Information Technology Reference
In-Depth Information
Chapter 2
The Structural Network Properties of Biological Systems
Matteo Brilli
1
and Pietro Lio
2
1
Laboratoire Biometrie et Biologie Evolutive UMR CNRS 5558, Universite Lyon
2
Computer Laboratory, University of Cambridge, Cambridge, UK
2.1. Introduction
Many signicant bio-medical innovations of the last years have resulted from a
more accurate understanding of the fundamental properties of complex systems
that reside inside the cell, such as the topological properties of biological networks,
that is crucial for exploiting an integrated knowledge of living beings.
The NCBI Genomes database records data for 49 archaeal, 940 bacterial and
162 eukaryotic genomes in September 2008. While giving an enormous amount of
information on the biological properties of many organisms, genome sequences can't
tell us directly which metabolites, molecular structures or organelles are present,
the genes that are expressed in a given conditions, the possible splicing variants and
the post-translational modications of proteins. Moreover, cell physiology depends
on thousands of genes and proteins that interact on several levels giving rise to a
plethora of networks. Genetic, biochemical and molecular biology techniques have
been used for decades to identify these interactions but only newly developed high-
throughput methodologies allow inferring genome-wide interaction maps. These,
paired with computational approaches can be used to infer networks of interactions
and causal relationships within the cell. The challenge of systems biology is to
integrate the dierent information to understand dynamical properties of cellular
systems [1] and be able to design semi-synthetic organisms or parts thereof.
Biological networks have evolved in billions of years under the pressure of nat-
ural selection which promoted the emergence of several important features. These
features are now inspiring the design of technological systems with high eciency
and robustness.
For sake of simplicity biological networks can be grouped in several categories
some of which will be discussed in more detail, by focusing on the experimental, the
computational and the integrated approach for their construction and subsequent
analysis.
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