Multi-equilibrium property of metabolic networks: MMN module

2012 ◽  
Vol 24 (10) ◽  
pp. 1505-1529 ◽  
Author(s):  
Jin Guo ◽  
Ji-Feng Zhang ◽  
Yanlong Zhao
Keyword(s):  
Metabolic Networks ◽  
Equilibrium Property

scholarly journals Multi-equilibrium property of metabolic networks: SSI module

2011 ◽  
Vol 5 (Suppl 1) ◽  
pp. S15
Author(s):  
Hong-Bo Lei ◽  
Ji-Feng Zhang ◽  
Luonan Chen
Keyword(s):  
Metabolic Networks ◽  
Equilibrium Property

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.

scholarly journals SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Justin Y. Lee ◽  
Britney Nguyen ◽  
Carlos Orosco ◽  
Mark P. Styczynski
Keyword(s):  
Machine Learning ◽  
Metabolic Networks ◽  
Sampling Frequency ◽  
Low Noise ◽  
Training Data ◽  
High Noise ◽  
Regulatory Interactions ◽  
Learning Framework ◽  
Metabolic Systems ◽  
Noise Data

Abstract Background The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms—two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR. Results We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6–27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification. Conclusions SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

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