usable by personnel who is not necessarily trained in, e.g., informa-
tion engineering.
The traditional biology laboratory of the past was mostly
concerned with the application of experimental techniques in dif-
ferent areas: e.g., microbiology, biochemistry, cell and developmen-
tal biology. The data generated by the experiments were easily
managed with a laboratory notebook, possibly together with a
collection of images from, e.g., microscopy or electrophoresis.
Then, in the past data management was not systematically aimed
at sharing of knowledge between researches, leaving this aspect to
the cooperation between the individual researchers, given the task
assignments by the lab’s head.
The organization of the traditional biology lab reflected the
view of biological research at the time, in which particular mechan-
isms were singled out and studied accurately by just a few research-
ers. In this framework, the originality of ideas could work very well
even in the absence of a tight organizational structure.
Nowadays, after the advent of high-throughput techniques, in
particular those related to molecular biology, the view of scientific
discovery in biology is much less linked to the single researcher’s
ingenuity and initiative, and is regarded as a complex process,
involving the analysis of massive amounts of data (possibly from
diverse investigational standpoints), to be interpreted in a system-
level perspective. As underlined by Kitano, “System-level under-
standing requires a shift in our notion of “what to look for” in
biology. While an understanding of genes and proteins continues to
be important, the focus is on understanding a system’s structure
and dynamics. Because a system is not just an assembly of genes and
proteins, its properties cannot be fully understood merely by draw-
ing diagrams of their interconnections” [13].
With such considerations, it is evident that advanced biological
research is only possible today with remarkable resources of data
management and analysis. In the next section, we will illustrate the
experience of our laboratory on the transition to such a new para-
digm for biological research.2.1 Quantitative Data
in Biology: An
Infrastructure for Data
Analysis and Exchange
In the everyday activity of the up-to-date experimental biology
laboratory, different techniques and methods are used, relative to
molecular sciences, statistical techniques, and System biology con-
straints. Experimental biology consists of the integration of typical
laboratory activities with scientific theories, drawn from several
domains: chemistry, physics, information engineering, and of
course biology, just to name a few.
It is widely recognized that available laboratory data (especially
from optical microscopy) are not fully exploited. Even though the
biologist’s insight is always extremely useful in assessing the rele-
vance of cell samples in different experimental conditions, never-
theless the need for more objective and repeatable assessments isImaging and Systems Biology 341