Constraint-based Modeling Approach Research Articles

GEM-based computational modeling for exploring metabolic interactions in a microbial community.

Microbial communities play fundamental roles in every complex ecosystem, such as soil, sea and the human body. The stability and diversity of the microbial community depend precisely on the composition of the microbiota. Any change in the composition of these communities affects microbial functions. An important goal of studying the interactions between species is to understand the behavior of microbes and their responses to perturbations. These interactions among species are mediated by the exchange of metabolites within microbial communities. We developed a computational model for the microbial community that has a separate compartment for exchanging metabolites. This model can predict possible metabolites that cause competition, commensalism, and mutual interactions between species within a microbial community. Our constraint-based community metabolic modeling approach provides insights to elucidate the pattern of metabolic interactions for each common metabolite between two microbes. To validate our approach, we used a toy model and a syntrophic co-culture of Desulfovibrio vulgaris and Methanococcus maripaludis, as well as another in co-culture between Geobacter sulfurreducens and Rhodoferax ferrireducens. For a more general evaluation, we applied our algorithm to the honeybee gut microbiome, composed of seven species, and the epiphyte strain Pantoea eucalypti 299R. The epiphyte strain Pe299R has been previously studied and cultured with six different phyllosphere bacteria. Our algorithm successfully predicts metabolites, which imply mutualistic, competitive, or commensal interactions. In contrast to OptCom, MRO, and MICOM algorithms, our COMMA algorithm shows that the potential for competitive interactions between an epiphytic species and Pe299R is not significant. These results are consistent with the experimental measurements of population density and reproductive success of the Pe299R strain.

Identification of Anticancer Enzymes and Biomarkers for Hepatocellular Carcinoma through Constraint-Based Modeling.

Hepatocellular carcinoma (HCC) results in the abnormal regulation of cellular metabolic pathways. Constraint-based modeling approaches can be utilized to dissect metabolic reprogramming, enabling the identification of biomarkers and anticancer targets for diagnosis and treatment. In this study, two genome-scale metabolic models (GSMMs) were reconstructed by employing RNA sequencing expression patterns of hepatocellular carcinoma (HCC) and their healthy counterparts. An anticancer target discovery (ACTD) framework was integrated with the two models to identify HCC targets for anticancer treatment. The ACTD framework encompassed four fuzzy objectives to assess both the suppression of cancer cell growth and the minimization of side effects during treatment. The composition of a nutrient may significantly affect target identification. Within the ACTD framework, ten distinct nutrient media were utilized to assess nutrient uptake for identifying potential anticancer enzymes. The findings revealed the successful identification of target enzymes within the cholesterol biosynthetic pathway using a cholesterol-free cell culture medium. Conversely, target enzymes in the cholesterol biosynthetic pathway were not identified when the nutrient uptake included a cholesterol component. Moreover, the enzymes PGS1 and CRL1 were detected in all ten nutrient media. Additionally, the ACTD framework comprises dual-group representations of target combinations, pairing a single-target enzyme with an additional nutrient uptake reaction. Additionally, the enzymes PGS1 and CRL1 were identified across the ten-nutrient media. Furthermore, the ACTD framework encompasses two-group representations of target combinations involving the pairing of a single-target enzyme with an additional nutrient uptake reaction. Computational analysis unveiled that cell viability for all dual-target combinations exceeded that of their respective single-target enzymes. Consequently, integrating a target enzyme while adjusting an additional exchange reaction could efficiently mitigate cell proliferation rates and ATP production in the treated cancer cells. Nevertheless, most dual-target combinations led to lower side effects in contrast to their single-target counterparts. Additionally, differential expression of metabolites between cancer cells and their healthy counterparts were assessed via parsimonious flux variability analysis employing the GSMMs to pinpoint potential biomarkers. The variabilities of the fluxes and metabolite flow rates in cancer and healthy cells were classified into seven categories. Accordingly, two secretions and thirteen uptakes (including eight essential amino acids and two conditionally essential amino acids) were identified as potential biomarkers. The findings of this study indicated that cancer cells exhibit a higher uptake of amino acids compared with their healthy counterparts.

Genome scale metabolic network modelling for metabolic profile predictions.

