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.

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 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 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 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.

Critical assessment of genome-scale metabolic models of Arabidopsis thaliana

2022 ◽  
Author(s):  
Javad Zamani ◽  
Sayed-Amir Marashi ◽  
Tahmineh Lohrasebi ◽  
Mohammad-Ali Malboobi ◽  
Esmail Foroozan
Keyword(s):  
Arabidopsis Thaliana ◽  
Flux Balance Analysis ◽  
Critical Assessment ◽  
Flux Balance ◽  
Balance Analysis ◽  
Metabolic Models ◽  
Living Organisms ◽  
Genome Scale

Genome-scale metabolic models (GSMMs) have enabled researchers to perform systems-level studies of living organisms. As a constraint-based technique, flux balance analysis (FBA) aids computation of reaction fluxes and prediction of...

scholarly journals The ModelSEED Database for the integration of metabolic annotations and the reconstruction, comparison, and analysis of metabolic models for plants, fungi, and microbes

2020 ◽  
Author(s):  
Samuel M. D. Seaver ◽  
Filipe Liu ◽  
Qizhi Zhang ◽  
James Jeffryes ◽  
José P. Faria ◽  
...  
Keyword(s):  
Metabolic Pathways ◽  
Draft Genome ◽  
Biochemical Network ◽  
Plant Genomes ◽  
Flux Balance ◽  
Rosetta Stone ◽  
Balance Analysis ◽  
Comparison And Analysis ◽  
Metabolic Models ◽  
Genome Scale

ABSTRACTFor over ten years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions;; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical “Rosetta Stone” to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies, and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org and KBase.

scholarly journals Simulating serial-target antibacterial drug synergies using flux balance analysis

2015 ◽  
Author(s):  
Andrew S Krueger ◽  
Christian Munck ◽  
Gautam Dantas ◽  
George M Church ◽  
James Galagan ◽  
...  
Keyword(s):  
Flux Balance Analysis ◽  
Enzyme Inhibitors ◽  
Metabolic Inhibitors ◽  
Metabolic Enzyme ◽  
Antibacterial Drug ◽  
Flux Balance ◽  
Single Target ◽  
Metabolic Systems ◽  
Balance Analysis ◽  
Genome Scale

Flux balance analysis (FBA) is an increasingly useful approach for modeling the behavior of metabolic systems. However, standard FBA modeling of genetic knockouts can not predict drug combination synergies observed between serial metabolic targets, even though such synergies give rise to some of the most widely used antibiotic treatments. Here we extend FBA modeling to simulate responses to chemical inhibitors at varying concentrations, by diverting enzymatic flux to a waste reaction. This flux diversion yields very similar qualitative predictions to prior methods for single target activity. However, we find very different predictions for combinations, where flux diversion, which mimics the kinetics of competitive metabolic inhibitors, can explain serial target synergies between metabolic enzyme inhibitors that we confirmed in Escherichia coli cultures. FBA flux diversion opens the possibility for more accurate genome-scale predictions of drug synergies, which can be used to suggest treatments for infections and other diseases.

scholarly journals Regulatory dynamic enzyme-cost flux balance analysis: A unifying framework for constraint-based modeling

2019 ◽  
Author(s):  
Lin Liu ◽  
Alexander Bockmayr
Keyword(s):  
Flux Balance Analysis ◽  
Optimization Problem ◽  
Linear Optimization ◽  
Production Costs ◽  
Mixed Integer ◽  
Time Interval ◽  
Modeling Framework ◽  
Flux Balance ◽  
Balance Analysis ◽  
Constraint Based Modeling

AbstractIntegrated modeling of metabolism and gene regulation continues to be a major challenge in computational biology. While there exist approaches like regulatory flux balance analysis (rFBA), dynamic flux balance analysis (dFBA), resource balance analysis (RBA) or dynamic enzyme-cost flux balance analysis (deFBA) extending classical flux balance analysis (FBA) in various directions, there have been no constraint-based methods so far that allow predicting the dynamics of metabolism taking into account both macromolecule production costs and regulatory events.In this paper, we introduce a new constraint-based modeling framework named regulatory dynamic enzyme-cost flux balance analysis (r-deFBA), which unifies dynamic modeling of metabolism, cellular resource allocation and transcriptional regulation in a hybrid discrete-continuous setting.With r-deFBA, we can predict discrete regulatory states together with the continuous dynamics of reaction fluxes, external substrates, enzymes, and regulatory proteins needed to achieve a cellular objective such as maximizing biomass over a time interval. The dynamic optimization problem underlying r-deFBA can be reformulated as a mixed-integer linear optimization problem, for which there exist efficient solvers.

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