The Organization of Metabolic Reaction Networks

2001 ◽  
Vol 3 (4) ◽  
pp. 362-379 ◽  
Author(s):  
A. Kremling ◽  
K. Bettenbrock ◽  
B. Laube ◽  
K. Jahreis ◽  
J.W. Lengeler ◽  
...  
Keyword(s):  
Reaction Networks ◽  
Metabolic Reaction

Optimization of regulatory architectures in metabolic reaction networks

1996 ◽  
Vol 52 (4) ◽  
pp. 485-500 ◽  
Author(s):  
Vassily Hatzimanikatis ◽  
Christodoulos A. Floudas ◽  
James E. Bailey
Keyword(s):  
Reaction Networks ◽  
Metabolic Reaction

Predicting Metabolic Reaction Networks with Perturbation-Theory Machine Learning (PTML) Models

2021 ◽  
Vol 21 ◽  
Author(s):  
Karel Diéguez-Santana ◽  
Gerardo M. Casañola-Martin ◽  
James R. Green ◽  
Bakhtiyor Rasulev ◽  
Humberto González-Díaz
Keyword(s):  
Machine Learning ◽  
Perturbation Theory ◽  
External Validation ◽  
Machine Learning Techniques ◽  
Reaction Networks ◽  
Data Series ◽  
Metabolic Reaction ◽  
Large Set ◽  
Validation Data ◽  
Specificity And Sensitivity

Background: Checking the connectivity (structure) of complex Metabolic Reaction Networks (MRNs) models proposed for new microorganisms with promising properties is an important goal for chemical biology. Objective: In principle, we can perform a hand-on checking (Manual Curation). However, this is a hard task due to the high number of combinations of pairs of nodes (possible metabolic reactions). Method: In this work, we used Combinatorial, Perturbation Theory, and Machine Learning, techniques to seek a CPTML model for MRNs >40 organisms compiled by Barabasis’ group. First, we quantified the local structure of a very large set of nodes in each MRN using a new class of node index called Markov linear indices fk. Next, we calculated CPT operators for 150000 combinations of query and reference nodes of MRNs. Last, we used these CPT operators as inputs of different ML algorithms. Results: The CPTML linear model obtained using LDA algorithm is able to discriminate nodes (metabolites) with correct assignation of reactions from not correct nodes with values of accuracy, specificity, and sensitivity in the range of 85-100% in both training and external validation data series. Conclusion: Meanwhile, PTML models based on Bayesian network, J48-Decision Tree and Random Forest algorithms were identified as the three best non-linear models with accuracy greater than 97.5%. The present work opens a door to the study of MRNs of multiple organisms using PTML models.

Perturbation-Theory Machine Learning (PTML) Models for Predicting Metabolic Reaction Networks

2021 ◽  
Author(s):  
Karel Diéguez-Santana ◽  
Gerardo Casañola-Martin ◽  
James Green ◽  
Bakhtiyor Rasulev
Keyword(s):  
Machine Learning ◽  
Perturbation Theory ◽  
Reaction Networks ◽  
Metabolic Reaction

scholarly journals Evolutionary Plasticity and Innovations in Complex Metabolic Reaction Networks

2009 ◽  
Vol 5 (12) ◽  
pp. e1000613 ◽  
Author(s):  
João F. Matias Rodrigues ◽  
Andreas Wagner
Keyword(s):  
Reaction Networks ◽  
Metabolic Reaction

Perturbation Theory Model of Metabolic Reaction Networks

2017 ◽  
Author(s):  
Gerardo M. Casañola-Martín ◽  
Facundo Pérez-Jiménez ◽  
Matilde Merino Sanjuan ◽  
James Green
Keyword(s):  
Perturbation Theory ◽  
Theory Model ◽  
Reaction Networks ◽  
Metabolic Reaction

scholarly journals Acorn: A grid computing system for constraint based modeling and visualization of the genome scale metabolic reaction networks via a web interface

2011 ◽  
Vol 12 (1) ◽  
pp. 196 ◽  
Author(s):  
Jacek Sroka ◽  
Łukasz Bieniasz-Krzywiec ◽  
Szymon Gwóźdź ◽  
Dariusz Leniowski ◽  
Jakub Łącki ◽  
...  
Keyword(s):  
Grid Computing ◽  
Computing System ◽  
Reaction Networks ◽  
Metabolic Reaction ◽  
Web Interface ◽  
Constraint Based Modeling ◽  
Genome Scale
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