Individual or population fit in simbiology

Hello Support-Team,

thanks to your attention i am able to analyse my data set. Going more into detailed understanding of the posibilities working with simbiology some more questions are coming up. So, do you have any advise how i could get more information about what fitting method whether individual or population could be the best for my purpose? The data i got results from measuring an substrate decay and the datapoints show a linear developing for the observed time course. Now i like to fit kinetic parameters of the prebuild Ordered Bi-Bi- Kinetic Law in Simbiology (v = (Vmax*cA*cB)/(kiA*kB+cA*kB+cB*kA*cA*cB)) to my data.

Using population fit i will be able to get a lot more information about the fitting procedure and the goodness of fit. But in that method there are a lot of things to consider such as choosing the right error model or the right method for approximate the NLME Model Liklihood and optimization function. The only thing i varied is the initial concentration of certain substrate. What i like to know is, what is the best set of methods to accomplish the parameter estimation for KiA, KA, KB, Vmax in that particular case? Do you have any recomendation or a good hint for literature? It also could be very helpful if you could give me some advise where to look for the exact details of certain fitting methods and its configuration sets because i need to justify later why i exactly have chosen certain method and lets say error models and stuff. I already checkt the Matlab documentation but unfortunately it only describes the functions properties but not where to use certain methods and config-sets and where not to use.

That would be great!

Thanks a lot!

Answer 1

[Arthur Goldsipe]

Hi,

First off, I now see you posted a similar follow-up comment on a previous question. I don't get notified of such replies, and I didn't think to check the page again until today. Please feel free to contact me directly if you don't get a reply in a couple of days.

It sounds like you are trying to estimate one set of parameters that applies to all experiments. In this case, you are not really doing a population fit. You should use a population fit when you want to estimate parameters that are specific to sub-groups of your data. For example, in drug trials we measure data on many different people and try to estimate parameters specific to each individual person.

Although there are more configuration options and more results for population fits, that doesn't necessarily mean it's "better" for your problem. Mostly, this just indicates that population methods are more complex. But if you need specific information or goodness-of-fit measures that don't seem to be provided by individual fitting, please let us know.

If you want more details on the specifics of the algorithms and configuration choices, I recommend looking at the reference pages for the underlying MATLAB functions. You may also want to look at some of the papers and books cited in the References sections. Here are links to some of the relevant reference pages:

• Individual fits use the function NLINFIT: http://www.mathworks.com/help/stats/nlinfit.html
• Population fits use the functions NLMEFIT or NLMEFITSA: http://www.mathworks.com/help/stats/nlmefit.html, http://www.mathworks.com/help/stats/nlmefitsa.html

I'm guessing one of the cited books is the best place to learn about things like error models. But I can also briefly comment here on that particular option. You choose your error model based on your knowledge of the uncertainty of the experimental data. For example, let's assume you measure two concentrations to be 10 and 100 mg/L. If both are accurate to 1 mg/L, that's a constant error. If you both are accurate to 10%, that's a proportional error.

-Arthur