Klara Cerk

Pablo Ugalde-Salas

Chabname Ghassemi Nedjad

Maxime Lecomte

Coralie Muller

David J Sherman

Falk Hildebrand

Simon Labarthe

Clémence Frioux
email: frioux@inria.fr>

Community-scale models of microbiomes: articulating metabolic modelling and metagenome sequencing

Keywords:

Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genomescale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species.

Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally-occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models.

We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third generation sequencing, and we discuss the opportunities of long read sequencing, strain-level characterisation, and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.

Introduction

The roles of microbiomes in the environment and in health-associated, agricultural or biotechnological applications is widely acknowledged [START_REF] Peixoto | Harnessing the microbiome to prevent global biodiversity loss[END_REF], Foundation et al., 2022, Wilmes et al., 2022].

These assemblies of microorganisms are governed by the interactions they have with their environment, and by those occurring among species [START_REF] Zengler | The social network of microorganisms -how auxotrophies shape complex communities[END_REF]. Such ecological interactions can generally be classified as neutral, positive, or negative; and can be inferred through network analysis [START_REF] Faust | Microbial interactions: From networks to models[END_REF]. Mechanistically, they operate at a similar level, called the metabolism. Metabolism encompasses all of the functions of cells, defined by biochemical reactions and by molecular exchanges between cells and the environment. Microbiomes are characterised by their biosynthetic potential and by the taxonomic diversity that they harbour [START_REF] Heintz-Buschart | Human gut microbiome: Function matters[END_REF]. While some microbiomes such as the gastro-intestinal tract in humans have been extensively studied, thereby revealing insights into the putative cooperation and competition networks among bacteria [START_REF] Coyte | Understanding competition and cooperation within the mammalian gut microbiome[END_REF], in general the precise mechanisms, dynamics and control levers of metabolic interactions remain poorly understood. These difficulties are even more prevalent in complex environmental microbiomes where the massive diversity of micro-organisms and their poor representation in public databases impede taxonomic and functional surveys [START_REF] Brown | Critical evaluation of short, long, and hybrid assembly for contextual analysis of antibiotic resistance genes in complex environmental metagenomes[END_REF].

Culture-independent methods can be used to broadly characterise microbiomes [START_REF] Zhang | Advancing functional and translational microbiome research using meta-omics approaches[END_REF].

Prominent among these is metagenomics, the random sequencing of all DNA in a given sample. Metagenomics is by now ubiquitously applied to decipher both the taxonomic and functional diversity of samples; a rich resource of public available metagenomes can support large scale studies available to even small labs with the required bioinformatics expertise [START_REF] Gurbich | MGnify Genomes: A resource for biome-specific microbial genome catalogues[END_REF]. Analysis of metagenomic data by bioinformatic tools can generate surveys of the functions embodied by the community [START_REF] Kindler | Genomeresolved metagenomics provides insights into the functional complexity of microbial mats in Blue Holes, Shark Bay[END_REF].

Metabolic models, either mechanistic models such as those reconstructed from genome-scale metabolic network reconstructions (GENREs), or phenomenological models describing phenotypic traits, go further than these surveys. The latter have been the modeller's choice for narrowing down the complexity of communitywide behaviour [van den Berg et al., 2022], whereas the former could originally only be applied to single species [START_REF] Varma | Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110[END_REF]. However the range of modelling tools has strongly increased during the past years, and now provides a broad collection of methods for answering critical questions about microbial communities [START_REF] Kumar | Modelling approaches for studying the microbiome[END_REF]: what are the roles of bacterial species in the ecosystem? What functions are carried and how redundant are they? How will the community behave over time? And, can we alter this behaviour, or modify the community composition to reach a desired state?

Recent developments in metabolic model inference facilitate this procedure and make it worthwhile to consider systematic reconstruction for collections of genomes [START_REF] Frioux | From bag-of-genes to bag-of-genomes: Metabolic modelling of communities in the era of metagenome-assembled genomes[END_REF]. Concurrently, algorithmic resources for obtaining such genomes from metagenomes have increased, and the technological development of third generation sequencing (TGS) has paved the way to a major change of paradigm in the study of microbiomes [START_REF] Sereika | Oxford Nanopore R10.4 long-read sequencing enables the generation of near-finished bacterial genomes from pure cultures and metagenomes without short-read or reference polishing[END_REF]. Our goal here is twofold: (1) to review to what extent metabolic modelling and metagenomics can interact in service of better comprehending microbial ecosystems, (2) to provide comprehensive lists of practical tools that can be used by non-specialists for metagenomics assembly, metabolic reconstruction, and microbiome modelling. We describe the different kinds of models that can be built from metagenomic data to predict the functions and the dynamics of a microbial community, and how they can be used to infer interactions among species. As these models rely on reconstructions of metagenomes, we present as well the latest sequencing techniques, bioinformatics tools and open challenges related to metagenomics, in an attempt to draw together these two domains of computational biology.

From gene functions to metabolic reconstructions 2.1 Aggregating functions into metabolic networks

Models of microbial communities can be derived from a subset of functions, reactions, or metabolic pathways; or on the contrary by analysing the entire metabolic potential of the genomes. In the latter case, the exploration of the link between the genome, enzymes, and the metabolic reactions they catalyse provides a system-wide understanding of the physiology of the cell and its links with its environment.

Genome-scale metabolic network reconstructions (GENREs) contain metabolic reactions predicted from the entire genomic content through gene-protein-reaction (GPR) relationships, but also spontaneous reactions and transport reactions connecting the cell to its environment or even to intracellular components [START_REF] Thiele | A protocol for generating a high-quality genome-scale metabolic reconstruction[END_REF]. In this section, we present the basis of such metabolic networks, whose fundamental concept is the association of genes to functions. Since GENREs are at the heart of mechanistic models of metabolism, the first step of community metabolic modelling is typically the acquisition of a GENRE for each of its members.

GENREs have been built for more than twenty years, with a spectacular acceleration of the publications over the past years, as a result of both the increased availability of genomes, and of the efforts of the community for tools development. In 2019, a review tallied more than 6,000 published GENREs, 94% of which were for bacteria [START_REF] Gu | Current status and applications of genome-scale metabolic models[END_REF]. However, the numbers rise even more when including systematic and automatic reconstructions performed on publicly available genomes by platforms such as BioCyc [START_REF] Caspi | The MetaCyc database of metabolic pathways and enzymes-a 2019 update[END_REF], or AGORA2 for human-associated micro-organisms [START_REF] Heinken | Genome-scale metabolic reconstruction of 7,302 human microorganisms for personalized medicine[END_REF]. Table 1 gathers databases that can be used as general or specialised resources to functionally annotate genomes prior to GENRE generation, as well as collections of metabolic reconstructions. In addition, de novo reconstruction is facilitated by a variety of software programmes and toolboxes created for high throughput processing of genomes, an important criterion when tackling large-scale communities of micro-organisms. While many of tools, catalogued in dedicated reviews [START_REF] Mendoza | A systematic assessment of current genome-scale metabolic reconstruction tools[END_REF], enable this task, only a small number can do so fully automatically, without user interaction. We hereafter present a sample of tools that we believe are suitable for systematic metabolic reconstruction of GENREs starting from a list of annotated genomes.

Gapseq [START_REF] Zimmermann | Gapseq: Informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models[END_REF] builds GENREs using a curated reaction database and a dedicated gap-filling algorithm. The tool was notably applied to a four-strain microbial community whose associated [START_REF] Kanehisa | KEGG: Integrating viruses and cellular organisms[END_REF] General annotation of genes and genomes that can be directly or indirectly (via reconstruction tools) associated to metabolic reactions uses a top down approach consisting in pruning (carving) a universal curated model of the microbial metabolism for rapid generation of GENREs starting from genomes. While construction with Gapseq takes up to two hours, a Carveme-generated reconstruction can be obtained in a few minutes. Both computation times are compatible with a systematic generation of GENREs for microbiomes. Pathway

Tools [START_REF] Karp | Pathway Tools version 23.0 update: Software for pathway/genome informatics and systems biology[END_REF] builds pathway-genome databases relying on the MetaCyc database of genomes [START_REF] Caspi | The MetaCyc database of metabolic pathways and enzymes-a 2019 update[END_REF] and on functionally annotations, e.g. from EggNOG-mapper [START_REF] Cantalapiedra | eggNOGmapper v2: Functional annotation, orthology assignments, and domain prediction at the metagenomic scale[END_REF],

Prokka [Seemann, 2014] or RAST [START_REF] Overbeek | The SEED and the rapid annotation of microbial genomes using subsystems technology (RAST)[END_REF]. While Pathway Tools is originally based on an interactive single-organism interface, which can impede its use at the scale of large communities, it is possible to automatise the process using, for instance, the Python package mpwt [START_REF] Belcour | Metage2Metabo, microbiota-scale metabolic complementarity for the identification of key species[END_REF]. The latter was applied to a collection of 336 reference genomes obtained after metagenomic read mapping, in order to identify the presence of pathways of interest [START_REF] Ruuskanen | Links between gut microbiome composition and fatty liver disease in a large population sample[END_REF]. The latest developments of Pathway Tools additionally permit importing a set of genomes or metagenome-assembled genomes (MAGs) (see section 7) into the interface to run the reconstruction on each organism. AutoKEGGRec [START_REF] Karlsen | Automated generation of genome-scale metabolic draft reconstructions based on KEGG[END_REF] is a Matlab programme that creates draft GENREs by retrieving reactions and GPR relationships from the KEGG database for one or a set of organisms, making it suitable for community modelling. In addition, online platforms provide reconstruction services, such as KBase [START_REF] Arkin | KBase: The united states department of[END_REF], which proposes a large variety of analyses including metabolic reconstruction, with extensive efforts towards reproducibility through the use of narratives; or ModelSEED [START_REF] Seaver | The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes[END_REF], which performs metabolic annotation and GENRE reconstruction for microbes, plants and fungi.

