Literature DB >> 26515531

Computational resources and strategies to construct single-molecule metabolic models of microbial cells.

Denise Gameiro, Martín Pérez-Pérez, Gael Pérez-Rodríguez, Gonçalo Monteiro, Nuno F Azevedo, Anália Lourenço.   

Abstract

Recent computational methodologies, such as individual-based modelling, pave the way to the search for explanatory insight into the collective behaviour of molecules. Many reviews offer an up-to-date perspective about such methodologies, but little is discussed about the practical information requirements involved. The biological information used as input should be easily and routinely determined in the laboratory, publicly available and, preferably, organized in programmatically accessible databases. This review is the first to provide a systematic and comprehensive overview of available resources for the modelling of metabolic events at the molecular scale. The glycolysis pathway of Escherichia coli, which is one of the most studied pathways in Microbiology, serves as case study. This curation addressed structural information about E. coli (i.e. defining the simulation environment), the reactions forming the glycolysis pathway including the enzymes and the metabolites (i.e. the molecules to be represented), the kinetics of each reaction (i.e. behavioural logic of the molecules) and diffusion parameters for all enzymes and metabolites (i.e. molecule movement in the environment). Furthermore, the interpretation of relevant biological features, such as molecular diffusion and enzyme kinetics, and the connection of experimental determination and simulation validation are detailed. Notably, the information from classical theories, such as enzymatic rates and diffusion coefficients, is translated to simulation parameters, such as collision efficiency and particle velocity.
© The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  biochemical systems; in silico cell simulation; molecular diffusion; single-molecule precision; spatial location

Mesh:

Year:  2015        PMID: 26515531     DOI: 10.1093/bib/bbv096

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  2 in total

1.  Particle-Based Simulation Reveals Macromolecular Crowding Effects on the Michaelis-Menten Mechanism.

Authors:  Daniel R Weilandt; Vassily Hatzimanikatis
Journal:  Biophys J       Date:  2019-06-25       Impact factor: 4.033

Review 2.  Heterogeneity in Pure Microbial Systems: Experimental Measurements and Modeling.

Authors:  Rebeca González-Cabaleiro; Anca M Mitchell; Wendy Smith; Anil Wipat; Irina D Ofiţeru
Journal:  Front Microbiol       Date:  2017-09-20       Impact factor: 5.640

  2 in total

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