Advertisement
Minireview | Novel Systems Biology Techniques

From Network Analysis to Functional Metabolic Modeling of the Human Gut Microbiota

Eugen Bauer, Ines Thiele
Naseer Sangwan, Editor
DOI: 10.1128/mSystems.00209-17

ABSTRACT

An important hallmark of the human gut microbiota is its species diversity and complexity. Various diseases have been associated with a decreased diversity leading to reduced metabolic functionalities. Common approaches to investigate the human microbiota include high-throughput sequencing with subsequent correlative analyses. However, to understand the ecology of the human gut microbiota and consequently design novel treatments for diseases, it is important to represent the different interactions between microbes with their associated metabolites. Computational systems biology approaches can give further mechanistic insights by constructing data- or knowledge-driven networks that represent microbe interactions. In this minireview, we will discuss current approaches in systems biology to analyze the human gut microbiota, with a particular focus on constraint-based modeling. We will discuss various community modeling techniques with their advantages and differences, as well as their application to predict the metabolic mechanisms of intestinal microbial communities. Finally, we will discuss future perspectives and current challenges of simulating realistic and comprehensive models of the human gut microbiota.

INTRODUCTION

Microbial communities are abundantly present throughout nature and form many beneficial symbiotic interactions with different eukaryotic hosts. In many cases, the host acquires novel functionalities with mutualists that can contribute to host fitness and well-being (1) by, e.g., the supplementation of essential nutrients (2). These functional microbes can be found on the interface of nutrient absorption in the intestinal ecosystem (3) and gut-associated structures, such as bacteriomes (4). Removal of these symbiotic microbes can lead to host fitness decrease, demonstrating the host dependence on the symbionts (2) and the relevance of symbiotic interactions.

While the symbiosis between the human gut ecosystem and its host is less well understood than other symbioses, intestinal microbial communities have been associated with human well-being (5). In particular, microbial metabolism is considered relevant for nutrient provisioning and complementary digestion of food (6). The human gut microbiota consists of more than thousands of microbial species (7), which can form manifold metabolic interactions between themselves and with their host. Interestingly, the microbiota metabolism is more conserved between human individuals than the species composition, which suggests that redundant functionalities are present in these microbes, complementing each other (8). The metabolic functions of various gut microbes are suggested to benefit human health with the provisioning of vitamins (9) and fermentation products (10). Microbial fermentation products can be utilized by the human host as an additional energy source (11) and benefit the immune system (12), therefore playing a pivotal role for human well-being.

A loss of microbial and metabolic diversity can lead to various microbiota-associated diseases, such as obesity (13), type 2 diabetes (14), and inflammatory bowel disease (IBD) (15). Treatments for gut-associated diseases also aim to modulate the human gut microbiota such that it exhibits healthy characteristics. Such treatments can include fecal microbiota transplantation (16), probiotics (17), and dietary change in the form of prebiotics (18). Since the microbiota varies between individuals, treatments are ideally personalized to support each patient’s unique needs. Consequently, it is important to understand the mechanism by which the microbiota is influenced to thereby enable the design of novel treatment with higher efficacies.

With the advent of high-throughput sequencing technologies, we can now enumerate and describe the microbial and functional diversity of the human gut with respect to different diseases and conditions. These analyses have unraveled a complex microbial ecosystem that is influenced by diet and environmental factors (19). Furthermore, transcriptomic and proteomic analyses have allowed researchers to probe the metabolic activity of the microbiota, revealing high activity of fermentative pathways and carbohydrate utilization (20). Whole-genome sequencing of single microbes can give hints on the functions and capabilities of individual members of the intestinal microbial community (21). Recently, it has become possible to reconstruct individual genomes from metagenomic data (22). Thus, omics analyses have broadened our understanding of the human gut microbiota in terms of the possible metabolic function and microbes that occur in this complex ecosystem. However, it is still difficult to assess the ecology in terms of metabolic interactions between microbes and how each microbe contributes to the intestinal microbial community.

To understand the ecological mechanisms that drive the human gut microbiota better, it is important to go beyond the descriptive nature of high-throughput data analysis. This can be achieved by formulating knowledge- or data-driven models, which can describe the underlying ecosystem and give mechanistic insights for hypothesis generation and designing subsequent experiments. A recent review described the relevance of such models in giving novel causal relationships in a field that is dominated by data and correlation (23). Here, we will discuss current systems biology approaches that have been applied to the human gut microbiota. As highlighted above, metabolism is one of the key features in the gut microbiota and microbes in general; we will thus focus on this aspect. First, we outline network topology analyses that are based on newly generated data or already existing knowledge. Then, we will describe modeling approaches in the field of constraint-based modeling that allow researchers to simulate interactions within microbial communities. Finally, we will highlight the advantages and limitations of these approaches and give suggestions for further studies.

NETWORK-BASED APPROACHES APPLIED TO THE HUMAN GUT MICROBIOTA

Network-based approaches are used to identify relevant microbes or metabolites of the human gut microbiota. Networks are usually represented by interactions in the form of edges that connect biological components in the form of nodes. With respect to the human gut microbiota, these components can represent species that interact (24) or metabolites that are converted through biochemical reactions (25). The goal of such networks is to provide a global overview of the underlying system and possible mechanisms, which makes it possible to identify relevant microbes or metabolites that play important roles in the network by connecting a variety of components or exhibiting key features. In the next paragraphs, we will discuss networks that are created top-down with newly generated data and knowledge-derived networks that are created bottom-up with existing information (Fig. 1).

FIG 1 
FIG 1 

Examples of data-driven network reconstruction based on high-throughput data from different patients or conditions (A) and knowledge-driven networks based on genome-scale metabolic reconstructions (B).

Data-driven (top-down) networks.Species cooccurrence networks can be used to investigate ecological interactions between microbes. Based on high-throughput data analyses, simple networks can be constructed with the species abundance information for different patients (24). Such networks contain the information of cooccurring species based on calculation of correlation coefficients for each pair of microbes (Fig. 1A). Consequently, a positive interaction can be deduced if two microbes have a positive correlation, e.g., cooccur with each other, and a negative interaction can be deduced if the coefficient is negative, e.g., excludes the presence of each other (24). This information can be relevant for understanding ecological concepts that drive exclusion mechanisms and niche differentiation. Furthermore, the information on species that interact positively or negatively can enable further experimental studies to investigate mechanisms by which these interactions may take place, e.g., production of antibiotics or metabolic cross-feeding. Despite their benefits and merits, such network-based approaches have the limitation that they do not provide hypotheses on potential mechanisms for the species interactions.

To overcome these limitations, metabolism-based networks have been used to assess the functional aspect of the human gut microbiota. Therefore, metabolic functions or enzymes annotated from high-throughput sequencing of different human individuals can be used to identify pathway or metabolite differences (26). By connecting enzymes based on the reactions that they are involved in, it is possible to construct metabolic networks specific for each patient (26). Topological analyses of these networks can reveal patient-specific differences and global differences between healthy controls and patients (26). Additional information can be gained by finding differences in enzymatic modules consisting of multiple related metabolic functions, which are connected and thus influence each other. This information can be important when trying to find potential components for treatments or assessing off target effects (26). While such networks are primarily constructed from patient data, the connections between the reactions are retrieved from previous knowledge, e.g., biochemical reaction databases (Fig. 1).

Knowledge-driven (bottom-up) networks.In the bottom-up network approach, biochemical knowledge is used to generate specific networks, e.g., biochemical networks, from information provide in databases, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) (27), MetaCyc (28), and ModelSEED/Kbase (29, 30). In a subsequent step, the high-throughput data can then be overlaid with such networks to provide novel insight and facilitate the generation of hypotheses. For instance, biochemical reaction information has been used to construct a global view of the metabolic reactions and pathways that take place in the human microbiota to assess its overall metabolic capacity (20). Useful visualizations of these networks can be achieved by maps representing how reactions interact with each other and share metabolites (20). By subselecting these maps according to patient data, it is possible to find differences in terms of various gut locations (20). With these maps, it is possible to link metabolic functions and generate a global overview; however, the information on which metabolic functions are carried out by specific microbes and how they interact is missing in such analyses.

