  It is becoming increasingly appreciated that the signal transduction systems
used by eukaryotic cells to achieve a variety of essential responses represent
highly complex networks rather than simple linear pathways. While significant
effort is being made to experimentally measure the rate constants for
individual steps in these signaling networks, many of the parameters required
to describe the behavior of these systems remain unknown, or at best,
estimates. With these goals and caveats in mind, we use methods of statistical
mechanics to extract useful predictions for complex cellular signaling
networks. To establish the usefulness of our approach, we have applied our
methods towards modeling the nerve growth factor (NGF)-induced differentiation
of neuronal cells. Using our approach, we are able to extract predictions that
are highly specific and accurate, thereby enabling us to predict the influence
of specific signaling modules in determining the integrated cellular response
to the two growth factors. We show that extracting biologically relevant
predictions from complex signaling models appears to be possible even in the
absence of measurements of all the individual rate constants. Our methods also
raise some interesting insights into the design and possible evolution of
cellular systems, highlighting an inherent property of these systems wherein
particular ''soft'' combinations of parameters can be varied over wide ranges
without impacting the final output and demonstrating that a few ''stiff''
parameter combinations center around the paramount regulatory steps of the
network. We refer to this property -- which is distinct from robustness -- as
''sloppiness.''
