Exploring the Cellular Objective in Flux Balance Constraint-Based Models

2014 ◽  
pp. 211-224 ◽  
Author(s):  
Rafael S. Costa ◽  
Son Nguyen ◽  
Andras Hartmann ◽  
Susana Vinga
Keyword(s):  
Flux Balance ◽  
Constraint Based Models

scholarly journals Gap Detection for Genome-Scale Constraint-Based Models

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
J. Paul Brooks ◽  
William P. Burns ◽  
Stephen S. Fong ◽  
Chris M. Gowen ◽  
Seth B. Roberts
Keyword(s):  
Culture Media ◽  
Model Organism ◽  
Metabolic Reconstruction ◽  
Exchange Reactions ◽  
Working Models ◽  
Flux Balance ◽  
Working Model ◽  
Balance Analysis ◽  
Genome Scale ◽  
Constraint Based Models

Constraint-based metabolic models are currently the most comprehensive system-wide models of cellular metabolism. Several challenges arise when building an in silico constraint-based model of an organism that need to be addressed before flux balance analysis (FBA) can be applied for simulations. An algorithm called FBA-Gap is presented here that aids the construction of a working model based on plausible modifications to a given list of reactions that are known to occur in the organism. When applied to a working model, the algorithm gives a hypothesis concerning a minimal medium for sustaining the cell in culture. The utility of the algorithm is demonstrated in creating a new model organism and is applied to four existing working models for generating hypotheses about culture media. In modifying a partial metabolic reconstruction so that biomass may be produced using FBA, the proposed method is more efficient than a previously proposed method in that fewer new reactions are added to complete the model. The proposed method is also more accurate than other approaches in that only biologically plausible reactions and exchange reactions are used.

scholarly journals The Systems Biology Markup Language (SBML) Level 3 Package: Flux Balance Constraints

2015 ◽  
Vol 12 (2) ◽  
pp. 660-690 ◽  
Author(s):  
Brett G. Olivier ◽  
Frank T. Bergmann
Keyword(s):  
Biological Networks ◽  
Optimization Techniques ◽  
Stoichiometric Matrix ◽  
Flux Balance ◽  
Base Level ◽  
Balance Analysis ◽  
Level 3 ◽  
Constraint Based Modeling ◽  
Genome Scale ◽  
Constraint Based Models

Summary Constraint-based modeling is a well established modelling methodology used to analyze and study biological networks on both a medium and genome scale. Due to their large size, genome scale models are typically analysed using constraint-based optimization techniques. One widely used method is Flux Balance Analysis (FBA) which, for example, requires a modelling description to include: the definition of a stoichiometric matrix, an objective function and bounds on the values that fluxes can obtain at steady state.The Flux Balance Constraints (FBC) Package extends SBML Level 3 and provides a standardized format for the encoding, exchange and annotation of constraint-based models. It includes support for modelling concepts such as objective functions, flux bounds and model component annotation that facilitates reaction balancing. The FBC package establishes a base level for the unambiguous exchange of genome-scale, constraint-based models, that can be built upon by the community to meet future needs (e. g. by extending it to cover dynamic FBC models).

scholarly journals SBML Level 3 Package: Flux Balance Constraints version 2

2018 ◽  
Vol 15 (1) ◽  
Author(s):  
Brett G. Olivier ◽  
Frank T. Bergmann
Keyword(s):  
Biological Networks ◽  
Optimization Techniques ◽  
Open Platform ◽  
Flux Balance ◽  
Modeling Methodology ◽  
Balance Analysis ◽  
Level 3 ◽  
Constraint Based Modeling ◽  
Genome Scale ◽  
Constraint Based Models

AbstractConstraint-based modeling is a well established modeling methodology used to analyze and study biological networks on both a medium and genome scale. Due to their large size and complexity such steady-state flux models are, typically, analyzed using constraint-based optimization techniques, for example, flux balance analysis (FBA). The Flux balance constraints (FBC) Package extends SBML Level 3 and provides a standardized format for the encoding, exchange and annotation of constraint-based models. It includes support for modeling concepts such as objective functions, flux bounds and model component annotation that facilitates reaction balancing. Version two expands on the original release by adding official support for encoding gene-protein associations and their associated elements. In addition to providing the elements necessary to unambiguously encode existing constraint-based models, the FBC Package provides an open platform facilitating the continued, cross-community development of an interoperable, constraint-based model encoding format.

