Finding elementary flux modes in metabolic networks based on flux balance analysis and flux coupling analysis: application to the analysis of Escherichia coli metabolism

Elementary modes (EMs) are steady-state metabolic flux vectors with minimal set of active reactions. Each EM corresponds to a metabolic pathway. Therefore, studying EMs is helpful for analyzing the production of biotechnologically important metabolites. However, memory requirements for computing EMs may hamper their applicability as, in most genome-scale metabolic models, no EM can be computed due to running out of memory. In this study, we present a method for computing randomly sampled EMs. In this approach, a network reduction algorithm is used for EM computation, which is based on flux balance-based methods. We show that this approach can be used to recover the EMs in the medium- and genome-scale metabolic network models, while the EMs are sampled in an unbiased way. The applicability of such results is shown by computing “estimated” control-effective flux values in Escherichia coli metabolic network.     

5′-AMP-activated Protein Kinase (AMPK) Supports the Growth of Aggressive Experimental Human Breast Cancer Tumors

Rapid tumor growth can establish metabolically stressed microenvironments that activate 5′-AMP-activated protein kinase (AMPK), a ubiquitous regulator of ATP homeostasis. Previously, we investigated the importance of AMPK for the growth of experimental tumors prepared from HRAS-transformed mouse embryo fibroblasts and for primary brain tumor development in a rat model of neurocarcinogenesis. Here, we used triple-negative human breast cancer cells in which AMPK activity had been knocked down to investigate the contribution of AMPK to experimental tumor growth and core glucose metabolism. We found that AMPK supports the growth of fast-growing orthotopic tumors prepared from MDA-MB-231 and DU4475 breast cancer cells but had no effect on the proliferation or survival of these cells in culture. We used in vitro and in vivo metabolic profiling with [¹³C]glucose tracers to investigate the contribution of AMPK to core glucose metabolism in MDA-MB-231 cells, which have a Warburg metabolic phenotype; these experiments indicated that AMPK supports tumor glucose metabolism in part through positive regulation of glycolysis and the nonoxidative pentose phosphate cycle. We also found that AMPK activity in the MDA-MB-231 tumors could systemically perturb glucose homeostasis in sensitive normal tissues (liver and pancreas). Overall, our findings suggest that the contribution of AMPK to the growth of aggressive experimental tumors has a critical microenvironmental component that involves specific regulation of core glucose metabolism.     

Molecular Mechanism of SLC5A8 Inactivation in Breast Cancer

SLC5A8 is a putative tumor suppressor that is inactivated in more than 10 different types of cancer, but neither the oncogenic signaling responsible for SLC5A8 inactivation nor the functional relevance of SLC5A8 loss to tumor growth has been elucidated. Here, we identify oncogenic HRAS (HRAS^G12V) as a potent mediator of SLC5A8 silencing in human nontransformed normal mammary epithelial cell lines and in mouse mammary tumors through DNMT1. Further, we demonstrate that loss of Slc5a8 increases cancer-initiating stem cell formation and promotes mammary tumorigenesis and lung metastasis in an HRAS-driven murine model of mammary tumors. Mammary-gland-specific overexpression of Slc5a8 (mouse mammary tumor virus-Slc5a8 transgenic mice), as well as induction of endogenous Slc5a8 in mice with inhibitors of DNA methylation, protects against HRAS-driven mammary tumors. Collectively, our results provide the tumor-suppressive role of SLC5A8 and identify the oncogenic HRAS as a mediator of tumor-associated silencing of this tumor suppressor in mammary glands. These findings suggest that pharmacological approaches to reactivate SLC5A8 expression in tumor cells have potential as a novel therapeutic strategy for breast cancer treatment.     

Physiologically Shrinking the Solution Space of a Saccharomyces cerevisiae Genome-Scale Model Suggests the Role of the Metabolic Network in Shaping Gene Expression Noise

