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.     

From network models to network responses: integration of thermodynamic and kinetic properties of yeast genome-scale metabolic networks

Many important problems in cell biology arise from the dense nonlinear interactions between functional modules. The importance of mathematical modelling and computer simulation in understanding cellular processes is now indisputable and widely appreciated. Genome-scale metabolic models have gained much popularity and utility in helping us to understand and test hypotheses about these complex networks. However, there are some caveats that come with the use and interpretation of different types of metabolic models, which we aim to highlight here. We discuss and illustrate how the integration of thermodynamic and kinetic properties of the yeast metabolic networks in network analyses can help in understanding and utilizing this organism more successfully in the areas of metabolic engineering, synthetic biology and disease treatment.     

Bringing metabolic networks to life: convenience rate law and thermodynamic constraints

Background  

Translating a known metabolic network into a dynamic model requires rate laws for all chemical reactions. The mathematical expressions depend on the underlying enzymatic mechanism; they can become quite involved and may contain a large number of parameters. Rate laws and enzyme parameters are still unknown for most enzymes.     

Identification of genome-scale metabolic network models using experimentally measured flux profiles

Genome-scale metabolic network models can be reconstructed for well-characterized organisms using genomic annotation and literature information. However, there are many instances in which model predictions of metabolic fluxes are not entirely consistent with experimental data, indicating that the reactions in the model do not match the active reactions in the in vivo system. We introduce a method for determining the active reactions in a genome-scale metabolic network based on a limited number of experimentally measured fluxes. This method, called optimal metabolic network identification (OMNI), allows efficient identification of the set of reactions that results in the best agreement between in silico predicted and experimentally measured flux distributions. We applied the method to intracellular flux data for evolved Escherichia coli mutant strains with lower than predicted growth rates in order to identify reactions that act as flux bottlenecks in these strains. The expression of the genes corresponding to these bottleneck reactions was often found to be downregulated in the evolved strains relative to the wild-type strain. We also demonstrate the ability of the OMNI method to diagnose problems in E. coli strains engineered for metabolite overproduction that have not reached their predicted production potential. The OMNI method applied to flux data for evolved strains can be used to provide insights into mechanisms that limit the ability of microbial strains to evolve towards their predicted optimal growth phenotypes. When applied to industrial production strains, the OMNI method can also be used to suggest metabolic engineering strategies to improve byproduct secretion. In addition to these applications, the method should prove to be useful in general for reconstructing metabolic networks of ill-characterized microbial organisms based on limited amounts of experimental data.     

Candidate metabolic network states in human mitochondria. Impact of diabetes, ischemia, and diet

The human mitochondrial metabolic network was recently reconstructed based on proteomic and biochemical data. Linear programming and uniform random sampling were applied herein to identify candidate steady states of the metabolic network that were consistent with the imposed physico-chemical constraints and available experimental data. The activity of the mitochondrion was studied under four metabolic conditions: normal physiologic, diabetic, ischemic, and dietetic. Pairwise correlations between steady-state reaction fluxes were calculated in each condition to evaluate the dependence among the reactions in the network. Applying constraints on exchange fluxes resulted in predictions for intracellular fluxes that agreed with experimental data. Analyses of the steady-state flux distributions showed that the experimentally observed reduced activity of pyruvate dehydrogenase in vivo could be a result of stoichiometric constraints and therefore would not necessarily require enzymatic inhibition. The observed changes in the energy metabolism of the mitochondrion under diabetic conditions were used to evaluate the impact of previously suggested treatments. The results showed that neither normalized glucose uptake nor decreased ketone body uptake have a positive effect on the mitochondrial energy metabolism or network flexibility. Taken together, this study showed that sampling of the steady-state flux space is a powerful method to investigate network properties under different conditions and provides a basis for in silico evaluations of effects of potential disease treatments.     