A general limitation of GENREs lies in the metabolic landscape covered in reaction databases; many proteins have no attributed functions even in well-studied organisms, and experimental evidence of functions are even scarcer [START_REF] Monk | Optimizing genome-scale network reconstructions[END_REF]. It must be noted that automatically reconstructed GENREs cannot compete with manually curated ones with respect to the quality of the contents and the accuracy of the simulations they permit. High-quality GENREs exist mostly for model organisms; automatically reconstructed GENREs may suffer from erroneous GPR relationships or reversibility constraints, and inaccuracy in biomass composition [START_REF] Gu | Current status and applications of genome-scale metabolic models[END_REF]. Most automated tools produce GENREs that are gap-filled in order for the associated model to simulate the production of biomass [START_REF] Feist | The biomass objective function[END_REF], which is a common step for flux prediction (see section 3.1). Unfortunately, automated gap-filling, while useful, is not as reliable as manual curation for producing high quality GENREs [Latendresse and[START_REF] Karp | How accurate is automated gap filling of metabolic models?[END_REF]. Additionally, growth is simulated in media that likely do not sustain the auxotrophies of non-cultivable organisms, which may depend on complex metabolic interactions with other microbes to grow [West et al., 2007, Zengler andZaramela, 2018]. On the other hand, it is possible to work with non-gapfilled GENREs, for example using Pathway Tools or AutoKEGGRec. Overall, the use of simulation-ready GENREs creates a risk of missing interactions occurring in communities (false negative interaction) while using non-gap-filled GENREs may falsely predict interactions to sustain certain functions that could actually be performed by individual microbes (false positive interaction). We refer to [START_REF] Bernstein | Addressing uncertainty in genome-scale metabolic model reconstruction and analysis[END_REF] for a description of all the steps of the GENRE reconstruction and simulation processes where uncertainty may occur and lead to inconsistent results. More generally, with the increasing use of metagenomic-assembled genomes (see section 7), more attention is paid to the effect of genome incompleteness on the inference of functions and metabolic potentials [START_REF] Eisenhofer | Impact of microbial genome completeness on metagenomic functional inference[END_REF], Belcour et al., 2020]. The examination of multiple GENREs when building models of microbiomes must therefore consider these potential shortcomings, and address them through a careful choice of model paradigm and a cautious interpretation of results.

Obtaining a GENRE for each member of a microbial community is already per se a valuable resource for querying and comparing the metabolic processes that can occur within species, even before modelling and simulation. It can consist in identifying functional differences between GENREs: strain-specific patterns, gene essentiality or biosynthetic capacity [START_REF] Biggs | Metabolic network modeling of microbial communities[END_REF]. In [START_REF] Weiss | In vitro interaction network of a synthetic gut bacterial community[END_REF], GENREs corresponding to the twelve bacterial members of a synthetic bacterial community were compared at the level of metabolic pathway, as a complement to results of in vitro interaction experiments. More generally, any approach aiming at comparing metabolic networks could be applied to GENREs of co-occurring species [START_REF] Ay | Metabolic network alignment in large scale by network compression[END_REF]. Likewise, studying the pan-reactome and accessory reactome of species using GENREs is valuable [START_REF] Prigent | Reconstruction of 24 Penicillium genome-scale metabolic models shows diversity based on their secondary metabolism[END_REF], Belcour et al., 2023] and these approaches can be applied to ecosystems.

Metabolic models for microbial communities: from GENREs to dynamic simulations

GENREs can be the input of metabolic modelling at the genome or community scale. In this section, we describe and focus on mechanistic constraint-based models. Alternative GENRE-based models and their relationship to other modelling fields are presented in section 5.

When modelling a community, the first step is defining a collection of individual GENREs representative of the community. The selection can be 'reductionist', meaning a reduced number of genomes is selected as functionally representative of the community and a GENRE is obtained for each, either from de novo reconstruction, or from databases (Table1). A second 'exhaustive' approach consists in building a GENRE for each genome encountered in the community e.g. from metagenomic analysis. Then, two alternative conceptual views exist to build a community model, with methodological and computational pros and cons (Fig. 1): one consisting in merging GENREs to build a a community-wide model, also termed bag-of-genes model and the other articulating individual GENREs into a community of GENREs (bag-of-genomes) [START_REF] Frioux | From bag-of-genes to bag-of-genomes: Metabolic modelling of communities in the era of metagenome-assembled genomes[END_REF].

Genome-scale metabolic modelling

Metabolic networks, metabolic models and dynamic simulations A GENRE has a matrix representation termed the stoichiometric matrix S, with as many rows as metabolites and as many columns as reactions (Fig. 1). The coefficient S ij is the stoichiometric coefficient of the metabolite i in the reaction j, with a negative or positive value indicating whether i is a substrate or a product of j, or a zero value if the metabolite i does not appear in reaction j [START_REF] Orth | What is flux balance analysis?[END_REF]. The matrix S allows modelling the dynamics of the metabolite concentrations c (mmol L -1 ) b (g L -1 ) through the mass-balance equation

∂ t c = Sν(c, b) (1)
where ∂ t is the time derivative operator and ν(c, b) (mmol h -1 L) is a vector of the reaction fluxes that depends both on c and on the bacterial population b [START_REF] Jamshidi | Mass Action Stoichiometric Simulation Models: Incorporating Kinetics and Regulation into Stoichiometric Models[END_REF]. Equation (1) highlights that modelling concentration variation comprises two distinct components: (1) the metabolic network, i.e. the static functional potential carried by the micro-organism genome represented by S, and (2) the distribution of the metabolic fluxes on this network, i.e. the activated functions in the current state of the system, represented by ν. The resulting model is referred to as a genome-scale metabolic model (GEM).

The algebraic analysis of S already gives biological insights into the metabolic graph, through the use of approaches such as elementary flux modes (EFM) [START_REF] Stelling | Metabolic network structure determines key aspects of functionality and regulation[END_REF].

Constraint-based models for flux prediction

To predict ν for a single species or a merged community (see fig. 1), several approaches are available. Kinetic modelling adds the enzymes catalysing the reactions to the system and evaluates ν using Michaelis-Menten-like equations that link substrate and enzyme concentrations to the reaction kinetics [START_REF] Costa | Kinetic modeling of cell metabolism for microbial production[END_REF]. However, due to the difficulty of model parametrisation, which requires enzyme kinetic rates, practical use is limited at a genome scale [van Rosmalen et al., 2021] and even more so at metagenome scale. Constraint-based metabolic models are a widely used alternative for predicting flux distributions, that assumes a quasi steady-state of the intracellular metabolites in eq. 1. Flux Balance Analysis (FBA) [START_REF] Orth | What is flux balance analysis?[END_REF] solves a linear optimisation of an objective function-usually the biomass production-and taking into account thermodynamic constraints modelled as flux bounds.

Known limitations of FBA are the need for high-quality or curated GENREs as input, and its sensitivity to the definition of the biomass function, which is organism-dependent [START_REF] Xavier | Integration of biomass formulations of genome-scale metabolic models with experimental data reveals universally essential cofactors in prokaryotes[END_REF] and is hard to establish for a community-wide model. Another limitation is the existence of many alternative optimal solutions to the problem. Numerous variations exist, such as Parsimonious FBA (pFBA) [START_REF] Lewis | Omic data from evolved E. Coli are consistent with computed optimal growth from genome-scale models[END_REF],

which focuses on sparse flux distributions; Flux Variability Analysis (FVA) [START_REF] Mahadevan | The effects of alternate optimal solutions in constraint-based genome-scale metabolic models[END_REF],

which explores the range of alternative solutions to identify essential and blocked reactions; Thermodynamic Flux Analysis (TFA) [START_REF] Henry | Thermodynamics-Based Metabolic Flux Analysis[END_REF], which identifies thermodynamically feasible flux distribution; dynamic FBA (dFBA) [START_REF] Mahadevan | Dynamic Flux Balance Analysis of Diauxic Growth in Escherichia coli[END_REF], which iterates flux evaluations and concentration updates to model the dynamics of the system; and Resource Balance Analysis (RBA) [START_REF] Goelzer | Quantitative prediction of genome-wide resource allocation in bacteria[END_REF],

which extends the optimisation problem to take into account the internal machinery of protein production. We refer to [START_REF] León | Toward merging bottom-up and top-down model-based designing of synthetic microbial communities[END_REF] for an extensive review of constraint-based methods discussing the biological trade-offs they achieve. The Cobra toolbox [START_REF] Heirendt | Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0[END_REF] and its Python interface [START_REF] Ebrahim | COBRApy: COnstraints-Based Reconstruction and Analysis for Python[END_REF] provide numerous practical tools and implementations for constraint-based modelling.

