Literature DB >> 23894104

BioFNet: biological functional network database for analysis and synthesis of biological systems.

Hiroyuki Kurata, Kazuhiro Maeda, Toshikazu Onaka, Takenori Takata.   

Abstract

In synthetic biology and systems biology, a bottom-up approach can be used to construct a complex, modular, hierarchical structure of biological networks. To analyze or design such networks, it is critical to understand the relationship between network structure and function, the mechanism through which biological parts or biomolecules are assembled into building blocks or functional networks. A functional network is defined as a subnetwork of biomolecules that performs a particular function. Understanding the mechanism of building functional networks would help develop a methodology for analyzing the structure of large-scale networks and design a robust biological circuit to perform a target function. We propose a biological functional network database, named BioFNet, which can cover the whole cell at the level of molecular interactions. The BioFNet takes an advantage in implementing the simulation program for the mathematical models of the functional networks, visualizing the simulated results. It presents a sound basis for rational design of biochemical networks and for understanding how functional networks are assembled to create complex high-level functions, which would reveal design principles underlying molecular architectures.
© The Author 2013. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Keywords:  biological database; functional network; network motif; rational design; simulator

Mesh:

Year:  2013        PMID: 23894104     DOI: 10.1093/bib/bbt048

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


  7 in total

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Journal:  J R Soc Interface       Date:  2016-07-20       Impact factor: 4.118

2.  Analytical study of robustness of a negative feedback oscillator by multiparameter sensitivity.

Authors:  Kazuhiro Maeda; Hiroyuki Kurata
Journal:  BMC Syst Biol       Date:  2014-12-12

3.  Self-replenishment cycles generate a threshold response.

Authors:  Hiroyuki Kurata
Journal:  Sci Rep       Date:  2019-11-20       Impact factor: 4.379

4.  Evolution and extinction can occur rapidly: a modeling approach.

Authors:  Vitaly A Likhoshvai; Tamara M Khlebodarova
Journal:  PeerJ       Date:  2021-04-13       Impact factor: 2.984

5.  Comprehensive network modeling from single cell RNA sequencing of human and mouse reveals well conserved transcription regulation of hematopoiesis.

Authors:  Shouguo Gao; Zhijie Wu; Xingmin Feng; Sachiko Kajigaya; Xujing Wang; Neal S Young
Journal:  BMC Genomics       Date:  2020-12-29       Impact factor: 3.969

6.  Modeling and simulation of the redox regulation of the metabolism in Escherichia coli at different oxygen concentrations.

Authors:  Yu Matsuoka; Hiroyuki Kurata
Journal:  Biotechnol Biofuels       Date:  2017-07-14       Impact factor: 6.040

7.  Improvement of the memory function of a mutual repression network in a stochastic environment by negative autoregulation.

Authors:  A B M Shamim Ul Hasan; Hiroyuki Kurata; Sebastian Pechmann
Journal:  BMC Bioinformatics       Date:  2019-12-27       Impact factor: 3.169

  7 in total

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