Metabolic Networks and Genetic Interactions

2016 ◽  
pp. 122-153
Keyword(s):  
Metabolic Networks ◽  
Genetic Interactions

scholarly journals Plasticity of genetic interactions in metabolic networks of yeast

2007 ◽  
Vol 104 (7) ◽  
pp. 2307-2312 ◽  
Author(s):  
R. Harrison ◽  
B. Papp ◽  
C. Pal ◽  
S. G. Oliver ◽  
D. Delneri
Keyword(s):  
Metabolic Networks ◽  
Genetic Interactions

Analysis on Electricigen Genomes and Metabolic Networks

2012 ◽  
Vol 18 (6) ◽  
pp. 1075
Author(s):  
Jing GUO ◽  
Zixiang XU ◽  
Yaxing FU ◽  
Biyun LIU ◽  
Jing MENG ◽  
...  
Keyword(s):  
Metabolic Networks

MetaGen: a Promising Tool for Modeling Metabolic Networks From KEGG*

2010 ◽  
Vol 37 (1) ◽  
pp. 63-68 ◽  
Author(s):  
Ting-Ting ZHOU ◽  
Kin-Fung YUNG ◽  
Chung Keith CHAN Chun ◽  
Zheng-Hua WANG ◽  
Yun-Ping ZHU ◽  
...  
Keyword(s):  
Metabolic Networks ◽  
Promising Tool

Learning from metabolic networks: current trends and future directions for precision medicine.

2020 ◽  
Vol 28 ◽  
Author(s):  
Ilaria Granata ◽  
Mario Manzo ◽  
Ari Kusumastuti ◽  
Mario R Guarracino
Keyword(s):  
Systems Biology ◽  
Precision Medicine ◽  
Metabolic Networks ◽  
Information Modeling ◽  
Model Systems ◽  
Specific Information ◽  
Important Indicator ◽  
Predictive Values ◽  
Metabolic Systems ◽  
Metabolic Models

Purpose: Systems biology and network modeling represent, nowadays, the hallmark approaches for the development of predictive and targeted-treatment based precision medicine. The study of health and disease as properties of the human body system allows the understanding of the genotype-phenotype relationship through the definition of molecular interactions and dependencies. In this scenario, metabolism plays a central role as its interactions are well characterized and it is considered an important indicator of the genotype-phenotype associations. In metabolic systems biology, the genome-scale metabolic models are the primary scaffolds to integrate multi-omics data as well as cell-, tissue-, condition-specific information. Modeling the metabolism has both investigative and predictive values. Several methods have been proposed to model systems, which involve steady-state or kinetic approaches, and to extract knowledge through machine and deep learning. Method: This review collects, analyzes, and compares the suitable data and computational approaches for the exploration of metabolic networks as tools for the development of precision medicine. To this extent, we organized it into three main sections: "Data and Databases", "Methods and Tools", and "Metabolic Networks for medicine". In the first one, we have collected the most used data and relative databases to build and annotate metabolic models. In the second section, we have reported the state-of-the-art methods and relative tools to reconstruct, simulate, and interpret metabolic systems. Finally, we have reported the most recent and innovative studies which exploited metabolic networks for the study of several pathological conditions, not only those directly related to the metabolism. Conclusion: We think that this review can be a guide to researchers of different disciplines, from computer science to biology and medicine, in exploring the power, challenges and future promises of the metabolism as predictor and target of the so-called P4 medicine (predictive, preventive, personalized and participatory).

Brief Survey of Biological Network Alignment and a Variant with Incorporation of Functional Annotations

2018 ◽  
Vol 14 (1) ◽  
pp. 4-10
Author(s):  
Fang Jing ◽  
Shao-Wu Zhang ◽  
Shihua Zhang
Keyword(s):  
Gene Ontology ◽  
Topological Structure ◽  
Metabolic Networks ◽  
Biological Network ◽  
Genomic Sequence ◽  
Network Alignment ◽  
Future Directions ◽  
Functional Annotations ◽  
Protein Protein Interaction ◽  
Ppi Networks

Background:Biological network alignment has been widely studied in the context of protein-protein interaction (PPI) networks, metabolic networks and others in bioinformatics. The topological structure of networks and genomic sequence are generally used by existing methods for achieving this task.Objective and Method:Here we briefly survey the methods generally used for this task and introduce a variant with incorporation of functional annotations based on similarity in Gene Ontology (GO). Making full use of GO information is beneficial to provide insights into precise biological network alignment.Results and Conclusion:We analyze the effect of incorporation of GO information to network alignment. Finally, we make a brief summary and discuss future directions about this topic.

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