I am working with large panel of human tissue cells that are collected from different centres. Its a paired design i.e same sample has treated and untreated rna-seq data.

Here is how my colData looks like

> colData
                purity subjects treat  Center   Year  BMI  Gender  Age
sample1_treat      95    1      treat    Miami  2011  <NA>   M   <NA>
sample1_untreat    95    1      untreat  Miami  2011  <NA>   M   <NA>
sample2_treat      90    2      treat    Milano 2011  <NA>   F   <NA>
sample2_untreat    90    2      untreat  Milano 2011  <NA>   F   <NA>
sample3_treat      75    3      treat    Geneva 2011   20    F    50
sample3_untreat    75    3      untreat  Geneva 2011   20    F    50
. . .
. . .

As I have been believing that the paired design takes any batch effects in to account, I used the following design for DE analysis to look for differential genes between treated vs untreated :

design(dds) <- formula(~ subjects + treat)

I am also intended to do exploratory data analysis, for that I am using a matrix of log Fold change values generated by:

norm <- assay(normTransform(dds))
i <- seq.int(1L,72,by = 2L) # 72 is the total number of samples.
norm.fc <- norm[,i]-norm[,i+1]​
write.table(norm.fc, file="normalised_FCMatrix.txt", sep="\t")

And I am using the norm.fc for doing clustering and PCA analysis. 

I would like to know if my design for DE analysis is correct. Do I need to incorporate any other information to account for tissue collection / sequencing centre etc.

Your analysis takes any batch effects into effect, contingent upon those batch effects being consistent, where by 'consistent' I mean 'on average the same within batch'. This is likely to be true of things like tissue collection centers, where the average gene expression value may be different between centers, but is expected to be relatively consistent within each center.

The pairing won't account for any interaction term for things like sex or age. In other words, there may be a whole set of genes that are affected differently by treatment in males as compared to females. Or the treatment may have a different effect on cells from older people as compared to younger people. So while pairing would be expected to account for any batch effects associated with center, you may still need to account for sex or age. Or not, depending on what the treatment is.