Reconstruction of a genome-scale metabolic network of Rhodococcus erythropolis for desulfurization studies

The remarkable catabolic diversity of Rhodococcus erythropolis makes it an interesting organism for bioremediation and fuel desulfurization. However, a model that can describe and explain the combined influence of various intracellular metabolic activities on its desulfurizing capabilities is missing from the literature. Such a model can greatly aid the development of R. erythropolis as an effective desulfurizing biocatalyst. This work reports the reconstruction of the first genome-scale metabolic model for R. erythropolis using the available genomic, experimental, and biochemical information. We have validated our in silico model by successfully predicting cell growth results and explaining several experimental observations in the literature on biodesulfurization using dibenzothiophene. We report several in silico experiments and flux balance analyses to propose minimal media, determine gene and reaction essentiality, and compare effectiveness of carbon, nitrogen, and sulfur sources. We demonstrate the usefulness of our model by studying a few in silico mutants of R. erythropolis for improved biodesulfurization, and comparing the desulfurization abilities of R. erythropolis with an in silico mutant of E. coli.     

Systematic analysis of conservation relations in Escherichia coli genome-scale metabolic network reveals novel growth media

A biochemical species is called producible in a constraints-based metabolic model if a feasible steady-state flux configuration exists that sustains its nonzero concentration during growth. Extreme semipositive conservation relations (ESCRs) are the simplest semipositive linear combinations of species concentrations that are invariant to all metabolic flux configurations. In this article, we outline a fundamental relationship between the ESCRs of a metabolic network and the producibility of a biochemical species under a nutrient media. We exploit this relationship in an algorithm that systematically enumerates all minimal nutrient sets that render an objective species weakly producible (i.e., producible in the absence of thermodynamic constraints) through a simple traversal of ESCRs. We apply our results to a recent genome scale model of Escherichia coli metabolism, in which we traverse the 51 anhydrous ESCRs of the metabolic network to determine all 928 minimal aqueous nutrient media that render biomass weakly producible. Applying irreversibility constraints, we find 287 of these 928 nutrient sets to be thermodynamically feasible. We also find that an additional 365 of these nutrient sets are thermodynamically feasible in the presence of oxygen. Since biomass producibility is commonly used as a surrogate for growth in genome scale metabolic models, our results represent testable hypotheses of alternate growth media derived from in silico analysis of the E. coli genome scale metabolic network.     

Two-phase resolution of polyploidy in the Arabidopsis metabolic network gives rise to relative and absolute dosage constraints

The abundance of detected ancient polyploids in extant genomes raises questions regarding evolution after whole-genome duplication (WGD). For instance, what rules govern the preservation or loss of the duplicated genes created by WGD? We explore this question by contrasting two possible preservation forces: selection on relative and absolute gene dosages. Constraints on the relative dosages of central network genes represent an important force for maintaining duplicates (the dosage balance hypothesis). However, preservation may also result from selection on the absolute abundance of certain gene products. The metabolic network of the model plant Arabidopsis thaliana is a powerful system for comparing these hypotheses. We analyzed the surviving WGD-produced duplicate genes in this network, finding evidence that the surviving duplicates from the most recent WGD (WGD-α) are clustered in the network, as predicted by the dosage balance hypothesis. A flux balance analysis suggests an association between the survival of duplicates from a more ancient WGD (WGD-β) and reactions with high metabolic flux. We argue for an interplay of relative and absolute dosage constraints, such that the relative constraints imposed by the recent WGD are still being resolved by evolution, while they have been essentially fully resolved for the ancient event.     

