Literature DB >> 15380661

Toward large-scale modeling of the microbial cell for computer simulation.

Nobuyoshi Ishii1, Martin Robert, Yoichi Nakayama, Akio Kanai, Masaru Tomita.   

Abstract

In the post-genomic era, the large-scale, systematic, and functional analysis of all cellular components using transcriptomics, proteomics, and metabolomics, together with bioinformatics for the analysis of the massive amount of data generated by these "omics" methods are the focus of intensive research activities. As a consequence of these developments, systems biology, whose goal is to comprehend the organism as a complex system arising from interactions between its multiple elements, becomes a more tangible objective. Mathematical modeling of microorganisms and subsequent computer simulations are effective tools for systems biology, which will lead to a better understanding of the microbial cell and will have immense ramifications for biological, medical, environmental sciences, and the pharmaceutical industry. In this review, we describe various types of mathematical models (structured, unstructured, static, dynamic, etc.), of microorganisms that have been in use for a while, and others that are emerging. Several biochemical/cellular simulation platforms to manipulate such models are summarized and the E-Cell system developed in our laboratory is introduced. Finally, our strategy for building a "whole cell metabolism model", including the experimental approach, is presented.

Mesh:

Year:  2004        PMID: 15380661     DOI: 10.1016/j.jbiotec.2004.04.038

Source DB:  PubMed          Journal:  J Biotechnol        ISSN: 0168-1656            Impact factor:   3.307


  13 in total

Review 1.  Manipulating corynebacteria, from individual genes to chromosomes.

Authors:  Alain A Vertès; Masayuki Inui; Hideaki Yukawa
Journal:  Appl Environ Microbiol       Date:  2005-12       Impact factor: 4.792

2.  Reconstruction of a kinetic model of the chromatophore vesicles from Rhodobacter sphaeroides.

Authors:  Tihamér Geyer; Volkhard Helms
Journal:  Biophys J       Date:  2006-05-19       Impact factor: 4.033

Review 3.  Protein abundance ratios for global studies of prokaryotes.

Authors:  Qiangwei Xia; Erik L Hendrickson; Tiansong Wang; Richard J Lamont; John A Leigh; Murray Hackett
Journal:  Proteomics       Date:  2007-08       Impact factor: 3.984

4.  Individual-based modelling: an essential tool for microbiology.

Authors:  Jordi Ferrer; Clara Prats; Daniel López
Journal:  J Biol Phys       Date:  2008-07-19       Impact factor: 1.365

5.  Silicon dreams of cells into symbols.

Authors:  Jeremy Gunawardena
Journal:  Nat Biotechnol       Date:  2012-09       Impact factor: 54.908

Review 6.  CANDO and the infinite drug discovery frontier.

Authors:  Mark Minie; Gaurav Chopra; Geetika Sethi; Jeremy Horst; George White; Ambrish Roy; Kaushik Hatti; Ram Samudrala
Journal:  Drug Discov Today       Date:  2014-06-26       Impact factor: 7.851

Review 7.  Insights into the biology of Escherichia coli through structural proteomics.

Authors:  Allan Matte; Zongchao Jia; S Sunita; J Sivaraman; Miroslaw Cygler
Journal:  J Struct Funct Genomics       Date:  2007-08-01

8.  Hybrid dynamic/static method for large-scale simulation of metabolism.

Authors:  Katsuyuki Yugi; Yoichi Nakayama; Ayako Kinoshita; Masaru Tomita
Journal:  Theor Biol Med Model       Date:  2005-10-04       Impact factor: 2.432

Review 9.  Metabolic Network Modeling of Microbial Interactions in Natural and Engineered Environmental Systems.

Authors:  Octavio Perez-Garcia; Gavin Lear; Naresh Singhal
Journal:  Front Microbiol       Date:  2016-05-18       Impact factor: 5.640

10.  Proteomic and network analysis characterize stage-specific metabolism in Trypanosoma cruzi.

Authors:  Seth B Roberts; Jennifer L Robichaux; Arvind K Chavali; Patricio A Manque; Vladimir Lee; Ana M Lara; Jason A Papin; Gregory A Buck
Journal:  BMC Syst Biol       Date:  2009-05-16
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