are these types of ODE models that are built to explain experimental data.

These are called identifiable models.

These are system-specific, and the models are directly fitted to experimental datas.

These are most commonly used in drug action, and so while the model

parameters are fitted to experimental data and the fitting is quite

it has a level of accuracy that can be statistically characterized.

The model parameters may often not be connected

to molecular details, molecular details so one might not

get a mechanistic understanding of behavior, but one gets

a precise quantitative description of behavior in identifiable models.

So when one goes from mathematical representations to numerical

simulations, what one needs to do is to get the reactions parameterized.

So the initial concentrations and the reaction

rates have to be reaction rates are

unneeded and these are not always easy

to obtained as biochemical and cell biological

experiments a vast majority of these which were done for the last 50 or so years,

had not been really geared towards getting rate measurements or absolute values of

components within cells.

As we get more and more into these kinds

of modeling systems, one, people are starting to collect

these type of data, but still there is very

little of this data that is really available very easily.

They often need, one needs to read experimental papers

and make some assumptions to extract these kind of data.

Sometimes these parameters need to be guesstimated

based on known value for similar parameters, in

one where you can think of there's a

little bit like homology modeling of structures, where

if you know, one protein structure then a protein, an ISO forming a, a have

similar structures sort of based, and you think

sort of calculate the structure based on computation

similarly if one has values for say, GQ, one could, and these kinetic

parameters may be applicable to the other G protein in this category such as GS.

And this is what, this is what one means by guesstimated parameters.

And sometimes parameters need to be estimated

from indirect measurements such as time course.

And these can be quite accurate, although you might not get

a parameter that is directly associated with one component or another.

For instance, if there is a time course of ras activation

one can accurately estimate the relative activities of the get and

the gap, but one may not be able to precisely mea, estimate

the kinetic parameters associated with that get or a gap alone.

So there are curve fitting programs that's COPASI allows us one to estimate COPASI

that allows one to estimate these types of programs, these types of parameters.

So your models are only as real as your kinetic parameters, and this is sort of a

very, very fundamental sort of concept that one needs to keep in place.

So, there are some sort of rules we need to follow when building models.

These are rules that we follow in my own lab all the

time, and I would encourage other people who build these models to

follow these rules so that your models are likely to be realistic

or reasonably realistic representation of the systems that you wish to study.

So the first rule is do not oversimplify the model.

And as I told you previously, when

you require different isoforms of the receptors.

Identify the isoforms and use them as di-, distinct entities, and compute your model.

You, incorporating these levels of details.