Enhancement of rapamycin production by metabolic engineering in <Emphasis Type="Italic">Streptomyces hygroscopicus</Emphasis> based on genome-scale metabolic model

Rapamycin, as a macrocyclic polyketide with immunosuppressive, antifungal, and anti-tumor activity produced by Streptomyces hygroscopicus, is receiving considerable attention for its significant contribution in medical field. However, the production capacity of the wild strain is very low. Hereby, a computational guided engineering approach was proposed to improve the capability of rapamycin production. First, a genome-scale metabolic model of Streptomyces hygroscopicus ATCC 29253 was constructed based on its annotated genome and biochemical information. The model consists of 1003 reactions, 711 metabolites after manual refinement. Subsequently, several potential genetic targets that likely guaranteed an improved yield of rapamycin were identified by flux balance analysis and minimization of metabolic adjustment algorithm. Furthermore, according to the results of model prediction, target gene pfk (encoding 6-phosphofructokinase) was knocked out, and target genes dahP (encoding 3-deoxy-d-arabino-heptulosonate-7-phosphate synthase) and rapK (encoding chorismatase) were overexpressed in the parent strain ATCC 29253. The yield of rapamycin increased by 30.8% by knocking out gene pfk and increased by 36.2 and 44.8% by overexpression of rapK and dahP, respectively, compared with parent strain. Finally, the combined effect of the genetic modifications was evaluated. The titer of rapamycin reached 250.8 mg/l by knockout of pfk and co-expression of genes dahP and rapK, corresponding to a 142.3% increase relative to that of the parent strain. The relationship between model prediction and experimental results demonstrates the validity and rationality of this approach for target identification and rapamycin production improvement.     

Development of a metabolic network design and optimization framework incorporating implementation constraints: a succinate production case study

We have developed a pathway design and optimization scheme that accommodates genetically and/or environmentally derived operational constraints. We express the full set of theoretically optimal pathways in terms of the underlying elementary flux modes and then examine the sensitivity of the optimal yield to a wide class of physiological perturbations. Though the scheme is general it is best appreciated in a concrete context: we here take succinate production as our model system. The scheme produces novel pathway designs and leads to the construction of optimal succinate production pathway networks. The model predictions compare very favorably with experimental observations.     

GSMN-TB: a web-based genome-scale network model of <Emphasis Type="Italic">Mycobacterium tuberculosis</Emphasis>metabolism

Background  

An impediment to the rational development of novel drugs against tuberculosis (TB) is a general paucity of knowledge concerning the metabolism of Mycobacterium tuberculosis, particularly during infection. Constraint-based modeling provides a novel approach to investigating microbial metabolism but has not yet been applied to genome-scale modeling of M. tuberculosis.     

Reconstruction and verification of a genome-scale metabolic model for <Emphasis Type="Italic">Synechocystis</Emphasis> sp. PCC6803

In terms of generating sustainable energy resources, the prospect of producing energy and other useful materials using cyanobacteria has been attracting increasing attention since these processes require only carbon dioxide and solar energy. To establish production processes with a high productivity, in silico models to predict the metabolic activity of cyanobacteria are highly desired. In this study, we reconstructed a genome-scale metabolic model of the cyanobacterium Synechocystis sp. PCC6803, which included 465 metabolites and 493 metabolic reactions. Using this model, we performed constraint-based metabolic simulations to obtain metabolic flux profiles under various environmental conditions. We evaluated the simulated results by comparing these with experimental results from ¹³C-tracer metabolic flux analyses, which were obtained under heterotrophic and mixotrophic conditions. There was a good agreement of simulation and experimental results under both conditions. Furthermore, using our model, we evaluated the production of ethanol by Synechocystis sp. PCC6803, which enabled us to estimate quantitatively how its productivity depends on the environmental conditions. The genome-scale metabolic model provides useful information for the evaluation of the metabolic capabilities, and prediction of the metabolic characteristics, of Synechocystis sp. PCC6803.     

