scholarly journals Metabolic constraints for a novel symbiosis

2016 ◽  
Vol 3 (3) ◽  
pp. 150708 ◽  
Author(s):  
Megan E. S. Sørensen ◽  
Duncan D. Cameron ◽  
Michael A. Brockhurst ◽  
A. Jamie Wood
Keyword(s):  
The Novel ◽  
Free Living ◽  
Evolutionary Transitions ◽  
Metabolic Modelling ◽  
Flux Balance ◽  
Metabolic Adaptations ◽  
Endosymbiotic Event ◽  
Balance Analysis ◽  
Evolutionary Event ◽  
Life On Earth

Ancient evolutionary events are difficult to study because their current products are derived forms altered by millions of years of adaptation. The primary endosymbiotic event formed the first photosynthetic eukaryote resulting in both plants and algae, with vast consequences for life on Earth. The evolutionary time that passed since this event means the dominant mechanisms and changes that were required are obscured. Synthetic symbioses such as the novel interaction between Paramecium bursaria and the cyanobacterium Synechocystis PC6803, recently established in the laboratory, permit a unique window on the possible early trajectories of this critical evolutionary event. Here, we apply metabolic modelling, using flux balance analysis (FBA), to predict the metabolic adaptations necessary for this previously free-living symbiont to transition to the endosymbiotic niche. By enforcing reciprocal nutrient trading, we are able to predict the most efficient exchange nutrients for both host and symbiont. During the transition from free-living to obligate symbiosis, it is likely that the trading parameters will change over time, which leads in our model to discontinuous changes in the preferred exchange nutrients. Our results show the applicability of FBA modelling to ancient evolutionary transitions driven by metabolic exchanges, and predict how newly established endosymbioses, governed by conflict, will differ from a well-developed one that has reached a mutual-benefit state.

scholarly journals An evolutionary algorithm for designing microbial communities via environmental modification

2021 ◽  
Vol 18 (179) ◽  
pp. 20210348
Author(s):  
Alan R. Pacheco ◽  
Daniel Segrè
Keyword(s):  
Microbial Communities ◽  
Rational Design ◽  
Experimental Testing ◽  
Computational Prediction ◽  
Metabolic Modelling ◽  
Dynamic Flux Balance Analysis ◽  
Flux Balance ◽  
Balance Analysis ◽  
Combinatorial Space ◽  
Metabolic Exchange

Despite a growing understanding of how environmental composition affects microbial communities, it remains difficult to apply this knowledge to the rational design of synthetic multispecies consortia. This is because natural microbial communities can harbour thousands of different organisms and environmental substrates, making up a vast combinatorial space that precludes exhaustive experimental testing and computational prediction. Here, we present a method based on the combination of machine learning and metabolic modelling that selects optimal environmental compositions to produce target community phenotypes. In this framework, dynamic flux balance analysis is used to model the growth of a community in candidate environments. A genetic algorithm is then used to evaluate the behaviour of the community relative to a target phenotype, and subsequently adjust the environment to allow the organisms to approach this target. We apply this iterative process to thousands of in silico communities of varying sizes, showing how it can rapidly identify environments that yield desired taxonomic compositions and patterns of metabolic exchange. Moreover, this combination of approaches produces testable predictions for the assembly of experimental microbial communities with specific properties and can facilitate rational environmental design processes for complex microbiomes.

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.

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 A simplified metabolic network reconstruction to promote understanding and development of flux balance analysis tools

2019 ◽  
Vol 105 ◽  
pp. 64-71 ◽  
Author(s):  
Kristopher D. Rawls ◽  
Bonnie V. Dougherty ◽  
Edik M. Blais ◽  
Ethan Stancliffe ◽  
Glynis L. Kolling ◽  
...  
Keyword(s):  
Metabolic Network ◽  
Flux Balance Analysis ◽  
Network Reconstruction ◽  
Metabolic Network Reconstruction ◽  
Flux Balance ◽  
Analysis Tools ◽  
Balance Analysis
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