The dynamics of biochemical reaction systems can be modeled either 
deterministically or stochastically. Typically, the equations governing 
the dynamics of these models are quite complex. Further, there is 
oftentimes little knowledge about the exact values of the different
system 
parameters, and, worse still, these system parameter values may vary
from 
cell to cell. However, the network structure of a given system induces
the 
corresponding equations (up to parameter values) governing its dynamics.
I 
will show in this talk how this fact may be exploited to infer
qualitative 
properties of large classes of biochemical systems and, most
importantly, 
to learn which properties are independent of the details of the system 
parameters. I will give results for both stochastically and 
deterministically modeled systems. I will also discuss some recent work
on 
numerical methods for the simulation of sample paths for stochastically 
modeled systems. The use of such methods is quickly increasing
throughout 
the biology and biochemistry communities and therefore warrants more 
careful study.