Though with constraints imposed by endosymbiosis,preferential attachment is still a plausible mechanism responsible for the evolution of the chloroplast metabolic network

Chloroplasts evolved as a result of endosymbiosis, during which sophisticated mechanisms evolved to translocate nucleus‐encoded plastid‐targeted enzymes into the chloroplast to form the chloroplast metabolic network. Given the constraints and complexity of endosymbiosis, will preferential attachment still be a plausible mechanism for chloroplast metabolic network evolution? We answer this question by analysing the metabolic network properties of the chloroplast and a cyanobacterium, Synechococcus sp. WH8102 (syw). First, we found that enzymes related to more ancient pathways are more connected, and synthetases have the highest connectivity. Most of the enzymes shared by the two densest cores between the chloroplast and syw are synthetases. Second, the highly conserved functional modules mainly consist of highly connected enzymes. Finally, isozymes and enzymes from endosymbiotic gene transfer (EGT) were distributed mainly in conserved modules and showed higher connectivity than nonisozymes or non‐EGT enzymes. These results suggest that even with severe evolutionary constraints imposed by endosymbiosis, preferential attachment is still a plausible mechanism responsible for the evolution of the chloroplast metabolic network. However, the current analysis may not completely differentiate whether the chloroplast network properties reflect the evolution of the chloroplast network through preferential attachment or has been inherited from its cyanobacterial ancestor. To fully differentiate these two possibilities, further analyses of the metabolic network structure properties of organisms at various intermediate evolutionary stages between cyanobacteria and the chloroplast are needed.     

A network thermodynamic method for numerical solution of the Nernst-Planck and Poisson equation system with application to ionic transport through membranes

Simple techniques of network thermodynamics are used to obtain the numerical solution of the Nernst-Planck and Poisson equation system. A network model for a particular physical situation, namely ionic transport through a thin membrane with simultaneous diffusion, convection and electric current, is proposed. Concentration and electric field profiles across the membrane, as well as diffusion potential, have been simulated using the electric circuit simulation program, SPICE. The method is quite general and extremely efficient, permitting treatments of multi-ion systems whatever the boundary and experimental conditions may be.     

The genome-scale metabolic network analysis <Emphasis Type=Italic>of Zymomonas mobilis</Emphasis> ZM4 explains physiological features and suggests ethanol and succinic acid production strategies

Background  

Zymomonas mobilis ZM4 is a Gram-negative bacterium that can efficiently produce ethanol from various carbon substrates, including glucose, fructose, and sucrose, via the Entner-Doudoroff pathway. However, systems metabolic engineering is required to further enhance its metabolic performance for industrial application. As an important step towards this goal, the genome-scale metabolic model of Z. mobilis is required to systematically analyze in silico the metabolic characteristics of this bacterium under a wide range of genotypic and environmental conditions.     

metaSHARK: software for automated metabolic network prediction from DNA sequence and its application to the genomes of Plasmodium falciparum and Eimeria tenella

The metabolic SearcH And Reconstruction Kit (metaSHARK) is a new fully automated software package for the detection of enzyme-encoding genes within unannotated genome data and their visualization in the context of the surrounding metabolic network. The gene detection package (SHARKhunt) runs on a Linux system and requires only a set of raw DNA sequences (genomic, expressed sequence tag and/or genome survey sequence) as input. Its output may be uploaded to our web-based visualization tool (SHARKview) for exploring and comparing data from different organisms. We first demonstrate the utility of the software by comparing its results for the raw Plasmodium falciparum genome with the manual annotations available at the PlasmoDB and PlasmoCyc websites. We then apply SHARKhunt to the unannotated genome sequences of the coccidian parasite Eimeria tenella and observe that, at an E-value cut-off of 10⁻²⁰, our software makes 142 additional assertions of enzymatic function compared with a recent annotation package working with translated open reading frame sequences. The ability of the software to cope with low levels of sequence coverage is investigated by analyzing assemblies of the E.tenella genome at estimated coverages from 0.5× to 7.5×. Lastly, as an example of how metaSHARK can be used to evaluate the genomic evidence for specific metabolic pathways, we present a study of coenzyme A biosynthesis in P.falciparum and E.tenella.