In another study, the metabolic exchange between microbes has been represented in a global interaction map, which helped the identification of metabolic deficiencies in certain diseases (31). On the basis of data in databases and scientific literature, microbes have been represented by their known transport of various metabolites, which can act in a beneficial or detrimental manner to the cooccurring species. The global view of all transports gave clues as to which nutrients were converted by specific microbes and then potentially supplemented to the host. When mapping patient data to this network of transporters, it was possible to identify potential exchange deficiencies compared to healthy controls (31). While these analyses can reveal potential metabolites or interactions that are differentially regulated in patients, they lack the view on the complete metabolism of each microbe species.

In a complementary approach, genome-scale metabolic reconstructions of organisms are constructed based on genomic and biochemical data to represent comprehensively their metabolism to investigate the metabolic potential. To facilitate the reconstruction process, available automatic reconstruction pipelines, such as ModelSEED (29), can be used to generate an initial reconstruction based on the genome annotation, which can be, e.g., retrieved from RAST (32). On the basis of the annotation of each gene, enzymes are predicted, which carry out one or multiple reactions. In the automated reconstruction process, biochemical reactions are then retrieved from a database, such as KEGG (27) or MetaCyc (28). Subsequently, biochemical reactions are represented in a stoichiometrically accurate manner by their metabolite educts and products and their thermodynamic directionality (reversible or irreversible). To ensure correct reaction stoichiometry (33) and directionality (34, 35), further curation of the database entries may be necessary (36) (see also below). The comprehensive set of reactions retrieved by this process constitutes the metabolic reconstruction and are mathematically represented in form of the stoichiometric matrix (S-matrix) (Fig. 1). The rows in the S-matrix represent the metabolites, and the columns represent the biochemical reactions. Entries are stoichiometric coefficients for each metabolite participating in a reaction. Through sharing different metabolites, reactions are connected with each other and therefore represent a metabolic network. In a recent study, we applied the automatic pipeline of ModelSEED (29) to reconstruct 300 representative microbes that are present in the human gut (37). We compared the metabolic networks with each other to find taxon-specific differences between the gut microbes. On the basis of this analysis, we found that microbial strains can be more metabolically different than predicted by phylogeny, which highlights the need for taking a diversity of microbes into consideration to understand the complete metabolism of the human gut microbiota.

While the automatic reconstruction process provides a good approximation of a species’ metabolism, the manual curation of reactions is essential for metabolic modeling. On the basis of biochemical knowledge of reaction directionality and substrate uptake, reactions are refined within the metabolic reconstruction to be more congruent with experiments (36). This manual effort cleans up mistakes that are still prevalent in automatic reconstructions and further expands the metabolic network for biologically relevant simulation. Several resources and databases are available with various degrees of curation status (Table 1). In a recent publication (25), we retrieved automatically reconstructed gut microbe models and applied manual curation efforts to provide a resource for further refinements and metabolic modeling efforts.

View this table:
TABLE 1 

Resources and databases to retrieve genome-scale metabolic models of human gut microbes with their respective curation status

CONSTRAINT-BASED MODELING OF INTESTINAL MICROBIAL COMMUNITIES

Constraint-based reconstruction and analysis. Constraint-based reconstruction and analysis (COBRA) is based on genome-scale metabolic reconstructions, which are formalized as metabolic models to simulate biological relevant physiological states. By applying condition-specific constraints, reconstructions are converted into metabolic models (Fig. 2). Often, a particular linear metabolic objective is defined, e.g., a biomass reaction, which summarizes all biochemical precursors known to be required to form a new cell or organism. The following mathematical problem can then be formulated while assuming the biological system to be at a steady state: maximize vB, which is subject to S × v = 0, and vi,min ≤ vi ≤ vi,max, where v represents the flux values (minimum and maximum values) through all model reactions i while maximizing the flux vBthrough the metabolic objective reaction B. The steady-state assumption implies that metabolites cannot accumulate and the total flux into the network must equal the total outflux, and it is represented by S × v = 0, where S is the aforementioned stoichiometric matrix. The constraints, represented as vi,min ≤ vi ≤ vi,max, can reflect medium conditions, in which the uptake of metabolites via exchange reactions is limited or limitations of internal reaction fluxes that come from experimental data (36). The biomass flux then predicts how much biomass the organism can produce under the given condition. Fluxes that flow through the network predict the metabolic pathways that are used by the organism in order to achieve the metabolic objective. The process of finding the solution to the stated optimization problem is called flux balance analysis (FBA) (40) and can be solved using linear programming. To help with the interpretation and data integration, fluxes are typically scaled to millimoles per gram (dry weight) per hour, and the biomass is usually given in gram (dry weight) per hour (41). Constraint-based modeling approaches can be applied to the human gut microbiota by either analyzing the metabolic capabilities of single species (42–45) or combining metabolic models of multiple species in a community modeling approach (46–50). Computational toolboxes, such as the COBRA toolbox (51), facilitate such investigations by the systems biology and the microbiome research communities.

FIG 2 
FIG 2 

Flux balance simulations of individual genome-scale metabolic models and the emergence of alternative optimal flux distributions.

An advantage of the COBRA approach is that it can assess the spectrum of metabolism for an organism (52). The steady-state solution space contains all possible flux distributions consistent with the applied constraints for a given condition-specific model. To deal with this degenerative nature of FBA, i.e., that there are typically more than one possible flux distribution, parsimonious FBA can be used to select the flux distribution that minimizes the sum of all reactions fluxes and thereby estimates the minimization of enzyme usage (53). This particular flux distribution can be computed with an additional linear programming problem, which minimizes the total flux while ensuring the calculated biomass objective (53). Furthermore, flux variability analysis (FVA) (54, 55) can be applied, in which each reaction in the model is minimized and maximized with the aim to find the range of flux values for each reaction (i.e., the flux span) that can be carried by each reaction. These computations enable the overall assessment of metabolic functionalities an organism can achieve.

Compartmentalized community models.Compartmentalized microbial community models can be constructed by integrating individual microbial metabolic reconstructions via their S-matrices (Fig. 3A). In this combined model, the individual microbes are separated from each other by occupying different compartments, in which they can secrete and take up metabolites from a shared environment (56). Therefore, the microbes can compete for nutrients in this environment but can also support each other by releasing metabolites to be used by other microbes. The optimized biomass of this community is usually composed as a combination of the individual biomass reactions of each microbe (56). Additionally, coupling constraints can be applied to ensure that the flux through each biochemical reaction is scaled with the achievable biomass reaction (57). Further developments of the community objective include a multiobjective optimization, in which the egoistic growth interest of each microbe is optimized while ensuring an overall community growth (58). These methods allow the prediction of metabolic interactions based on combined S-matrices.

FIG 3 
FIG 3 

Flux balance simulation (FBA) of compartment-based communities (A) as well as dynamic FBA of population-based (B) and individual-based models (C).

Pairwise models of microbial communities can give new insights in terms of the metabolic interactions that occur in the human gut. In a recent study, we applied pairwise community models to the human gut microbiota by modeling all possible pairs of more than 700 microbes and found strikingly mostly negative metabolic interactions, e.g., competition for resources, of different microbes (25), which underlines the ecological concept that negative interactions shape diversity in the human gut microbiota (59). Several other studies have investigated pairwise interactions of gut bacteria and discovered novel metabolic interactions and the exchange of key metabolites (57, 60, 61). By simply adding additional microbes to the compartment-based strategy, it is possible to model intestinal microbe communities of multiple species (49). A study based on 11 metabolic models (62) revealed a vivid exchange of fermentation products and the conversion of metabolites involved in neurotransmitter production, which can have important implications in various neurological diseases. The concept of modeling multiple species has been further applied to model the influence of different diets on the metabolic interactions of different gut bacteria (63). While such generated information of compartmentalized community models can be useful in finding new hypothesis of metabolic interactions, they do not represent the temporal dynamics of the interactions between the species.

