nonlinear dynamics, network modeling, influence of biophysical
constraints, operating on different scales, rather than solely focus-
ing on building numerical mathematical or computer models [10].
Cancer is systemic by nature and reductionist approaches have
failed to improve treatment and understanding substantially.
Despite the variability in the nature of diseases related to cancer, it
is expected that systems biology can make essential contributions to
[11]:
l The identification of early biomarkers for a noninvasive progno-
sis of tumor development.
l Personalized medicine by building computer models predicting
different stages of the disease.
l Improving treatment of later stages by comparing biochemical
networks and gene expression levels in primary tumors and
metastases [11].Computational and mathematical approaches used in systems
biology are highly versatile; a few categories of general methodol-
ogies have emerged for specific purposes in cancer research. One
class is integrative statistical analysis of large-scale cancer multi-
omics and clinical data. These unbiased data-driven analyses have
identified key biological processes underlying cancer pathogenesis,
prognostic biomarkers, and predictive signatures for drug
response [4].
Another class is mathematical modeling of interaction networks
such as intracellular signaling pathways or extracellular crosstalk’s
between tumor and the microenvironment. These models have
proved useful at unraveling mechanisms of drug resistance and in
optimizing combinatorial targeted therapy. Furthermore, evolu-
tionary models that simulate tumor growth and progression have
provided important insights into the evolution dynamics of tumor
and have led to the discoveries of more effective dosing schedules.
Overall, the application of systems biology approaches has led to
substantial improvements in our understanding of cancer initiation
and progression and to the discovery and implementation of more
effective anticancer therapeutic strategies [4].
The cancer complexity has been probed at the genomic, pro-
tein, post-translational, and tissue levels [4]. The architecture of
signal transduction pathways is not where the complexity of cancer
ends. Being parts of the cell, signaling networks are affected by
additional levels of organization, for instance, as many proteins are
not uniformly distributed over the cell. Areas with high protein
concentrations might lead to macromolecular crowding and cause
steep spatial gradients of activated signaling proteins. Numerous
interactions at the supra-cellular level make the cancer system even
more complex [8].Parameters Estimation in Phase-Space Landscape Reconstruction of Cell Fate... 129