title: Multi -omics and metabolic modelling pipelines: challenges and tools for systems microbiology.
abstract: Integrated -omics approaches are quickly spreading across microbiology research labs, leading to (i) the possibility of detecting previously hidden features of microbial cells like multi-scale spatial organization and (ii) tracing molecular components across multiple cellular functional states. This promises to reduce the knowledge gap between genotype and phenotype and poses new challenges for computational microbiologists. We underline how the capability to unravel the complexity of microbial life will strongly depend on the integration of the huge and diverse amount of information that can be derived today from -omics experiments. In this work, we present opportunities and challenges of multi -omics data integration in current systems biology pipelines. We here discuss which layers of biological information are important for biotechnological and clinical purposes, with a special focus on bacterial metabolism and modelling procedures. A general review of the most recent computational tools for performing large-scale datasets integration is also presented, together with a possible framework to guide the design of systems biology experiments by microbiologists.
authors: Florence Computational Biology Group (ComBo), University of Florence, Via Madonna del Piano 6, Sesto Fiorentino, Florence 50019, Italy; Laboratory of Microbial and Molecular Evolution, Department of Biology, University of Florence, Via Madonna del Piano 6, Sesto Fiorentino, Florence 50019, Italy. Electronic address: marco.fondi@unifi.it.
authors: Marco
authors: M
authors: Fondi
authors: University of Cambridge, Computer Laboratory, 15 JJ Thomson Avenue, CB3 0FD Cambridge, UK.
authors: Pietro
authors: P
authors: Liò
mesh_terms: D019295:Computational Biology / Q000379:methods; D023281:Genomics / Q000379:methods; D006801:Humans; D055432:Metabolomics / Q000379:methods; D008829:Microbiology; D008954:Models, Biological; D049490:Systems Biology / Q000379:methods
keywords: -omics integration; Computational biology; Metabolic modelling; Multi -omics; Systems microbiology