Flux-sum analysis: a metabolite-centric approach for understanding the metabolic network

<p>Abstract</p> <p>Background</p> <p>Constraint-based flux analysis of metabolic network model quantifies the reaction flux distribution to characterize the state of cellular metabolism. However, metabolites are key players in the metabolic network and the current reaction-centric approach may not account for the effect of metabolite perturbation on the cellular physiology due to the inherent limitation in model formulation. Thus, it would be practical to incorporate the metabolite states into the model for the analysis of the network.</p> <p>Results</p> <p>Presented herein is a metabolite-centric approach of analyzing the metabolic network by including the turnover rate of metabolite, known as flux-sum, as key descriptive variable within the model formulation. By doing so, the effect of varying metabolite flux-sum on physiological change can be simulated by resorting to mixed integer linear programming. From the results, we could classify various metabolite types based on the flux-sum profile. Using the <it>i</it>AF1260 <it>in silico </it>metabolic model of <it>Escherichia coli</it>, we demonstrated that this novel concept complements the conventional reaction-centric analysis.</p> <p>Conclusions</p> <p>Metabolite flux-sum analysis elucidates the roles of metabolites in the network. In addition, this metabolite perturbation analysis identifies the key metabolites, implicating practical application which is achievable through metabolite flux-sum manipulation in the areas of biotechnology and biomedical research.</p

Maximal Extraction of Biological Information from Genetic Interaction Data

Targeted genetic perturbation is a powerful tool for inferring gene function in model organisms. Functional relationships between genes can be inferred by observing the effects of multiple genetic perturbations in a single strain. The study of these relationships, generally referred to as genetic interactions, is a classic technique for ordering genes in pathways, thereby revealing genetic organization and gene-to-gene information flow. Genetic interaction screens are now being carried out in high-throughput experiments involving tens or hundreds of genes. These data sets have the potential to reveal genetic organization on a large scale, and require computational techniques that best reveal this organization. In this paper, we use a complexity metric based in information theory to determine the maximally informative network given a set of genetic interaction data. We find that networks with high complexity scores yield the most biological information in terms of (i) specific associations between genes and biological functions, and (ii) mapping modules of co-functional genes. This information-based approach is an automated, unsupervised classification of the biological rules underlying observed genetic interactions. It might have particular potential in genetic studies in which interactions are complex and prior gene annotation data are sparse

An Allosteric Mechanism for Switching between Parallel Tracks in Mammalian Sulfur Metabolism

Methionine (Met) is an essential amino acid that is needed for the synthesis of S-adenosylmethionine (AdoMet), the major biological methylating agent. Methionine used for AdoMet synthesis can be replenished via remethylation of homocysteine. Alternatively, homocysteine can be converted to cysteine via the transsulfuration pathway. Aberrations in methionine metabolism are associated with a number of complex diseases, including cancer, anemia, and neurodegenerative diseases. The concentration of methionine in blood and in organs is tightly regulated. Liver plays a key role in buffering blood methionine levels, and an interesting feature of its metabolism is that parallel tracks exist for the synthesis and utilization of AdoMet. To elucidate the molecular mechanism that controls metabolic fluxes in liver methionine metabolism, we have studied the dependencies of AdoMet concentration and methionine consumption rate on methionine concentration in native murine hepatocytes at physiologically relevant concentrations (40–400 µM). We find that both [AdoMet] and methionine consumption rates do not change gradually with an increase in [Met] but rise sharply (∼10-fold) in the narrow Met interval from 50 to 100 µM. Analysis of our experimental data using a mathematical model reveals that the sharp increase in [AdoMet] and the methionine consumption rate observed within the trigger zone are associated with metabolic switching from methionine conservation to disposal, regulated allosterically by switching between parallel pathways. This regulatory switch is triggered by [Met] and provides a mechanism for stabilization of methionine levels in blood over wide variations in dietary methionine intake

OptForce: An Optimization Procedure for Identifying All Genetic Manipulations Leading to Targeted Overproductions

Computational procedures for predicting metabolic interventions leading to the overproduction of biochemicals in microbial strains are widely in use. However, these methods rely on surrogate biological objectives (e.g., maximize growth rate or minimize metabolic adjustments) and do not make use of flux measurements often available for the wild-type strain. In this work, we introduce the OptForce procedure that identifies all possible engineering interventions by classifying reactions in the metabolic model depending upon whether their flux values must increase, decrease or become equal to zero to meet a pre-specified overproduction target. We hierarchically apply this classification rule for pairs, triples, quadruples, etc. of reactions. This leads to the identification of a sufficient and non-redundant set of fluxes that must change (i.e., MUST set) to meet a pre-specified overproduction target. Starting with this set we subsequently extract a minimal set of fluxes that must actively be forced through genetic manipulations (i.e., FORCE set) to ensure that all fluxes in the network are consistent with the overproduction objective. We demonstrate our OptForce framework for succinate production in Escherichia coli using the most recent in silico E. coli model, iAF1260. The method not only recapitulates existing engineering strategies but also reveals non-intuitive ones that boost succinate production by performing coordinated changes on pathways distant from the last steps of succinate synthesis

