A Promising Method for Calculating True Steady-State Metabolite Concentrations in Large-Scale Metabolic Reaction Network Models

2020 ◽  
Vol 17 (1) ◽  
pp. 27-36
Author(s):  
Atsuko Miyawaki-Kuwakado ◽  
Soichiro Komori ◽  
Fumihide Shiraishi
Keyword(s):  
Steady State ◽  
Large Scale ◽  
Reaction Network ◽  
Network Models ◽  
Promising Method ◽  
Metabolic Reaction ◽  
Metabolite Concentrations

scholarly journals The unraveling of balanced complexes in metabolic networks

2021 ◽  
Author(s):  
Damoun Langary ◽  
Anika Kueken ◽  
Zoran Nikoloski
Keyword(s):  
Steady State ◽  
Large Scale ◽  
Metabolic Networks ◽  
Network Models ◽  
Steady States ◽  
Biochemical Networks ◽  
Computational Approaches ◽  
Metabolic Models ◽  
Large Scale Networks ◽  
General Conditions

Balanced complexes in biochemical networks are at core of several theoretical and computational approaches that make statements about the properties of the steady states supported by the network. Recent computational approaches have employed balanced complexes to reduce metabolic networks, while ensuring preservation of particular steady-state properties; however, the underlying factors leading to the formation of balanced complexes have not been studied, yet. Here, we present a number of factorizations providing insights in mechanisms that lead to the origins of the corresponding balanced complexes. The proposed factorizations enable us to categorize balanced complexes into four distinct classes, each with specific origins and characteristics. They also provide the means to efficiently determine if a balanced complex in large-scale networks belongs to a particular class from the categorization. The results are obtained under very general conditions and irrespective of the network kinetics, rendering them broadly applicable across variety of network models. Application of the categorization shows that all classes of balanced complexes are present in large-scale metabolic models across all kingdoms of life, therefore paving the way to study their relevance with respect to different properties of steady states supported by these networks.

scholarly journals Bayesian metabolic flux analysis reveals intracellular flux couplings

2019 ◽  
Vol 35 (14) ◽  
pp. i548-i557 ◽  
Author(s):  
Markus Heinonen ◽  
Maria Osmala ◽  
Henrik Mannerström ◽  
Janne Wallenius ◽  
Samuel Kaski ◽  
...  
Keyword(s):  
Steady State ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Reaction Network ◽  
Reaction Rates ◽  
Supplementary Information ◽  
Flux Analysis ◽  
Metabolic Reaction ◽  
Metabolic Systems ◽  
Genome Scale

AbstractMotivationMetabolic flux balance analysis (FBA) is a standard tool in analyzing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place model assumptions on fluxes due to the convenience of formulating the problem as a linear programing model, while many methods do not consider the inherent uncertainty in flux estimates.ResultsWe introduce a novel paradigm of Bayesian metabolic flux analysis that models the reactions of the whole genome-scale cellular system in probabilistic terms, and can infer the full flux vector distribution of genome-scale metabolic systems based on exchange and intracellular (e.g. 13C) flux measurements, steady-state assumptions, and objective function assumptions. The Bayesian model couples all fluxes jointly together in a simple truncated multivariate posterior distribution, which reveals informative flux couplings. Our model is a plug-in replacement to conventional metabolic balance methods, such as FBA. Our experiments indicate that we can characterize the genome-scale flux covariances, reveal flux couplings, and determine more intracellular unobserved fluxes in Clostridium acetobutylicum from 13C data than flux variability analysis.Availability and implementationThe COBRA compatible software is available at github.com/markusheinonen/bamfa.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals Metabolic Reaction Network-based Recursive Metabolite Identification for Untargeted Metabolomics

2018 ◽  
Author(s):  
Xiaotao Shen ◽  
Xin Xiong ◽  
Ruohong Wang ◽  
Yandong Yin ◽  
Yuping Cai ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Biological Sample ◽  
Large Scale ◽  
Recursive Algorithm ◽  
Reaction Network ◽  
Metabolite Identification ◽  
Untargeted Metabolomics ◽  
Metabolic Reaction ◽  
Link Type ◽  
Functional Metabolomics

Metabolite identification is a long-standing challenge in untargeted metabolomics and a major hurdle for functional metabolomics studies. Here, we developed a metabolic reaction network-based recursive algorithm and webserver called MetDNA for the large-scale and unambiguous identification of metabolites (available at http://metdna.zhulab.cn). We showcased the versatility of our workflow using different instrument platforms, data acquisition methods, and biological sample types and demonstrated that over 2,000 metabolites could be identified from one experiment.