Metabolic profiling (metabolomics) aims at measuring small molecules (metabolites) in complex samples like blood or urine for human health studies. While biomarker-based assessment often relies on a single molecule, metabolic profiling combines several metabolites to create a more complex and more specific fingerprint of the disease. However, in contrast to genomics, there is no unique metabolomics setup able to measure the entire metabolome. This challenge leads to tedious and resource consuming preliminary studies to be able to design the right metabolomics experiment. In that context, computer assisted metabolic profiling can be of strong added value to design metabolomics studies more quickly and efficiently. We propose a constraint-based modelling approach which predicts in silico profiles of metabolites that are more likely to be differentially abundant under a given metabolic perturbation (e.g. due to a genetic disease), using flux simulation. In genome-scale metabolic networks, the fluxes of exchange reactions, also known as the flow of metabolites through their external transport reactions, can be simulated and compared between control and disease conditions in order to calculate changes in metabolite import and export. These import/export flux differences would be expected to induce changes in circulating biofluid levels of those metabolites, which can then be interpreted as potential biomarkers or metabolites of interest. In this study, we present SAMBA (SAMpling Biomarker Analysis), an approach which simulates fluxes in exchange reactions following a metabolic perturbation using random sampling, compares the simulated flux distributions between the baseline and modulated conditions, and ranks predicted differentially exchanged metabolites as potential biomarkers for the perturbation. We show that there is a good fit between simulated metabolic exchange profiles and experimental differential metabolites detected in plasma, such as patient data from the disease database OMIM, and metabolic trait-SNP associations found in mGWAS studies. These biomarker recommendations can provide insight into the underlying mechanism or metabolic pathway perturbation lying behind observed metabolite differential abundances, and suggest new metabolites as potential avenues for further experimental analyses.

3PG-MT-LSTM: A Hybrid Model under Biomass Compatibility Constraints for the Prediction of Long-Term Forest Growth to Support Sustainable Management

Climate change is posing new challenges to forestry management practices. Thinning reduces competitive pressure in the forest by repeatedly reducing the tree density of forest stands, thereby increasing the productivity of plantations. Considering the impact of thinning on vegetation and physiological and ecological traits, for this study, we used Norway spruce (Picea abies) data from three sites in the PROFOUND dataset to parameterize the 3-PG model in stages. The calibrated 3-PG model was used to simulate the stand diameter at breast height and the stem, root, and leaf biomass data on a monthly scale. The 3PG-MT-LSTM model uses 3-PG simulation data as the input variable. The model uses a long short-term memory neural network (LSTM) as a shared layer and introduces multi-task learning (MTL). Based on the compatibility rules, the interpretability of the model was further improved. The models were trained using single-site and multi-site data, respectively, and multiple indicators were used to evaluate the model accuracy and generalization ability. Our preliminary results show that, compared with the process model and LSTM algorithm without MTL and compatibility rules, the hybrid model has higher biomass simulation accuracy and shows a more realistic biomass response to environmental driving factors. To illustrate the potential applicability of the model, we applied light (10%), moderate (20%), and heavy thinning (30%) at intervals of 10, 15, 20, 25, 30 years. Then, we used three climate scenarios—SSP1-2.6, SSP2-4.5, and SSP5-8.5—to simulate the growth of Norway spruce. The hybrid model can effectively capture the impact of climate change and artificial management on stand growth. In terms of climate, temperature and solar radiation are the most important factors affecting forest growth, and under warm conditions, the positive significance of forest management is more obvious. In terms of forest management practices, less frequent light-to-moderate thinning can contribute more to the increase in forest carbon sink potential; high-intensity thinning can support large-diameter timber production. In summary, moderate thinning should be carried out every 10 years in the young-aged forest stage. It is also advisable to perform light thinning procedures after the forest has progressed into a middle-aged forest stage. This allows for a better trade-off of the growth relationship between stand yield and diameter at breast height (DBH). The physical constraint-based hybrid modeling approach is a practical and effective tool. It can be used to measure long-term dynamic changes in forest production and then guide management activities such as thinning to achieve sustainable forest management.