Sampling the solution space of genome-scale models is generally conducted to determine the feasible region for metabolic flux distribution. Because the region for actual metabolic states resides only in a small fraction of the entire space, it is necessary to shrink the solution space to improve the predictive power of a model. A common strategy is to constrain models by integrating extra datasets such as high-throughput datasets and C¹³-labeled flux datasets. However, studies refining these approaches by performing a meta-analysis of massive experimental metabolic flux measurements, which are closely linked to cellular phenotypes, are limited. In the present study, experimentally identified metabolic flux data from 96 published reports were systematically reviewed. Several strong associations among metabolic flux phenotypes were observed. These phenotype-phenotype associations at the flux level were quantified and integrated into a Saccharomyces cerevisiae genome-scale model as extra physiological constraints. By sampling the shrunken solution space of the model, the metabolic flux fluctuation level, which is an intrinsic trait of metabolic reactions determined by the network, was estimated and utilized to explore its relationship to gene expression noise. Although no correlation was observed in all enzyme-coding genes, a relationship between metabolic flux fluctuation and expression noise of genes associated with enzyme-dosage sensitive reactions was detected, suggesting that the metabolic network plays a role in shaping gene expression noise. Such correlation was mainly attributed to the genes corresponding to non-essential reactions, rather than essential ones. This was at least partially, due to regulations underlying the flux phenotype-phenotype associations. Altogether, this study proposes a new approach in shrinking the solution space of a genome-scale model, of which sampling provides new insights into gene expression noise.     

Metabolite-centric approaches for the discovery of antibacterials using genome-scale metabolic networks

Development of genome-scale metabolic models and various constraints-based flux analyses have enabled more sophisticated examination of metabolism. Recently reported metabolite essentiality studies are also based on the constraints-based modeling, but approaches metabolism from a metabolite-centric perspective, providing synthetic lethal combination of reactions and clues for the rational discovery of antibacterials. In this study, metabolite essentiality analysis was applied to the genome-scale metabolic models of four microorganisms: Escherichia coli, Helicobacter pylori, Mycobacterium tuberculosis and Staphylococcus aureus. Furthermore, chokepoints, metabolites surrounded by enzymes that uniquely consume and/or produce them, were also calculated based on the network properties of the above organisms. A systematic drug targeting strategy was developed by combining information from these two methods. Final drug target metabolites are presented and examined with knowledge from the literature.     

Genome-scale metabolic model for Lactococcus lactis MG1363 and its application to the analysis of flavor formation

Lactococcus lactis subsp. cremoris MG1363 is a paradigm strain for lactococci used in industrial dairy fermentations. However, despite of its importance for process development, no genome-scale metabolic model has been reported thus far. Moreover, current models for other lactococci only focus on growth and sugar degradation. A metabolic model that includes nitrogen metabolism and flavor-forming pathways is instrumental for the understanding and designing new industrial applications of these lactic acid bacteria. A genome-scale, constraint-based model of the metabolism and transport in L. lactis MG1363, accounting for 518 genes, 754 reactions, and 650 metabolites, was developed and experimentally validated. Fifty-nine reactions are directly or indirectly involved in flavor formation. Flux Balance Analysis and Flux Variability Analysis were used to investigate flux distributions within the whole metabolic network. Anaerobic carbon-limited continuous cultures were used for estimating the energetic parameters. A thorough model-driven analysis showing a highly flexible nitrogen metabolism, e.g., branched-chain amino acid catabolism which coupled with the redox balance, is pivotal for the prediction of the formation of different flavor compounds. Furthermore, the model predicted the formation of volatile sulfur compounds as a result of the fermentation. These products were subsequently identified in the experimental fermentations carried out. Thus, the genome-scale metabolic model couples the carbon and nitrogen metabolism in L. lactis MG1363 with complete known catabolic pathways leading to flavor formation. The model provided valuable insights into the metabolic networks underlying flavor formation and has the potential to contribute to new developments in dairy industries and cheese-flavor research.     

KRAS Genotype Correlates with Proteasome Inhibitor Ixazomib Activity in Preclinical In Vivo Models of Colon and Non-Small Cell Lung Cancer: Potential Role of Tumor Metabolism