Several methods integrate -omics data into constraint-based models, such as transcriptomic [START_REF] Jenior | Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments[END_REF] or metabolomic data [START_REF] Toya | Flux analysis and metabolomics for systematic metabolic engineering of microorganisms[END_REF]. Metatranscriptomics data can support the creation of condition-specific community metabolic models such as in the work of [START_REF] Zampieri | Metatranscriptomics-guided genome-scale metabolic modeling of microbial communities[END_REF]. Going beyond the analysis of functional potential-which may not be expressed-this approach takes into account whole transcriptome abundance of MAGs and gene transcript abundance, to scale and adapt metabolic reaction bounds. Such integrated solutions bring metabolic simulations closer to the biological reality.

Building constraint-based models using GENREs of individual microbiome members is a first step in the comparison of their biosynthetic potential [START_REF] Bartell | Comparative metabolic systems analysis of pathogenic Burkholderia[END_REF]. As one approach, CONGA compares GENREs pairwise to identify essential genes [START_REF] Hamilton | Identification of functional differences in metabolic networks using comparative genomics and constraint-based models[END_REF]. Pangenome analysis combined with flux analysis can also be used to detect strain specific metabolic features [START_REF] Bosi | Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity[END_REF] (see also section 6.3). A further step in the analysis is to combine individual GEMs in order to model the community and its interactions.

From genome to metagenome-scale metabolic modelling

Bag-of-genomes models A batch of GENREs can first be considered as a set of independent individuals interacting through extracellular metabolites. The community model is then obtained by the juxtaposition of the individual GENREs, which are used to predict individual metabolic fluxes, the sum of which determines the fate of the extracellular metabolites (Fig. 1). This paradigm is also termed a bag-of-genomes model [START_REF] Frioux | From bag-of-genes to bag-of-genomes: Metabolic modelling of communities in the era of metagenome-assembled genomes[END_REF].

However, the prediction of individual fluxes can require new methodological developments, in particular in a multi-species FBA framework where interactions are studied in a static regime [START_REF] Gottstein | Constraint-based stoichiometric modelling from single organisms to microbial communities[END_REF].

Indeed, the growth rate of an individual only depends on the availability of extracellular metabolites, whereas that of a community constraint-based model also depends on the instantaneous use of metabolites by organisms that limits metabolic availability for others. Individual flux prediction in communities is thus the result of a trade-off of metabolite uptake between the community members. Furthermore, the hypothesis of individual optimal growth, which is sound in monoculture, may not be valid in communities where complex ecological processes may occur. For example, symbiosis can induce sub-optimal growth for a symbiont but improve long-term persistence in the community. To predict this trade-off, several meth- Optcom [START_REF] Zomorrodi | OptCom: A Multi-Level Optimization Framework for the Metabolic Modeling and Analysis of Microbial Communities[END_REF] CASINO [START_REF] Shoaie | Quantifying Diet-Induced Metabolic Changes of the Human Gut Microbiome[END_REF] weighted squared sum A weighted square sum of individual flux weighted by microbe abundance as community-scale objective function MICOM [START_REF] Diener | MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota[END_REF] multi-objective FBA A Pareto front is computed as set of solutions of a multi-objective optimisation problem.

A solution on the Pareto front is chosen according to additional criteria (e.g. resource use) MO-FBA [START_REF] Budinich | A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems[END_REF] [ [START_REF] Heinken | Anoxic Conditions Promote Species-Specific Mutualism between Gut Microbes In Silico[END_REF] [ [START_REF] Heinken | Systems-level characterization of a host-microbe metabolic symbiosis in the mammalian gut[END_REF] EFM-based EFM-based individual models the kinetic rates of which is determined by a community-scale model CODY [START_REF] Geng | CODY enables quantitatively spatiotemporal predictions on in vivo gut microbial variability induced by diet intervention[END_REF] dFBA sum of fluxes Straightforward sum of individual fluxes (see fig. 1). Flux bounds are computed from extracellular compound concentrations that change over time according to consumption and production by community members.

µBialSim [START_REF] Popp | µBialSim: Constraint-Based Dynamic Simulation of Complex Microbiomes[END_REF] COMETS [START_REF] Dukovski | A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS)[END_REF] IndiMeSH [START_REF] Borer | Modeling metabolic networks of individual bacterial agents in heterogeneous and dynamic soil habitats (IndiMeSH)[END_REF] ods have been developed, that are conceptually very different in a static or a dynamic FBA framework.

These methods and example of corresponding toolboxes are recapitulated in table 2. Extensive reviews of community-wide metabolic models can be found in [START_REF] Colarusso | Computational modeling of metabolism in microbial communities on a genome-scale[END_REF], including comparison between FBA and EFM-based methods, in [START_REF] León | Toward merging bottom-up and top-down model-based designing of synthetic microbial communities[END_REF], for a comparison between top-down and bottom-up modelling approaches, or in [START_REF] García-Jiménez | Metabolic modelling approaches for describing and engineering microbial communities[END_REF], Zaramela et al., 2021], for a parallel between computational methods and engineered microbial communities.

The Microbiome Modelling Toolbox [Heinken and Thiele, 2022a] derives community models from metagenomics data for the human gut microbiome. This platform facilitates the extraction of GEMs corresponding to OTUs identified by metabarcoding from large curated collections such as AGORA 2 [START_REF] Heinken | Genome-scale metabolic reconstruction of 7,302 human microorganisms for personalized medicine[END_REF] or the Virtual Metabolic Human [START_REF] Noronha | The Virtual Metabolic Human database: Integrating human and gut microbiome metabolism with nutrition and disease[END_REF], and provides efficient computation and result visualisation. The main drawbacks of such large bag-of-genomes models are the computational cost, which scales with the number of GEMs taken into account [Heinken and Thiele, 2022a], and the difficulty of ensuring GENRE quality and accuracy for a large number of genomes. In particular, the presence of draft models inferred by automatic pipelines may impair the overall quality of the simulations [START_REF] Babaei | Challenges in modeling the human gut microbiome[END_REF].

Bag-of-genomes models can be embedded in an outer optimisation problem in order to find an optimal configuration of the microbial community according to a dedicated fitness function, like in FLYCOP [START_REF] García-Jiménez | FLYCOP: Metabolic modeling-based analysis and engineering microbial communities[END_REF].

Bag-of-genes models

A community-wide metabolic model can be built by assembling all the functions carried by the individual GENREs and factoring the common functions shared by different organisms (Fig. 1). In this paradigm, the community is seen as a meta or supra organism, an enzyme soup, whose functional potential results from the gathering of the whole set of functions [START_REF] Frioux | From bag-of-genes to bag-of-genomes: Metabolic modelling of communities in the era of metagenome-assembled genomes[END_REF]. The corresponding stoichiometric matrix S com is the concatenation of the individual stoichiometric matrices after removal of duplicated metabolites and functions.

Bag-of-genes models have been compared to the bag-of-genomes approach in the context of natural microbial communities in hot spring mats [START_REF] Taffs | In silico approaches to study mass and energy flows in microbial consortia: A syntrophic case study[END_REF]. A community-wide metabolic model has been reconstructed with KBase and compared with expression data in a bacterial photoautotroph-heterotroph consortium [START_REF] Henry | Microbial Community Metabolic Modeling: A Community Data-Driven Network Reconstruction[END_REF]. The same method was reused to reconstruct a bag-of-genes model of child gut microbiota for a modelling study of malnutrition-associated dysbiosis [START_REF] Kumar | Gut microbiota dysbiosis is associated with malnutrition and reduced plasma amino acid levels: Lessons from genome-scale metabolic modeling[END_REF].

Carveme [START_REF] Machado | Fast automated reconstruction of genomescale metabolic models for microbial species and communities[END_REF] provides an alternative between a community-wide GEM and a bagof-genomes model by assigning a compartment to each species and adding an extracellular compartment to connect them. A community biomass equation is inferred, enabling the use of the constraint-based models described above while keeping the knowledge of metabolite producers. HUMAnN2 can also help navigating between bag-of-genomes and bag-of-genes models by providing both community and speciesresolved functional potentials from metagenomic data [START_REF] Franzosa | Species-level functional profiling of metagenomes and metatranscriptomes[END_REF].

Models for large-scale metabolic exploration of microbial communities

The recurring goals, when considering a microbial community, are first to identify putative interactions among its members, then to globally assess the role of all species in the community. We focus here on models based on GENREs dedicated to the direct inference of putative interactions, and describe how discrete models of metabolism can be an alternative to constraint-based GEMs for scalable screening of metabolic potential in microbial communities.