In silico prediction of potential indigenous microbial biomarkers in Penaeus vannamei identified through meta-analysis and genome-scale metabolic modelling

BackgroundUnderstanding the microbiome is crucial as it contributes to the metabolic health of the host and, upon dysbiosis, may influence disease development. With the recent surge in high-throughput sequencing technology, the availability of microbial genomic data has increased dramatically. Amplicon sequence-based analyses majorly profile microbial abundance and determine taxonomic markers. Furthermore, the availability of genome sequences for various microbial organisms has prompted the integration of genome-scale metabolic modelling that provides insights into the metabolic interactions influencing host health. However, the analysis from a single study may not be consistent, necessitating a meta-analysis.ResultsWe conducted a meta-analysis and integrated with constraint-based metabolic modelling approach, focusing on the microbiome of pacific white shrimp Penaeus vannamei, an extensively cultured marine candidate species. Meta-analysis revealed that Acinetobacter and Alteromonas are significant indicators of "health" and "disease" specific taxonomic biomarkers, respectively. Further, we enumerated metabolic interactions among the taxonomic biomarkers by applying a constraint-based approach to the community metabolic models (4416 pairs). Under different nutrient environments, a constraint-based flux simulation identified five beneficial species: Acinetobacter spWCHA55, Acinetobacter tandoii SE63, Bifidobacterium pseudolongum 49 D6, Brevundimonas pondensis LVF1, and Lutibacter profundi LP1 mediating parasitic interactions majorly under sucrose environment in the pairwise community. The study also reports the healthy biomarkers that can co-exist and have functionally dependent relationships to maintain a healthy state in the host.ConclusionsToward this, we collected and re-analysed the amplicon sequence data of P. vannamei (encompassing 117 healthy and 142 disease datasets). By capturing the taxonomic biomarkers and modelling the metabolic interaction between them, our study provides a valuable resource, a first-of-its-kind analysis in aquaculture scenario toward a sustainable shrimp farming.

Time-Optimal Adaptation in Metabolic Network Models.

Analysis of metabolic models using constraint-based optimization has emerged as an important computational technique to elucidate and eventually predict cellular metabolism and growth. In this work, we introduce time-optimal adaptation (TOA), a new constraint-based modeling approach that allows us to evaluate the fastest possible adaptation to a pre-defined cellular state while fulfilling a given set of dynamic and static constraints. TOA falls into the mathematical problem class of time-optimal control problems, and, in its general form, can be broadly applied and thereby extends most existing constraint-based modeling frameworks. Specifically, we introduce a general mathematical framework that captures many existing constraint-based methods and define TOA within this framework. We then exemplify TOA using a coarse-grained self-replicator model and demonstrate that TOA allows us to explain several well-known experimental phenomena that are difficult to explore using existing constraint-based analysis methods. We show that TOA predicts accumulation of storage compounds in constant environments, as well as overshoot uptake metabolism after periods of nutrient scarcity. TOA shows that organisms with internal temporal degrees of freedom, such as storage, can in most environments outperform organisms with a static intracellular composition. Furthermore, TOA reveals that organisms adapted to better growth conditions than present in the environment (“optimists”) typically outperform organisms adapted to poorer growth conditions (“pessimists”).

Metabolic modeling of the International Space Station microbiome reveals key microbial interactions

BackgroundRecent studies have provided insights into the persistence and succession of microbes aboard the International Space Station (ISS), notably the dominance of Klebsiella pneumoniae. However, the interactions between the various microbes aboard the ISS and how they shape the microbiome remain to be clearly understood. In this study, we apply a computational approach to predict possible metabolic interactions in the ISS microbiome and shed further light on its organization.ResultsThrough a combination of a systems-based graph-theoretical approach, and a constraint-based community metabolic modeling approach, we demonstrated several key interactions in the ISS microbiome. These complementary approaches provided insights into the metabolic interactions and dependencies present amongst various microbes in a community, highlighting key interactions and keystone species. Our results showed that the presence of K. pneumoniae is beneficial to many other microorganisms it coexists with, notably those from the Pantoea genus. Species belonging to the Enterobacteriaceae family were often found to be the most beneficial for the survival of other microorganisms in the ISS microbiome. However, K. pneumoniae was found to exhibit parasitic and amensalistic interactions with Aspergillus and Penicillium species, respectively. To prove this metabolic prediction, K. pneumoniae and Aspergillus fumigatus were co-cultured under normal and simulated microgravity, where K. pneumoniae cells showed parasitic characteristics to the fungus. The electron micrography revealed that the presence of K. pneumoniae compromised the morphology of fungal conidia and degenerated its biofilm-forming structures.ConclusionOur study underscores the importance of K. pneumoniae in the ISS, and its potential positive and negative interactions with other microbes, including potential pathogens. This integrated modeling approach, combined with experiments, demonstrates the potential for understanding the organization of other such microbiomes, unravelling key organisms and their interdependencies.-HMRmrRFAwPDF1qD73q3qYVideo