Population-based models.Population-based COBRA modeling can be used to analyze temporal dynamics of metabolic interactions within microbial communities. With the optimization of biomass growth, FBA intrinsically models growth of microbe populations. The combined S-matrix approach can also be used to model population dynamics with respect to a temporal scale (64). Essentially, the biomass optimization of each microbe is solved iteratively and independently. Each iteration represents a discrete time interval, in which new biomass (as defined by the biomass reaction) is produced and metabolites are secreted or taken up. The units of the fluxes and biomass are scaled according to the time interval. Similar to the compartmentalized community models, metabolites are secreted into a shared compartment. Therefore, the same positive or negative interactions between microbes, as discussed above, can potentially take place. However, through the introduction of a temporal scale via the time intervals, it is possible to investigate the evolution and emergence of these interactions under different conditions. This extension can help with the understanding of how different interactions are temporally dependent, as it is thought to be the case for the human gut microbiota where nutrient intake is dynamically changing throughout the day (65). Experimental time series data can be integrated in such temporal dynamics to adjust the simulation results (66). Recent approaches also add a spatial dimension to such community models to represent the colony growth of organisms (67). The inclusion of a spatial dimension allows the representation of metabolite concentrations, which can diffuse and create different gradients. Such gradients are thought to strongly influence the gut microbiota by forming different niches in which the microbial community differentiates (68). Population-based community modeling can therefore help to investigate what metabolites affect and dynamically change the microbial community.

Population-based COBRA modeling can give new insights in the dynamic change of metabolic interactions of human gut microbial communities. A recent approach applied population-based metabolic models to a simple community of six microbes (69). The results of this analysis demonstrated a highly metabolic active community that exchanges a variety of different metabolites through a complex interaction network. Within this interaction network, many cross-feeding interactions have been validated against knowledge from literature. Notably, these interactions can change over time and are therefore highly dynamic (69). By calibrating the relative abundances of a representative intestinal microbial community, a recent study (70) could also reproduce the dynamic change of the community structure in response to different diets. A significant assumption of such models is that each microbe consists of a population of homogenous cells, which operate under similar environmental niche conditions. This approach can be useful to investigate the metabolic behavior of microbial populations but is limited in predicting metabolic interactions between individuals of one population.

Individual-based community models.The metabolic interactions between distinct microbial organisms can be modeled by combining the COBRA approach with individual-based modeling (IBM). IBM is a method also used in classical ecology (71) to model species populations in terms of single independent individuals by discrete time steps and a spatial environment. Individuals in this environment can interact according to predefined rules, which determine their states. Through these local interactions, global population structures can emerge that determine the system state. Therefore, population dynamics can help to understand how single cells shape and structure populations of species in an ecosystem. Several approaches have been published that combined IBM with COBRA modeling to study the metabolism of single cells in a population of a single species (72) or multiple species (73, 74). Briefly, each single cell is represented by a metabolic model and simulates its metabolism according to the spatial position in the environment, which includes certain metabolites. Metabolites can diffuse through this environment, described using partial differential equations, to reflect concentration gradients that dominate the community and to create niches that allow the activation of different metabolic pathways. Each species is thus represented by metabolically heterogeneous individuals, which permits the simulation of the full metabolic potential (73). Thus, this method allows researchers to predict the metabolic interactions between species and within species that can have different metabolic phenotypes depending on the spatial resource allocation (Fig. 3C).

Individual-based COBRA modeling can give insights into the spatial and temporal community structure of the human gut microbiota. For instance, a recent study has explored the effects of antibiotics on a two-species community model of human gut microbes, represented by a combination of individual-based and kinetic modeling (75). Similarly, the combined approach of IBM and COBRA has been applied to analyze microbial communities in the human gut (73). The community model contained seven representative microbes that have previously been experimentally shown to represent the properties of the gut microbiota (76). The simulations recapitulated experimentally known metabolite concentrations but also predicted novel cross-feeding interactions through fermentation products exchanged within the microbes of the community (73). Furthermore, by applying a spatial gradient of mucous glycans, spatial niche differentiation of microbial cells could be observed, consistent with experimental microscopy studies. This observation further strengthens the fact that metabolism is an important factor in shaping the gut microbiota and inducing ecological interactions. Recently, this approach has been further expanded to integrate metagenomic data of patients and healthy controls to predict personalized dietary treatments (77). Such findings demonstrate the relevance of the integration of ecological methods with COBRA to understand the metabolic mechanisms that shape the temporal and spatial community structure. There are several tools available that allow constraint-based modeling of microbial communities (Table 2).

View this table:
TABLE 2 

List of available tools as freely accessible software packages for constraint-based modeling of microbial communities

CURRENT CHALLENGES OF GUT MICROBIOTA MODELING

The COBRA approaches for analyzing microbial communities provide promising tools for investigating the metabolic effect of the human gut microbiota. Several studies have been conducted in this context, mostly focusing on small communities representative of the intestinal microbiota (Table 3). These studies have revealed several important aspects of the human gut microbiota and its metabolism through the use of mechanistic metabolic models, which allow researchers to identify relevant metabolic pathways underlying the observed metabolic interactions between species. These analyses can improve our understanding of the metabolic mechanisms that shape intestinal microbial communities, but there are several limitations and challenges that need to be considered when applying COBRA community modeling to the human gut microbiota.

View this table:
TABLE 3 

List of the different constraint-based community modeling approaches that have been applied to model microbial consortia of the human gut microbiota

Horizontal gene transfer.A challenging aspect in community modeling of the human gut microbiota is horizontal gene transfer (HGT). HGT seems to occur frequently within the human gut microbiota (78, 79) and represents a source for evolutionary innovation. Though the effect of HGT on gut-associated community has not been studied yet, metabolic models could be used to address such questions (80). Thus, the combination of evolutionary and ecological modeling could give novel insights into the dynamics of the human gut microbiota.

Scalability and model complexity.One of the most striking hallmarks of the human gut microbiota is its species diversity, which poses challenges to the model simulations that need to be addressed. Simplified microbiota models of less than 10 species are relevant for studying metabolic interactions in general and can be used to simulate experiments that are conducted with small microbial communities in gnotobiotic animals (76) or in vitro (82). However, such models will never be able to capture and explain the high complexity of the human gut microbiota. Why are there so many different species? Why is the human gut microbiota more diverse than other body sites? Why are some diseases associated with a lower microbiota diversity? These are some of the questions that can be addressed only with more comprehensive microbiota model. In a recent publication (25), we created a resource of more than 700 curated metabolic models of gut microbes. Combining those into a community model poses several difficulties, such as the simulation time. Compartmentalized models are extended versions of single metabolic models, which become more challenging to solve as the number of variables and mass-balances increases. In particular, extensive calculations associated with simulation methods, such as FVA, require dedicated algorithms that make effective use of, for instance, parallelization of the computations (55, 83). By doing so, we were able to simulate with microbiota and host-microbiota models containing hundreds of thousand variables and linear equations (50, 81), demonstrating the scalability of this approach. Population-based models, on the other hand, simulate each microbe independently and scale therefore with the number of species in the community. Individual-based community models are generally independent of the number of species but scale linearly with the number of individuals (73) and are therefore limited in modeling a small spatial scale. Taken together, each simulation approach requires the consideration of the trade-off between model complexity and simulation time.

Model complexity also poses the problem of subsequent data analysis and visualization. By simulating large-scale microbiota models with a high number of variables, it becomes difficult to identify the explaining pathways. The large amount of simulations and the complexity of the models further require data analysis approaches to find the most relevant parameters influencing the modeled system with the aim to propose an experimentally testable hypothesis. State-of-the art machine learning approaches have already been used to aid the analyses of COBRA simulation results (84, 85) but have yet to be systematically integrated with the COBRA modeling of the human gut microbiota.

