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

2020 ◽  
Author(s):  
Fernando Santos-Beneit ◽  
Vytautas Raškevičius ◽  
Vytenis A. Skeberdis ◽  
Sergio Bordel
Keyword(s):  
Literature Search ◽  
Rna Viruses ◽  
Metabolic Modeling ◽  
Bioactive Molecules ◽  
Modeling Approach ◽  
Triacsin C ◽  
Protein Palmitoylation ◽  
Positive Sense ◽  
Different Types ◽  
Docking Calculations

Abstract In this study we have developed a metabolic modeling approach 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 FastMM: an efficient toolbox for personalized constraint-based metabolic modeling

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Gong-Hua Li ◽  
Shaoxing Dai ◽  
Feifei Han ◽  
Wenxing Li ◽  
Jingfei Huang ◽  
...  
Keyword(s):  
Flux Balance Analysis ◽  
Large Scale ◽  
Computing Time ◽  
Metabolic Modeling ◽  
The Cancer Genome Atlas ◽  
Flux Balance ◽  
Mcmc Sampling ◽  
Genome Wide ◽  
Balance Analysis ◽  
User Friendly

Abstract Background Constraint-based metabolic modeling has been applied to understand metabolism related disease mechanisms, to predict potential new drug targets and anti-metabolites, and to identify biomarkers of complex diseases. Although the state-of-art modeling toolbox, COBRA 3.0, is powerful, it requires substantial computing time conducting flux balance analysis, knockout analysis, and Markov Chain Monte Carlo (MCMC) sampling, which may limit its application in large scale genome-wide analysis. Results Here, we rewrote the underlying code of COBRA 3.0 using C/C++, and developed a toolbox, termed FastMM, to effectively conduct constraint-based metabolic modeling. The results showed that FastMM is 2~400 times faster than COBRA 3.0 in performing flux balance analysis and knockout analysis and returns consistent outputs. When applied to MCMC sampling, FastMM is 8 times faster than COBRA 3.0. FastMM is also faster than some efficient metabolic modeling applications, such as Cobrapy and Fast-SL. In addition, we developed a Matlab/Octave interface for fast metabolic modeling. This interface was fully compatible with COBRA 3.0, enabling users to easily perform complex applications for metabolic modeling. For example, users who do not have deep constraint-based metabolic model knowledge can just type one command in Matlab/Octave to perform personalized metabolic modeling. Users can also use the advance and multiple threading parameters for complex metabolic modeling. Thus, we provided an efficient and user-friendly solution to perform large scale genome-wide metabolic modeling. For example, FastMM can be applied to the modeling of individual cancer metabolic profiles of hundreds to thousands of samples in the Cancer Genome Atlas (TCGA). Conclusion FastMM is an efficient and user-friendly toolbox for large-scale personalized constraint-based metabolic modeling. It can serve as a complementary and invaluable improvement to the existing functionalities in COBRA 3.0. FastMM is under GPL license and can be freely available at GitHub site: https://github.com/GonghuaLi/FastMM.

scholarly journals Multiscale Metabolic Modeling: Dynamic Flux Balance Analysis on a Whole-Plant Scale

2013 ◽  
Vol 163 (2) ◽  
pp. 637-647 ◽  
Author(s):  
E. Grafahrend-Belau ◽  
A. Junker ◽  
A. Eschenroder ◽  
J. Muller ◽  
F. Schreiber ◽  
...  
Keyword(s):  
Flux Balance Analysis ◽  
Metabolic Modeling ◽  
Dynamic Flux Balance Analysis ◽  
Flux Balance ◽  
Whole Plant ◽  
Balance Analysis

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 When Does HARKing Hurt? Identifying When Different Types of Undisclosed Post Hoc Hypothesizing Harm Scientific Progress

2017 ◽  
Vol 21 (4) ◽  
pp. 308-320 ◽  
Author(s):  
Mark Rubin
Keyword(s):  
Philosophy Of Science ◽  
Present Article ◽  
Literature Search ◽  
A Priori ◽  
Scientific Progress ◽  
Research Report ◽  
Research Results ◽  
Different Types ◽  
Replication Crisis ◽  
Post Hoc

Hypothesizing after the results are known, or HARKing, occurs when researchers check their research results and then add or remove hypotheses on the basis of those results without acknowledging this process in their research report ( Kerr, 1998 ). In the present article, I discuss 3 forms of HARKing: (a) using current results to construct post hoc hypotheses that are then reported as if they were a priori hypotheses; (b) retrieving hypotheses from a post hoc literature search and reporting them as a priori hypotheses; and (c) failing to report a priori hypotheses that are unsupported by the current results. These 3 types of HARKing are often characterized as being bad for science and a potential cause of the current replication crisis. In the present article, I use insights from the philosophy of science to present a more nuanced view. Specifically, I identify the conditions under which each of these 3 types of HARKing is most and least likely to be bad for science. I conclude with a brief discussion about the ethics of each type of HARKing.

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...

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