Modeling Constraints Can Identify Winning Arguments in Multi-Party Interactions (Student Abstract)

In contexts where debate and deliberation is the norm, participants are regularly presented with new information that conflicts with their original beliefs. When required to update their beliefs (belief alignment), they may choose arguments that align with their worldview (confirmation bias). We test this and competing hypotheses in a constraint-based modeling approach to predict the winning arguments in multi-party interactions in the Reddit ChangeMyView dataset. We impose structural constraints that reflect competing hypotheses on a hierarchical generative Variational Auto-encoder. Our findings suggest that when arguments are further from the initial belief state of the target, they are more likely to succeed.

A Constraint-based modeling approach to reach an improved chemically defined minimal medium for recombinant antiEpEX-scFv production by Escherichia coli

Increasing demand for recombinant therapeutic proteins highlights the necessity of their yield improvement. Culture medium formulation is a popular approach for bioprocess optimization to improve therapeutic protein production. Constraint-based modeling can empower high-precision optimization based on information on how media compounds affect metabolism and cell growth. Here, a genome-scale metabolic model (GEMs) of Escherichia coli cells was employed to design strategies of minimal medium supplementation for higher antiEpEX-scFv production. Dynamic flux balance analysis of the recombinant E. coli cell model predicted that ammonium was depleted during the process. Based on the simulations, three amino acids (Asn, Gln and Arg) were chosen to be added to the medium to compensate for ammonium depletion. Experimental validation suggested that the addition of these amino acids can indeed improve cell growth and recombinant protein production. Then, design of experiment was used to optimize the concentrations of amino acids in the growth medium. About two-fold increase was observed in the growth rate and total scFv expression level in M9 supplemented with all three amino acids compared to the minimal medium. It can be concluded that the GEMs-based approach can provide insights into an effective feeding strategy to improve the production of recombinant protein in E. coli.

A Dynamic Constraint-based Modelling (DCBM) Approach With Alternative Metabolic Objective Functions Predicts The impact of Oxidative Stress on Stored Red Blood Cells (RBCs)

Mathematical metabolic modelling is a systematic endeavour to allow identifying the main causes of an observed metabolic change and to estimate the consequences of an imposed metabolic perturbation regarding a biosystem. Dynamic Constraint-based modelling (DCBM) has delivered promising results in metabolic engineering and in bioprocess design by providing mechanistically relevant systems-level knowledge of a network of bioreactions. Here, we seek to establish a DCBM approach that leverages convex optimization and nonlinear regression mathematical toolkit to estimate dynamic intracellular metabolic flux distributions in stored Red Blood Cells (RBCs) for transfusion purposes. First, we developed an ad-hoc metabolic network including 77 reactions and 74 metabolites, second, we adapted Flux Variability Analysis (FVA) technique to quantify the connection between exometabolomic dynamics and the dynamics of feasible intracellular reaction flux ranges. We have obtained fine-grained flux range dynamics of the intracellular reactions for the benchmark data published in (Bordbar et al., 2016). Then, we defined four objective functions regarding the accumulation of oxidative stress in stored RBCs for performing a dynamic Flux Balance Analysis (DFBA). In all four cases, time-resolved flux predictions were obtained respecting the imposed equality and inequality constraints. Last, we adapted a quadratic programming (QP) approach to calculate the Euclidean distance between the dynamic optimum flux vectors. The DCBM approach we have developed herein along with the developed metabolic network showed being suitable for the computational analysis of RBCs metabolic behaviour, and it is thought to be useful for other biosystems.

In Silico Prediction of Novel Probiotic Species Limiting Pathogenic Vibrio Growth Using Constraint-Based Genome Scale Metabolic Modeling.