scholarly journals Network organization of cell metabolism: monosaccharide interconversion

1997 ◽  
Vol 324 (1) ◽  
pp. 103-111 ◽  
Author(s):  
Juan C. NUÑO ◽  
Ignaciol SÁNCHEZ-VALDENEBRO ◽  
Carolina PÉREZ-IRATXETA ◽  
Enrique MELÉNDEZ-HEVIA ◽  
Francisco MONTERO
Keyword(s):  
Steady State ◽  
Cell Metabolism ◽  
Reaction Network ◽  
Network Models ◽  
Metabolic Process ◽  
Structural Factors ◽  
Complex Reaction ◽  
Present Contribution ◽  
Boundary Constraints ◽  
The One

The structural properties of carbohydrate metabolism are being studied. The present contribution focuses mainly on those processes involving the transfer of carbon fragments among sugars. It is shown how enzymic activities fix the way the system self-organizes stoichiometrically at the steady state. It is proven that there exists a specific correspondence between the set of all possible enzymic activities, the activity set, and the set of stoichiometrically compatible flux distributions through the pathway. On the one hand, there are enzymic activities that do not allow a stoichiometrically feasible coupling at the steady state of the reactions involved in the conversion. On the other hand, there are enzymic activities that are related to one or more flux distributions at the steady state (i.e. with one or several rate vectors respectively). For this latter group, it can be demonstrated that the structure of the system depends on other non-structural factors, such as boundary constraints and the kinetic parameters. As a consequence, it is suggested that this kind of metabolic process must be viewed as a complex reaction network instead of a sequential number of steps. Some implications of these derivations are illustrated for the particular conversion CO2 →C3. General remarks are also discussed within the framework of network models of cell metabolism.

scholarly journals Exploiting network topology for large-scale inference of nonlinear reaction models

2019 ◽  
Vol 16 (152) ◽  
pp. 20180766 ◽  
Author(s):  
Nikhil Galagali ◽  
Youssef M. Marzouk
Keyword(s):  
Species Interactions ◽  
Large Scale ◽  
Network Inference ◽  
Chemical Species ◽  
Reaction Network ◽  
Network Models ◽  
Model Space ◽  
Unknown Parameters ◽  
Nonlinear Reaction ◽  
Reaction Models

The development of chemical reaction models aids understanding and prediction in areas ranging from biology to electrochemistry and combustion. A systematic approach to building reaction network models uses observational data not only to estimate unknown parameters but also to learn model structure. Bayesian inference provides a natural approach to this data-driven construction of models. Yet traditional Bayesian model inference methodologies that numerically evaluate the evidence for each model are often infeasible for nonlinear reaction network inference, as the number of plausible models can be combinatorially large. Alternative approaches based on model-space sampling can enable large-scale network inference, but their realization presents many challenges. In this paper, we present new computational methods that make large-scale nonlinear network inference tractable. First, we exploit the topology of networks describing potential interactions among chemical species to design improved ‘between-model’ proposals for reversible-jump Markov chain Monte Carlo. Second, we introduce a sensitivity-based determination of move types which, when combined with network-aware proposals, yields significant additional gains in sampling performance. These algorithms are demonstrated on inference problems drawn from systems biology, with nonlinear differential equation models of species interactions.

scholarly journals Identification of a Metabolic Reaction Network from Time-Series Data of Metabolite Concentrations

PLoS ONE ◽  
2013 ◽  
Vol 8 (1) ◽  
pp. e51212 ◽  
Author(s):  
Kansuporn Sriyudthsak ◽  
Fumihide Shiraishi ◽  
Masami Yokota Hirai
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Reaction Network ◽  
Series Data ◽  
Metabolic Reaction ◽  
Metabolite Concentrations

Methods of Network Planning and Management when Implementing Territory Planning Projects

2019 ◽  
pp. 49-54
Keyword(s):  
Large Scale ◽  
Housing Stock ◽  
Network Planning ◽  
Network Models ◽  
Point Of View ◽  
Residential Areas ◽  
Basic Principles ◽  
Planning And Management ◽  
Planning Project ◽  
Complex Process