Data integration.High-throughput omics data can be used to contextualize genome-scale metabolic models (86, 87). Similarly, omics data from microbiome studies can be integrated into COBRA community microbiota models to generate context-specific models. For instance, in our recently published resource of gut microbe reconstructions (25), we have mapped metagenomic data of microbial abundances onto our set of microbes, which resulted in microbiota models consisting of about 100 microbes, indicating the need of comprehensive and scalable modeling approaches to analyze this data. Additionally, relative microbial abundances estimated from metagenomic data can be used to calibrate the microbial community (50). As such, the microbiota models are sample and/or person specific and permit the assessment of microbial capabilities unique to an individual, or a patient group. However, attention should be paid to the biological interpretation of this integration in different community modeling approaches. In compartment-based models, the bacterial abundances can be scaled with the community biomass (50, 81), which assumes that the microbial abundance correlates with the weight (dry weight) of each organism. In contrast, in individual-based models, the cell count of the species populations is scaled by the microbial abundances (77). As an alternative measure, the replication rates can be estimated from metagenomic data (88) and used as a proxy for microbial growth rates. If available, further data, such as meta-transcriptomic, meta-proteomic, and meta-metabolomic data (89), could be integrated into microbial community models to further constrain the models, thereby making them more representative of the real biological system.

Model validation.Experimental validation of community simulation results plays an important role to assess the predictive potential of the microbial community models. While community models can give interesting novel hypotheses with their simulation, particular attention should be paid to the biological relevance of the predictions. It is therefore important to relate and validate at least part of the simulations with experimental values that come from existing knowledge or direct experiments. Existing knowledge can be used to validate the predictions of community models, e.g., by comparing simulated with measured metabolite concentrations (63). This will also help to assess the relevance of the community models and how they should be interpreted. Since models of the human gut microbiota can be quite complicated and extensive, it is also difficult to find appropriate data or design experiments that can be used for validation. Part of the simulation results will thus be a novel hypothesis that can be validated only with new experiments. This can guide the targeted design of experimental studies, which becomes important in the field of the human gut microbiota to reduce the complexity to simple findings.

CONCLUSIONS AND FUTURE PERSPECTIVES

The introduced COBRA community modeling approaches are promising tools to give novel insights and hypotheses of microbial consortia in the human gut. The models allow for the integration of multidimensional omics data and go beyond the descriptive nature of more traditional high-throughput data analysis approaches with the potential to unravel novel mechanisms. The different discussed modeling paradigms rely on various assumptions that need to be assessed before starting an analysis. On the basis of this assessment, the model that is least complicated and most explanatory for a specific research question should be chosen. It could also be fruitful to combine different approaches to see their consistency or potential differences for a specific problem. For example, it would be interesting to model single isolated species and communities separately to identify beneficial or detrimental effects on the single species level. Further comparisons can be made between network-based and modeling approaches. Can, for example, the negative and positive interactions in a coexistence correlation network be explained by the underlying metabolic interactions? Comparisons between algorithms could give further hints on candidate experiments that can be performed for validation. This can drive further research on the human microbiota, mechanistically predicting different treatments for various diseases.

ACKNOWLEDGMENTS

This work was supported by the Luxembourg National Research Fund (FNR) through an ATTRACT program grant (FNR/A12/01) and an Aides a la Formation Recherche (FNR/6783162) grant.

