Information Technology Reference
In-Depth Information
8.1. Introduction
The network paradigm is the prevailing metaphor in nowadays biology. We can
read about gene networks, protein networks, metabolic networks, ecological
networks, protein folding networks, as well as signaling networks. The network
paradigm is an horizontal construct, basically different from the classical top-
down molecular biology view, dominant until not so many years ago, in which
there was a privileged flux of information from DNA down to RNA and proteins.
The shift from a top-down approach to the network paradigm stems from both
the dramatic failure in terms of biotechnological applications and the decline of
the molecular biology central dogma after the discovery of alternative and in
many cases much more efficient fluxes of information with respect to the
classical DNA-RNA-protein cascade, RNA editing, epigenetics heritage, post-
translational modification.
This sudden crisis of molecular biology fundamentals just after the
completion of the genome sequencing enterprise stimulated a resurgence of
interest of a system-based biology in a style very similar to the cybernetic wave
of the fifties with two basic differences with respect to Norbert Wiener and von
Bertalanffy Post II World War speculations on general systems theory: the
massive use of computers and the emphasis on molecular data with respect to
physiological signals.
The general concept of a network as a collection of elements (nodes) and the
relationships among those (arcs), cannot be separated by the definition of a
"system" in dynamical systems theory, where the basic elements (nodes) are time
varying functions and relationships are differential or difference equations. In
this respect, purely topological approaches in which all the nodes and edges are
considered as equivalent, and dynamical approaches in which the relations take
the form of differential equations are two sides of the same coin, from a purely
graphical point of view, being the separation of the purely topological and
dynamical approaches based only on practical circumstances coming from the
difficulty to get reliable kinetic data from biological experimentation.
Thus when we talk about topological properties of biological networks and
discuss how different kinds of wiring architectures like random or small-world
network, essentially we are asking ourselves the basic question 'how far we can
go knowing only the wiring diagram of a biological system?', one hundreds year
ago in life sciences the same question should be formulated as 'what we could
infer from the pure anatomical perspective without any information about
physiology?'.