The prevalence of bacterial diseases and the application of probiotics to prevent them is a common practice in shrimp aquaculture. A wide range of bacterial species/strains is utilized in probiotic formulations, with proven beneficial effects. However, knowledge of their role in inhibiting the growth of a specific pathogen is restricted. In this study, we employed constraint-based genome-scale metabolic modeling approach to screen and identify the beneficial bacteria capable of limiting the growth of V. harveyi, a common pathogen in shrimp culture. Genome-scale models were built for 194 species (including strains from the genera Bacillus, Lactobacillus, and Lactococcus and the pathogenic strain V. harveyi) to explore the metabolic potential of these strains under different nutrient conditions in a consortium. In silico-based phenotypic analysis on 193 paired models predicted six candidate strains with growth enhancement and pathogen suppression. Growth simulations reveal that mannitol and glucoronate environments mediate parasitic interactions in a pairwise community. Furthermore, in a mannitol environment, the shortlisted six strains were purely metabolite consumers without donating metabolites to V. harveyi. The production of acetate by the screened species in a paired community suggests the natural metabolic end product’s role in limiting pathogen survival. Our study employing in silico approach successfully predicted three novel candidate strains for probiotic applications, namely, Bacillus sp 1 (identified as B. licheniformis in this study), Bacillus weihaiensis Alg07, and Lactobacillus lindneri TMW 1.1993. The study is the first to apply genomic-scale metabolic models for aquaculture applications to detect bacterial species limiting Vibrio harveyi growth.

An extended reconstruction of human gut microbiota metabolism of dietary compounds

Understanding how diet and gut microbiota interact in the context of human health is a key question in personalized nutrition. Genome-scale metabolic networks and constraint-based modeling approaches are promising to systematically address this complex problem. However, when applied to nutritional questions, a major issue in existing reconstructions is the limited information about compounds in the diet that are metabolized by the gut microbiota. Here, we present AGREDA, an extended reconstruction of diet metabolism in the human gut microbiota. AGREDA adds the degradation pathways of 209 compounds present in the human diet, mainly phenolic compounds, a family of metabolites highly relevant for human health and nutrition. We show that AGREDA outperforms existing reconstructions in predicting diet-specific output metabolites from the gut microbiota. Using 16S rRNA gene sequencing data of faecal samples from Spanish children representing different clinical conditions, we illustrate the potential of AGREDA to establish relevant metabolic interactions between diet and gut microbiota.

Integration of relative metabolomics and transcriptomics time-course data in a metabolic model pinpoints effects of ribosome biogenesis defects on Arabidopsis thaliana metabolism

Ribosome biogenesis is tightly associated to plant metabolism due to the usage of ribosomes in the synthesis of proteins necessary to drive metabolic pathways. Given the central role of ribosome biogenesis in cell physiology, it is important to characterize the impact of different components involved in this process on plant metabolism. Double mutants of the Arabidopsis thaliana cytosolic 60S maturation factors REIL1 and REIL2 do not resume growth after shift to moderate 10 ^{circ }hbox {C} chilling conditions. To gain mechanistic insights into the metabolic effects of this ribosome biogenesis defect on metabolism, we developed TC-iReMet2, a constraint-based modelling approach that integrates relative metabolomics and transcriptomics time-course data to predict differential fluxes on a genome-scale level. We employed TC-iReMet2 with metabolomics and transcriptomics data from the Arabidopsis Columbia 0 wild type and the reil1-1 reil2-1 double mutant before and after cold shift. We identified reactions and pathways that are highly altered in a mutant relative to the wild type. These pathways include the Calvin–Benson cycle, photorespiration, gluconeogenesis, and glycolysis. Our findings also indicated differential NAD(P)/NAD(P)H ratios after cold shift. TC-iReMet2 allows for mechanistic hypothesis generation and interpretation of system biology experiments related to metabolic fluxes on a genome-scale level.

Model-assisted identification of metabolic engineering strategies for Jatropha curcas lipid pathways.