FOOTNOTES

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

REFERENCES

  1. 1.↵
    1. Douglas AE
    . 2011. Lessons from studying insect symbioses. Cell Host Microbe 10:359–367. doi:10.1016/j.chom.2011.09.001.
    OpenUrlCrossRefPubMedWeb of Science
  2. 2.↵
    1. Salem H,
    2. Bauer E,
    3. Strauss AS,
    4. Vogel H,
    5. Marz M,
    6. Kaltenpoth M
    . 2014. Vitamin supplementation by gut symbionts ensures metabolic homeostasis in an insect host. Proc R Soc B Biol Sci 281:20141838. doi:10.1098/rspb.2014.1838.
    OpenUrlAbstract/FREE Full Text
  3. 3.↵
    1. Dillon RJ,
    2. Dillon VM
    . 2004. The gut bacteria of insects: nonpathogenic interactions. Annu Rev Entomol 49:71–92. doi:10.1146/annurev.ento.49.061802.123416.
    OpenUrlCrossRefPubMedWeb of Science
  4. 4.↵
    1. Braendle C,
    2. Miura T,
    3. Bickel R,
    4. Shingleton AW,
    5. Kambhampati S,
    6. Stern DL
    . 2003. Developmental origin and evolution of bacteriocytes in the aphid–Buchnera symbiosis. PLoS Biol 1:E21. doi:10.1371/journal.pbio.0000021.
    OpenUrlCrossRefPubMed
  5. 5.↵
    1. Clemente JC,
    2. Ursell LK,
    3. Parfrey LW,
    4. Knight R
    . 2012. The impact of the gut microbiota on human health: an integrative view. Cell 148:1258–1270. doi:10.1016/j.cell.2012.01.035.
    OpenUrlCrossRefPubMedWeb of Science
  6. 6.↵
    1. den Besten G,
    2. van Eunen K,
    3. Groen AK,
    4. Venema K,
    5. Reijngoud D-J,
    6. Bakker BM
    . 2013. The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism. J Lipid Res 54:2325–2340. doi:10.1194/jlr.R036012.
    OpenUrlAbstract/FREE Full Text
  7. 7.↵
    1. Zoetendal EG,
    2. Rajilić-Stojanović M,
    3. De Vos WM
    . 2008. High-throughput diversity and functionality analysis of the gastrointestinal tract microbiota. Gut 57:1605–1615. doi:10.1136/gut.2007.133603.
    OpenUrlAbstract/FREE Full Text
  8. 8.↵
    1. Human Microbiome Project Consortium
    . 2012. Structure, function and diversity of the healthy human microbiome. Nature 486:207–214. doi:10.1038/nature11234.
    OpenUrlCrossRefPubMedWeb of Science
  9. 9.↵
    1. Magnúsdóttir S,
    2. Ravcheev D,
    3. de Crécy-Lagard V,
    4. Thiele I
    . 2015. Systematic genome assessment of B-vitamin biosynthesis suggests co-operation among gut microbes. Front Genet 6:148. doi:10.3389/fgene.2015.00148.
    OpenUrlCrossRefPubMed
  10. 10.↵
    1. Parvez S,
    2. Malik KA,
    3. Ah Kang S,
    4. Kim HY
    . 2006. Probiotics and their fermented food products are beneficial for health. J Appl Microbiol 100:1171–1185. doi:10.1111/j.1365-2672.2006.02963.x.
    OpenUrlCrossRefPubMedWeb of Science
  11. 11.↵
    1. den Besten G,
    2. Lange K,
    3. Havinga R,
    4. van Dijk TH,
    5. Gerding A,
    6. van Eunen K,
    7. Müller M,
    8. Groen AK,
    9. Hooiveld GJ,
    10. Bakker BM,
    11. Reijngoud DJ
    . 2013. Gut-derived short-chain fatty acids are vividly assimilated into host carbohydrates and lipids. Am J Physiol Gastrointest Liver Physiol 305:G900–G910. doi:10.1152/ajpgi.00265.2013.
    OpenUrlCrossRefPubMed
  12. 12.↵
    1. Furusawa Y,
    2. Obata Y,
    3. Fukuda S,
    4. Endo TA,
    5. Nakato G,
    6. Takahashi D,
    7. Nakanishi Y,
    8. Uetake C,
    9. Kato K,
    10. Kato T,
    11. Takahashi M,
    12. Fukuda NN,
    13. Murakami S,
    14. Miyauchi E,
    15. Hino S,
    16. Atarashi K,
    17. Onawa S,
    18. Fujimura Y,
    19. Lockett T,
    20. Clarke JM,
    21. Topping DL,
    22. Tomita M,
    23. Hori S,
    24. Ohara O,
    25. Morita T,
    26. Koseki H,
    27. Kikuchi J,
    28. Honda K,
    29. Hase K,
    30. Ohno H
    . 2013. Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells. Nature 504:446–450. doi:10.1038/nature12721.
    OpenUrlCrossRefPubMedWeb of Science
  13. 13.↵
    1. Zhang H,
    2. DiBaise JK,
    3. Zuccolo A,
    4. Kudrna D,
    5. Braidotti M,
    6. Yu Y,
    7. Parameswaran P,
    8. Crowell MD,
    9. Wing R,
    10. Rittmann BE,
    11. Krajmalnik-Brown R
    . 2009. Human gut microbiota in obesity and after gastric bypass. Proc Natl Acad Sci U S A 106:2365–2370. doi:10.1073/pnas.0812600106.
    OpenUrlAbstract/FREE Full Text
  14. 14.↵
    1. Qin J,
    2. Li Y,
    3. Cai Z,
    4. Li S,
    5. Zhu J,
    6. Zhang F,
    7. Liang S,
    8. Zhang W,
    9. Guan Y,
    10. Shen D,
    11. Peng Y,
    12. Zhang D,
    13. Jie Z,
    14. Wu W,
    15. Qin Y,
    16. Xue W,
    17. Li J,
    18. Han L,
    19. Lu D,
    20. Wu P,
    21. Dai Y,
    22. Sun X,
    23. Li Z,
    24. Tang A,
    25. Zhong S,
    26. Li X,
    27. Chen W,
    28. Xu R,
    29. Wang M,
    30. Feng Q,
    31. Gong M,
    32. Yu J,
    33. Zhang Y,
    34. Zhang M,
    35. Hansen T,
    36. Sanchez G,
    37. Raes J,
    38. Falony G,
    39. Okuda S,
    40. Almeida M,
    41. LeChatelier E,
    42. Renault P,
    43. Pons N,
    44. Batto JM,
    45. Zhang Z,
    46. Chen H,
    47. Yang R,
    48. Zheng W,
    49. Li S,
    50. Yang H,
    51. Wang J,
    52. Ehrlich SD,
    53. Nielsen R,
    54. Pedersen O,
    55. Kristiansen K,
    56. Wang J
    . 2012. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490:55–60. doi:10.1038/nature11450.
    OpenUrlCrossRefPubMedWeb of Science
  15. 15.↵
    1. Frank DN,
    2. St Amand AL,
    3. Feldman RA,
    4. Boedeker EC,
    5. Harpaz N,
    6. Pace NR
    . 2007. Molecular-phylogenetic characterization of microbial community imbalances in human inflammatory bowel diseases. Proc Natl Acad Sci U S A 104:13780–13785. doi:10.1073/pnas.0706625104.
    OpenUrlAbstract/FREE Full Text
  16. 16.↵
    1. Bakken JS,
    2. Borody T,
    3. Brandt LJ,
    4. Brill JV,
    5. Demarco DC,
    6. Franzos MA,
    7. Kelly C,
    8. Khoruts A,
    9. Louie T,
    10. Martinelli LP,
    11. Moore TA,
    12. Russell G,
    13. Surawicz C, Fecal Microbiota Transplantation Workgroup
    . 2011. Treating Clostridium difficile infection with fecal microbiota transplantation. Clin Gastroenterol Hepatol 9:1044–1049. doi:10.1016/j.cgh.2011.08.014.
    OpenUrlCrossRefPubMed
  17. 17.↵
    1. Gareau MG,
    2. Sherman PM,
    3. Walker WA
    . 2010. Probiotics and the gut microbiota in intestinal health and disease. Nat Rev Gastroenterol Hepatol 7:503–514. doi:10.1038/nrgastro.2010.117.
    OpenUrlCrossRefPubMed
  18. 18.↵
    1. Cani PD,
    2. Lecourt E,
    3. Dewulf EM,
    4. Sohet FM,
    5. Pachikian BD,
    6. Naslain D,
    7. De Backer F,
    8. Neyrinck AM,
    9. Delzenne NM
    . 2009. Gut microbiota fermentation of prebiotics increases satietogenic and incretin gut peptide production with consequences for appetite sensation and glucose response after a meal. Am J Clin Nutr 90:1236–1243. doi:10.3945/ajcn.2009.28095.
    OpenUrlAbstract/FREE Full Text
  19. 19.↵
    1. David LA,
    2. Maurice CF,
    3. Carmody RN,
    4. Gootenberg DB,
    5. Button JE,
    6. Wolfe BE,
    7. Ling AV,
    8. Devlin AS,
    9. Varma Y,
    10. Fischbach MA,
    11. Biddinger SB,