The success of the Program of housing stock renovation in Moscow depends on the efficiency of resource management. One of the main urban planning documents that determine the nature of the reorganization of residential areas included in the Program of renovation is the territory planning project. The implementation of the planning project is a complex process that has a time point of its beginning and end, and also includes a set of interdependent parallel-sequential activities. From an organizational point of view, it is convenient to use network planning and management methods for project implementation. These methods are based on the construction of network models, including its varieties – a Gantt chart. A special application has been developed to simulate the implementation of planning projects. The article describes the basic principles and elements of modeling. The list of the main implementation parameters of the Program of renovation obtained with the help of the developed software for modeling is presented. The variants of using the results obtained for a comprehensive analysis of the implementation of large-scale urban projects are proposed.

scholarly journals On the structural connectivity of large-scale models of brain networks at cellular level

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Giuseppe Giacopelli ◽  
Domenico Tegolo ◽  
Emiliano Spera ◽  
Michele Migliore
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Network Models ◽  
Brain Regions ◽  
Cellular Level ◽  
Scale Model ◽  
Mathematical Framework ◽  
Underlying Mechanisms ◽  
Experimental Findings

AbstractThe brain’s structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited. Their predicting power ultimately depends on how closely a model’s connectivity represents the real system. Here we argue that the data-driven probabilistic rules, widely used to build neuronal network models, may not be appropriate to represent the dynamics of the corresponding biological system. To solve this problem, we propose to use a new mathematical framework able to use sparse and limited experimental data to quantitatively reproduce the structural connectivity of biological brain networks at cellular level.

Cyclic Breathing Simulations in Large Scale Models of the Lung Airway From the Oronasal Opening to the Terminal Bronchioles

2012 ◽  
Author(s):  
D. Keith Walters ◽  
Greg W. Burgreen ◽  
Robert L. Hester ◽  
David S. Thompson ◽  
David M. Lavallee ◽  
...  
Keyword(s):  
Steady State ◽  
Boundary Conditions ◽  
Large Scale ◽  
Whole Body ◽  
Patient Specific ◽  
Cfd Simulations ◽  
Reduced Order ◽  
Scale Models ◽  
Steady State Simulation ◽  
Conducting Zone

Computational fluid dynamics (CFD) simulations were performed for unsteady periodic breathing conditions, using large-scale models of the human lung airway. The computational domain included fully coupled representations of the orotracheal region and large conducting zone up to generation four (G4) obtained from patient-specific CT data, and the small conducting zone (to G16) obtained from a stochastically generated airway tree with statistically realistic geometrical characteristics. A reduced-order geometry was used, in which several airway branches in each generation were truncated, and only select flow paths were retained to G16. The inlet and outlet flow boundaries corresponded to the oronasal opening (superior), the inlet/outlet planes in terminal bronchioles (distal), and the unresolved airway boundaries arising from the truncation procedure (intermediate). The cyclic flow was specified according to the predicted ventilation patterns for a healthy adult male at three different activity levels, supplied by the whole-body modeling software HumMod. The CFD simulations were performed using Ansys FLUENT. The mass flow distribution at the distal boundaries was prescribed using a previously documented methodology, in which the percentage of the total flow for each boundary was first determined from a steady-state simulation with an applied flow rate equal to the average during the inhalation phase of the breathing cycle. The distal pressure boundary conditions for the steady-state simulation were set using a stochastic coupling procedure to ensure physiologically realistic flow conditions. The results show that: 1) physiologically realistic flow is obtained in the model, in terms of cyclic mass conservation and approximately uniform pressure distribution in the distal airways; 2) the predicted alveolar pressure is in good agreement with previously documented values; and 3) the use of reduced-order geometry modeling allows accurate and efficient simulation of large-scale breathing lung flow, provided care is taken to use a physiologically realistic geometry and to properly address the unsteady boundary conditions.

The Research of Production Process Statistical Steady-State Based on the Hypothesis Test

2011 ◽  
Vol 314-316 ◽  
pp. 2433-2438
Author(s):  
Wei Zhi Wang
Keyword(s):  
Quality Control ◽  
Steady State ◽  
Production Process ◽  
Quantitative Method ◽  
Large Scale ◽  
Hypothesis Test ◽  
Influence Factors ◽  
Normal Operation ◽  
Scale Production ◽  
Large Scale Production

By only applying a after the event exam in the quality control of the batch production is not enough to meet the needs of modern large-scale production. To a certain extent, modern quality control is a dynamic process of the steady-state judge and adjustment. A simple and reliable steady-state judge rule and method is the premise to guarantee the normal operation. This paper provides a quantitative method to evaluate production process steady-state by analyzing influence factors based on mathematical statistics. The method is both suitable for simple production process and complex production process with sub-processes.

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