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.
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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.
The LNCS journal Transactions on Computational Systems Biology is devoted to inter- and multidisciplinary research in the fields of computer science and life sciences and supports a paradigmatic shift in the techniques from computer and information science to cope with the new challenges arising from the systems-oriented point of view of biological phenomena. This, the third Transactions on Computational Systems Biology volume, edited by Emanuela Merelli, Pedro Pablo Gonzalez and Andrea Omicini, is devoted to considerably extended versions of selected papers presented at the International Workshop on Network Tools and Applications in Biology (NETTAB 2004), held at the University of Camerino, in Camerino, Italy, in September 2004. Dedicated especially to models and metaphors from biology to bioinformatics tools, the 10 papers selected for the special issue cover a wide range of bioinformatics research such as data visualisation, protein/RNA structure prediction, motif finding, modelling and simulation of protein interaction, genetic linkage analysis, and notations and models for systems biology.
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