Efficient approaches to increase plant lipid production are necessary to meet current industrial demands for this important resource. While Jatropha curcas cell culture can be used for in vitro lipid production, scaling up the system for industrial applications requires an understanding of how growth conditions affect lipid metabolism and yield. Here we present a bottom-up metabolic reconstruction of J.curcas supported with labeling experiments and biomass characterization under three growth conditions. We show that the metabolic model can accurately predict growth and distribution of fluxes in cell cultures and use these findings to pinpoint energy expenditures that affect lipid biosynthesis and metabolism. In addition, by using constraint-based modeling approaches we identify network reactions whose joint manipulation optimizes lipid production. The proposed model and computational analyses provide a stepping stone for future rational optimization of other agronomically relevant traits in J.curcas.

ACBM: An Integrated Agent and Constraint Based Modeling Framework for Simulation of Microbial Communities

The development of new methods capable of more realistic modeling of microbial communities necessitates that their results be quantitatively comparable with experimental findings. In this research, a new integrated agent and constraint based modeling framework abbreviated ACBM has been proposed that integrates agent-based and constraint-based modeling approaches. ACBM models the cell population in three-dimensional space to predict spatial and temporal dynamics and metabolic interactions. When used to simulate the batch growth of C. beijerinckii and two-species communities of F. prausnitzii and B. adolescent., ACBM improved on predictions made by two previous models. Furthermore, when transcriptomic data were integrated with a metabolic model of E. coli to consider intracellular constraints in the metabolism, ACBM accurately predicted growth rate, half-rate constant, and concentration of biomass, glucose, and acidic products over time. The results also show that the framework was able to predict the metabolism changes in the early stationary compared to the log phase. Finally, ACBM was implemented to estimate starved cells under heterogeneous feeding and it was concluded that a percentage of cells are always subject to starvation in a bioreactor with high volume.

Abstract A09: Understanding the role of colorectal cancer-associated microbes in colorectal cancer

Abstract Accumulating evidence suggests that dysbiosis, a state of pathologic imbalance in the human gut microbiome, is present in patients suffering from colorectal cancer (CRC). Several microbiome studies identified specific bacteria that are associated with CRC, among which Fusobacteria were shown to directly interact with cancer or immune cells of their host. However, only a limited number of CRC-associated microbes have been studied for host-microbial interactions; hence, the role of bacteria in the etiology of the disease remains unknown. Accordingly, our work aims at the development of a methodologic workflow for studying CRC-associated bacteria and their role in colon cancer tumor initiation and progression. In a first step, we identified CRC-associated bacteria that are enriched at the tumor site of CRC patients. Therefore, we used publicly available datasets and an in-house patient sample collection. Then, we predicted and optimized bacterial growth in silico by using a genome-scale metabolic reconstruction model combined with a constraint-based modeling approach. Finally, we implemented CRC-associated bacteria together with established primary CRC patient cultures into the microfluidics-based human-microbial crosstalk model (HuMiX). Our workflow allowed to analyze host-microbial interaction mechanisms of CRC-associated bacteria on a transcriptomic, proteomic, and metabolomic level. Citation Format: Dominik Ternes, Martine Schmitz, Léa Grandmougin, Mina Tsenkova, Eric Koncina, Aurélien Ginolhac, Jessica Karta, Diana Kuhn, Javier Ramiro Garcia, Kacy Greenhalgh, Paul Wilmes, Elisabeth Letellier, Serge Haan. Understanding the role of colorectal cancer-associated microbes in colorectal cancer [abstract]. In: Proceedings of the AACR Special Conference on the Microbiome, Viruses, and Cancer; 2020 Feb 21-24; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2020;80(8 Suppl):Abstract nr A09.

Deciphering the metabolic capabilities of Bifidobacteria using genome-scale metabolic models

Bifidobacteria, the initial colonisers of breastfed infant guts, are considered as the key commensals that promote a healthy gastrointestinal tract. However, little is known about the key metabolic differences between different strains of these bifidobacteria, and consequently, their suitability for their varied commercial applications. In this context, the present study applies a constraint-based modelling approach to differentiate between 36 important bifidobacterial strains, enhancing their genome-scale metabolic models obtained from the AGORA (Assembly of Gut Organisms through Reconstruction and Analysis) resource. By studying various growth and metabolic capabilities in these enhanced genome-scale models across 30 different nutrient environments, we classified the bifidobacteria into three specific groups. We also studied the ability of the different strains to produce short-chain fatty acids, finding that acetate production is niche- and strain-specific, unlike lactate. Further, we captured the role of critical enzymes from the bifid shunt pathway, which was found to be essential for a subset of bifidobacterial strains. Our findings underline the significance of analysing metabolic capabilities as a powerful approach to explore distinct properties of the gut microbiome. Overall, our study presents several insights into the nutritional lifestyles of bifidobacteria and could potentially be leveraged to design species/strain-specific probiotics or prebiotics.