Dynamic flux balance analysis of biomass and lipid production by Antarctic thraustochytrid Oblongichytrium sp. RT2316‐13

2020 ◽  
Vol 117 (10) ◽  
pp. 3006-3017 ◽  
Author(s):  
Carolina Shene ◽  
Paris Paredes ◽  
Liset Flores ◽  
Allison Leyton ◽  
Juan A. Asenjo ◽  
...  
Keyword(s):  
Flux Balance Analysis ◽  
Lipid Production ◽  
Dynamic Flux Balance Analysis ◽  
Flux Balance ◽  
Balance Analysis

scholarly journals Understanding the host-microbe interactions using metabolic modeling

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Jack Jansma ◽  
Sahar El Aidy
Keyword(s):  
Human Health ◽  
Flux Balance Analysis ◽  
Bacterial Species ◽  
Flux Balance ◽  
Interaction Field ◽  
In Silico Simulation ◽  
Balance Analysis ◽  
Microbe Interactions ◽  
Host Microbe Interactions ◽  
Genome Scale

AbstractThe human gut harbors an enormous number of symbiotic microbes, which is vital for human health. However, interactions within the complex microbiota community and between the microbiota and its host are challenging to elucidate, limiting development in the treatment for a variety of diseases associated with microbiota dysbiosis. Using in silico simulation methods based on flux balance analysis, those interactions can be better investigated. Flux balance analysis uses an annotated genome-scale reconstruction of a metabolic network to determine the distribution of metabolic fluxes that represent the complete metabolism of a bacterium in a certain metabolic environment such as the gut. Simulation of a set of bacterial species in a shared metabolic environment can enable the study of the effect of numerous perturbations, such as dietary changes or addition of a probiotic species in a personalized manner. This review aims to introduce to experimental biologists the possible applications of flux balance analysis in the host-microbiota interaction field and discusses its potential use to improve human health.

scholarly journals A metabolic modeling approach reveals promising therapeutic targets and antiviral drugs to combat COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fernando Santos-Beneit ◽  
Vytautas Raškevičius ◽  
Vytenis A. Skeberdis ◽  
Sergio Bordel
Keyword(s):  
Literature Search ◽  
Metabolic Modeling ◽  
Bioactive Molecules ◽  
Flux Balance ◽  
Triacsin C ◽  
Protein Palmitoylation ◽  
Positive Sense ◽  
Different Types ◽  
Balance Analysis ◽  
Docking Calculations

AbstractIn this study we have developed a method based on Flux Balance Analysis to identify human metabolic enzymes which can be targeted for therapeutic intervention against COVID-19. A literature search was carried out in order to identify suitable inhibitors of these enzymes, which were confirmed by docking calculations. In total, 10 targets and 12 bioactive molecules have been predicted. Among the most promising molecules we identified Triacsin C, which inhibits ACSL3, and which has been shown to be very effective against different viruses, including positive-sense single-stranded RNA viruses. Similarly, we also identified the drug Celgosivir, which has been successfully tested in cells infected with different types of viruses such as Dengue, Zika, Hepatitis C and Influenza. Finally, other drugs targeting enzymes of lipid metabolism, carbohydrate metabolism or protein palmitoylation (such as Propylthiouracil, 2-Bromopalmitate, Lipofermata, Tunicamycin, Benzyl Isothiocyanate, Tipifarnib and Lonafarnib) are also proposed.

scholarly journals Why does yeast ferment? A flux balance analysis study

2010 ◽  
Vol 38 (5) ◽  
pp. 1225-1229 ◽  
Author(s):  
Evangelos Simeonidis ◽  
Ettore Murabito ◽  
Kieran Smallbone ◽  
Hans V. Westerhoff
Keyword(s):  
Flux Balance Analysis ◽  
Energy Cost ◽  
Yeast Fermentation ◽  
Minimal Amount ◽  
Aerobic Growth ◽  
Analysis Study ◽  
Flux Balance ◽  
Balance Analysis ◽  
Genome Scale ◽  
Excess Glucose

Advances in biological techniques have led to the availability of genome-scale metabolic reconstructions for yeast. The size and complexity of such networks impose limits on what types of analyses one can perform. Constraint-based modelling overcomes some of these restrictions by using physicochemical constraints to describe the potential behaviour of an organism. FBA (flux balance analysis) highlights flux patterns through a network that serves to achieve a particular objective and requires a minimal amount of data to make quantitative inferences about network behaviour. Even though FBA is a powerful tool for system predictions, its general formulation sometimes results in unrealistic flux patterns. A typical example is fermentation in yeast: ethanol is produced during aerobic growth in excess glucose, but this pattern is not present in a typical FBA solution. In the present paper, we examine the issue of yeast fermentation against respiration during growth. We have studied a number of hypotheses from the modelling perspective, and novel formulations of the FBA approach have been tested. By making the observation that more respiration requires the synthesis of more mitochondria, an energy cost related to mitochondrial synthesis is added to the FBA formulation. Results, although still approximate, are closer to experimental observations than earlier FBA analyses, at least on the issue of fermentation.

Estimating optimal profiles of genetic alterations using constraint-based models

2004 ◽  
Vol 89 (2) ◽  
pp. 243-251 ◽  
Author(s):  
Kapil G. Gadkar ◽  
Francis J. Doyle III ◽  
Jeremy S. Edwards ◽  
Radhakrishnan Mahadevan
Keyword(s):  
Genetic Alterations ◽  
Constraint Based Models
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