model {
for (i in 1:2){
count[i,1:5] ~ dmulti(q[i,1:5], M[i])
for (r in 1:5) {
q[i,r] <- phi[i, r] / sum(phi[i, ])
log(phi[i, r]) <- a[r] + b.treat[r] * treat[i]
}
}
for (r in 2:5){
a[r] ~ dnorm(0, 0.00001)
}
a[1] <- 0
b.treat[1] <- 0
b.treat[2] ~ dnorm(0, 0.00001)
or.treat <- exp(b.treat[2])   
for (r in 3:5) {
b.treat[r] <- 0
}
treat[1] <- 0
treat[2] <- 1
}

Data:
list(count=structure(.Data=c(210,60,0,1,1,
                                       66,32,0,0,2),.Dim=c(2,5)),
   M=c(272, 100))

Inits:
list(a = c(NA, 0, 0, 0, 0), b.treat = c(NA, 0, NA, NA, NA))


   node   mean   sd   MC error   2.5%   median   97.5%   start   sample
   or.treat   1.718   0.4566   0.01769   0.9899   1.672   2.798   4001   6000
   q[1,1]   0.7684   0.02534   9.308E-4   0.7169   0.7687   0.8142   4001   6000
   q[1,2]   0.2206   0.02507   9.07E-4   0.1751   0.2204   0.2711   4001   6000
   q[1,3]   2.679E-6   7.726E-5   1.35E-6   0.0   0.0   1.16E-7   4001   6000
   q[1,4]   0.002745   0.002862   8.859E-5   7.079E-5   0.001846   0.01036   4001   6000
   q[1,5]   0.008323   0.004679   1.075E-4   0.001878   0.007449   0.01956   4001   6000
   q[2,1]   0.6702   0.04553   0.001336   0.5786   0.6709   0.7583   4001   6000
   q[2,2]   0.3202   0.04607   0.001356   0.2312   0.319   0.4126   4001   6000
   q[2,3]   2.18E-6   6.024E-5   1.051E-6   0.0   0.0   1.024E-7   4001   6000
   q[2,4]   0.002394   0.002505   7.774E-5   6.522E-5   0.001599   0.008891   4001   6000
   q[2,5]   0.00727   0.004142   9.662E-5   0.001613   0.006541   0.01733   4001   6000

Using the Poisson trick:...
model {
for(i in 1:2){
for (r in 1:5) {
count[i,r] ~ dpois(mu[i,r])
log(mu[i,r]) <- lambda[i] + a[r] + b.treat[r] * treat[i]
}
lambda[i] ~ dflat()
}
a[1] <- 0
for (r in 2:5){
a[r] ~ dnorm(0, 0.00001)
}
b.treat[1] <- 0
b.treat[2] ~ dnorm(0, 0.00001)
or.treat <- exp(b.treat[2])   
for (r in 3:5) {
b.treat[r] <- 0
}
treat[1] <- 0
treat[2] <- 1
}

Data:
list(count=structure(.Data=c(210,60,0,1,1,
66,32,0,0,2),.Dim=c(2,5)))

Inits:
list(a = c(NA, 0, 0, 0, 0), b.treat = c(NA, 0, NA, NA, NA), lambda=c(0,0))