Repeated vs Replicates vs multiple observations and nesting

[#1]

Hi JMP community,

I have been working on a biological data set where I need help in which model should I use to analyze my data. There are 2 different strains of mice (2 mice per strain) and 1 leg of each mouse has been given treatment 1, the other leg of the same mouse has been given treatment 2. I have taken 2-5 measurements during my experiment (chemical composition) per leg of each mouse. I am interested in using mixed model to compare means of chemical composition between each strain within a treatment and between treatment within a strain of mouse. Here is an example JMP table date set.

I don't particularly want to average the multiple observations, for the sake of making use of the in-sample variability in my stats. Thus, looking for a method where I can compare means, without losing the information that I get from making multiple observations, but also don't want to consider each observation as an independent observations or sample. 

I use this particular table, and go to fit model> mixed model> then have mineral amount as a y variable, fixed effect as strain, treatment condition, strain* treatment condition, random effect as mouse number. I don't do any nesting and repeated measures. Should I be considering any? 

Thank you for your help! 

[#2]

Whether you use the individual observations or the mean observation determines the meaning of the errors. This interpretation is important in any hypothesis tests that you use such as the F ratio for the whole model in the analysis of variance or the t ratio for a parameter estimate. The errors represent the random variation in the response that occur when you do not change conditions or when you return to the same condition. They provide a benchmark for assessing effects when conditions change. How much of the effect is due to the new condition and how much of it is just random change that would happen anyway?

Since you are investigating the effect of mouse strain and treatment, the error term should ideally represent the accumulation of all the perturbations (sum) associated with any run. That is a single observation. If you repeat the entire run, not just make another measurement, that run is a replicate. If you do not repeat the run but make another measurement, then that is not replication.

It seems like you made multiple measures within each run due to concern about measurement accuracy (bias + precision). That is to say, you did not sacrifice a mouse more than once! In that case, I would model the mean response.