Assessing competition and cooperation potentials of microbial communities

Cooperation through metabolic exchanges and competition for shared nutrients are the main metabolic interactions that occur in microbial communities. As a result, being able to estimate cooperation and competition potentials is essential for characterising, comparing and selecting communities. Approaches dedicated to this purpose can be separated in two categories, the first one composed of methods that structurally analyse and compare individual GENREs, and the second comparing the individual growth rates of bacterial species to those obtained in community using GEMs (Fig. 2 upper right panel). Reverse ecology [START_REF] Levy | Advances in experimental medicine and biology[END_REF] aims at inferring ecological mechanisms through the characterisation of the composition of the environment and the interactions occurring in it. A core step is the detection of exogenously-acquired compounds, or seeds, that NetSeed [START_REF] Borenstein | Large-scale reconstruction and phylogenetic analysis of metabolic environments[END_REF] computes using GENRE's strongly-connected components. NetCooperate [START_REF] Levy | NetCooperate: A network-based tool for inferring host-microbe and microbe-microbe cooperation[END_REF] quantifies the potential for pairwise syntrophic relationships. The biosynthetic support score quantifies the proportion of an endosymbiont's seeds occuring in a host's GENRE. The metabolic complementarity index of two bacterial species is the fraction of one GENRE's seeds that are products of the second GENRE, and not in its own seed set. Comparing GENREs pairwise can also be used to determine a competition potential. In [START_REF] Levy | Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules[END_REF], the metabolic competition index is the fraction of an organism's seeds that also belong to the seeds of another. NetCompt [START_REF] Kreimer | NetCmpt: A network-based tool for calculating the metabolic competition between bacterial species[END_REF] calculates the effective metabolic overlap, combining seed calculation with the network expansion algorithm (see section 4.2), and estimating the reduction of a species's metabolic potential in the absence of source compounds that are shared with other species. These metrics have been used for determining a large-scale evolutionary trend in the loss of biosynthetic capacity over time in parasites [START_REF] Borenstein | Topological signatures of species interactions in metabolic networks[END_REF], and detecting enzymes operating on exogenously-acquired compounds in diseased individuals [START_REF] Greenblum | Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease[END_REF]. They were also integrated in analysis frameworks such as RevEcoR [START_REF] Cao | RevEcoR: An R package for the reverse ecology analysis of microbiomes[END_REF] or PhyloMint [START_REF] Lam | Model-based and phylogenetically adjusted quantification of metabolic interaction between microbial species[END_REF]. It is worth noticing that the seed set is not only the set of nutrients required for growth but includes compounds that may theoretically enable the production of any metabolite in the network, and can therefore underestimate or overestimate interaction potentials [START_REF] Lam | Model-based and phylogenetically adjusted quantification of metabolic interaction between microbial species[END_REF].

Other methods use the computation of flux distributions with constraint-based modelling to determine scores of competition and cooperation. [START_REF] Freilich | Competitive and cooperative metabolic interactions in bacterial communities[END_REF] calculate the potential competition score and the potential cooperation score to characterise metabolic interactions between nearly 7000 combinations of 118 bacterial GEMs. The authors additionally infer win-lose and give-and-take relationships between bacterial pairs by assessing their growth and co-growth rates in competition-and cooperation-inducing media. MMinte [START_REF] Mendes-Soares | MMinte: An application for predicting metabolic interactions among the microbial species in a community[END_REF]] compares the growth rates of species in pairwise interacting communities to their individual growth rates in order to predict whether one species can benefit from or be negatively impacted by the presence of another. SMETANA overcomes the limitation of pairwise simulation and computes interaction scores for microbial communities by solving mixed integer linear programming problems [START_REF] Zelezniak | Metabolic dependencies drive species co-occurrence in diverse microbial communities[END_REF]. It computes the metabolic interaction potential which represents the extent to which metabolites produced by the community can be reused by species for their growth. The nutrients required for growth in a non-interacting community can be compared between species, leading to the metabolic resource overlap, an estimation of competition potential. [START_REF] Machado | Polarization of microbial communities between competitive and cooperative metabolism[END_REF] uses these scores and describes a polarisation of microbial communities between competitive and cooperative metabolisms. Finally, the use of taxon knock-outs in MICOM [START_REF] Diener | MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota[END_REF] can also be a means to evaluate the positive or negative impacts of species on the growth of other community members.

A Boolean abstraction of metabolic producibility to scale up to large communities

As depicted in the above sections, most approaches for modelling interactions in microbial communities are applied pairwise, and more generally, constraint-based models are not applied to large communities due to both the curation needs for the individual GENREs and the computational costs associated with simulations. This motivates the use of alternative paradigms for modelling. One approach is metabolic network percolation [START_REF] Bernstein | Metabolic network percolation quantifies biosynthetic capabilities across the human oral microbiome[END_REF], a probabilistic concept that randomly samples environmental compositions to assess the consistency of metabolite production in microbiomes. Applied to the human oral microbiome, it permitted the creation of an atlas describing metabolite biosynthesis potentials while being more tolerant to missing reactions than the classical constraint-based simulations.

Discrete and reasoning-based models are further approaches for estimating the ability of a species to synthesise, or not, metabolic compounds from available substrates in its environments. The network expansion algorithm [START_REF] Ebenhöh | Structural analysis of expanding metabolic networks[END_REF] provides a Boolean approximation that converts the stoichiometric matrix into a matrix of incidence of 0, 1 and -1, representing solely the participation of molecules in metabolic reactions, hence discarding mass-conservation. The metabolic potential of a species, called scope, is calculated from a set of available nutrients, the seeds: metabolic reaction activation depends on the availability of its reactants either from the seeds or as the products of other activated reactions. MeneTools implements network expansion to query metabolic capabilities of GENREs [START_REF] Aite | Traceability, reproducibility and wiki-exploration for "à-la-carte" reconstructions of genome-scale metabolic models[END_REF].

Metabolic scope can be used to determine the metabolic potential of several organisms in a community [START_REF] Christian | Metabolic synergy: Increasing biosynthetic capabilities by network cooperation[END_REF]. [START_REF] Ofaim | Analysis of microbial functions in the rhizosphere using a metabolic-network based framework for metagenomics interpretation[END_REF] built a framework considering the relationship between taxonomy, function and environment, and attempted to use metabolic modelling to explain the co-occurrence of species in the plant rhizosphere or the soil. A similar approach applies to deciphering symbiont-host interactions: [START_REF] Opatovsky | Modeling trophic dependencies and exchanges among insects' bacterial symbionts in a host-simulated environment[END_REF] simulated the metabolism of obligatory and facultative symbionts associated with a phloem-feeding whitefly. As a microbiota can involve hundreds of species living in a shared environment, the scalability of network expansion is a property accounting for the associated metabolic diversity (Fig. 2 bottom right panel). The community-wide implementation of network expansion in MiSCoTo [START_REF] Frioux | Scalable and exhaustive screening of metabolic functions carried out by microbial consortia[END_REF] permits computing metabolic potentials of communities and selecting minimal size communities exhibiting functions of interest. Metage2Metabo integrates such analyses together with GENRE reconstruction to systematically compare the metabolic potential of metagenomic samples or genome collections [START_REF] Belcour | Metage2Metabo, microbiota-scale metabolic complementarity for the identification of key species[END_REF]. Discrete models of this kind were used to screen microbial metabolic potentials [START_REF] Ramalho | Selection of anabaena sp. PCC 7938 as a cyanobacterium model for biological ISRU on mars[END_REF], to predict potential symbiotic relationships between MAGs from patients with Crohn's disease and healthy individuals [START_REF] Sabater | Arabinoxylan and pectin metabolism in crohn's disease microbiota: An in silico study[END_REF], and to analyse metabolic complementarity [START_REF] Karimi | Robustness analysis of metabolic predictions in algal microbial communities based on different annotation pipelines[END_REF].

Towards genomics-informed phenomenological models of microbial communities

The use of mechanistic GEMs in the context of large microbial ecosystems can be impeded by poorlyreconstructed GENREs [START_REF] Babaei | Challenges in modeling the human gut microbiome[END_REF], or by issues related to the high dimension of the system, such as uncertainties, accumulations, analysis of massive outputs or computational load. On the other hand, a large number of phenomenological microbial community models, has proved to be efficient in capturing macroscopic features without involving genome-scale inputs. In this section, we discuss opportunities for coupling GEMs with phenomenological and hybrid models to achieve scalability for complex microbiome, while allowing genome-resolved -omics integration. as arguably they can be complementary to one another [START_REF] Hellweger | Advancing microbial sciences by individual-based modelling[END_REF], and since ODE models can be derived as large-population approximations of IBM models [START_REF] Darrigade | A PDMP model of the epithelial cell turn-over in the intestinal crypt including microbiota-derived regulations[END_REF]. Due to their computational efficiency, models (1)-( 4) can be spatialised, using e.g. partial differential equations (PDEs)

Existing models that could benefit from metabolic modelling

as [START_REF] Labarthe | A mathematical model to investigate the key drivers of the biogeography of the colon microbiota[END_REF], Moorthy et al., 2015, Smith et al., 2021] did for gut microbiota.

GEMs can be considered not only as a category by themselves, but can also be included in models of the form (1)-(4).

Coupling GEMs with consumer-resource models It must be stressed that bag-of-genomes models (see Fig. 1) can be interpreted as particular cases of consumer-resource models (Fig. 3, Fig. 2 bottom left panel), where resource consumption, metabolite production and growth rates are modelled through FBA fluxes. Hence, existing consumer-resource models can be easily supplemented with a genome-scale metabolic model. As such, a consumer-resource model that qualitatively reproduces experimentally observed ecological patterns of the Earth Microbiome Project by considering the exchange of secondary metabolites [START_REF] Marsland | A minimal model for microbial biodiversity can reproduce experimentally observed ecological patterns[END_REF]] could be refined with GEMs: export fluxes for the metabolites included in the consumer-resource model could be computed dynamically with the GEM while remaining essential metabolites would be given a steady-state value.