    12. Dutton RJ,
    13. Turnbaugh PJ
    . 2014. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505:559–563. doi:10.1038/nature12820.
    OpenUrlCrossRefPubMedWeb of Science
  20. 20.↵
    1. Zoetendal EG,
    2. Raes J,
    3. Van Den Bogert B,
    4. Arumugam M,
    5. Booijink CC,
    6. Troost FJ,
    7. Bork P,
    8. Wels M,
    9. De Vos WM,
    10. Kleerebezem M
    . 2012. The human small intestinal microbiota is driven by rapid uptake and conversion of simple carbohydrates. ISME J 6:1415–1426. doi:10.1038/ismej.2011.212.
    OpenUrlCrossRefPubMedWeb of Science
  21. 21.↵
    1. van Passel MW,
    2. Kant R,
    3. Zoetendal EG,
    4. Plugge CM,
    5. Derrien M,
    6. Malfatti SA,
    7. Chain PS,
    8. Woyke T,
    9. Palva A,
    10. de Vos WM,
    11. Smidt H
    . 2011. The genome of Akkermansia muciniphila, a dedicated intestinal mucin degrader, and its use in exploring intestinal metagenomes. PLoS One 6:e16876. doi:10.1371/journal.pone.0016876.
    OpenUrlCrossRefPubMed
  22. 22.↵
    1. Sangwan N,
    2. Xia F,
    3. Gilbert JA
    . 2016. Recovering complete and draft population genomes from metagenome datasets. Microbiome 4:8. doi:10.1186/s40168-016-0154-5.
    OpenUrlCrossRefPubMed
  23. 23.↵
    1. Wolkenhauer O
    . 2014. Why model? Front Physiol 5:21. doi:10.3389/fphys.2014.00021.
    OpenUrlCrossRefPubMed
  24. 24.↵
    1. Faust K,
    2. Sathirapongsasuti JF,
    3. Izard J,
    4. Segata N,
    5. Gevers D,
    6. Raes J,
    7. Huttenhower C
    . 2012. Microbial co-occurrence relationships in the human microbiome. PLoS Comput Biol 8:e1002606. doi:10.1371/journal.pcbi.1002606.
    OpenUrlCrossRefPubMed
  25. 25.↵
    1. Magnúsdóttir S,
    2. Heinken A,
    3. Kutt L,
    4. Ravcheev DA,
    5. Bauer E,
    6. Noronha A,
    7. Greenhalgh K,
    8. Jäger C,
    9. Baginska J,
    10. Wilmes P,
    11. Fleming RM,
    12. Thiele I
    . 2017. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat Biotechnol 35:81–89. doi:10.1038/nbt.3703.
    OpenUrlCrossRef
  26. 26.↵
    1. Greenblum S,
    2. Turnbaugh PJ,
    3. Borenstein E
    . 2012. Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease. Proc Natl Acad Sci U S A 109:594–599. doi:10.1073/pnas.1116053109.
    OpenUrlAbstract/FREE Full Text
  27. 27.↵
    1. Kanehisa M,
    2. Goto S
    . 2000. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 28:27–30. doi:10.1093/nar/28.1.27.
    OpenUrlCrossRefPubMedWeb of Science
  28. 28.↵
    1. Caspi R,
    2. Altman T,
    3. Dale JM,
    4. Dreher K,
    5. Fulcher CA,
    6. Gilham F,
    7. Kaipa P,
    8. Karthikeyan AS,
    9. Kothari A,
    10. Krummenacker M,
    11. Latendresse M,
    12. Mueller LA,
    13. Paley S,
    14. Popescu L,
    15. Pujar A,
    16. Shearer AG,
    17. Zhang P,
    18. Karp PD
    . 2010. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 38:D473–D479. doi:10.1093/nar/gkp875.
    OpenUrlCrossRefPubMedWeb of Science
  29. 29.↵
    1. Henry CS,
    2. DeJongh M,
    3. Best AA,
    4. Frybarger PM,
    5. Linsay B,
    6. Stevens RL
    . 2010. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol 28:977–982. doi:10.1038/nbt.1672.
    OpenUrlCrossRefPubMedWeb of Science
  30. 30.↵
    1. Arkin AP,
    2. Stevens RL,
    3. Cottingham RW,
    4. Maslov S,
    5. Henry CS,
    6. Dehal P,
    7. Ware D,
    8. Perez F,
    9. Harris NL,
    10. Canon S
    . 2016. The DOE systems biology KnowledgeBase (KBase). bioRxiv https://doi.org/10.1101/096354.
  31. 31.↵
    1. Sung J,
    2. Kim S,
    3. Cabatbat JJT,
    4. Jang S,
    5. Jin Y-S,
    6. Jung GY,
    7. Chia N,
    8. Kim P-J
    . 2017. Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis. Nat Commun 8:15393. doi:10.1038/ncomms15393.
    OpenUrlCrossRef
  32. 32.↵
    1. Overbeek R,
    2. Begley T,
    3. Butler RM,
    4. Choudhuri JV,
    5. Chuang H-Y,
    6. Cohoon M
    . 2005. The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res 33:5691–5702. doi:10.1093/nar/gki866.
    OpenUrlCrossRefPubMedWeb of Science
  33. 33.↵
    1. Thorleifsson SG,
    2. Thiele I
    . 2011. rBioNet: a COBRA toolbox extension for reconstructing high-quality biochemical networks. Bioinformatics 27:2009–2010. doi:10.1093/bioinformatics/btr308.
    OpenUrlCrossRefPubMedWeb of Science
  34. 34.↵
    1. Fleming RM,
    2. Thiele I
    . 2011. von Bertalanffy 1.0: a COBRA toolbox extension to thermodynamically constrain metabolic models. Bioinformatics 27:142–143. doi:10.1093/bioinformatics/btq607.
    OpenUrlCrossRefPubMed
  35. 35.↵
    1. Haraldsdóttir HS,
    2. Thiele I,
    3. Fleming RM
    . 2012. Quantitative assignment of reaction directionality in a multicompartmental human metabolic reconstruction. Biophys J 102:1703–1711. doi:10.1016/j.bpj.2012.02.032.
    OpenUrlCrossRefPubMed
  36. 36.↵
    1. Thiele I,
    2. Palsson BØ
    . 2010. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc 5:93–121. doi:10.1038/nprot.2009.203.
    OpenUrlCrossRefPubMedWeb of Science
  37. 37.↵
    1. Bauer E,
    2. Laczny CC,
    3. Magnusdottir S,
    4. Wilmes P,
    5. Thiele I
    . 2015. Phenotypic differentiation of gastrointestinal microbes is reflected in their encoded metabolic repertoires. Microbiome 3:55. doi:10.1186/s40168-015-0121-6.
    OpenUrlCrossRef
  38. 38.
    1. King ZA,
    2. Lu J,
    3. Dräger A,
    4. Miller P,
    5. Federowicz S,
    6. Lerman JA,
    7. Ebrahim A,
    8. Palsson BO,
    9. Lewis NE
    . 2016. BiGG models: a platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res 44:D515–D522. doi:10.1093/nar/gkv1049.
    OpenUrlCrossRefPubMed
  39. 39.
    1. Pornputtapong N,
    2. Nookaew I,
    3. Nielsen J
    . 2015. Human metabolic atlas: an online resource for human metabolism. Database 2015:bav068. doi:10.1093/database/bav068.
    OpenUrlCrossRefPubMed
  40. 40.↵
    1. Palsson B
    . 2006. Systems biology: properties of reconstructed networks. Cambridge University Press, Cambridge, United Kingdom.
  41. 41.↵
    1. Orth JD,
    2. Thiele I,
    3. Palsson BØ
    . 2010. What is flux balance analysis? Nat Biotechnol 28:245–248. doi:10.1038/nbt.1614.
    OpenUrlCrossRefPubMedWeb of Science
  42. 42.↵
    1. Thiele I,
    2. Vo TD,
    3. Price ND,
    4. Palsson BØ
    . 2005. Expanded metabolic reconstruction of Helicobacter pylori (iIT341 GSM/GPR): an in silico genome-scale characterization of single- and double-deletion mutants. J Bacteriol 187:5818–5830. doi:10.1128/JB.187.16.5818-5830.2005.
    OpenUrlAbstract/FREE Full Text
  43. 43.↵
    1. Heinken A,
    2. Khan MT,
    3. Paglia G,
    4. Rodionov DA,
    5. Harmsen HJ,
    6. Thiele I
    . 2014. Functional metabolic map of Faecalibacterium prausnitzii, a beneficial human gut microbe. J Bacteriol 196:3289–3302. doi:10.1128/JB.01780-14.
    OpenUrlAbstract/FREE Full Text
  44. 44.↵
    1. Orth JD,
    2. Conrad TM,
    3. Na J,
    4. Lerman JA,
    5. Nam H,
    6. Feist AM,
    7. Palsson BØ