In non-clinical studies, the proteasome inhibitor ixazomib inhibits cell growth in a broad panel of solid tumor cell lines in vitro. In contrast, antitumor activity in xenograft tumors is model-dependent, with some solid tumors showing no response to ixazomib. In this study we examined factors responsible for ixazomib sensitivity or resistance using mouse xenograft models. A survey of 14 non-small cell lung cancer (NSCLC) and 6 colon xenografts showed a striking relationship between ixazomib activity and KRAS genotype; tumors with wild-type (WT) KRAS were more sensitive to ixazomib than tumors harboring KRAS activating mutations. To confirm the association between KRAS genotype and ixazomib sensitivity, we used SW48 isogenic colon cancer cell lines. Either KRAS-G13D or KRAS-G12V mutations were introduced into KRAS-WT SW48 cells to generate cells that stably express activated KRAS. SW48 KRAS WT tumors, but neither SW48-KRAS-G13D tumors nor SW48-KRAS-G12V tumors, were sensitive to ixazomib in vivo. Since activated KRAS is known to be associated with metabolic reprogramming, we compared metabolite profiling of SW48-WT and SW48-KRAS-G13D tumors treated with or without ixazomib. Prior to treatment there were significant metabolic differences between SW48 WT and SW48-KRAS-G13D tumors, reflecting higher oxidative stress and glucose utilization in the KRAS-G13D tumors. Ixazomib treatment resulted in significant metabolic regulation, and some of these changes were specific to KRAS WT tumors. Depletion of free amino acid pools and activation of GCN2-eIF2α-pathways were observed both in tumor types. However, changes in lipid beta oxidation were observed in only the KRAS WT tumors. The non-clinical data presented here show a correlation between KRAS genotype and ixazomib sensitivity in NSCLC and colon xenografts and provide new evidence of regulation of key metabolic pathways by proteasome inhibition.     

Genome-Scale NAD(H/+) Availability Patterns as a Differentiating Feature between Saccharomyces cerevisiae and Scheffersomyces stipitis in Relation to Fermentative Metabolism

Scheffersomyces stipitis is a yeast able to ferment pentoses to ethanol, unlike Saccharomyces cerevisiae, it does not present the so-called overflow phenomenon. Metabolic features characterizing the presence or not of this phenomenon have not been fully elucidated. This work proposes that genome-scale metabolic response to variations in NAD(H/⁺) availability characterizes fermentative behavior in both yeasts. Thus, differentiating features in S. stipitis and S. cerevisiae were determined analyzing growth sensitivity response to changes in available reducing capacity in relation to ethanol production capacity and overall metabolic flux span. Using genome-scale constraint-based metabolic models, phenotypic phase planes and shadow price analyses, an excess of available reducing capacity for growth was found in S. cerevisiae at every metabolic phenotype where growth is limited by oxygen uptake, while in S. stipitis this was observed only for a subset of those phenotypes. Moreover, by using flux variability analysis, an increased metabolic flux span was found in S. cerevisiae at growth limited by oxygen uptake, while in S. stipitis flux span was invariant. Therefore, each yeast can be characterized by a significantly different metabolic response and flux span when growth is limited by oxygen uptake, both features suggesting a higher metabolic flexibility in S. cerevisiae. By applying an optimization-based approach on the genome-scale models, three single reaction deletions were found to generate in S. stipitis the reducing capacity availability pattern found in S. cerevisiae, two of them correspond to reactions involved in the overflow phenomenon. These results show a close relationship between the growth sensitivity response given by the metabolic network and fermentative behavior.     

Mitophagy protein PINK1 suppresses colon tumor growth by metabolic reprogramming via p53 activation and reducing acetyl-CoA production

Colorectal cancer (CRC) is the third leading cause of cancer-related deaths in the US. Understanding the mechanisms of CRC progression is essential to improve treatment. Mitochondria is the powerhouse for healthy cells. However, in tumor cells, less energy is produced by the mitochondria and metabolic reprogramming is an early hallmark of cancer. The metabolic differences between normal and cancer cells are being interrogated to uncover new therapeutic approaches. Mitochondria targeting PTEN-induced kinase 1 (PINK1) is a key regulator of mitophagy, the selective elimination of damaged mitochondria by autophagy. Defective mitophagy is increasingly associated with various diseases including CRC. However, a significant gap exists in our understanding of how PINK1-dependent mitophagy participates in the metabolic regulation of CRC. By mining Oncomine, we found that PINK1 expression was downregulated in human CRC tissues compared to normal colons. Moreover, disruption of PINK1 increased colon tumorigenesis in two colitis-associated CRC mouse models, suggesting that PINK1 functions as a tumor suppressor in CRC. PINK1 overexpression in murine colon tumor cells promoted mitophagy, decreased glycolysis and increased mitochondrial respiration potentially via activation of p53 signaling pathways. In contrast, PINK1 deletion decreased apoptosis, increased glycolysis, and reduced mitochondrial respiration and p53 signaling. Interestingly, PINK1 overexpression in vivo increased apoptotic cell death and suppressed colon tumor xenograft growth. Metabolomic analysis revealed that acetyl-CoA was significantly reduced in tumors with PINK1 overexpression, which was partly due to activation of the HIF-1α-pyruvate dehydrogenase (PDH) kinase 1 (PDHK1)-PDHE1α axis. Strikingly, treating mice with acetate increased acetyl-CoA levels and rescued PINK1-suppressed tumor growth. Importantly, PINK1 disruption simultaneously increased xenografted tumor growth and acetyl-CoA production. In conclusion, mitophagy protein PINK1 suppresses colon tumor growth by metabolic reprogramming and reducing acetyl-CoA production.Subject terms: Tumour-suppressor proteins, Cancer metabolism     

