Biologist for decades have adopted a reductionist approach to investigate the secret of life. In recent years however, thanks to the genome sequencing efforts, new/old disciplines gained prominence with the specific aim of studying at a global scale biological processes in terms of their molecular constituents and their functional interactions. This is the case of Systems Biology, not new in terms of name and definition that presently takes advantage of new technological developments to investigate biological processes at cell, organism or communicative level. Based on high-throughput quantitative measurements, Systems Biology adopts computational modelling, mathematical, control and optimization theories with the purpose of reconstructing cellular systems and their functioning. Even more recently, Synthetic Biology seeks to engineer complex artificial biological systems starting from molecular components to investigate natural biological phenomena for a variety of applications to come. Can Bioinformatics gap the bridge from molecular to multi-level and integrated modelling and be the link among Systems and Synthetic Biology? One major problem in large-sequence projects is the annotation of those genes which have no counterpart in the database of presently known sequences with a given function. And then we may ask: can we contribute to the annotation process with predictive methods? Our group has implemented machine learning-based predictors capable of performing with some success in different tasks, including the topology of membrane proteins and porins and the presence of signal peptides. All our predictors take advantage of evolution information derived from the structural alignments of homologous proteins and derived from the sequence and structure databases. We integrated our tools in suits of programs (HUNTER, MANHUNTER), which take as input the proteomes both of prokaryotes and eukaryotes, predict subcellular localisation with BaCello, a new developed predictor, and then based upon predictive scores, cluster globular, inner and outer membrane proteins.
Bioinformatics for Systems and Synthetic Biology / Casadio R.. - STAMPA. - (2006), pp. 41-41. (Intervento presentato al convegno VII Convegno Nazionale su Scienze della vita, Consorzio Interuniversitario 'Istituto Nazionale di Biostrutture e Biosistemi' (INBB) Sessione "Nanobiotecnologie e Bioinformatica" tenutosi a Aula Marconi, CNR, Roma nel 19-20/10/2006).
Bioinformatics for Systems and Synthetic Biology
CASADIO, RITA
2006
Abstract
Biologist for decades have adopted a reductionist approach to investigate the secret of life. In recent years however, thanks to the genome sequencing efforts, new/old disciplines gained prominence with the specific aim of studying at a global scale biological processes in terms of their molecular constituents and their functional interactions. This is the case of Systems Biology, not new in terms of name and definition that presently takes advantage of new technological developments to investigate biological processes at cell, organism or communicative level. Based on high-throughput quantitative measurements, Systems Biology adopts computational modelling, mathematical, control and optimization theories with the purpose of reconstructing cellular systems and their functioning. Even more recently, Synthetic Biology seeks to engineer complex artificial biological systems starting from molecular components to investigate natural biological phenomena for a variety of applications to come. Can Bioinformatics gap the bridge from molecular to multi-level and integrated modelling and be the link among Systems and Synthetic Biology? One major problem in large-sequence projects is the annotation of those genes which have no counterpart in the database of presently known sequences with a given function. And then we may ask: can we contribute to the annotation process with predictive methods? Our group has implemented machine learning-based predictors capable of performing with some success in different tasks, including the topology of membrane proteins and porins and the presence of signal peptides. All our predictors take advantage of evolution information derived from the structural alignments of homologous proteins and derived from the sequence and structure databases. We integrated our tools in suits of programs (HUNTER, MANHUNTER), which take as input the proteomes both of prokaryotes and eukaryotes, predict subcellular localisation with BaCello, a new developed predictor, and then based upon predictive scores, cluster globular, inner and outer membrane proteins.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