    . 2011. A comprehensive genome-scale reconstruction of Escherichia coli metabolism—2011. Mol Syst Biol 7:535. doi:10.1038/msb.2011.65.
    OpenUrlAbstract/FREE Full Text
  45. 45.↵
    1. Bartell JA,
    2. Blazier AS,
    3. Yen P,
    4. Thøgersen JC,
    5. Jelsbak L,
    6. Goldberg JB,
    7. Papin JA
    . 2017. Reconstruction of the metabolic network of Pseudomonas aeruginosa to interrogate virulence factor synthesis. Nat Commun 8:14631. doi:10.1038/ncomms14631.
    OpenUrlCrossRef
  46. 46.↵
    1. Biggs MB,
    2. Medlock GL,
    3. Kolling GL,
    4. Papin JA
    . 2015. Metabolic network modeling of microbial communities. Wiley Interdiscip Rev Syst Biol Med 7:317–334. doi:10.1002/wsbm.1308.
    OpenUrlCrossRef
  47. 47.↵
    1. Heinken A,
    2. Thiele I
    . 2015. Systems biology of host-microbe metabolomics. Wiley Interdiscip Rev Syst Biol Med 7:195–219. doi:10.1002/wsbm.1301.
    OpenUrlCrossRefPubMed
  48. 48.↵
    1. Shoaie S,
    2. Karlsson F,
    3. Mardinoglu A,
    4. Nookaew I,
    5. Bordel S,
    6. Nielsen J
    . 2013. Understanding the interactions between bacteria in the human gut through metabolic modeling. Sci Rep 3:2532. doi:10.1038/srep02532.
    OpenUrlCrossRefPubMed
  49. 49.↵
    1. Thiele I,
    2. Heinken A,
    3. Fleming RM
    . 2013. A systems biology approach to studying the role of microbes in human health. Curr Opin Biotechnol 24:4–12. doi:10.1016/j.copbio.2012.10.001.
    OpenUrlCrossRefPubMed
  50. 50.↵
    1. Heinken A,
    2. Ravcheev DA,
    3. Baldini F,
    4. Heirendt L,
    5. Fleming RMT,
    6. Thiele I
    . 2017. Personalized modeling of the human gut microbiome reveals distinct bile acid deconjugation and biotransformation potential in healthy and IBD individuals. BioRxiv https://doi.org/10.1101/229138.
  51. 51.↵
    1. Heirendt L,
    2. Arreckx S,
    3. Pfau T,
    4. Mendoza SN,
    5. Richelle A,
    6. Heinken A,
    7. Haraldsdottir HS,
    8. Keating SM,
    9. Vlasov V,
    10. Wachowiak J,
    11. Magnusdottir S,
    12. Ng CY,
    13. Preciat G,
    14. Zagare A,
    15. Chan SHJ,
    16. Aurich MK,
    17. Clancy CM,
    18. Modamio J,
    19. Sauls JT,
    20. Noronha A,
    21. Bordbar A,
    22. Cousins B,
    23. El Assal DC,
    24. Ghaderi S,
    25. Ahookhosh M,
    26. Ben Guebila M,
    27. Apaolaza I,
    28. Kostromins A,
    29. Le HM,
    30. Ma D,
    31. Sun Y,
    32. Valcarcel LV,
    33. Wang L,
    34. Yurkovich JT,
    35. Vuong PT,
    36. El Assal LP,
    37. Hinton S,
    38. Bryant WA,
    39. Aragon Artacho FJ,
    40. Planes FJ,
    41. Stalidzans E,
    42. Maass A,
    43. Vempala S,
    44. Hucka M,
    45. Saunders MA,
    46. Maranas CD,
    47. Lewis NE,
    48. Sauter T,
    49. Palsson BO,
    50. Thiele I, et al.
    2017. Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0. ArXiv arXiv:1710.04038v2 [q-bio.QM].
  52. 52.↵
    1. Feist AM,
    2. Palsson BØ
    . 2008. The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli. Nat Biotechnol 26:659–667. doi:10.1038/nbt1401.
    OpenUrlCrossRefPubMedWeb of Science
  53. 53.↵
    1. Lewis NE,
    2. Nagarajan H,
    3. Palsson BO
    . 2012. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol 10:291–305. doi:10.1038/nrmicro2737.
    OpenUrlCrossRefPubMed
  54. 54.↵
    1. Mahadevan R,
    2. Schilling CH
    . 2003. The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng 5:264–276. doi:10.1016/j.ymben.2003.09.002.
    OpenUrlCrossRefPubMedWeb of Science
  55. 55.↵
    1. Gudmundsson S,
    2. Thiele I
    . 2010. Computationally efficient flux variability analysis. BMC Bioinformatics 11:489. doi:10.1186/1471-2105-11-489.
    OpenUrlCrossRefPubMed
  56. 56.↵
    1. Klitgord N,
    2. Segrè D
    . 2010. Environments that induce synthetic microbial ecosystems. PLoS Comput Biol 6:e1001002. doi:10.1371/journal.pcbi.1001002.
    OpenUrlCrossRefPubMed
  57. 57.↵
    1. Heinken A,
    2. Sahoo S,
    3. Fleming RM,
    4. Thiele I
    . 2013. Systems-level characterization of a host-microbe metabolic symbiosis in the mammalian gut. Gut Microbes 4:28–40. doi:10.4161/gmic.22370.
    OpenUrlCrossRefPubMed
  58. 58.↵
    1. Zomorrodi AR,
    2. Maranas CD
    . 2012. OptCom: a multi-level optimization framework for the metabolic modeling and analysis of microbial communities. PLoS Comput Biol 8:e1002363. doi:10.1371/journal.pcbi.1002363.
    OpenUrlCrossRefPubMed
  59. 59.↵
    1. Coyte KZ,
    2. Schluter J,
    3. Foster KR
    . 2015. The ecology of the microbiome: networks, competition, and stability. Science 350:663–666. doi:10.1126/science.aad2602.
    OpenUrlAbstract/FREE Full Text
  60. 60.↵
    1. El-Semman IE,
    2. Karlsson FH,
    3. Shoaie S,
    4. Nookaew I,
    5. Soliman TH,
    6. Nielsen J
    . 2014. Genome-scale metabolic reconstructions of Bifidobacterium adolescentis L2-32 and Faecalibacterium prausnitzii A2-165 and their interaction. BMC Syst Biol 8:41. doi:10.1186/1752-0509-8-41.
    OpenUrlCrossRefPubMed
  61. 61.↵
    1. Mendes-Soares H,
    2. Mundy M,
    3. Soares LM,
    4. Chia N
    . 2016. MMinte: an application for predicting metabolic interactions among the microbial species in a community. BMC Bioinformatics 17:343. doi:10.1186/s12859-016-1230-3.
    OpenUrlCrossRef
  62. 62.↵
    1. Heinken A,
    2. Thiele I
    . 2015. Anoxic conditions promote species-specific mutualism between gut microbes in silico. Appl Environ Microbiol 81:4049–4061. doi:10.1128/AEM.00101-15.
    OpenUrlAbstract/FREE Full Text
  63. 63.↵
    1. Shoaie S,
    2. Ghaffari P,
    3. Kovatcheva-Datchary P,
    4. Mardinoglu A,
    5. Sen P,
    6. Pujos-Guillot E,
    7. de Wouters T,
    8. Juste C,
    9. Rizkalla S,
    10. Chilloux J,
    11. Hoyles L,
    12. Nicholson JK, MICRO-Obes Consortium,
    13. Dore J,
    14. Dumas ME,
    15. Clement K,
    16. Bäckhed F,
    17. Nielsen J
    . 2015. Quantifying diet-induced metabolic changes of the human gut microbiome. Cell Metab 22:320–331. doi:10.1016/j.cmet.2015.07.001.
    OpenUrlCrossRefPubMed
  64. 64.↵
    1. Zhuang K,
    2. Izallalen M,
    3. Mouser P,
    4. Richter H,
    5. Risso C,
    6. Mahadevan R,
    7. Lovley DR
    . 2011. Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments. ISME J 5:305–316. doi:10.1038/ismej.2010.117.
    OpenUrlCrossRefPubMedWeb of Science
  65. 65.↵
    1. Turnbaugh PJ,
    2. Ridaura VK,
    3. Faith JJ,
    4. Rey FE,
    5. Knight R,
    6. Gordon JI
    . 2009. The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci Transl Med 1:6ra14. doi:10.1126/scitranslmed.3000322.
    OpenUrlAbstract/FREE Full Text
  66. 66.↵
    1. Louca S,
    2. Doebeli M
    . 2015. Calibration and analysis of genome-based models for microbial ecology. Elife 4:e08208. doi:10.7554/eLife.08208.
    OpenUrlAbstract/FREE Full Text
  67. 67.↵
    1. Harcombe WR,
    2. Riehl WJ,
    3. Dukovski I,
    4. Granger BR,
    5. Betts A,
    6. Lang AH,
    7. Bonilla G,
    8. Kar A,
    9. Leiby N,
    10. Mehta P,
    11. Marx CJ,
    12. Segrè D