Deciphering gLV interactions with GEMs gLV models proved to be efficient in analysing interactions across the gut microbiome by fitting time-series of population counts [START_REF] Stein | Ecological modeling from time-series inference: Insight into dynamics and stability of intestinal microbiota[END_REF]. Population data obtained from high-throughput sequencing reports relative abundances, inducing inference difficulties [START_REF] Remien | Structural identifiability of the generalized Lotka-Volterra model for microbiome studies[END_REF]. That drawback can be overcome by providing priors to the inference methods [van den Berg et al., 2022], for instance from GEMs. In an example coming from anaerobic digestion (AD), GEMs were reconstructed for each of the 800 species present in the community, and FBA was used to understand the syntrophic relations between micro-organisms [START_REF] Basile | Revealing metabolic mechanisms of interaction in the anaerobic digestion microbiome by flux balance analysis[END_REF], that could be used as priors of gLV model parameters. Conversely, a pairwise interaction identified in time-series with a gLV model could be interpreted using pairwise GEMs.

Supplementing trait-based models with GEMs. Bioreactor modelling is a good example that relates to both trait-based models (since they often involve abiotic parameters such as pH or temperature)

and consumer-resource models. Two examples of widely implemented bioreactors are wastewater treatment plants based on activated sludge (AS) [START_REF] Tchobanoglus | Wastewater engineering: Treatment and reuse[END_REF], and biogas production from AD of organic waste [Moletta, 2015]. AS [Leslie Grady Jr et al., 1986] and AD [START_REF] Batstone | The IWA anaerobic digestion model no 1 (ADM1)[END_REF] systems have been modelled in the framework of community-wide ODE systems (see Fig. 1), representing microbial species as functional guilds, i.e. a population of microbes responsible for a metabolic pathway. AD models have been used in various contexts such as ruminal [START_REF] Muñoz-Tamayo | Mechanistic modelling of in vitro fermentation and methane production by rumen microbiota[END_REF], environmental [Desmond-Le Quéméner et al., 2021] or gut microbiota [START_REF] Kettle | microPop: Modelling microbial populations and communities in R[END_REF], Muñoz-Tamayo et al., 2010].

Since considerable efforts have been made to sequence microbes present in AD and AS at a global scale [START_REF] Dueholm | MiDAS 4: A global catalogue of full-length 16S rRNA gene sequences and taxonomy for studies of bacterial communities in wastewater treatment plants[END_REF], one could readily envision enhancing parts of the AD and AS with metabolic models. However, FBA analysis alone may not suffice to represent abiotic inhibition arising in AD systems [START_REF] Basile | Modelling of microbial interactions in anaerobic digestion: From black to glass box[END_REF].

Coupling IBM with GEMs. In an IBM model, each individual can be provided with a GEM, which can be used to predict its metabolism with FBA. Such a framework has been already included in platforms like BacArena [START_REF] Bauer | BacArena: Individual-based metabolic modeling of heterogeneous microbes in complex communities[END_REF], ACBM [START_REF] Karimian | ACBM: An Integrated Agent and Constraint Based Modeling Framework for Simulation of Microbial Communities[END_REF], IndiMeSH [START_REF] Borer | Modeling metabolic networks of individual bacterial agents in heterogeneous and dynamic soil habitats (IndiMeSH)[END_REF] or CROMICS [Angeles-Martinez and Hatzimanikatis, 2021], that were recently compared [START_REF] Scott | A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia[END_REF].

These frameworks allow spatialisation: the IBM can include movement functions modelling microbial motility, and complex multi-physics features, e.g. the complex diffusive pattern in the rhizospheric area for IndiMesh.

Since coupling system dynamics, IBM and GEMs involve different mathematical formalisms, hybrid models provide a unifying framework that can help implementing model coupling, as described in the next section.

Extending the scope of community modelling with hybrid models

The complex behaviours of microbiomes arise from the interaction of different functional processes with different dynamics, that can be considered as continuous, discrete or random, depending on their time scale, dictating appropriate mathematical formalisms [START_REF] Assar | Modeling acclimatization by hybrid systems: Condition changes alter biological system behavior models[END_REF]: numerical ODEs for continuous dynamics, automata for discrete instantaneous changes, nondeterministic or stochastic models for uncertain or random transitions. This highlights the need for a flexible framework to explore conjointly these processes at the community scale.

Hybrid models are such a framework, by assembling modules modelled using different formalisms. In [START_REF] Liu | Hybrid modelling of biological systems: Current progress and future prospects[END_REF] five complementary combinations of modelling formalisms are used for biological systems: hybrid discre-continuous methods, often used for intracellular modelling as above; hybrid stochasticdeterministic methods, introducing random delays; hybrid flux balance analysis methods, integrating metabolic, regulatory, and signalling networks; hybrid logic-quantitative methods, combining quantitative methods with Boolean networks or fuzzy logic; and spatial hybrid methods, combining spatial stochastic and deterministic models to bridge three scales: time, abundance, and space [START_REF] Widmer | Bridging intracellular scales by mechanistic computational models[END_REF].

More formally, hybrid dynamical systems often combine continuous state and control variables plus a discrete mode variable, the transition of which is determined by guard conditions defined on the continuous variables [Henzinger, 1996]. In switched systems, the mode activates, deactivates, and combines modules of a hierarchical model [Branicky, 1994, Shorten et al., 2007]. In this framework, phenomenological models and GEMs can be coupled in a flexible and rigorous way. A good example can be found in [START_REF] Assar | Modeling acclimatization by hybrid systems: Condition changes alter biological system behavior models[END_REF], that switches between two ODE models [START_REF] Coleman | Temperature-dependent kinetic model for nitrogen-limited wine fermentations[END_REF], Scaglia et al., 2009] and a dFBA model of wine fermentation kinetics [START_REF] Pizarro | Coupling kinetic expressions and metabolic networks for predicting wine fermentations[END_REF]] according to the system state.

From environmental samples to functional and taxonomic diversity

The preceding has taken a high-level view of the modelling process and the insights models can provide, and has assumed that the collection of models for a community accurately reflects the taxonomic composition of its members and the biomolecular functions they provide. These will vary greatly between environments and from sample to sample: different species provide different functions, and within those species strain differences and horizontal gene transfer will further affect gene content. In this section we consider how molecular identification of taxonomic and functional diversity is employed to build accurate characterisations of real communities.

Two DNA sequencing approaches are used when building metabolic models from environmental samples [START_REF] Pérez-Cobas | Metagenomic approaches in microbial ecology: An update on whole-genome and marker gene sequencing analyses[END_REF]. Amplicon sequencing of marker genes characterises taxonomic diversity within a sample and can be used to infer functional profiles, map community members to existing models [START_REF] Heinken And Thiele ; Heinken | Microbiome Modelling Toolbox 2.0: Efficient, tractable modelling of microbiome communities[END_REF], and select genomes prior to GENRE reconstruction. Whole metagenome shotgun sequencing characterises both taxonomic and functional diversity within a sample by determining the gene content of community members, and can be used to infer new GENREs of novel species and strains. In both approaches, third generation sequencing (TGS) using long reads can complement, or advantageously replace, NGS using short reads.

Amplicon sequencing and shotgun metagenomic sequencing have demonstrated the high diversity of microbial communities, for bacteria and archaea, but also eukaryotes such as fungi [START_REF] Tedersoo | Global diversity and geography of soil fungi[END_REF]. Tools or steps related specifically to TGS, eukaryotic metagenomics and strain resolution are highlighted with dotted boxes.

Amplicon sequencing

A good marker gene is universally present in target populations and taxonomically robust because its infrequent mutation is not biased by functional selection. Typical choices for marker genes are 16S ribosomal RNA genes in prokaryotes, and either 18S ribosomal RNA genes or different lineage-specific markers in eukaryotes [START_REF] Earl | Species-level bacterial community profiling of the healthy sinonasal microbiome using Pacific Biosciences sequencing of full-length 16S rRNA genes[END_REF], Johnson et al., 2019, Bradley et al., 2016, Burki et al., 2020, Tekle et al., 2010].

For a given environmental sample, the variable regions of the selected marker genes are amplified using PCR and later sequenced. Until recently, inexpensive short-read NGS sequencing has been used exclusively [START_REF] Kim | Comparative analysis of 7 short-read sequencing platforms using the Korean Reference Genome: MGI and Illumina sequencing benchmark for whole-genome sequencing[END_REF], Bukin et al., 2019], although this limits the size of the variable region that can be used, may be subject to amplification bias, and may limit taxonomic resolution to phylum and genus due to 16S

polymorphism [START_REF] Knight | Best practices for analysing microbiomes[END_REF], Mitchell et al., 2020, Sieber et al., 2018a]. Long-read TGS sequencing of full-length 16S rRNA has recently been shown to reduce bias, broaden the choice of marker genes, and increase taxonomic resolution to species and subspecies [de Muinck et al., 2017, Johnson et al., 2019, de Oliveira Martins et al., 2019].

Amplicon sequences are clustered into operational taxonomic units (OTUs) or into amplicon sequencing variants (ASVs) that provide "species-like" delineation of organisms and estimations of relative abundance.

Bioinformatics pipelines for amplicon sequencing analysis are reviewed in [START_REF] Hakimzadeh | A pile of pipelines: An overview of the bioinformatics software for metabarcoding data analyses[END_REF]. While the functions present in a microbiome cannot be directly identified by amplicon sequencing [START_REF] Almeida | A unified catalog of 204,938 reference genomes from the human gut microbiome[END_REF],

approximations may be indirectly inferred using phylogenetic placement of markers [Douglas et al., 2020, Bowman andDucklow, 2015], alignment [START_REF] Wemheuer | Tax4Fun2: Prediction of habitat-specific functional profiles and functional redundancy based on 16S rRNA gene sequences[END_REF], Narayan et al., 2020], or incorporation of biological knowledge in the form of taxonomic and functional profiles [START_REF] Mallick | Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences[END_REF], Jing et al., 2021].