Identification of metabolic engineering targets through analysis of optimal and sub-optimal routes

Identification of optimal genetic manipulation strategies for redirecting substrate uptake towards a desired product is a challenging task owing to the complexity of metabolic networks, esp. in terms of large number of routes leading to the desired product. Algorithms that can exploit the whole range of optimal and suboptimal routes for product formation while respecting the biological objective of the cell are therefore much needed. Towards addressing this need, we here introduce the notion of structural flux, which is derived from the enumeration of all pathways in the metabolic network in question and accounts for the contribution towards a given biological objective function. We show that the theoretically estimated structural fluxes are good predictors of experimentally measured intra-cellular fluxes in two model organisms, namely, Escherichia coli and Saccharomyces cerevisiae. For a small number of fluxes for which the predictions were poor, the corresponding enzyme-coding transcripts were also found to be distinctly regulated, showing the ability of structural fluxes in capturing the underlying regulatory principles. Exploiting the observed correspondence between in vivo fluxes and structural fluxes, we propose an in silico metabolic engineering approach, iStruF, which enables the identification of gene deletion strategies that couple the cellular biological objective with the product flux while considering optimal as well as sub-optimal routes and their efficiency.This work was supported by the Portuguese Science Foundation [grant numbers MIT-Pt/BS-BB/0082/2008, SFRH/BPD/44180/2008 to ZS] (http://www.fct.pt/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Impact of the solvent capacity constraint on E. coli metabolism

<p>Abstract</p> <p>Background</p> <p>Obtaining quantitative predictions for cellular metabolic activities requires the identification and modeling of the physicochemical constraints that are relevant at physiological growth conditions. Molecular crowding in a cell's cytoplasm is one such potential constraint, as it limits the solvent capacity available to metabolic enzymes.</p> <p>Results</p> <p>Using a recently introduced flux balance modeling framework (FBAwMC) here we demonstrate that this constraint determines a metabolic switch in <it>E. coli </it>cells when they are shifted from low to high growth rates. The switch is characterized by a change in effective optimization strategy, the excretion of acetate at high growth rates, and a global reorganization of <it>E. coli </it>metabolic fluxes, the latter being partially confirmed by flux measurements of central metabolic reactions.</p> <p>Conclusion</p> <p>These results implicate the solvent capacity as an important physiological constraint acting on <it>E. coli </it>cells operating at high metabolic rates and for the activation of a metabolic switch when they are shifted from low to high growth rates. The relevance of this constraint in the context of both the aerobic ethanol excretion seen in fast growing yeast cells (Crabtree effect) and the aerobic glycolysis observed in rapidly dividing cancer cells (Warburg effect) should be addressed in the future.</p

Large-Scale Bi-Level Strain Design Approaches and Mixed-Integer Programming Solution Techniques

The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ∼10 days to ∼5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering

Epistasis for Growth Rate and Total Metabolic Flux in Yeast

Studies of interactions between gene deletions repeatedly show that the effect of epistasis on the growth of yeast cells is roughly null or barely positive. These observations relate generally to the pace of growth, its costs in terms of required metabolites and energy are unknown. We measured the maximum rate at which yeast cultures grow and amounts of glucose they consume per synthesized biomass for strains with none, single, or double gene deletions. Because all strains were maintained under a fermentative mode of growth and thus shared a common pattern of metabolic processes, we used the rate of glucose uptake as a proxy for the total flux of metabolites and energy. In the tested sample, the double deletions showed null or slightly positive epistasis both for the mean growth and mean flux. This concordance is explained by the fact that average efficiency of converting glucose into biomass was nearly constant, that is, it did not change with the strength of growth effect. Individual changes in the efficiency caused by gene deletions did have a genetic basis as they were consistent over several environments and transmitted between single and double deletion strains indicating that the efficiency of growth, although independent of its rate, was appreciably heritable. Together, our results suggest that data on the rate of growth can be used as a proxy for the rate of total metabolism when the goal is to find strong individual interactions or estimate the mean epistatic effect. However, it may be necessary to assay both growth and flux in order to detect smaller individual effects of epistasis

An Automated Phenotype-Driven Approach (GeneForce) for Refining Metabolic and Regulatory Models

Integrated constraint-based metabolic and regulatory models can accurately predict cellular growth phenotypes arising from genetic and environmental perturbations. Challenges in constructing such models involve the limited availability of information about transcription factor—gene target interactions and computational methods to quickly refine models based on additional datasets. In this study, we developed an algorithm, GeneForce, to identify incorrect regulatory rules and gene-protein-reaction associations in integrated metabolic and regulatory models. We applied the algorithm to refine integrated models of Escherichia coli and Salmonella typhimurium, and experimentally validated some of the algorithm's suggested refinements. The adjusted E. coli model showed improved accuracy (∼80.0%) for predicting growth phenotypes for 50,557 cases (knockout mutants tested for growth in different environmental conditions). In addition to identifying needed model corrections, the algorithm was used to identify native E. coli genes that, if over-expressed, would allow E. coli to grow in new environments. We envision that this approach will enable the rapid development and assessment of genome-scale metabolic and regulatory network models for less characterized organisms, as such models can be constructed from genome annotations and cis-regulatory network predictions