Inspecting the Solution Space of Genome-Scale Metabolic Models

Genome-scale metabolic models are frequently used in computational biology. They offer an integrative view on the metabolic network of an organism without the need to know kinetic information in detail. However, the huge solution space which comes with the analysis of genome-scale models by using, e.g., Flux Balance Analysis (FBA) poses a problem, since it is hard to thoroughly investigate and often only an arbitrarily selected individual flux distribution is discussed as an outcome of FBA. Here, we introduce a new approach to inspect the solution space and we compare it with other approaches, namely Flux Variability Analysis (FVA) and CoPE-FBA, using several different genome-scale models of lactic acid bacteria. We examine the extent to which different types of experimental data limit the solution space and how the robustness of the system increases as a result. We find that our new approach to inspect the solution space is a good complementary method that offers additional insights into the variance of biological phenotypes and can help to prevent wrong conclusions in the analysis of FBA results.

How to Tackle Underdeterminacy in Metabolic Flux Analysis? A Tutorial and Critical Review

Metabolic flux analysis is often (not to say almost always) faced with system underdeterminacy. Indeed, the linear algebraic system formed by the steady-state mass balance equations around the intracellular metabolites and the equality constraints related to the measurements of extracellular fluxes do not define a unique solution for the distribution of intracellular fluxes, but instead a set of solutions belonging to a convex polytope. Various methods have been proposed to tackle this underdeterminacy, including flux pathway analysis, flux balance analysis, flux variability analysis and sampling. These approaches are reviewed in this article and a toy example supports the discussion with illustrative numerical results.

Long Term UV Continuum and Line Variability in NGC 1275

In this paper, we present our results for the first time on long term emission-line and continuum variability studies using the International Ultraviolet Explorer’s final archive of UV spectroscopic data obtained in the wavelength region from 1150 Å to 3200 Å for NGC 1275, a dust dominated BL Lac characterized by the Rmax and F-variance parameter. The UV continuum flux variability analysis presented in this paper covers more number of emission-line free continuum windows in the UV region centred at 1710 Å, 1800 Å, 2625 Å, 2875 Å & 3025 Å. We have obtained a higher value of Fvar ~ 45 % at 1710 Å and a lower value of ~ 30 % at 1800 Å for the IUE's observational period of 1978 - 1989. The Lyα, C IV, C III] and Mg II emission lines have been observed as weaker line features on fewer occasions intermittently.

VFFVA: dynamic load balancing enables large-scale flux variability analysis

Background Genome-scale metabolic models are increasingly employed to predict the phenotype of various biological systems pertaining to healthcare and bioengineering. To characterize the full metabolic spectrum of such systems, Fast Flux Variability Analysis (FFVA) is commonly used in parallel with static load balancing. This approach assigns to each core an equal number of biochemical reactions without consideration of their solution complexity. Results Here, we present Very Fast Flux Variability Analysis (VFFVA) as a parallel implementation that dynamically balances the computation load between the cores in runtime which guarantees equal convergence time between them. VFFVA allowed to gain a threefold speedup factor with coupled models and up to 100 with ill-conditioned models along with a 14-fold decrease in memory usage. Conclusions VFFVA exploits the parallel capabilities of modern machines to enable biological insights through optimizing systems biology modeling. VFFVA is available in C, MATLAB, and Python at https://github.com/marouenbg/VFFVA.

Curation and Analysis of a Saccharomyces cerevisiae Genome-Scale Metabolic Model for Predicting Production of Sensory Impact Molecules under Enological Conditions

One approach for elucidating strain-to-strain metabolic differences is the use of genome-scale metabolic models (GSMMs). To date GSMMs have not focused on the industrially important area of flavor production and, as such; do not cover all the pathways relevant to flavor formation in yeast. Moreover, current models for Saccharomyces cerevisiae generally focus on carbon-limited and/or aerobic systems, which is not pertinent to enological conditions. Here, we curate a GSMM (iWS902) to expand on the existing Ehrlich pathway and ester formation pathways central to aroma formation in industrial winemaking, in addition to the existing sulfur metabolism and medium-chain fatty acid (MCFA) pathways that also contribute to production of sensory impact molecules. After validating the model using experimental data, we predict key differences in metabolism for a strain (EC 1118) in two distinct growth conditions, including differences for aroma impact molecules such as acetic acid, tryptophol, and hydrogen sulfide. Additionally, we propose novel targets for metabolic engineering for aroma profile modifications employing flux variability analysis with the expanded GSMM. The model provides mechanistic insights into the key metabolic pathways underlying aroma formation during alcoholic fermentation and provides a potential framework to contribute to new strategies to optimize the aroma of wines.

