Literature DB >> 26476430

An overview of bioinformatics methods for modeling biological pathways in yeast.

Jie Hou, Lipi Acharya, Dongxiao Zhu, Jianlin Cheng.   

Abstract

The advent of high-throughput genomics techniques, along with the completion of genome sequencing projects, identification of protein-protein interactions and reconstruction of genome-scale pathways, has accelerated the development of systems biology research in the yeast organism Saccharomyces cerevisiae In particular, discovery of biological pathways in yeast has become an important forefront in systems biology, which aims to understand the interactions among molecules within a cell leading to certain cellular processes in response to a specific environment. While the existing theoretical and experimental approaches enable the investigation of well-known pathways involved in metabolism, gene regulation and signal transduction, bioinformatics methods offer new insights into computational modeling of biological pathways. A wide range of computational approaches has been proposed in the past for reconstructing biological pathways from high-throughput datasets. Here we review selected bioinformatics approaches for modeling biological pathways inS. cerevisiae, including metabolic pathways, gene-regulatory pathways and signaling pathways. We start with reviewing the research on biological pathways followed by discussing key biological databases. In addition, several representative computational approaches for modeling biological pathways in yeast are discussed.
© The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  Saccharomyces cerevisiae; gene regulatory network; metabolic pathway; signaling regulation

Mesh:

Year:  2015        PMID: 26476430      PMCID: PMC5065356          DOI: 10.1093/bfgp/elv040

Source DB:  PubMed          Journal:  Brief Funct Genomics        ISSN: 2041-2649            Impact factor:   4.241


  117 in total

1.  KEGG: kyoto encyclopedia of genes and genomes.

Authors:  M Kanehisa; S Goto
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  Network constrained clustering for gene microarray data.

Authors:  Dongxiao Zhu; Alfred O Hero; Hong Cheng; Ritu Khanna; Anand Swaroop
Journal:  Bioinformatics       Date:  2005-09-01       Impact factor: 6.937

Review 3.  The model organism as a system: integrating 'omics' data sets.

Authors:  Andrew R Joyce; Bernhard Ø Palsson
Journal:  Nat Rev Mol Cell Biol       Date:  2006-03       Impact factor: 94.444

Review 4.  Glucose and glutamine metabolism control by APC and SCF during the G1-to-S phase transition of the cell cycle.

Authors:  Irving Omar Estévez-García; Verónica Cordoba-Gonzalez; Eleazar Lara-Padilla; Abel Fuentes-Toledo; Ramcés Falfán-Valencia; Rafael Campos-Rodríguez; Edgar Abarca-Rojano
Journal:  J Physiol Biochem       Date:  2014-03-07       Impact factor: 4.158

5.  BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions.

Authors:  Jan Schellenberger; Junyoung O Park; Tom M Conrad; Bernhard Ø Palsson
Journal:  BMC Bioinformatics       Date:  2010-04-29       Impact factor: 3.169

Review 6.  Regulation mechanisms and signaling pathways of autophagy.

Authors:  Congcong He; Daniel J Klionsky
Journal:  Annu Rev Genet       Date:  2009       Impact factor: 16.830

7.  Boosting probabilistic graphical model inference by incorporating prior knowledge from multiple sources.

Authors:  Paurush Praveen; Holger Fröhlich
Journal:  PLoS One       Date:  2013-06-24       Impact factor: 3.240

8.  Further developments towards a genome-scale metabolic model of yeast.

Authors:  Paul D Dobson; Kieran Smallbone; Daniel Jameson; Evangelos Simeonidis; Karin Lanthaler; Pınar Pir; Chuan Lu; Neil Swainston; Warwick B Dunn; Paul Fisher; Duncan Hull; Marie Brown; Olusegun Oshota; Natalie J Stanford; Douglas B Kell; Ross D King; Stephen G Oliver; Robert D Stevens; Pedro Mendes
Journal:  BMC Syst Biol       Date:  2010-10-28

9.  NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways.

Authors:  Sylvain Brohée; Karoline Faust; Gipsi Lima-Mendez; Olivier Sand; Rekin's Janky; Gilles Vanderstocken; Yves Deville; Jacques van Helden
Journal:  Nucleic Acids Res       Date:  2008-06-04       Impact factor: 16.971

Review 10.  Protein-protein interaction detection: methods and analysis.

Authors:  V Srinivasa Rao; K Srinivas; G N Sujini; G N Sunand Kumar
Journal:  Int J Proteomics       Date:  2014-02-17
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  5 in total

1.  Next-Generation Genome-Scale Models Incorporating Multilevel 'Omics Data: From Yeast to Human.

Authors:  Tunahan Çakır; Emel Kökrek; Gülben Avşar; Ecehan Abdik; Pınar Pir
Journal:  Methods Mol Biol       Date:  2019

2.  Non-linearity of Metabolic Pathways Critically Influences the Choice of Machine Learning Model.

Authors:  Ophélie Lo-Thong-Viramoutou; Philippe Charton; Xavier F Cadet; Brigitte Grondin-Perez; Emma Saavedra; Cédric Damour; Frédéric Cadet
Journal:  Front Artif Intell       Date:  2022-06-10

3.  Seminal plasma modulates the immune-cytokine network in the porcine uterine tissue and pre-ovulatory follicles.

Authors:  Dagmar Waberski; Jana Schäfer; Alexandra Bölling; Manon Scheld; Heiko Henning; Nina Hambruch; Hans-Joachim Schuberth; Christiane Pfarrer; Christine Wrenzycki; Ronald H F Hunter
Journal:  PLoS One       Date:  2018-08-28       Impact factor: 3.240

4.  Genetic interactions derived from high-throughput phenotyping of 6589 yeast cell cycle mutants.

Authors:  Jenna E Gallegos; Neil R Adames; Mark F Rogers; Pavel Kraikivski; Aubrey Ibele; Kevin Nurzynski-Loth; Eric Kudlow; T M Murali; John J Tyson; Jean Peccoud
Journal:  NPJ Syst Biol Appl       Date:  2020-05-06

5.  Apoptosis, Induced by Human α-Synuclein in Yeast, Can Occur Independent of Functional Mitochondria.

Authors:  Damilare D Akintade; Bhabatosh Chaudhuri
Journal:  Cells       Date:  2020-09-29       Impact factor: 6.600

  5 in total

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