Flux Balance Analysis Approach Research Articles

Accurate flux predictions using tissue-specific gene expression in plant metabolic modeling.

The accurate prediction of complex phenotypes such as metabolic fluxes in living systems is a grand challenge for systems biology and central to efficiently identifying biotechnological interventions that can address pressing industrial needs. The application of gene expression data to improve the accuracy of metabolic flux predictions using mechanistic modeling methods such as flux balance analysis (FBA) has not been previously demonstrated in multi-tissue systems, despite their biotechnological importance. We hypothesized that a method for generating metabolic flux predictions informed by relative expression levels between tissues would improve prediction accuracy. Relative gene expression levels derived from multiple transcriptomic and proteomic datasets were integrated into FBA predictions of a multi-tissue, diel model of Arabidopsis thaliana's central metabolism. This integration dramatically improved the agreement of flux predictions with experimentally based flux maps from 13C metabolic flux analysis compared with a standard parsimonious FBA approach. Disagreement between FBA predictions and MFA flux maps was measured using weighted averaged percent error values, and for parsimonious FBA this was169%-180% for high light conditions and 94%-103% for low light conditions, depending on the gene expression dataset used. This fell to 10%-13% and 9%-11% upon incorporating expression data into the modeling process, which also substantially altered the predicted carbon and energy economy of the plant. Code and data generated as part of this study are available from https://github.com/Gibberella/ArabidopsisGeneExpressionWeights.

Analysis of the carbon metabolism of Rhodopseudomonas palustris for biohydrogen production

Hydrogen can be produced renewably and sustainably by the purple non-sulfur bacterium Rhodopseudomonas palustris from sucrose. To improve hydrogen production, detailed insight is needed, which can be obtained by studying the coupling of carbon fluxes with the light utilization apparatus and the hydrogen producing enzymes. In this study, the flux balance analysis approach was used to construct a model of the central carbon metabolism of this organism and solve the resulting network for a chosen objective function. The model was able to closely reproduce key qualitative and quantitative aspects of an independent experimental study. Further insight was obtained by additional case studies. Specifically, it was found that extreme light intensities resulted in the decrease of hydrogen production, that hydrogen production could be possible even when no light is provided, and a mix of sucrose and an organic acid could improve hydrogen production, which can be explained and supported by prior work on this organism. Further investigation is necessary to investigate the connections between metabolic network components, such the antagonistic relationship between hydrogen and polyhydroxybutyrate, which is a reserve product of this microorganism.

Global connectivity in genome-scale metabolic networks revealed by comprehensive FBA-based pathway analysis

BackgroundGraph-based analysis (GBA) of genome-scale metabolic networks has revealed system-level structures such as the bow-tie connectivity that describes the overall mass flow in a network. However, many pathways obtained by GBA are biologically impossible, making it difficult to study how the global structures affect the biological functions of a network. New method that can calculate the biologically relevant pathways is desirable for structural analysis of metabolic networks.ResultsHere, we present a new method to determine the bow-tie connectivity structure by calculating possible pathways between any pairs of metabolites in the metabolic network using a flux balance analysis (FBA) approach to ensure that the obtained pathways are biologically relevant. We tested this method with 15 selected high-quality genome-scale metabolic models from BiGG database. The results confirmed the key roles of central metabolites in network connectivity, locating in the core part of the bow-tie structure, the giant strongly connected component (GSC). However, the sizes of GSCs revealed by GBA are significantly larger than those by FBA approach. A great number of metabolites in the GSC from GBA actually cannot be produced from or converted to other metabolites through a mass balanced pathway and thus should not be in GSC but in other subsets of the bow-tie structure. In contrast, the bow-tie structural classification of metabolites obtained by FBA is more biologically relevant and suitable for the study of the structure-function relationships of genome scale metabolic networks.ConclusionsThe FBA based pathway calculation improve the biologically relevant classification of metabolites in the bow-tie connectivity structure of the metabolic network, taking us one step further toward understanding how such system-level structures impact the biological functions of an organism.

Abstract P1-02-06: Computational modeling predicts drugs response to targeting metabolism in breast cancer cells

Abstract Background Reprogramming of metabolism is a hallmark in cancer. In previous works we observed differences in glucose metabolism between tumors from different breast cancer subtypes, suggesting the possibility to use drugs against metabolism in this disease. Flux Balance Analysis (FBA) is widely used to study biochemical networks, allowing to predict growth rates and to simulate drug response. Material and methods Breast cancer cell lines and different drugs against metabolic targets were evaluated with dose-response curves, and pharmacological parameters for each condition were calculated. Proteomics data from breast cancer cells lines treated with sub-lethal doses and controls were obtained applying a mass spectrometry-based approach. Differences in protein expression between treated vs. control were assessed. An FBA approach using the human metabolic reconstruction Recon2 and including the protein expression values from perturbation experiments was also applied. Model predictions were validated using dynamic FBA and growth rate for each sample was estimated. With the aim to compare the activity of the different pathway fluxes between control and treated cells, flux activity was calculated for each condition and for each pathway and response predictive models were performed. Results Drug response was diverse across different breast cancer cells. Mass spectrometry from cell samples allows identifying and quantifying 4,114 proteins. FBA predicted that growth rates decrease in treated cells vs. control, as observed in cell viability assays. Dynamic FBA showed that our model correctly reflects cell growth rates. Finally, using flux activities, it is possible to build models which could predict response against these drugs. Conclusions Proteomics provide insights of the mechanisms responsible of cells' response to metabolism drugs. A validated computational model able to predict tumor growth using data from proteomics was developed. Model predicts growth rates and also dysregulation of biological processes triggered by drug treatment. Moreover, these computational approaches could be used to propose new mechanisms of action and effects of metabolic drugs. Acknowledgments This work was supported by grant PI15/01310 from Instituto de Salud Carlos III, Spanish Economy and Competitiveness Ministry, Spain and co-funded by FEDER program, “Una forma de hacer Europa”. L.T.-F is supported by Spanish Economy and Competitiveness Ministry grant DI-15-07614. Competing interest JAFV, AG-P and EE are stakeholders of Biomedica Molecular Medicine S.L. and Biomedica Molecular Medicine Ltd. LT-F is an employee of Biomedica Molecular Medicine S.L. The authors have declared no other conflict of interest. Citation Format: Zamora Auñón P, Trilla-Fuertes L, Díaz-Almirón M, Gamez-Pozo A, Prado-Vázquez G, Zapater-Moros A, Llorente-Armijo S, Gaya Romero F, Espinosa Arranz E, Fresno-Vara JA. Computational modeling predicts drugs response to targeting metabolism in breast cancer cells [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P1-02-06.

Modeling and Simulation of Optimal Resource Management during the Diurnal Cycle in Emiliania huxleyi by Genome-Scale Reconstruction and an Extended Flux Balance Analysis Approach.

The coccolithophorid unicellular alga Emiliania huxleyi is known to form large blooms, which have a strong effect on the marine carbon cycle. As a photosynthetic organism, it is subjected to a circadian rhythm due to the changing light conditions throughout the day. For a better understanding of the metabolic processes under these periodically-changing environmental conditions, a genome-scale model based on a genome reconstruction of the E. huxleyi strain CCMP 1516 was created. It comprises 410 reactions and 363 metabolites. Biomass composition is variable based on the differentiation into functional biomass components and storage metabolites. The model is analyzed with a flux balance analysis approach called diurnal flux balance analysis (diuFBA) that was designed for organisms with a circadian rhythm. It allows storage metabolites to accumulate or be consumed over the diurnal cycle, while keeping the structure of a classical FBA problem. A feature of this approach is that the production and consumption of storage metabolites is not defined externally via the biomass composition, but the result of optimal resource management adapted to the diurnally-changing environmental conditions. The model in combination with this approach is able to simulate the variable biomass composition during the diurnal cycle in proximity to literature data.

Exploring the differences in metabolic behavior of astrocyte and glioblastoma: a flux balance analysis approach.

Brain cancers demonstrate a complex metabolic behavior so as to adapt the external hypoxic environment and internal stress generated by reactive oxygen species. To survive in these stringent conditions, glioblastoma cells develop an antagonistic metabolic phenotype as compared to their predecessors, the astrocytes, thereby quenching the resources expected for nourishing the neurons. The complexity and cumulative effect of the large scale metabolic functioning of glioblastoma is mostly unexplored. In this study, we reconstruct a metabolic network comprising of pathways that are known to be deregulated in glioblastoma cells as compared to the astrocytes. The network, consisted of 147 genes encoding for enzymes performing 247 reactions distributed across five distinct model compartments, was then studied using constrained-based modeling approach by recreating the scenarios for astrocytes and glioblastoma, and validated with available experimental evidences. From our analysis, we predict that glycine requirement of the astrocytes are mostly fulfilled by the internal glycine-serine metabolism, whereas glioblastoma cells demand an external uptake of glycine to utilize it for glutathione production. Also, cystine and glucose were identified to be the major contributors to glioblastoma growth. We also proposed an extensive set of single and double lethal reaction knockouts, which were further perturbed to ascertain their role as probable chemotherapeutic targets. These simulation results suggested that, apart from targeting the reactions of central carbon metabolism, knockout of reactions belonging to the glycine-serine metabolism effectively reduce glioblastoma growth. The combinatorial targeting of glycine transporter with any other reaction belonging to glycine-serine metabolism proved lethal to glioblastoma growth.

Developing next generation predictive models: a systems biology approach

The applicability and reliability of existing predictive models can definitely be improved by unraveling the underlying mechanisms and incorporating intracellular (microscopic) information. Following a systems biology approach, the link between the intracellular fluxes and the extracellular measurements is established by techniques of metabolic flux analysis. Flux balance analysis (FBA) uses an objective function to derive, through optimization over the solution space of this underdetermined linear system, an intracellular flux distribution. This paper discusses the background of the FBA approach and focuses on important issues with respect to the implementation of FBA analysis when modeling dynamic systems.

Why does yeast ferment? A flux balance analysis study

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.

Capturing the essence of a metabolic network: A flux balance analysis approach

As genome-scale metabolic reconstructions emerge, tools to manage their size and complexity will be increasingly important. Flux balance analysis (FBA) is a constraint-based approach widely used to study the metabolic capabilities of cellular or subcellular systems. FBA problems are highly underdetermined and many different phenotypes can satisfy any set of constraints through which the metabolic system is represented. Two of the main concerns in FBA are exploring the space of solutions for a given metabolic network and finding a specific phenotype which is representative for a given task such as maximal growth rate. Here, we introduce a recursive algorithm suitable for overcoming both of these concerns. The method proposed is able to find the alternate optimal patterns of active reactions of an FBA problem and identify the minimal subnetwork able to perform a specific task as optimally as the whole. Our method represents an alternative to and an extension of other approaches conceived for exploring the space of solutions of an FBA problem. It may also be particularly helpful in defining a scaffold of reactions upon which to build up a dynamic model, when the important pathways of the system have not yet been well-defined.

Flux Balance Analysis of a Genome‐Scale Yeast Model Constrained by Exometabolomic Data Allows Metabolic System Identification of Genetically Different Strains

A systems approach to biology requires a principled approach to pathway identification. In this study, the two nuclear petite yeast mutants K1Deltapet191a and K1Deltapet191ab and their parental industrial strain K1 were cultured in glucose-containing microaerobic chemostats. Exometabolomic profiles were used to infer the differences in the fermentation characteristics and respiration capacity of the strains. The ability of the metabolite measurement information to describe genetically different strains was investigated using a genome-scale yeast model. Flux balance analysis (FBA) of the model reveals that the objective function of minimal oxygen consumption enables the identification of the effect of genotypic differences when combined with the knowledge of the extracellular state of metabolism. The predicted decrease in oxygen consumption flux of K1Deltapet191a and K1Deltapet191ab strains with respect to the parental strain is about 80% and 100%, respectively, which coincides with the respiratory deficiencies of the strains. The expected increase in ethanol production rates in response to the decrease in the respiratory capacity was also predicted to be very close to the experimental values. This study shows the predictive power of the integrated analysis of genome-scale models with exometabolomic profiles, since accurate predictions could be made without any information about the respiration capacity of the strains. The FBA approach thereby enables identification of responsive pathways and so permits the elucidation of the genetic characteristics of strains in terms of expressed metabolite profiles.

Analysis of Growth of Lactobacillus plantarum WCFS1 on a Complex Medium Using a Genome-scale Metabolic Model

A genome-scale metabolic model of the lactic acid bacterium Lactobacillus plantarum WCFS1 was constructed based on genomic content and experimental data. The complete model includes 721 genes, 643 reactions, and 531 metabolites. Different stoichiometric modeling techniques were used for interpretation of complex fermentation data, as L. plantarum is adapted to nutrient-rich environments and only grows in media supplemented with vitamins and amino acids. (i) Based on experimental input and output fluxes, maximal ATP production was estimated and related to growth rate. (ii) Optimization of ATP production further identified amino acid catabolic pathways that were not previously associated with free-energy metabolism. (iii) Genome-scale elementary flux mode analysis identified 28 potential futile cycles. (iv) Flux variability analysis supplemented the elementary mode analysis in identifying parallel pathways, e.g. pathways with identical end products but different co-factor usage. Strongly increased flexibility in the metabolic network was observed when strict coupling between catabolic ATP production and anabolic consumption was relaxed. These results illustrate how a genome-scale metabolic model and associated constraint-based modeling techniques can be used to analyze the physiology of growth on a complex medium rather than a minimal salts medium. However, optimization of biomass formation using the Flux Balance Analysis approach, reported to successfully predict growth rate and by product formation in Escherichia coli and Saccharomyces cerevisiae, predicted too high biomass yields that were incompatible with the observed lactate production. The reason is that this approach assumes optimal efficiency of substrate to biomass conversion, and can therefore not predict the metabolically inefficient lactate formation.