Systematic assignment of thermodynamic constraints in metabolic network models

Background  

The availability of genome sequences for many organisms enabled the reconstruction of several genome-scale metabolic network models. Currently, significant efforts are put into the automated reconstruction of such models. For this, several computational tools have been developed that particularly assist in identifying and compiling the organism-specific lists of metabolic reactions. In contrast, the last step of the model reconstruction process, which is the definition of the thermodynamic constraints in terms of reaction directionalities, still needs to be done manually. No computational method exists that allows for an automated and systematic assignment of reaction directions in genome-scale models.     

Reconstructing genome-scale metabolic models with merlin

The Metabolic Models Reconstruction Using Genome-Scale Information (merlin) tool is a user-friendly Java application that aids the reconstruction of genome-scale metabolic models for any organism that has its genome sequenced. It performs the major steps of the reconstruction process, including the functional genomic annotation of the whole genome and subsequent construction of the portfolio of reactions. Moreover, merlin includes tools for the identification and annotation of genes encoding transport proteins, generating the transport reactions for those carriers. It also performs the compartmentalisation of the model, predicting the organelle localisation of the proteins encoded in the genome and thus the localisation of the metabolites involved in the reactions promoted by such enzymes. The gene-proteins-reactions (GPR) associations are automatically generated and included in the model. Finally, merlin expedites the transition from genomic data to draft metabolic models reconstructions exported in the SBML standard format, allowing the user to have a preliminary view of the biochemical network, which can be manually curated within the environment provided by merlin.     

Construction of a Genome-Scale Metabolic Model of Arthrospira platensis NIES-39 and Metabolic Design for Cyanobacterial Bioproduction

Arthrospira (Spirulina) platensis is a promising feedstock and host strain for bioproduction because of its high accumulation of glycogen and superior characteristics for industrial production. Metabolic simulation using a genome-scale metabolic model and flux balance analysis is a powerful method that can be used to design metabolic engineering strategies for the improvement of target molecule production. In this study, we constructed a genome-scale metabolic model of A. platensis NIES-39 including 746 metabolic reactions and 673 metabolites, and developed novel strategies to improve the production of valuable metabolites, such as glycogen and ethanol. The simulation results obtained using the metabolic model showed high consistency with experimental results for growth rates under several trophic conditions and growth capabilities on various organic substrates. The metabolic model was further applied to design a metabolic network to improve the autotrophic production of glycogen and ethanol. Decreased flux of reactions related to the TCA cycle and phosphoenolpyruvate reaction were found to improve glycogen production. Furthermore, in silico knockout simulation indicated that deletion of genes related to the respiratory chain, such as NAD(P)H dehydrogenase and cytochrome-c oxidase, could enhance ethanol production by using ammonium as a nitrogen source.     

Construction and Analysis of the Model of Energy Metabolism in E. coli

Genome-scale models of metabolism have only been analyzed with the constraint-based modelling philosophy and there have been several genome-scale gene-protein-reaction models. But research on the modelling for energy metabolism of organisms just began in recent years and research on metabolic weighted complex network are rare in literature. We have made three research based on the complete model of E. coli’s energy metabolism. We first constructed a metabolic weighted network using the rates of free energy consumption within metabolic reactions as the weights. We then analyzed some structural characters of the metabolic weighted network that we constructed. We found that the distribution of the weight values was uneven, that most of the weight values were zero while reactions with abstract large weight values were rare and that the relationship between w (weight values) and v (flux values) was not of linear correlation. At last, we have done some research on the equilibrium of free energy for the energy metabolism system of E. coli. We found that (free energy rate input from the environment) can meet the demand of (free energy rate dissipated by chemical process) and that chemical process plays a great role in the dissipation of free energy in cells. By these research and to a certain extend, we can understand more about the energy metabolism of E. coli.     

