In the last few decades, advances in molecular biology and in the
research - frastructure in this ?eld has given rise to the "omics"
revolution in molecular biology, alongwiththeexplosionofdatabases:
fromgenomicstotranscriptomics, proteomics, interactomics, and
metabolomics. However, the huge amount of b-
logicalinformationavailablehasleftabottleneckindataprocessing:
information over?ow has called for innovative techniques for their
visualization, modelling, interpretationandanalysis.The
manyresultsfromthe ?eldsofcomputerscience
andengineeringhavethenmetwithbiology, leadingto new, emergingdisciplines
such as bioinformatics and systems biology. So, for instance, as the
result of - plicationoftechniquessuchasmachinelearning,
self-organizingmaps, statistical algorithms,
clusteringalgorithmsandmulti-agentsystemstomodernbiology, we can
actually model and simulate some functions of the cell (e.g., protein
inter- tion, gene expression and gene regulation), make inferences from
the molecular biology database, make connections among biological data,
and derive useful predictions. Today, and more generally, two di?erent
scenarios characterize the po- genomic era. On the one hand, the huge
amount of datasets made available by biological research all over the
world mandates for suitable techniques, tools and methods meant at
modelling biological processes and analyzing biological sequences. On
the other hand, biological systems work as the sources of a wide range
of new computational models and paradigms, which are now ready to be
applied in the context of computer-based systems.