    . 2014. Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics. Cell Rep 7:1104–1115. doi:10.1016/j.celrep.2014.03.070.
    OpenUrlCrossRefPubMed
  68. 68.↵
    1. Earle KA,
    2. Billings G,
    3. Sigal M,
    4. Lichtman JS,
    5. Hansson GC,
    6. Elias JE,
    7. Amieva MR,
    8. Huang KC,
    9. Sonnenburg JL
    . 2015. Quantitative imaging of gut microbiota spatial organization. Cell Host Microbe 18:478–488. doi:10.1016/j.chom.2015.09.002.
    OpenUrlCrossRefPubMed
  69. 69.↵
    1. Granger BR,
    2. Chang Y-C,
    3. Wang Y,
    4. DeLisi C,
    5. Segrè D,
    6. Hu Z
    . 2016. Visualization of metabolic interaction networks in microbial communities using VisANT 5.0. PLoS Comput Biol 12:e1004875. doi:10.1371/journal.pcbi.1004875.
    OpenUrlCrossRef
  70. 70.↵
    1. Chan SHJ,
    2. Simons MN,
    3. Maranas CD
    . 2017. SteadyCom: predicting microbial abundances while ensuring community stability. PLoS Comput Biol 13:e1005539. doi:10.1371/journal.pcbi.1005539.
    OpenUrlCrossRef
  71. 71.↵
    1. Judson OP
    . 1994. The rise of the individual-based model in ecology. Trends Ecol Evol 9:9–14. doi:10.1016/0169-5347(94)90225-9.
    OpenUrlCrossRefPubMedWeb of Science
  72. 72.↵
    1. Biggs MB,
    2. Papin JA
    . 2013. Novel multiscale modeling tool applied to Pseudomonas aeruginosa biofilm formation. PLoS One 8:e78011. doi:10.1371/journal.pone.0078011.
    OpenUrlCrossRefPubMed
  73. 73.↵
    1. Bauer E,
    2. Zimmermann J,
    3. Baldini F,
    4. Thiele I,
    5. Kaleta C
    . 2017. BacArena: individual-based metabolic modeling of heterogeneous microbes in complex communities. PLoS Comput Biol 13:e1005544. doi:10.1371/journal.pcbi.1005544.
    OpenUrlCrossRef
  74. 74.↵
    1. Van Hoek MJA,
    2. Merks RMH
    . 2017. Emergence of microbial diversity due to cross-feeding interactions in a spatial model of gut microbial metabolism. BMC Syst Biol 11:56. doi:10.1186/s12918-017-0430-4.
    OpenUrlCrossRef
  75. 75.↵
    1. Shashkova T,
    2. Popenko A,
    3. Tyakht A,
    4. Peskov K,
    5. Kosinsky Y,
    6. Bogolubsky L,
    7. Raigorodskii A,
    8. Ischenko D,
    9. Alexeev D,
    10. Govorun V
    . 2016. Agent based modeling of human gut microbiome interactions and perturbations. PLoS One 11:e0148386. doi:10.1371/journal.pone.0148386.
    OpenUrlCrossRef
  76. 76.↵
    1. Becker N,
    2. Kunath J,
    3. Loh G,
    4. Blaut M
    . 2011. Human intestinal microbiota: characterization of a simplified and stable gnotobiotic rat model. Gut Microbes 2:25–33. doi:10.4161/gmic.2.1.14651.
    OpenUrlCrossRefPubMed
  77. 77.↵
    1. Bauer E,
    2. Thiele I
    . 2017. From metagenomic data to personalized computational microbiotas: predicting dietary supplements for Crohn’s disease. arXiv arXiv:1709.06007 [q-bio.QM].
  78. 78.↵
    1. Brito IL,
    2. Yilmaz S,
    3. Huang K,
    4. Xu L,
    5. Jupiter SD,
    6. Jenkins AP,
    7. Naisilisili W,
    8. Tamminen M,
    9. Smillie CS,
    10. Wortman JR,
    11. Birren BW,
    12. Xavier RJ,
    13. Blainey PC,
    14. Singh AK,
    15. Gevers D,
    16. Alm EJ
    . 2016. Mobile genes in the human microbiome are structured from global to individual scales. Nature 535:435–439. doi:10.1038/nature18927.
    OpenUrlCrossRefPubMed
  79. 79.↵
    1. Smillie CS,
    2. Smith MB,
    3. Friedman J,
    4. Cordero OX,
    5. David LA,
    6. Alm EJ
    . 2011. Ecology drives a global network of gene exchange connecting the human microbiome. Nature 480:241–244. doi:10.1038/nature10571.
    OpenUrlCrossRefPubMedWeb of Science
  80. 80.↵
    1. Szappanos B,
    2. Fritzemeier J,
    3. Csörgő B,
    4. Lázár V,
    5. Lu X,
    6. Fekete G,
    7. Bálint B,
    8. Herczeg R,
    9. Nagy I,
    10. Notebaart RA,
    11. Lercher MJ,
    12. Pál C,
    13. Papp B
    . 2016. Adaptive evolution of complex innovations through stepwise metabolic niche expansion. Nat Commun 7:11607. doi:10.1038/ncomms11607.
    OpenUrlCrossRef
  81. 81.↵
    1. Thiele I,
    2. Sahoo S,
    3. Heinken A,
    4. Heirendt L,
    5. Aurich MK,
    6. Noronha A,
    7. Fleming RMT
    . 2018. When metabolism meets physiology: Harvey and Harvetta. bioRxiv https://doi.org/10.1101/255885.
  82. 82.↵
    1. Shah P,
    2. Fritz JV,
    3. Glaab E,
    4. Desai MS,
    5. Greenhalgh K,
    6. Frachet A,
    7. Niegowska M,
    8. Estes M,
    9. Jäger C,
    10. Seguin-Devaux C,
    11. Zenhausern F,
    12. Wilmes P
    . 2016. A microfluidics-based in vitro model of the gastrointestinal human-microbe interface. Nat Commun 7:11535. doi:10.1038/ncomms11535.
    OpenUrlCrossRef
  83. 83.↵
    1. Heirendt L,
    2. Thiele I,
    3. Fleming RMT
    . 2017. DistributedFBA.jl: high-level, high-performance flux balance analysis in Julia. Bioinformatics 33:1421–1423. doi:10.1093/bioinformatics/btw838.
    OpenUrlCrossRefPubMed
  84. 84.↵
    1. Monk JM,
    2. Charusanti P,
    3. Aziz RK,
    4. Lerman JA,
    5. Premyodhin N,
    6. Orth JD,
    7. Feist AM,
    8. Palsson BØ
    . 2013. Genome-scale metabolic reconstructions of multiple Escherichia coli strains highlight strain-specific adaptations to nutritional environments. Proc Natl Acad Sci U S A 110:20338–20343. doi:10.1073/pnas.1307797110.
    OpenUrlAbstract/FREE Full Text
  85. 85.↵
    1. Shaked I,
    2. Oberhardt MA,
    3. Atias N,
    4. Sharan R,
    5. Ruppin E
    . 2016. Metabolic network prediction of drug side effects. Cell Syst 2:209–213. doi:10.1016/j.cels.2016.03.001.
    OpenUrlCrossRef
  86. 86.↵
    1. Joyce AR,
    2. Palsson BØ
    . 2006. The model organism as a system: integrating “omics” data sets. Nat Rev Mol Cell Biol 7:198–210. doi:10.1038/nrm1857.
    OpenUrlCrossRefPubMedWeb of Science
  87. 87.↵
    1. Saha R,
    2. Chowdhury A,
    3. Maranas CD
    . 2014. Recent advances in the reconstruction of metabolic models and integration of omics data. Curr Opin Biotechnol 29:39–45. doi:10.1016/j.copbio.2014.02.011.
    OpenUrlCrossRefPubMedWeb of Science
  88. 88.↵
    1. Brown CT,
    2. Olm MR,
    3. Thomas BC,
    4. Banfield JF
    . 2016. Measurement of bacterial replication rates in microbial communities. Nat Biotechnol 34:1256–1263. doi:10.1038/nbt.3704.
    OpenUrlCrossRef
  89. 89.↵
    1. Heintz-Buschart A,
    2. May P,
    3. Laczny CC,
    4. Lebrun LA,
    5. Bellora C,
    6. Krishna A,
    7. Wampach L,
    8. Schneider JG,
    9. Hogan A,
    10. de Beaufort C,
    11. Wilmes P
    . 2016. Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes. Nat Microbiol 2:16180. doi:10.1038/nmicrobiol.2016.180.
    OpenUrlCrossRef
  90. 90.
    1. Heinken A,
    2. Thiele I
    . 2015. Systematic prediction of health-relevant human-microbial co-metabolism through a computational framework. Gut Microbes 6:120–130. doi:10.1080/19490976.2015.1023494.
    OpenUrlCrossRefPubMed
View Abstract
Back to top
Download PDF
Citation Tools
Print
Alerts
Email

KEYWORDS

computational modeling
constraint-based modeling
gut microbiome
metabolic modeling
network approaches

Related Articles

Cited By...

Advertisement