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.