Okey, I have in this case 3 conditions, control (no surgery), sham (surgery at 2 times, 45 min and 24 hrs) and non ischemic samples (mice with surgery and Ischemia reperfusion). I want to find out the genes varying by condition over time. 

I have already done LRT test on each condition alone on the 2 time points, and I checked for the co-expressed genes between sham and non-ischemic samples. But I thought that it can be easier to achieve so by directly checking genes varying in each condition over time by interaction of condition and time.

Here I just have two conditions but later I have ischemic samples also, so when I did LRT on each condition alone, it was complicated to separate genes that are coexpressed.

Starting a new comment thread for visual simplicity...

You can use a design ~time + condition:time, but you need to recode "control" as "sham". Then there is an issue with using the design as formula (so when you specify the design just use ~time or anything here). You have to remove a column from the model matrix like so:

full <- model.matrix(~time + condition:time, colData(dds))

You will then need to remove the column with all 0's.

full <- full[ , -x]

...where 'x' is the number of the column with all 0's.

For testing whether there is a difference between sham and non ischemic, you can make a matrix 'reduced' where you also remove from 'full' the last two columns that are associated with time 1 and 2 and with non ischemic samples.

Then use:

dds <- DESeq(dds, test="LRT", full=full, reduced=reduced)