scholarly journals Understanding FBA Solutions under Multiple Nutrient Limitations

Metabolites ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 257
Author(s):  
Eunice van Pelt-KleinJan ◽  
Daan H. de Groot ◽  
Bas Teusink
Keyword(s):  
Cell Metabolism ◽  
Reaction Rates ◽  
Scale Modeling ◽  
Cell Components ◽  
Balance Analysis ◽  
A Cell ◽  
Biotechnological Processes ◽  
Metabolic Behavior ◽  
Metabolic Strategies ◽  
Genome Scale

Genome-scale stoichiometric modeling methods, in particular Flux Balance Analysis (FBA) and variations thereof, are widely used to investigate cell metabolism and to optimize biotechnological processes. Given (1) a metabolic network, which can be reconstructed from an organism’s genome sequence, and (2) constraints on reaction rates, which may be based on measured nutrient uptake rates, FBA predicts which reactions maximize an objective flux, usually the production of cell components. Although FBA solutions may accurately predict the metabolic behavior of a cell, the actual flux predictions are often hard to interpret. This is especially the case for conditions with many constraints, such as for organisms growing in rich nutrient environments: it remains unclear why a certain solution was optimal. Here, we rationalize FBA solutions by explaining for which properties the optimal combination of metabolic strategies is selected. We provide a graphical formalism in which the selection of solutions can be visualized; we illustrate how this perspective provides a glimpse of the logic that underlies genome-scale modeling by applying our formalism to models of various sizes.

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.

Genome-scale Modeling of Metabolism and Macromolecular Expression and Their Applications

2020 ◽  
Vol 25 (6) ◽  
pp. 931-943
Author(s):  
Sanjeev Dahal ◽  
Jiao Zhao ◽  
Laurence Yang
Keyword(s):  
Scale Modeling ◽  
Genome Scale

G-protein-coupled receptors signalling at the cell nucleus: an emerging paradigmThis paper is one of a selection of papers published in this Special Issue, entitled The Nucleus: A Cell Within A Cell.

2006 ◽  
Vol 84 (3-4) ◽  
pp. 287-297 ◽  
Author(s):  
Fernand Gobeil ◽  
Audrey Fortier ◽  
Tang Zhu ◽  
Michela Bossolasco ◽  
Martin Leduc ◽  
...  
Keyword(s):  
G Protein ◽  
Cell Nucleus ◽  
Intracellular Signaling ◽  
Molecular Mechanisms ◽  
Nuclear Translocation ◽  
Cell Metabolism ◽  
Hormone Secretion ◽  
G Protein Coupled Receptors ◽  
A Cell ◽  
G Protein Coupled

G-protein-coupled receptors (GPCRs) comprise a wide family of monomeric heptahelical glycoproteins that recognize a broad array of extracellular mediators including cationic amines, lipids, peptides, proteins, and sensory agents. Thus far, much attention has been given towards the comprehension of intracellular signaling mechanisms activated by cell membrane GPCRs, which convert extracellular hormonal stimuli into acute, non-genomic (e.g., hormone secretion, muscle contraction, and cell metabolism) and delayed, genomic biological responses (e.g., cell division, proliferation, and apoptosis). However, with respect to the latter response, there is compelling evidence for a novel intracrine mode of genomic regulation by GPCRs that implies either the endocytosis and nuclear translocation of peripheral-liganded GPCR and (or) the activation of nuclearly located GPCR by endogenously produced, nonsecreted ligands. A noteworthy example of the last scenario is given by heptahelical receptors that are activated by bioactive lipoids (e.g., PGE2 and PAF), many of which may be formed from bilayer membranes including those of the nucleus. The experimental evidence for the nuclear localization and signalling of GPCRs will be reviewed. We will also discuss possible molecular mechanisms responsible for the atypical compartmentalization of GPCRs at the cell nucleus, along with their role in gene expression.

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 Microgravity-Induced Cell-to-Cell Junctional Contacts Are Counteracted by Antioxidant Compounds in TCam-2 Seminoma Cells

2020 ◽  
Vol 10 (22) ◽  
pp. 8289
Author(s):  
Angela Catizone ◽  
Caterina Morabito ◽  
Marcella Cammarota ◽  
Chiara Schiraldi ◽  
Katia Corano Scheri ◽  
...  
Keyword(s):  
Plasma Membrane ◽  
Cell Line ◽  
Simulated Microgravity ◽  
Cell Metabolism ◽  
Ultrastructural Level ◽  
Antioxidant Compounds ◽  
Signal Molecules ◽  
Beta Catenin ◽  
Autophagy Induction ◽  
A Cell

The direct impact of microgravity exposure on male germ cells, as well as on their malignant counterparts, has not been largely studied. In previous works, we reported our findings on a cell line derived from a human seminoma lesion (TCam-2 cell line) showing that acute exposure to simulated microgravity altered microtubule orientation, induced autophagy, and modified cell metabolism stimulating ROS production. Moreover, we demonstrated that the antioxidant administration prevented both TCam-2 microgravity-induced microtubule disorientation and autophagy induction. Herein, expanding previous investigations, we report that simulated microgravity exposure for 24 h induced the appearance, at an ultrastructural level, of cell-to-cell junctional contacts that were not detectable in cells grown at 1 g. In line with this result, pan-cadherin immunofluorescence analyzed by confocal microscopy, revealed the clustering of this marker at the plasma membrane level on microgravity exposed TCam-2 cells. The upregulation of cadherin was confirmed by Western blot analyses. Furthermore, we demonstrated that the microgravity-induced ROS increase was responsible for the distribution of cadherin nearby the plasma membrane, together with beta-catenin since the administration of antioxidants prevented this microgravity-dependent phenomenon. These results shed new light on the microgravity-induced modifications of the cell adhesive behavior and highlight the role of ROS as microgravity activated signal molecules.

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.

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