Integrative network analysis reveals active microRNAs and their functions in gastric cancer

Background

Ralstonia eutropha H16, found in both soil and water, is a Gram-negative lithoautotrophic bacterium that can utillize CO₂ and H₂ as its sources of carbon and energy in the absence of organic substrates. R. eutropha H16 can reach high cell densities either under lithoautotrophic or heterotrophic conditions, which makes it suitable for a number of biotechnological applications. It is the best known and most promising producer of polyhydroxyalkanoates (PHAs) from various carbon substrates and is an environmentally important bacterium that can degrade aromatic compounds. In order to make R. eutropha H16 a more efficient and robust biofactory, system-wide metabolic engineering to improve its metabolic performance is essential. Thus, it is necessary to analyze its metabolic characteristics systematically and optimize the entire metabolic network at systems level.

Results

We present the lithoautotrophic genome-scale metabolic model of R. eutropha H16 based on the annotated genome with biochemical and physiological information. The stoichiometic model, RehMBEL1391, is composed of 1391 reactions including 229 transport reactions and 1171 metabolites. Constraints-based flux analyses were performed to refine and validate the genome-scale metabolic model under environmental and genetic perturbations. First, the lithoautotrophic growth characteristics of R. eutropha H16 were investigated under varying feeding ratios of gas mixture. Second, the genome-scale metabolic model was used to design the strategies for the production of poly[R-(-)-3hydroxybutyrate] (PHB) under different pH values and carbon/nitrogen source uptake ratios. It was also used to analyze the metabolic characteristics of R. eutropha when the phosphofructokinase gene was expressed. Finally, in silico gene knockout simulations were performed to identify targets for metabolic engineering essential for the production of 2-methylcitric acid in R. eutropha H16.

Conclusion

The genome-scale metabolic model, RehMBEL1391, successfully represented metabolic characteristics of R. eutropha H16 at systems level. The reconstructed genome-scale metabolic model can be employed as an useful tool for understanding its metabolic capabilities, predicting its physiological consequences in response to various environmental and genetic changes, and developing strategies for systems metabolic engineering to improve its metabolic performance.     

WNT Signaling: an Emerging Mediator of Cancer Cell Metabolism?

WNT signaling was discovered in tumor models and has been recognized as a regulator of cancer development and progression for over 3 decades. Recent work has highlighted a critical role for WNT signaling in the metabolic homeostasis of mammals, where its misregulation has been heavily implicated in diabetes. While the majority of WNT metabolism research has focused on nontransformed tissues, the role of WNT in cancer metabolism remains underinvestigated. Cancer is also a metabolic disease where oncogenic signaling pathways regulate energy production and macromolecular synthesis to fuel rapidly proliferating tumors. This review highlights the emerging evidence for WNT signaling in the reprogramming of cancer cell metabolism and examines the role of these signaling pathways as mediators of tumor bioenergetics.     

Literature mining supports a next-generation modeling approach to predict cellular byproduct secretion

The metabolic byproducts secreted by growing cells can be easily measured and provide a window into the state of a cell; they have been essential to the development of microbiology, cancer biology, and biotechnology. Progress in computational modeling of cells has made it possible to predict metabolic byproduct secretion with bottom-up reconstructions of metabolic networks. However, owing to a lack of data, it has not been possible to validate these predictions across a wide range of strains and conditions. Through literature mining, we were able to generate a database of Escherichia coli strains and their experimentally measured byproduct secretions. We simulated these strains in six historical genome-scale models of E. coli, and we report that the predictive power of the models has increased as they have expanded in size and scope. The latest genome-scale model of metabolism correctly predicts byproduct secretion for 35/89 (39%) of designs. The next-generation genome-scale model of metabolism and gene expression (ME-model) correctly predicts byproduct secretion for 40/89 (45%) of designs, and we show that ME-model predictions could be further improved through kinetic parameterization. We analyze the failure modes of these simulations and discuss opportunities to improve prediction of byproduct secretion.     