Nonetheless, limitations resulting from the importance of the accessory genome in a species with respect to its core genome will impact the accuracy of the predictions.

Building mechanistic models of metabolism requires accurate taxonomic classification, both for identifying existing genomes and GENREs and characterising diversity. The KSGP database [START_REF] Grant | Improved taxonomic annotation of Archaea communities using LotuS2, the Genome Taxonomy Database and RNAseq data[END_REF] combines the GTDB unified taxonomy [START_REF] Parks | A complete domain-to-species taxonomy for Bacteria and Archaea[END_REF] with a collection of environmentally sampled full-length sequences, to substantially increase taxonomic assignment of environmental samples and links to reference genomes. Resolution within reference genomes is typically limited to species and will miss most strain-specific variability. [START_REF] Estaki | QIIME 2 enables comprehensive End-to-End analysis of diverse microbiome data and comparative studies with publicly available data[END_REF] DADA2 [START_REF] Callahan | DADA2: High-resolution sample inference from Illumina amplicon data[END_REF] LotuS2 [Özkurt et al., 2022] Function inference §6.1

PICRUSt2 [START_REF] Douglas | PICRUSt2 for prediction of metagenome functions[END_REF] paprica [START_REF] Bowman | Microbial communities can be described by metabolic structure: A general framework and application to a seasonally variable, depth-stratified microbial community from the coastal west antarctic peninsula[END_REF] Tax4Fun2 [START_REF] Wemheuer | Tax4Fun2: Prediction of habitat-specific functional profiles and functional redundancy based on 16S rRNA gene sequences[END_REF] Piphillin [START_REF] Narayan | Piphillin predicts metagenomic composition and dynamics from DADA2-corrected 16S rDNA sequences[END_REF] Metabolic features §6.1

MelonnPan [START_REF] Mallick | Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences[END_REF] Meta-Apo [START_REF] Jing | Meta-Apo improves accuracy of 16S-amplicon-based prediction of microbiome function[END_REF] Taxonomic classification §6.1 KSGP [START_REF] Grant | Improved taxonomic annotation of Archaea communities using LotuS2, the Genome Taxonomy Database and RNAseq data[END_REF] GTDB [START_REF] Parks | A complete domain-to-species taxonomy for Bacteria and Archaea[END_REF] Adapter removal §7.1

Trimmomatic [START_REF] Bolger | Trimmomatic: A flexible trimmer for Illumina sequence data[END_REF] sdm [START_REF] Hildebrand | LotuS: An efficient and user-friendly OTU processing pipeline[END_REF] AdapterRemoval [START_REF] Schubert | AdapterRemoval v2: Rapid adapter trimming, identification, and read merging[END_REF] Quality control §7.1

FastQC [START_REF] Andrews | FastQC[END_REF] SeqKit [START_REF] Shen | SeqKit: A cross-platform and ultrafast toolkit for FASTA/Q file manipulation[END_REF] Long read analysis §7.1

LongQC [START_REF] Fukasawa | LongQC: A quality control tool for third generation sequencing long read data[END_REF] nanoQC [START_REF] Coster | NanoPack: Visualizing and processing long-read sequencing data[END_REF] Cramino [START_REF] Coster | NanoPack2: Population-scale evaluation of long-read sequencing data[END_REF] Minimap2 [Li, 2018] Taxonomic filtering §7.1 [START_REF] Blanco-Miguez | Extending and improving metagenomic taxonomic profiling with uncharacterized species with MetaPhlAn 4. bioRxiv : the preprint server for biology[END_REF] mOTUs3 [START_REF] Ruscheweyh | Reference genome-independent taxonomic profiling of microbiomes with mOTUs3. bioRxiv : the preprint server for biology[END_REF] Functional profiling §7.1 Kaiju [START_REF] Menzel | Fast and sensitive taxonomic classification for metagenomics with Kaiju[END_REF] DIAMOND [START_REF] Buchfink | Fast and sensitive protein alignment using DIAMOND[END_REF] MMseqs2 [START_REF] Steinegger | MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets[END_REF] Short read assembly §7.2 metaSPAdes [START_REF] Nurk | metaSPAdes: A new versatile metagenomic assembler[END_REF] IDBA-UD [START_REF] Peng | IDBA-UD: A de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth[END_REF] MEGAHIT [START_REF] Li | MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph[END_REF] Long read assembly §7.2

MetaPhlAn4

MetaFlye [START_REF] Kolmogorov | metaFlye: Scalable long-read metagenome assembly using repeat graphs[END_REF] Canu [START_REF] Koren | Canu: Scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation[END_REF] Opera-MS [START_REF] Bertrand | Hybrid metagenomic assembly enables high-resolution analysis of resistance determinants and mobile elements in human microbiomes[END_REF] hifiasm-meta [START_REF] Feng | Metagenome assembly of high-fidelity long reads with hifiasm-meta[END_REF] MetaMDBG [START_REF] Benoit | Efficient high-quality metagenome assembly from long accurate reads using minimizer-space de bruijn graphs[END_REF] Assembly evaluation §7.2

MetaQUAST [START_REF] Mikheenko | MetaQUAST: Evaluation of metagenome assemblies[END_REF] Gene prediction §7.2.1

Prodigal [START_REF] Hyatt | Prodigal: Prokaryotic gene recognition and translation initiation site identification[END_REF] MetaEuk [START_REF] Karin | MetaEuk-sensitive, high-throughput gene discovery, and annotation for large-scale eukaryotic metagenomics[END_REF] Reference gene catalogues §7.2.1 CD-HIT [START_REF] Fu | CD-HIT: Accelerated for clustering the next-generation sequencing data[END_REF] MMseqs2 [START_REF] Steinegger | MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets[END_REF] Binning §7.2.2 CONCOCT [START_REF] Alneberg | Binning metagenomic contigs by coverage and composition[END_REF] MaxBin 2 [START_REF] Wu | MaxBin 2.0: An automated binning algorithm to recover genomes from multiple metagenomic datasets[END_REF] MetaBAT 2 [START_REF] Kang | MetaBAT 2: An adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies[END_REF] Bin consolidation §7.2.2 VAMB [START_REF] Nissen | Improved metagenome binning and assembly using deep variational autoencoders[END_REF] SemiBin2 [START_REF] Pan | A deep siamese neural network improves metagenomeassembled genomes in microbiome datasets across different environments[END_REF] MetaWRAP [START_REF] Uritskiy | MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis[END_REF] DAS Tool [Sieber et al., 2018b] Chimerism and contamination §7.2.2 CheckM [START_REF] Parks | CheckM: Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes[END_REF] GUNC [START_REF] Orakov | GUNC: Detection of chimerism and contamination in prokaryotic genomes[END_REF] MAG creation pipelines §7.2.2 MAGNETO [START_REF] Churcheward | MAGNETO: An automated workflow for genome-resolved metagenomics[END_REF] nf-core/mag [START_REF] Krakau | Nf-core/mag: A best-practice pipeline for metagenome hybrid assembly and binning[END_REF] anvi'o [START_REF] Eren | Community-led, integrated, reproducible multi-omics with anvi'o[END_REF] MATAFILER [START_REF] Hildebrand | Antibiotics-induced monodominance of a novel gut bacterial order[END_REF] MetaGEM [START_REF] Zorrilla | metaGEM: Reconstruction of genome scale metabolic models directly from metagenomes[END_REF]

Shotgun metagenomics

Whole metagenomic sequencing uses total DNA extracted from the environmental sample to build a complete picture of the genomes of the organisms present in the sample. Also called untargeted, shotgun, random, or WGS sequencing, whole metagenomic sequencing does not target a specific amplified marker gene, and sequencing a larger amount of genetic material comes with increased expense. However, it avoids many of the shortcomings of amplicon sequencing: it can taxonomically resolve species, and even strains with sufficient data; it can identify the genes carried by species on a sample-specific basis, even for unknown taxa; and it can infer microbial functions including strain-specific variants in the accessory genome [START_REF] Djemiel | Inferring microbiota functions from taxonomic genes: A review[END_REF], Hildebrand, 2021, Hildebrand et al., 2019, Hildebrand et al., 2021].

Table 3 summarises the short-read and long-read sequencing technologies used for assembling genomes, and reconstructing metagenomes and MAGs [START_REF] Metahit | Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota[END_REF], Nielsen et al., 2014, Pan et al., 2023].

As above, long TGS reads can significantly improve results [START_REF] Kim | HiFi metagenomic sequencing enables assembly of accurate and complete genomes from human gut microbiota[END_REF]. PacBio HiFi long read sequencing has low error rates and provides near-finished microbial genomes from pure cultures or metagenomes, albeit at relatively high cost [START_REF] Amarasinghe | Opportunities and challenges in long-read sequencing data analysis[END_REF]. Oxford Nanopore (ONT) sequencing produces even longer reads at higher throughput on average, but with higher rates of homopolymer indels that may lead to gene calling errors [START_REF] Watson | Errors in long-read assemblies can critically affect protein prediction[END_REF]. Combining long-read ONT sequencing with short reads for post-assembly error correction is a common compromise [START_REF] Delahaye | Sequencing DNA with nanopores: Troubles and biases[END_REF], and more recently PacBio metagenomic assembly using metaMDBG [START_REF] Benoit | Efficient high-quality metagenome assembly from long accurate reads using minimizer-space de bruijn graphs[END_REF] has improved on that. Both PacBio and ONT are rapidly advancing their technological development, and due to improvements in sequence length, quality, and throughput will likely replace short read NGS technologies entirely in the near term.

Metagenomic strains

In order to fully understand microbial communities, it is necessary to resolve them into individual strains, the fundamental unit of microbiological diversity [Segata, 2018, Hildebrand, 2021]. Escherichia coli, which has a highly variable genome, is a well-known example of the importance of strains in functional characterisation: some are harmless commensals while others are harmful pathogens [START_REF] Leimbach | E. coli as an all-rounder: The thin line between commensalism and pathogenicity[END_REF].

Resolving metagenomic data at the strain level brings a new dimension to functional prediction and to the comparison of the metabolic potential of samples. A metagenomic strain is a set of clonal descendants of a single cell, with such low levels of recombination with other strains that it can be distinguished genetically from them. Members of a metagenome strain may differ substantially, particularly in the rapidly evolving accessory genome [START_REF] Maistrenko | Disentangling the impact of environmental and phylogenetic constraints on prokaryotic within-species diversity[END_REF]. Reconstructing metagenomic strains is difficult since sequencing depth may be insufficient for genome reconstruction and conspecific strains with overlapping genetic regions may be present. Strain-resolved genomes can be determined by sequencing cultured isolates or single cells, but the former are not representative of the community and the latter remain too expensive and low-throughput for many applications. For these reasons, there is a practical need for efficient methods that can profile microbial communities at high genomic resolution [START_REF] Bickhart | Generating lineage-resolved, complete metagenome-assembled genomes from complex microbial communities[END_REF].

Reference-based methods for delineating microbial strains in metagenomes rely in general on aligning metagenomic reads to a reference database and inferring strain identity based on single nucleotide polymorphisms (SNPs) [START_REF] Scholz | Strain-level microbial epidemiology and population genomics from shotgun metagenomics[END_REF], Truong et al., 2017, Albanese and Donati, 2017, Fischer et al., 2017, Silva et al., 2021]. These methods identify gene family presence, strain identification for dominant strains, and abundance estimates.

Strain-resolved metagenomics compares de novo reconstructed MAGs from different metagenomes to each other, without use of a reference database. Gene catalogues can be used to trace core genes in MAGs across samples, which can be compared to create within-species strain-resolved phylogenies of metagenomes and avoid pan-genome-induced phylogenetic idiosyncrasies [START_REF] Hildebrand | Dispersal strategies shape persistence and evolution of human gut bacteria[END_REF].

When investigating conspecific strains in the same metagenomic sample, specialised tools model SNP frequencies across assemblies to infer bacterial haplotypes and identify conspecific strains, even to providing nearly complete pan genome reconstructions, or delineate strains using TGS data of low-complexity metagenomes [Quince et al., 2017a, Quince et al., 2021, Vicedomini et al., 2021]. Metagenomic strains can be mapped to GENREs, as shown in [Basile et al., 2023a] where longitudinal community models were built to describe metabolic changes over time of a well sampled microbiome.

Single-cell technologies can also rise to the challenge of recovering genomes of low abundant strains and microbial dark matter (see [START_REF] Lloréns-Rico | Single-cell approaches in human microbiome research[END_REF] for review), especially through the generation of single amplified genomes (SAGs) and the promise of modelling rare species [START_REF] Bowers | Dissecting the dominant hot spring microbial populations based on community-wide sampling at single-cell genomic resolution[END_REF], Lyalina et al., 2022, Zheng et al., 2022, Arikawa et al., 2021]. Single-cell transcriptomics also promises to contribute to context-specific models [START_REF] Hrovatin | Towards modelling metabolic state from single-cell transcriptomics[END_REF].

From metagenomic data to assembled genomes 7.1 First steps of data processing and assembly-free analysis

A standard metagenomic analysis workflow comprises: (1) sample collection, DNA extraction, and DNA sequencing;

(2) data pre-processing and read curation, including quality control and filtering; (3) taxonomic profiling via either "read-based" or "assembly-based" approaches; and (4) further downstream analyses of genomic and functional characteristics, such as gene prediction, and functional or taxonomic annotation [Quince et al., 2017b] (Fig. 4). In this simplified workflow, (2), ( 3) and ( 4) are typically executed in a single pipeline, or can be adjusted to the analytical needs in a project. A crucial decision in the bioinformatics analysis is the choice between reliance on assemblies, or on analysing metagenomes independent of this.

Assemblies will in most cases require further computational resources and user expertise, but offer in exchange a much richer datascape to explore in the follow up analysis. For example, to build models of metabolism and GENREs in particular, sequence assembly and MAG reconstruction are required to delineate the functional role of bacterial species in the community.

Once sequences for the chosen samples have been acquired, they are screened; trimmed to remove lowquality ends, bar codes, adaptors and technical artefacts; and filtered for quality (Table 4). Specific tools are available for quality filtering of long-read sequencing data [START_REF] Fukasawa | LongQC: A quality control tool for third generation sequencing long read data[END_REF], Coster et al., 2018, Coster and Rademakers, 2023, Li, 2018]. Host-associated samples such as gut and oral metagenomes may be contaminated with reads originating in the host organism, that should be removed to avoid chimeric assemblies or false-positive assigment to bacterial taxa [Li et al., 2009, Li and Durbin, 2009, Schmieder and Edwards, 2011].

While assembly-based metagenomics is the approach of choice for building mechanistic models of metabolism, we briefly present here assembly-independent alternatives for taxonomic functional inference.

Pre-processed metagenomic reads can also be used to infer the microbial taxonomic and functional potential composition in the sequenced samples, using DNA, marker gene, or protein-based approaches. Read-based taxonomic profilers estimate the relative abundance of microbes in an environmental sample by aligning the pre-processed sequencing reads to a reference sequence database such as GTDB [START_REF] Parks | A complete domain-to-species taxonomy for Bacteria and Archaea[END_REF] or NCBI RefSeq [START_REF] O'leary | Reference sequence (RefSeq) database at NCBI: Current status, taxonomic expansion, and functional annotation[END_REF], using either mapping [START_REF] Langmead | Fast gapped-read alignment with Bowtie 2[END_REF] or k-mer matching [START_REF] Lu | Metagenome analysis using the Kraken software suite[END_REF]. The reference genome search space can be drastically reduced by including only conserved marker genes that are specific to certain taxonomic groups [START_REF] Blanco-Miguez | Extending and improving metagenomic taxonomic profiling with uncharacterized species with MetaPhlAn 4. bioRxiv : the preprint server for biology[END_REF], Ruscheweyh et al., 2022]. Read-based profilers can also be used for functional inference [START_REF] Beghini | Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3[END_REF].

Protein-based functional profilers use 6-frame translations into amino acid sequences [START_REF] Menzel | Fast and sensitive taxonomic classification for metagenomics with Kaiju[END_REF], Buchfink et al., 2014, Steinegger and Söding, 2017], with the advantage of greater sensitivity because of the lower mutation rates in amino acids, but at the cost of significant increase in run time [START_REF] Ye | Benchmarking Metagenomics Tools for Taxonomic Classification[END_REF].

The choice of reference database plays an important role in read-based taxonomic profiling. Microbes lacking a reference genome will remain undetected, which is a problem in metagenomics as much of the microbial diversity is not yet represented in reference databases [START_REF] Solden | The bright side of microbial dark matter: Lessons learned from the uncultivated majority[END_REF]. In addition, the readbased approach does not permit the direct association of microbial diversity to metabolic functions. While it can be used for building a community-wide model as a super-organism associated to a metagenome, a common protocol is instead to assemble the reads and reconstruct genomes.

Metagenomic assemblies

Reconstructing microbial genes or partial genomes by assembling sequences from environmental samples overcomes limitations of reference-based metagenomic approaches, but requires significant sequencing read depth and bioinformatics expertise. While shallow shotgun sequencing (ca. 0.5 million short reads per sample) can be enough to capture the taxonomic and functional diversity of microbiome samples at relatively low cost, it does not provide sufficient coverage for de novo assembly of novel genes and genomes [START_REF] Lapidus | Metagenomic Data Assembly -The Way of Decoding Unknown Microorganisms[END_REF].

Quality filtered reads can be directly assembled using metagenomic-specific assemblers, designed for short reads, long TGS reads, or in hybrid combinations (Table 4), see [START_REF] Brown | Critical evaluation of short, long, and hybrid assembly for contextual analysis of antibiotic resistance genes in complex environmental metagenomes[END_REF] for review.

Assemblers that specifically target high-fidelity long reads perform well in general on middling-complex communities such as the gut microbiome, and have an acceptable computational efficiency for large scale metagenomes [START_REF] Feng | Metagenome assembly of high-fidelity long reads with hifiasm-meta[END_REF], Benoit et al., 2023]. When sampled metagenomes are expected to be highly similar to each other, as in e.g. time series data of the same ecosystem with reasonably stable strain persistence, co-assemblies of time series samples can be performed to increase assembly quality [START_REF] Hildebrand | Antibiotics-induced monodominance of a novel gut bacterial order[END_REF], Hildebrand et al., 2021].

