loops, and simultaneously inducing liver-specific positive feedback loops, liver cancer can be
successfully antagonized, and even “reverted.”
The complexity of these “loops” is extensively investigated by O. Wolkenhauer and his
team, by proposing an integrative workflow to study large-scale biochemical disease net-
works by combining techniques from bioinformatics and systems biology. Integrating
experimental and clinical data with the workflow allows vindicating specific hypotheses,
namely by aiming at identifying smaller modules/molecular signatures for tumor-specific
disease phenotypes. The workflow discussed herein can be applied to any large-scale bio-
chemical network to unravel the mechanisms underlying complex biological traits and
diseases.
The appraisal of specific biological processes through a Systems Biology approach needs,
therefore, to capture their dynamics by considering a number of kinetic and thermodynamic
parameters over wide spatial and temporal scales in order to integrate in the model the
influence of nonlocal factors belonging to higher level of organization. This issue is specifi-
cally addressed by the contribution from F. Cardarelli, in which a method to probe the
“diffusion law” of molecules directly from imaging is described. The method principally
refers to a fluorescence fluctuation-based approach. Of note, the presented approach does
not require extraction of the molecular trajectories nor the use of bright fluorophores.
S.A. Ramsay, A. Colosimo, and L. Casadei also discuss specific methodological issues. In
his contribution, S.A. Ramsay describes a computational workflow for cross-species visuali-
zation and comparison of mRNA-sequence transcriptome profiling data. The workflow is
based on gene set variation analysis (GSVA) and is illustrated using commands in the R
programming language. In addition, a complete step-by-step procedure for the workflow
using mRNA-sequence data sets is provided.
The contribution from A. Colosimo addresses a very intriguing aspect, i.e., the modu-
lation of the collective behavior—with the emergence and spreading of synchronous activ-
ities—exerted by environmental force fields. This is a wide diffused phenomenon, even if
rarely investigated. The chapter discusses the collective behavior of different kinds of
populations, ranging from shape-changing cells in aPetri dishto functionally correlated
brain areas in vivo, by means of a fruitful, unifying methodological approach, based upon a
Multi-Agent Simulation (MAS) paradigm as incorporated in the NETLOGO™interpreter.
L. Casadei et al. furnish a compelling example of the application of Systems Biology
principles in planning the study of the metabolic, dynamic profile (the so-called metabolo-
mic fingerprint) of both cell cultures and individuals. This approach is of particular interest
in evidencing how a disease may influence the overall metabolic response of patients, but
also can modulate key factors/pathway that could be exploited by specific treatments.
Finally, D’Avenio et al. analyze how a Systems Biology approach can benefit from
quantitative morphological studies in which several shape parameters (namely the Fractal
Dimension) can provide useful information about a system’s behavior.
Systems Biology taught us that physiological and pathological processes are complex
context-dependent entities to which our genes make a necessary but only partial contribu-
tion [25]. Yet, Systems Biology is not a simple collection of “new theoretical” principle.
Rather, the conceptual premises imply a profound reconsideration of the methodological
framework. We have to rethink how an experiment is planned, what kind of parameters are
worthy of investigation, and how their mutual relationship should be described by means of
a different mathematical modeling spanning through different space and temporal scales.
That task requires an open-minded attitude and a true multidisciplinary approach. I mean
x Preface