Cystine transporter SLC7A11/xCT in cancer:ferroptosis,nutrient dependency,and cancer therapy

The cystine/glutamate antiporter SLC7A11 (also com-monly known as xCT) functions to import cystine for glutathione biosynthesis and antioxidant defense and is overexpressed in multiple human cancers.Recent studies revealed that SLC7A11 overexpression pro-motes tumor growth partly through suppressing fer-roptosis,a form of regulated cell death induced by excessive lipid peroxidation.However,cancer cells with high expression of SLC7A11 (SLC7A11high) also have to endure the significant cost associated with SLC7A11-mediated metabolic reprogramming,leading to glucose-and glutamine-dependency in SLC7A11high cancer cells,which presents potential metabolic vulnerabilities for therapeutic targeting in SLC7A11high cancer.In this review,we summarize diverse regulatory mechanisms of SLC7A11 in cancer,discuss ferroptosis-dependent and-independent functions of SLC7A11 in promoting tumor development,explore the mechanistic basis of SLC7A11-induced nutrient dependency in cancer cells,and conceptualize therapeutic strategies to target SLC7A11 in cancer treatment.This review will provide the foundation for further understanding SLC7A11 in ferroptosis,nutrient dependency,and tumor biology and for developing novel effective cancer therapies.     

Intestinal PTGS2 mRNA Levels,PTGS2 Gene Polymorphisms,and Colorectal Carcinogenesis

Background & Aims

Inflammation is a major risk factor for development of colorectal cancer (CRC). Prostaglandin synthase cyclooxygenase-2 (COX-2) encoded by the PTGS2 gene is the rate limiting enzyme in prostaglandin synthesis and therefore plays a distinct role as regulator of inflammation.

Methods

PTGS2 mRNA levels were determined in intestinal tissues from 85 intestinal adenoma cases, 115 CRC cases, and 17 healthy controls. The functional PTGS2 polymorphisms A-1195G (rs689466), G-765C (rs20417), T8473C (rs5275) were assessed in 200 CRC cases, 991 adenoma cases and 399 controls from the Norwegian KAM cohort.

Results

PTGS2 mRNA levels were higher in mild/moderate adenoma tissue compared to morphologically normal tissue from the same individual (P<0.0001) and (P<0.035) and compared to mucosa from healthy individuals (P<0.0039) and (P<0.0027), respectively. In CRC patients, PTGS2 mRNA levels were 8–9 times higher both in morphologically normal tissue and in cancer tissue, compared to healthy individuals (P<0.0001). PTGS2 A-1195G variant allele carriers were at reduced risk of CRC (odds ratio (OR) = 0.52, 95% confidence interval (95% CI): 0.28–0.99, P = 0.047). Homozygous carriers of the haplotype encompassing the A-1195G and G-765C wild type alleles and the T8473C variant allele (PTGS2 AGC) were at increased risk of CRC as compared to homozygous carriers of the PTGS2 AGT (A-1195G, G-765C, T8473C) haplotype (OR = 5.37, 95% CI: 1.40–20.5, P = 0.014). No association between the investigated polymorphisms and PTGS2 mRNA levels could be detected.

Conclusion

High intestinal PTGS2 mRNA level is an early event in colorectal cancer development as it occurs already in mild/moderate dysplasia. PTGS2 polymorphisms that have been associated with altered PTGS2 mRNA levels/COX-2 activity in some studies, although not the present study, were associated with colorectal cancer risk. Thus, both PTGS2 polymorphisms and PTGS2 mRNA levels may provide information regarding CRC risk.     

Metabolic reconstruction and modeling of nitrogen fixation in Rhizobium etli

Rhizobiaceas are bacteria that fix nitrogen during symbiosis with plants. This symbiotic relationship is crucial for the nitrogen cycle, and understanding symbiotic mechanisms is a scientific challenge with direct applications in agronomy and plant development. Rhizobium etli is a bacteria which provides legumes with ammonia (among other chemical compounds), thereby stimulating plant growth. A genome-scale approach, integrating the biochemical information available for R. etli, constitutes an important step toward understanding the symbiotic relationship and its possible improvement. In this work we present a genome-scale metabolic reconstruction (iOR363) for R. etli CFN42, which includes 387 metabolic and transport reactions across 26 metabolic pathways. This model was used to analyze the physiological capabilities of R. etli during stages of nitrogen fixation. To study the physiological capacities in silico, an objective function was formulated to simulate symbiotic nitrogen fixation. Flux balance analysis (FBA) was performed, and the predicted active metabolic pathways agreed qualitatively with experimental observations. In addition, predictions for the effects of gene deletions during nitrogen fixation in Rhizobia in silico also agreed with reported experimental data. Overall, we present some evidence supporting that FBA of the reconstructed metabolic network for R. etli provides results that are in agreement with physiological observations. Thus, as for other organisms, the reconstructed genome-scale metabolic network provides an important framework which allows us to compare model predictions with experimental measurements and eventually generate hypotheses on ways to improve nitrogen fixation.