From Physiology, Genomes, Systems, and Self-Organization to Systems Biology: The Historical Roots of a Twenty-First Century Approach to Complexity - Systems Biology of Metabolic and Signaling Networks - page 12

• The ability and the computational power to mathematically model very compli-
cated systems, and analyze their control and regulation, as well as predict
changes in qualitative behavior
• An arsenal of theoretical tools (each with its own plethora of methods)
• High throughput technologies that allow simultaneous monitoring of an enor-
mous number of variables
• Automation and accessibility of databases by newly developing methods of
bioinformatics
• Powerful imaging methods and online monitoring systems that provide the
means of studying living systems at high spatial and temporal resolution of
several variables simultaneously
• The possibility of employing detailed enough bottom-up mathematical models
that may help rationalize the use of key integrative variables, such as the
membrane potential of cardiomyocytes or neurons, in top-down conceptual
models with a few state variables.
A Complex Systems Approach integrating Systems Biology with nonlinear
dynamic systems analysis, using the concepts and analytical tools of chaos, fractals,
critical phenomena, and networks has been proposed (Aon and Cortassa 2009 ). This
approximation is needed because the focus of the integrative physiological
approach applied to biology and medicine is shifting toward studies of the
properties of complex networks of reactions and processes of different nature,
and how these control the behavior of cells and organisms in health and disease
(Cortassa et al. 2012 ; Lloyd and Rossi 2008 ; Saks et al. 2007 , 2012 ).
The mass-energy transformation networks, comprising metabolic and trans-
port processes (e.g., metabolic pathways, electrochemical gradients), give rise to
the metabolome and fluxome, which account for the whole set of metabolites and
fluxes, respectively, sustained by the cell. The information-carrying networks
include the genome, transcriptome, and proteome, which account for the whole
set of genes, transcripts, and proteins, respectively, possessed by the cell.
Signaling networks modulate (activating or repressing) the interactions between
information and mass-energy transducing networks, thus mediating between the
genome-transcriptome-proteome and metabolome-fluxome. As such, signaling
networks pervade the whole cellular network playing the crucial role of
influencing the unfolding of its function in space and time. The output of
signaling networks consists of concentration levels of intracellular metabolites
(e.g., second messengers such as cAMP, AMP, phosphoinositides, reactive
oxygen, or nitrogen species), ions, proteins or small peptides, growth factors,
and transcriptional factors.
The underlying difficulty of the question of how the mass-energy and informa-
tion networks of the cell interact with each other to produce a certain phenotype
arises from the dual role of, e.g., metabolites or transcriptional factors; they are at the
same time a result of the mass-energy or information networks while being active
components of the signaling networks that will activate or repress the networks that
produced them (see Chap. 2 ). The presence of these loops, in which the components