. Or you might get a diffusion constant that is 10 [exp] 10 rather than what might be expected, viz, 10 [exp] 5.
The agreement of a model to reality is not a definite proof of its veracity. The Haldane model worked quite well within a certain range, however, the idea of critical supersaturation limits was incorrect.
Dr Deco :doctor:
