Flux Variability Analysis Research Articles

Inferring oncoenzymes in a genome-scale metabolic network for hepatocytes using bilevel optimization framework

Abstract Cancer cells exhibit unusual metabolic activity, characterized by high rates of glucose consumption and lactate production, even under aerobic conditions, as well as increased glutamine catabolism and amino acid metabolism. Based on the behaviors of metabolic reprogramming, we can infer oncogenesis from a genome-scale model of cancer cell metabolism. This study establishes a bilevel optimization formulation that integrates the genome-scale metabolic model of hepatocytes, the Warburg hypothesis, and LC/MS experimental data to detect multiple-hit enzyme deficiencies that induce metabolic reprogramming in hepatocytes. A nested hybrid differential evolution algorithm was employed to solve the bilevel optimization problem. The results predicted dopa decarboxylase (DDC) to be an influential enzyme that causes oncogenesis. A cluster analysis of the flux variations for different enzyme deficiencies obtained through a flux variability analysis showed that DDC is a dominant overexpressed enzyme, and it was classified into a group with similar trends of flux and metabolite alternations.

Understanding the Representative Gut Microbiota Dysbiosis in Metformin-Treated Type 2 Diabetes Patients Using Genome-Scale Metabolic Modeling.

Dysbiosis in the gut microbiome composition may be promoted by therapeutic drugs such as metformin, the world’s most prescribed antidiabetic drug. Under metformin treatment, disturbances of the intestinal microbes lead to increased abundance of Escherichia spp., Akkermansia muciniphila, Subdoligranulum variabile and decreased abundance of Intestinibacter bartlettii. This alteration may potentially lead to adverse effects on the host metabolism, with the depletion of butyrate producer genus. However, an increased production of butyrate and propionate was verified in metformin-treated Type 2 diabetes (T2D) patients. The mechanisms underlying these nutritional alterations and their relation with gut microbiota dysbiosis remain unclear. Here, we used Genome-scale Metabolic Models of the representative gut bacteria Escherichia spp., I. bartlettii, A. muciniphila, and S. variabile to elucidate their bacterial metabolism and its effect on intestinal nutrient pool, including macronutrients (e.g., amino acids and short chain fatty acids), minerals and chemical elements (e.g., iron and oxygen). We applied flux balance analysis (FBA) coupled with synthetic lethality analysis interactions to identify combinations of reactions and extracellular nutrients whose absence prevents growth. Our analyses suggest that Escherichia sp. is the bacteria least vulnerable to nutrient availability. We have also examined bacterial contribution to extracellular nutrients including short chain fatty acids, amino acids, and gasses. For instance, Escherichia sp. and S. variabile may contribute to the production of important short chain fatty acids (e.g., acetate and butyrate, respectively) involved in the host physiology under aerobic and anaerobic conditions. We have also identified pathway susceptibility to nutrient availability and reaction changes among the four bacteria using both FBA and flux variability analysis. For instance, lipopolysaccharide synthesis, nucleotide sugar metabolism, and amino acid metabolism are pathways susceptible to changes in Escherichia sp. and A. muciniphila. Our observations highlight important commensal and competing behavior, and their association with cellular metabolism for prevalent gut microbes. The results of our analysis have potential important implications for development of new therapeutic approaches in T2D patients through the development of prebiotics, probiotics, or postbiotics.

Accelerating flux balance calculations in genome-scale metabolic models by localizing the application of loopless constraints.

Genome-scale metabolic network models and constraint-based modeling techniques have become important tools for analyzing cellular metabolism. Thermodynamically infeasible cycles (TICs) causing unbounded metabolic flux ranges are often encountered. TICs satisfy the mass balance and directionality constraints but violate the second law of thermodynamics. Current practices involve implementing additional constraints to ensure not only optimal but also loopless flux distributions. However, the mixed integer linear programming problems required to solve become computationally intractable for genome-scale metabolic models. We aimed to identify the fewest needed constraints sufficient for optimality under the loopless requirement. We found that loopless constraints are required only for the reactions that share elementary flux modes representing TICs with reactions that are part of the objective function. We put forth the concept of localized loopless constraints (LLCs) to enforce this minimal required set of loopless constraints. By combining with a novel procedure for minimal null-space calculation, the computational time for loopless flux variability analysis (ll-FVA) is reduced by a factor of 10-150 compared to the original loopless constraints and by 4-20 times compared to the current fastest method Fast-SNP with the percent improvement increasing with model size. Importantly, LLCs offer a scalable strategy for loopless flux calculations for multi-compartment/multi-organism models of large sizes, for example, shortening the CPU time for ll-FVA from 35 h to less than 2 h for a model with more than104 reactions. Matlab functions are available in the Supplementary Material or at https://github.com/maranasgroup/lll-FVA. Supplementary data are available at Bioinformatics online.

Exploring candidate biomarkers for lung and prostate cancers using gene expression and flux variability analysis.

Genome-scale metabolic models have provided valuable resources for exploring changes in metabolism under normal and cancer conditions. However, metabolism itself is strongly linked to gene expression, so integration of gene expression data into metabolic models might improve the detection of genes involved in the control of tumor progression. Herein, we considered gene expression data as extra constraints to enhance the predictive powers of metabolic models. We reconstructed genome-scale metabolic models for lung and prostate, under normal and cancer conditions to detect the major genes associated with critical subsystems during tumor development. Furthermore, we utilized gene expression data in combination with an information theory-based approach to reconstruct co-expression networks of the human lung and prostate in both cohorts. Our results revealed 19 genes as candidate biomarkers for lung and prostate cancer cells. This study also revealed that the development of a complementary approach (integration of gene expression and metabolic profiles) could lead to proposing novel biomarkers and suggesting renovated cancer treatment strategies which have not been possible to detect using either of the methods alone.

Analyses of extracellular protein production in Bacillus subtilis – I: Genome-scale metabolic model reconstruction based on updated gene-enzyme-reaction data

Bacillus subtilis genome-scale model (GEM) reconstruction was stimulated by the recent sequencing and consequent re-annotations. The updated gene-enzyme-reaction data were collected from databases to reconstruct B. subtilis reaction network BsRN-2016 containing 1144 genes linked to 1955 reactions and 1103 metabolites. Thermodynamic analysis was conducted to identify reversibility and directionality of the reactions. By elimination of unconnected-reactions from BsRN-2016, reconstruction process of the first third-generation GEM iBsu1144 employing 1083 reactions linked to 719 genes was completed. The stoichiometric flux-balance based model was solved using time-profiles of serine alkaline protease fermentation-data at three different oxygen-transfer conditions creating perturbations on the intracellular reaction-network. Testing iBsu1144 dignity with three different objective functions indicated superior robustness of the GEM. Comparison of iBsu1144 results with a second-generation GEM was demonstrated. Insights obtained from flux distributions were used to determine metabolic engineering sites. Asparagine, isoleucine, threonine, and aspartic acid were determined as the primary rate-limiting amino acids to be considered as corresponding metabolic engineering sites in SAP synthesis in B. subtilis. Flux variability analysis carried out for the optimum condition reveals that 288 reactions are active and linked to 317 genes, so called substantial genes. The blocked 735 reactions linked to 533 genes, formed a platform for guided gene deletions in B. subtilis to generate simplified host strains.

SteadyCom: Predicting microbial abundances while ensuring community stability.

Genome-scale metabolic modeling has become widespread for analyzing microbial metabolism. Extending this established paradigm to more complex microbial communities is emerging as a promising way to unravel the interactions and biochemical repertoire of these omnipresent systems. While several modeling techniques have been developed for microbial communities, little emphasis has been placed on the need to impose a time-averaged constant growth rate across all members for a community to ensure co-existence and stability. In the absence of this constraint, the faster growing organism will ultimately displace all other microbes in the community. This is particularly important for predicting steady-state microbiota composition as it imposes significant restrictions on the allowable community membership, composition and phenotypes. In this study, we introduce the SteadyCom optimization framework for predicting metabolic flux distributions consistent with the steady-state requirement. SteadyCom can be rapidly converged by iteratively solving linear programming (LP) problem and the number of iterations is independent of the number of organisms. A significant advantage of SteadyCom is compatibility with flux variability analysis. SteadyCom is first demonstrated for a community of four E. coli double auxotrophic mutants and is then applied to a gut microbiota model consisting of nine species, with representatives from the phyla Bacteroidetes, Firmicutes, Actinobacteria and Proteobacteria. In contrast to the direct use of FBA, SteadyCom is able to predict the change in species abundance in response to changes in diets with minimal additional imposed constraints on the model. By randomizing the uptake rates of microbes, an abundance profile with a good agreement to experimental gut microbiota is inferred. SteadyCom provides an important step towards the cross-cutting task of predicting the composition of a microbial community in a given environment.

A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.

Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs) for simulating single-strain metabolic networks, we sought to study microbial ecosystems as a combination of single-strain metabolic networks that exchange nutrients. This study presents two multi-objective extensions of CBMs for modeling communities: multi-objective flux balance analysis (MO-FBA) and multi-objective flux variability analysis (MO-FVA). Both methods were applied to a hot spring mat model ecosystem. As a result, multiple trade-offs between nutrients and growth rates, as well as thermodynamically favorable relative abundances at community level, were emphasized. We expect this approach to be used for integrating genomic information in microbial ecosystems. Following models will provide insights about behaviors (including diversity) that take place at the ecosystem scale.

The use of genome-scale metabolic network reconstruction to predict fluxes and equilibrium composition of N-fixing versus C-fixing cells in a diazotrophic cyanobacterium, Trichodesmium erythraeum

BackgroundComputational, genome based predictions of organism phenotypes has enhanced the ability to investigate the biological phenomena that help organisms survive and respond to their environments. In this study, we have created the first genome-scale metabolic network reconstruction of the nitrogen fixing cyanobacterium T. erythraeum and used genome-scale modeling approaches to investigate carbon and nitrogen fluxes as well as growth and equilibrium population composition.ResultsWe created a genome-scale reconstruction of T. erythraeum with 971 reactions, 986 metabolites, and 647 unique genes. We then used data from previous studies as well as our own laboratory data to establish a biomass equation and two distinct submodels that correspond to the two cell types formed by T. erythraeum. We then use flux balance analysis and flux variability analysis to generate predictions for how metabolism is distributed to account for the unique productivity of T. erythraeum. Finally, we used in situ data to constrain the model, infer time dependent population compositions and metabolite production using dynamic Flux Balance Analysis. We find that our model predicts equilibrium compositions similar to laboratory measurements, approximately 15.5% diazotrophs for our model versus 10-20% diazotrophs reported in literature. We also found that equilibrium was the most efficient mode of growth and that equilibrium was stoichiometrically mediated. Moreover, the model predicts that nitrogen leakage is an essential condition of optimality for T. erythraeum; cells leak approximately 29.4% total fixed nitrogen when growing at the optimal growth rate, which agrees with values observed in situ.ConclusionThe genome-metabolic network reconstruction allows us to use constraints based modeling approaches to predict growth and optimal cellular composition in T. erythraeum colonies. Our predictions match both in situ and laboratory data, indicating that stoichiometry of metabolic reactions plays a large role in the differentiation and composition of different cell types. In order to realize the full potential of the model, advance modeling techniques which account for interactions between colonies, the environment and surrounding species need to be developed.

SolveME: fast and reliable solution of nonlinear ME models.

BackgroundGenome-scale models of metabolism and macromolecular expression (ME) significantly expand the scope and predictive capabilities of constraint-based modeling. ME models present considerable computational challenges: they are much (>30 times) larger than corresponding metabolic reconstructions (M models), are multiscale, and growth maximization is a nonlinear programming (NLP) problem, mainly due to macromolecule dilution constraints.ResultsHere, we address these computational challenges. We develop a fast and numerically reliable solution method for growth maximization in ME models using a quad-precision NLP solver (Quad MINOS). Our method was up to 45 % faster than binary search for six significant digits in growth rate. We also develop a fast, quad-precision flux variability analysis that is accelerated (up to 60× speedup) via solver warm-starts. Finally, we employ the tools developed to investigate growth-coupled succinate overproduction, accounting for proteome constraints.ConclusionsJust as genome-scale metabolic reconstructions have become an invaluable tool for computational and systems biologists, we anticipate that these fast and numerically reliable ME solution methods will accelerate the wide-spread adoption of ME models for researchers in these fields.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1240-1) contains supplementary material, which is available to authorized users.

A methodology for building a macroscopic FBA-based dynamical simulator of cell cultures through flux variability analysis

A dynamical FBA-based simulator of hybridoma cell fed-batch cultures predicting the dynamics of biomass growth, substrate consumption (glucose and glutamine), metabolites production (lactate, ammonium and alanine) and associate intracellular metabolism based on a simplified metabolic network is proposed. A preliminary comparison between the range of admissible flux distribution obtained based on all available measurements (the two substrates uptake rates and the three metabolite production rates) and based on only part of them (only the two substrates uptake rates) is performed to deal with the usual problem of system underdetermination in constraint-based modeling context. This comparative flux variability analysis allows the objective identification of some additional constraints (to be used in the final FBA-based simulator) so as to obtain similar admissible flux intervals in both cases. Moreover, the proposed approach legitimates the cost criterion used for the linear optimization, i.e. cell growth maximization. This methodology is validated on experimental data of two fed-batch cultures and cross validated on a batch culture of hybridoma cells HB-58. The flux distribution results are in agreement with overflow metabolism description available in literature.

Location and origin of gamma-rays in blazars

One of the most intriguing and challenging quests of current astrophysics is to understand the physical conditions and processes responsible for production of high-energy particles, and emission of γ-rays. A combination of high-resolution Very Long Baseline Interferometry (VLBI) images with broadband flux variability measurements is a unique way to probe the emission mechanisms at the bases of jets. Our analysis of γ-ray flux variability observed by the Fermi-LAT (Large Area Telescope) along with the parsec-scale jet kinematics suggests that the γ-ray emission in blazar S5 0716+714 has a significant correlation with the mm-VLBI core flux and the orientation of jet outflow on parsec scales. These results indicate that the inner jet morphology has a tight connection with the observed γ-ray flares. An overview of our current understanding on high-energy radiation processes, their origin, and location is presented here.

Genome-scale metabolic modeling of Mucor circinelloides and comparative analysis with other oleaginous species

We present a novel genome-scale metabolic model iWV1213 of Mucor circinelloides, which is an oleaginous fungus for industrial applications. The model contains 1213 genes, 1413 metabolites and 1326 metabolic reactions across different compartments. We demonstrate that iWV1213 is able to accurately predict the growth rates of M. circinelloides on various nutrient sources and culture conditions using Flux Balance Analysis and Phenotypic Phase Plane analysis. Comparative analysis of three oleaginous genome-scale models, including M. circinelloides (iWV1213), Mortierella alpina (iCY1106) and Yarrowia lipolytica (iYL619_PCP) revealed that iWV1213 possesses a higher number of genes involved in carbohydrate, amino acid, and lipid metabolisms that might contribute to its versatility in nutrient utilization. Moreover, the identification of unique and common active reactions among the Zygomycetes oleaginous models using Flux Variability Analysis unveiled a set of gene/enzyme candidates as metabolic engineering targets for cellular improvement. Thus, iWV1213 offers a powerful metabolic engineering tool for multi-level omics analysis, enabling strain optimization as a cell factory platform of lipid-based production.

PreProPath: An Uncertainty-Aware Algorithm for Identifying Predictable Profitable Pathways in Biochemical Networks.

Pathway analysis is a powerful approach to enable rational design or redesign of biochemical networks for optimizing metabolic engineering and synthetic biology objectives such as production of desired chemicals or biomolecules from specific nutrients. While experimental methods can be quite successful, computational approaches can enhance discovery and guide experimentation by efficiently exploring very large design spaces. We present a computational algorithm, Predictably Profitable Path (PreProPath), to identify target pathways best suited for engineering modifications. The algorithm utilizes uncertainties about the metabolic networks operating state inherent in the underdetermined linear equations representing the stoichiometric model. Flux Variability Analysis is used to determine the operational flux range. PreProPath identifies a path that is predictable in behavior, exhibiting small flux ranges, and profitable, containing the least restrictive flux-limiting reaction in the network. The algorithm is computationally efficient because it does not require enumeration of pathways. The results of case studies show that PreProPath can efficiently analyze variances in metabolic states and model uncertainties to suggest pathway engineering strategies that have been previously supported by experimental data.

Systems biology study of mucopolysaccharidosis using a human metabolic reconstruction network.

Mucopolysaccharidosis (MPS) is a group of lysosomal storage diseases (LSD), characterized by the deficiency of a lysosomal enzyme responsible for the degradation of glycosaminoglycans (GAG). This deficiency leads to the lysosomal accumulation of partially degraded GAG. Nevertheless, deficiency of a single lysosomal enzyme has been associated with impairment in other cell mechanism, such as apoptosis and redox balance. Although GAG analysis represents the main biomarker for MPS diagnosis, it has several limitations that can lead to a misdiagnosis, whereby the identification of new biomarkers represents an important issue for MPS. In this study, we used a system biology approach, through the use of a genome-scale human metabolic reconstruction to understand the effect of metabolism alterations in cell homeostasis and to identify potential new biomarkers in MPS. In-silico MPS models were generated by silencing of MPS-related enzymes, and were analyzed through a flux balance and variability analysis. We found that MPS models used approximately 2286 reactions to satisfy the objective function. Impaired reactions were mainly involved in cellular respiration, mitochondrial process, amino acid and lipid metabolism, and ion exchange. Metabolic changes were similar for MPS I and II, and MPS III A to C; while the remaining MPS showed unique metabolic profiles. Eight and thirteen potential high-confidence biomarkers were identified for MPS IVB and VII, respectively, which were associated with the secondary pathologic process of LSD. In vivo evaluation of predicted intermediate confidence biomarkers (β-hexosaminidase and β-glucoronidase) for MPS IVA and VI correlated with the in-silico prediction. These results show the potential of a computational human metabolic reconstruction to understand the molecular mechanisms this group of diseases, which can be used to identify new biomarkers for MPS.

Flux Balance Analysis with Objective Function Defined by Proteomics Data-Metabolism of Mycobacterium tuberculosis Exposed to Mefloquine.

We present a study of the metabolism of the Mycobacterium tuberculosis after exposure to antibiotics using proteomics data and flux balance analysis (FBA). The use of FBA to study prokaryotic organisms is well-established and allows insights into the metabolic pathways chosen by the organisms under different environmental conditions. To apply FBA a specific objective function must be selected that represents the metabolic goal of the organism. FBA estimates the metabolism of the cell by linear programming constrained by the stoichiometry of the reactions in an in silico metabolic model of the organism. It is assumed that the metabolism of the organism works towards the specified objective function. A common objective is the maximization of biomass. However, this goal is not suitable for situations when the bacterium is exposed to antibiotics, as the goal of organisms in these cases is survival and not necessarily optimal growth. In this paper we propose a new approach for defining the FBA objective function in studies when the bacterium is under stress. The function is defined based on protein expression data. The proposed methodology is applied to the case when the bacterium is exposed to the drug mefloquine, but can be easily extended to other organisms, conditions or drugs. We compare our method with an alternative method that uses experimental data for adjusting flux constraints. We perform comparisons in terms of essential enzymes and agreement using enzyme abundances. Results indicate that using proteomics data to define FBA objective functions yields less essential reactions with zero flux and lower error rates in prediction accuracy. With flux variability analysis we observe that overall variability due to alternate optima is reduced with the incorporation of proteomics data. We believe that incorporating proteomics data in the objective function used in FBA may help obtain metabolic flux representations that better support experimentally observed features.

Flux Balance Analysis and Thermodynamics: Trends and Strategies in Disease Biology

Computational systems biology emphasizes on modeling biological systems mathematically and computationally for analyzing their role in solving complex processes. One of the applications of systems biology is understanding disease networks, for which various strategies such as, decisionmaking stochastic networks, graph theory based methods, robust control theory, elementary flux modes, extreme pathways, convex analysis, flux variability analysis and phenotype phase planes are used. For judging the stability of a metabolic network, a sound knowledge of bioenergetics as well as free energy concepts regarding metabolic systems are essential. In this manuscript, we have explored the possible application of flux balance analysis in disease networks in correspondence with group contribution method pertaining to the field of thermodynamics for studying the stability of biological systems. Keywords: Carnitine translocase, flux balance analysis, Gibbs free energy, group contribution method, metabolic network, systems biology.

Flux balance analysis reveals acetate metabolism modulates cyclic electron flow and alternative glycolytic pathways in Chlamydomonas reinhardtii.

Cells of the green alga Chlamydomonas reinhardtii cultured in the presence of acetate perform mixotrophic growth, involving both photosynthesis and organic carbon assimilation. Under such conditions, cells exhibit a reduced capacity for photosynthesis but a higher growth rate, compared to phototrophic cultures. Better understanding of the down regulation of photosynthesis would enable more efficient conversion of carbon into valuable products like biofuels. In this study, Flux Balance Analysis (FBA) and Flux Variability Analysis (FVA) have been used with a genome scale model of C. reinhardtii to examine changes in intracellular flux distribution in order to explain their changing physiology. Additionally, a reaction essentiality analysis was performed to identify which reaction subsets are essential for a given growth condition. Our results suggest that exogenous acetate feeds into a modified tricarboxylic acid (TCA) cycle, which bypasses the CO2 evolution steps, explaining increases in biomass, consistent with experimental data. In addition, reactions of the oxidative pentose phosphate and glycolysis pathways, inactive under phototrophic conditions, show substantial flux under mixotrophic conditions. Importantly, acetate addition leads to an increased flux through cyclic electron flow (CEF), but results in a repression of CO2 fixation via Rubisco, explaining the down regulation of photosynthesis. However, although CEF enhances growth on acetate, it is not essential—impairment of CEF results in alternative metabolic pathways being increased. We have demonstrated how the reactions of photosynthesis interconnect with carbon metabolism on a global scale, and how systems approaches play a viable tool in understanding complex relationships at the scale of the organism.

RobOKoD: microbial strain design for (over)production of target compounds.

Sustainable production of target compounds such as biofuels and high-value chemicals for pharmaceutical, agrochemical, and chemical industries is becoming an increasing priority given their current dependency upon diminishing petrochemical resources. Designing these strains is difficult, with current methods focusing primarily on knocking-out genes, dismissing other vital steps of strain design including the overexpression and dampening of genes. The design predictions from current methods also do not translate well-into successful strains in the laboratory. Here, we introduce RobOKoD (Robust, Overexpression, Knockout and Dampening), a method for predicting strain designs for overproduction of targets. The method uses flux variability analysis to profile each reaction within the system under differing production percentages of target-compound and biomass. Using these profiles, reactions are identified as potential knockout, overexpression, or dampening targets. The identified reactions are ranked according to their suitability, providing flexibility in strain design for users. The software was tested by designing a butanol-producing Escherichia coli strain, and was compared against the popular OptKnock and RobustKnock methods. RobOKoD shows favorable design predictions, when predictions from these methods are compared to a successful butanol-producing experimentally-validated strain. Overall RobOKoD provides users with rankings of predicted beneficial genetic interventions with which to support optimized strain design.

FERMIMONITORING OF RADIO-LOUD NARROW-LINE SEYFERT 1 GALAXIES

We present detailed analysis of the gamma-ray flux variability and spectral properties of the five radio-loud narrow line Seyfert 1 (RL-NLSy1) galaxies, detected by the Large Area Telescope onboard the Fermi Gamma-ray Space Telescope, namely 1H 0323+342, SBS 0846+513, PMN J0948+0022, PKS 1502+036, and PKS 2004$-$447. The first three sources show significant flux variations including the rapid variability of a few hours by 1H 0323+342. The average gamma-ray spectrum of 1H 0323+342 and PMN J0948+0022 shows deviation from a simple power law (PL) behavior, whereas for other three sources, PL model gives better fit. The spectra of 1H 0323+342, SBS 0846+513, and PMN J0948+0022, respectively in low, flaring, and moderately active states, show significant curvature. Such curvature in the gamma-ray spectrum of 1H 0323+342 and PMN J0948+0022 can be due to emission region located inside the broad line region (BLR) where the primary mechanism of the gamma-ray emission is inverse-Compton (IC) scattering of BLR photons occurring in the Klein-Nishina regime. The gamma-ray emission of SBS 0846+513 is explained by IC scattering of dusty torus photons which puts the emission region to be outside the BLR and thus under the Thomson regime. Therefore, the observed curvature of SBS 0846+513 could be intrinsic to the particle energy distribution. The presence of curvature in the gamma-ray spectrum and flux variability amplitudes of some of the RL-NLSy1 galaxies suggest that these sources could be akin to low/moderate jet power flat spectrum radio quasars.

Designing metabolic engineering strategies with genome-scale metabolic flux modeling

Designing metabolic engineering strategies with genome-scale metabolic flux modeling Jiun Y Yen,1,2 Imen Tanniche,1 Amanda K Fisher,1–3 Glenda E Gillaspy,2 David R Bevan,2,3 Ryan S Senger,1 1Department of Biological Systems Engineering, 2Department of Biochemistry, 3Genomics, Bioinformatics, and Computational Biology Interdisciplinary Program, Virginia Tech, Blacksburg, VA, USA Abstract: New in silico tools that make use of genome-scale metabolic flux modeling are improving the design of metabolic engineering strategies. This review highlights the latest developments in this area, explains the interface between these in silico tools and the experimental implementation tools of metabolic engineers, and provides a way forward so that in silico predictions can better mimic reality and more experimental methods can be considered in simulation studies. The several methodologies for solving genome-scale models (eg, flux balance analysis [FBA], parsimonious FBA, flux variability analysis, and minimization of metabolic adjustment) all have unique advantages and applications. There are two basic approaches to designing metabolic engineering strategies in silico, and both have demonstrated success in the literature. The first involves: 1) making a genetic manipulation in a model; 2) testing for improved performance through simulation; and 3) iterating the process. The second approach has been used in more recently designed in silico tools and involves: 1) comparing metabolic flux profiles of a wild-type and ideally engineered state and 2) designing engineering strategies based on the differences in these flux profiles. Improvements in genome-scale modeling are anticipated in areas such as the inclusion of all relevant cellular machinery, the ability to understand and anticipate the results of combinatorial enrichment experiments, and constructing dynamic and flexible biomass equations that can respond to environmental and genetic manipulations. Keywords: genome-scale modeling, genome-scale modeling, flux balance analysis, flux variability analysis, minimization of metabolic adjustment, metabolic bottleneck, pathway optimization