Long-term optical and infrared variability characteristics of Fermi blazars

ABSTRACT We present long-term optical and near-infrared flux variability analysis of 37 blazars detected in the γ-ray band by the Fermi Gamma-Ray Space Telescope. Among them, 30 are flat spectrum radio quasars (FSRQs) and 7 are BL Lac objects (BL Lacs). The photometric data in the optical (BVR) and infrared (JK) bands were from the Small and Moderate Aperture Research Telescope System acquired between 2008–2018. From cross-correlation analysis of the light curves at different wavelengths, we did not find significant time delays between variations at different wavelengths, except for three sources, namely PKS 1144–379, PKS B1424–418, and 3C 273. For the blazars with both B- and J-band data, we found that in a majority of FSRQs and BL Lacs, the amplitude of variability (σm) in the J band is larger than that in B band, consistent with the dominance of the non-thermal jet over the thermal accretion disc component. Considering FSRQs and BL Lacs as a sample, there are indications of σm to increase gradually towards longer wavelengths in both, however, found to be statistically significant only between B and J bands in FSRQs. In the B−J v/s J-colour magnitude diagram, we noticed complicated spectral variability patterns. Most of the objects showed a redder when brighter (RWB) behaviour. Few objects showed a bluer when brighter (BWB) trend, while in some objects both BWB and RWB behaviours were noticed. These results on flux and colour characteristics indicate that the jet emission of FSRQs and BL Lacs is indistinguishable.

In silico Analysis of 1,4-butanediol Heterologous Pathway Impact on Escherichia coli Metabolism

The bio-based synthesis of 1,4-butanediol (BDO), a key compound in many industries, has recently been achieved in Escherichia coli, however the yield even in glucose was far below the theoretical maximum. Furthermore, the impact of the BDO pathway on cell metabolism is yet to be discovered. The main objective of this study was to in silico improve and analyze the production potential of BDO on glucose and glycerol and evaluate the interaction between native and non-native pathways for wild-type and mutant strains using a simple biosynthetic pathway. The maximum production potential and changes in metabolic fluxes were simulated by different objective functions (biomass and BDO) and the reactions with highest differences were identified under different environmental conditions. Considering the outcomes 80% of the reactions with significant flux change were identical for all conditions simulated. Flux variability analysis was carried out to decipher the variation of fluxes and flux span changes (SC) were calculated. To further analyse the reactions with SC over 1 mmol gDW-1h-1 and to calculate the correlation coefficients for WT and mutant strains uniform random sampling was carried out. Most important variations in correlation patterns were observed for reactions in the mutant model. On the other hand, the addition, elimination and optimization of different pathways significantly affected the pairwise correlation patterns as well as the entire solution space of the network. Keywords: 1,4-butanediol, E. coli, flux variability analysis, random sampling, in silico, COBRA

egKnock: identifying direct gene knockout strategies for microbial strain optimization based on metabolic network with gene-protein-reaction relationships

BackgroundGene knockout method has been used to improve the conversion ratio of industrial strains for many chemical products. There are a series of published algorithms to predict the targets for deletion. Based on metabolic networks, many of these algorithms are designed to predict the target of reaction or enzyme deletion. But as for the many-to-many relationship between genes and reactions, reaction or enzyme deletion is not the ideal strategy for metabolic engineering. GDLS algorithm aims to find direct gene deletion target by using local search, but it actually ignores the logic relationship of gene-protein-reaction.ResultsIn this study, we aim to find direct gene deletion targets for metabolic network, but the logic relationship of gene-protein-reaction (GPR) is considered. Our algorithm is call egKnock. At the same time, egKnock will provide the solution with multiple strategies and can maximize the minimum target flux of industrial objective in flux variability analysis. We compare egKnock with the algorithm of GDLS and OptORF by predicting the targets of gene deletion for several chemical products with their flux balance analysis testification, flux variability analysis testification and the main flux distribution.ConclusionsBy comparison with the algorithm of GDLS and OptORF, we can conclude that egKnock is a nice algorithm for identifying direct gene knockout strategies for microbial strain optimization.