Metabolic flux responses to deletion of 20 core enzymes reveal flexibility and limits of E. coli metabolism

Despite remarkable progress in mapping biochemistry and gene-protein-reaction relationships, quantitative systems-level understanding of microbial metabolism remains a persistent challenge. Here, 13C-metabolic flux analysis was applied to interrogate metabolic responses to 20 genetic perturbations in all viable Escherichia coli single gene knockouts in upper central metabolic pathways. Strains with severe growth defects displayed highly altered intracellular flux patterns and were the most difficult to predict using current constraint-based modeling approaches. In the ΔpfkA strain, an unexpected glucose-secretion phenotype was identified. The broad range of flux rewiring responses that were quantified suggest that some compensating pathways are more flexible than others, resulting in a more robust physiology. The fact that only 2 out of 20 strains displayed an increased net pathway-flux capacity points to a fundamental rate limitation of E. coli core metabolism. In cataloguing the various cellular responses, our results provide a critical resource for kinetic model development and efforts focused on genotype-to-phenotype predictions.

Regulatory mechanisms underlying coordination of amino acid and glucose catabolism in Escherichia coli

How microbes dynamically coordinate uptake and simultaneous utilization of nutrients in complex nutritional ecosystems is still an open question. Here, we develop a constraint-based modeling approach that exploits non-targeted exo-metabolomics data to unravel adaptive decision-making processes in dynamic nutritional environments. We thereby investigate metabolic adaptation of Escherichia coli to continuously changing conditions during batch growth in complex medium. Unexpectedly, model-based analysis of time resolved exo-metabolome data revealed that fastest growth coincides with preferred catabolism of amino acids, which, in turn, reduces glucose uptake and increases acetate overflow. We show that high intracellular levels of the amino acid degradation metabolites pyruvate and oxaloacetate can directly inhibit the phosphotransferase system (PTS), and reveal their functional role in mediating regulatory decisions for uptake and catabolism of alternative carbon sources. Overall, the proposed methodology expands the spectrum of possible applications of flux balance analysis to decipher metabolic adaptation mechanisms in naturally occurring habitats and diverse organisms.

Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models.

The ever-increasing availability of transcriptomic and metabolomic data can be used to deeply analyze and make ever-expanding predictions about biological processes, as changes in the reaction fluxes through genome-wide pathways can now be tracked. Currently, constraint-based metabolic modeling approaches, such as flux balance analysis (FBA), can quantify metabolic fluxes and make steady-state flux predictions on a genome-wide scale using optimization principles. However, relating the differential gene expression or differential metabolite abundances in different physiological states to the differential flux profiles remains a challenge. Here we present a novel method, named REMI (Relative Expression and Metabolomic Integrations), that employs genome-scale metabolic models (GEMs) to translate differential gene expression and metabolite abundance data obtained through genetic or environmental perturbations into differential fluxes to analyze the altered physiology for any given pair of conditions. REMI allows for gene-expression, metabolite abundance, and thermodynamic data to be integrated into a single framework, then uses optimization principles to maximize the consistency between the differential gene-expression levels and metabolite abundance data and the estimated differential fluxes and thermodynamic constraints. We applied REMI to integrate into the Escherichia coli GEM publicly available sets of expression and metabolomic data obtained from two independent studies and under wide-ranging conditions. The differential flux distributions obtained from REMI corresponding to the various perturbations better agreed with the measured fluxomic data, and thus better reflected the different physiological states, than a traditional model. Compared to the similar alternative method that provides one solution from the solution space, REMI was able to enumerate several alternative flux profiles using a mixed-integer linear programming approach. Using this important advantage, we performed a high-frequency analysis of common genes and their associated reactions in the obtained alternative solutions and identified the most commonly regulated genes across any two given conditions. We illustrate that this new implementation provides more robust and biologically relevant results for a better understanding of the system physiology.