My thoughts and comments are in between the points, labeled A-D, that Szallasi discusses in chapter 10 of "System Modeling in Cellular Biology":
A) "The overall size and complexity of intracellular networks"
From my experience working with social networks, I easily accept this to be a challenge. The complexity of a network grows quickly as the number of possible interactions grows
quadratically as each node is added.
In biological systems the complexity abounds as scientists have created models for everything form human social behavior on down to the an atom's behavior. Szallasi discusses the modular approach to Systems Biology and that can alleviate some of this complexity. He mentions that estimating the size of intracellular networks is the number of active genes in a given cell. (Of course, the number of genes is slightly in flux as new research continues to be done.)
B) "The general principles of biological measurements --- their technical and conceptual limitations."
When working with data there are always limitations based on your measurements. In some cases we often are not even measuring the right thing. Other times we are limited by the precision of our measurement devices.
I find it quite interesting that measurement devices often do not (and sometimes cannot) account for various environmental changes, which cause irregularities in the collected data.
C) "Concentration measurement versus kinetic parameter measurements"
Parameter estimation is important to get right, yet it is extremely challenging. In fact, both measurement techniques build upon the uncertainty acquired through data measurement.
D) "The actual target of the measurements"
Biological data can analyzed and measured using individual-centric or community-centric approaches, each of which are useful for in their own right. In each view, different problems exist and different interactions occur. Researchers should take great care to justify which measurements is being used to be how they are being viewed.
In conclusion, researchers must understand and accept the many limitations of biological data. As with any research, assumptions must be made and justified.