The drawback of assembly-based metagenomics is the high potential for errors in the assembly, and the inability to include a large part of the reads in the assembly, especially those from low-abundant genomes in highly diverse, complex ecosystems. The fraction of non-assembled reads depends on several factors, sequencing depth and microbiome diversity especially, and can range from < 5% in gut, to > 90% in soil metagenomes [START_REF] Hildebrand | Antibiotics-induced monodominance of a novel gut bacterial order[END_REF], Bahram et al., 2018]. Even with deeper short-read shotgun sequencing, de novo assembly remains a challenge due to the presence of repetitive DNA regions, shared genomic regions between strains, and the complexity of microbial communities with unknown and uneven representation of both diverse and closely-related strains [START_REF] Somerville | Long-read based de novo assembly of low-complexity metagenome samples results in finished genomes and reveals insights into strain diversity and an active phage system[END_REF], Ayling et al., 2020]. TGS can overcome many of the difficulties in metagenomic assembly by spanning repetitive sequences and highlyconserved regions. Before the development of the latest low-error technologies, the need for short-read data to overcome high error rates and low coverage made this approach costly and complex [START_REF] Kim | HiFi metagenomic sequencing enables assembly of accurate and complete genomes from human gut microbiota[END_REF].

The low error rate of PacBio HiFi reads constitutes a valuable alternative with high quality, circular genomes frequently being reported in the data currently available (e.g. [START_REF] Kim | HiFi metagenomic sequencing enables assembly of accurate and complete genomes from human gut microbiota[END_REF]), (Table 3).

Gene catalogue creation and gene prediction

Genes can be predicted on contigs, but having the complete sequence of a gene and its context makes its functional and taxonomic assignment and its annotation much easier and more reliable, facilitating the construction of community-wide GENREs [START_REF] Tamames | SqueezeMeta, A Highly Portable, Fully Automatic Metagenomic Analysis Pipeline[END_REF]].

To capture the set of proteins represented in a set of metagenomes, one can generate reference gene catalogues. These typically start with the genes predicted on contigs from metagenomic assemblies, that are subsequently clustered e.g. at 95% identity, in order to remove trivial differences between sequences due to fragmentary data (e.g. genes that miss the start or stop codons), sequencing errors or small, strainlevel variations [START_REF] Qin | A human gut microbial gene catalogue established by metagenomic sequencing[END_REF]. The clustering can be performed either at the nucleotide or amino acid level [Fu et al., 2012, Steinegger andSöding, 2017]; the former provides greater resolution for taxonomic classification, whereas the latter is better suited for identifying functionally-relevant similarities between distant orthologues [Commichaux et al., 2021, Wang andJia, 2016].

The underlying goal of the clustering process is to reproducibly group together sequences that have the same function or the same taxonomic origin, thereby identifying the genes from which the sequences are derived in a way that is consistent across samples. Each cluster is typically represented by one sequence, either a representative selected from the sequences clustered together, or a sequence that represents the consensus of the clustered sequences. Cluster representatives can be used in a broad range of sequencebased analyses (e.g. database searches, function or structure predictions), and also to estimate the relative abundance of a single gene across metagenomes using a gene catalogue [START_REF] Commichaux | A critical assessment of gene catalogs for metagenomic analysis[END_REF], Plaza Oñate et al., 2019]. As gene catalogues reduce the redundancy of the data, they can be used as a basis of exhaustive surveys that compare the metabolic potential of metagenomic samples by associating metabolic functions to the genes.

Creation of metagenome-assembled genomes (MAGs)

Most contigs obtained from assemblies are only fragments of pro-or eukaryotic genomes. TGS permits the assembly of near-complete genomes for abundant species. For less abundant organisms and when using short read sequencing data, it is necessary to add a binning step that sort contigs into "bins" according to the genome they likely originate from, based on assumptions of shared sequence composition, orthologous linkage data, and shared co-abundance within and between samples [START_REF] Alneberg | Binning metagenomic contigs by coverage and composition[END_REF], Wu et al., 2016, Kang et al., 2019, Nissen et al., 2021, Pan et al., 2022]. VAMB [START_REF] Nissen | Improved metagenome binning and assembly using deep variational autoencoders[END_REF] in particular allows splitting clusters into sample-specific bins according to their sample of origin, an approach termed "multisplit," which is useful for comparing pan genomes of conspecific strains. Bin consolidation assimilates bins created by different tools into a set of refined bins [START_REF] Uritskiy | MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis[END_REF], Sieber et al., 2018b].

Genome bins of sufficient quality to approximate a genome are commonly called "MAGs," for metagenomeassembled genome. Popular measures of MAG quality are the genome completeness-a desirable measure of the proportion of genome recovered for the species of interest-and genome contamination, the undesirable amount of genome fragments that are of different species origin and been mistakenly binned together [START_REF] Chen | Accurate and complete genomes from metagenomes[END_REF]. Tools exist for quality assessment using single-copy marker genes as well as detection of contamination and chimeric assemblies (Table 4). A MAG is said to be of high quality when it has over 90% of the expected count of SCGs with less than 5% redundancy of their prevalence, and containing assemblies of ribosomal RNA (rRNA) and transfer RNA (tRNA) genes [START_REF] Bowers | Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea[END_REF]. Abundance of MAGs in a sample can be estimated using (1) mapping raw metagenomic reads on the newly created MAG and (2) extracting informative marker genes for each MAG to which then either reads are mapped, or their abundance is derived from a gene catalogue (as used in [START_REF] Hildebrand | Dispersal strategies shape persistence and evolution of human gut bacteria[END_REF]).

Even though building MAGs is data-and computation-intensive, they provide significant insights into the genomic and functional diversity found in different habitats [START_REF] Almeida | A new genomic blueprint of the human gut microbiota[END_REF], Kashaf et al., 2022].

MAGs are contig-based clusters and thus preserve the relative order of genes along contigs, which is of importance as genes found in close physical proximity may encode biosynthetic pathways for the synthesis of specialised metabolites [START_REF] Almeida | A unified catalog of 204,938 reference genomes from the human gut microbiome[END_REF]. Dozens of high quality MAGs can be reconstructed from short-read data of a single gut metagenome, but rarely as continuous, circular genomes. Here, TGS technologies can potentially yield dozens or hundreds of closed genomes without binning, markedly increasing the quality of culture-independent genome reconstructions [START_REF] Kim | HiFi metagenomic sequencing enables assembly of accurate and complete genomes from human gut microbiota[END_REF].

All-in-one pipelines combine tools to reconstruct MAGs directly from sequencing reads [START_REF] Churcheward | MAGNETO: An automated workflow for genome-resolved metagenomics[END_REF], Krakau et al., 2022, Eren et al., 2021, Hildebrand et al., 2019]. MAGs can be used for GENRE reconstruction and thus enable metabolic modelling, as in [START_REF] Bernardini | Integrating metagenomic binning with flux balance analysis to unravel syntrophies in anaerobic CO2 methanation[END_REF] where the authors performed FBA simulations to examine interspecies metabolic interactions in anaerobic digestion. MetaGEM [START_REF] Zorrilla | metaGEM: Reconstruction of genome scale metabolic models directly from metagenomes[END_REF] is a MAG reconstruction pipeline that directly bridges the gap between metagenomics and metabolic modelling, as it proposes add additional pipeline steps for the automatic reconstruction of GENREs using Carveme and metabolic interaction inference using SMETANA. While all-in-one pipelines are valuable, the difficulty of metagenome assembly combined with the complexity of microbial ecosystems maintain the need for curation and additional analyses, especially to capture certain taxonomic clades such as eukaryotes (see subsection 7.2.3).

Extending metagenomic assembly to eukaryotes

Along with prokaryotes, eukaryotes are significant drivers of microbiomes. For instance, autotrophic and mixotrophic protists fix carbon in aquatic environments, and heterotrophic protists catalyse nutrient cycling in aquatic and terrestrial ecosystems [Worden et al., 2015, Massana andLópez-Escardó, 2022]. Fungi are much more prevalent soil and aquatic ecosystems [START_REF] Bahram | Metagenomic assessment of the global distribution of bacteria and fungi[END_REF], and play essential roles in global nutrient cycles and the plant rhizosphere [START_REF] Tedersoo | Global diversity and geography of soil fungi[END_REF]. Much can be learned about the diversity and role of eukaryotes in our environment from eukaryotic MAG retrieval, but several technical hurdles prevent this currently: (1) eukaryotic genomic complexity, due to larger genomes, multiple chromosomes, and the many non-coding and repetitive regions, complicates both metagenome assembly and MAG retrieval;

(2) commonly studied environments, such as the human gut, contain only very few microeukaryotes, at typically low abundance [START_REF] Bahram | Metagenomic assessment of the global distribution of bacteria and fungi[END_REF], Tito et al., 2019]; and (3) there is a bias in the currently available metagenomic computational tools towards the study of bacterial and archaeal members of the community [Massana andLópez-Escardó, 2022, Pronk andMedema, 2022].

The differences in gene structure between prokaryotes and eukaryotes is a major obstacle to the identification and annotation of eukaryotic genes. Prokaryotic tools applied to eukaryotic contigs tend to predict incomplete, erroneous and discontinuous genes and thus protein sequences. Consequently, eukaryotic proteins are neglected in microbiome data, with a risk of being truncated and assigned an unreliable annotations [Pronk andMedema, 2022, Karlicki et al., 2022]. Most public metagenomic repos-itories use annotation pipelines tailored for prokaryotes, regardless of the taxonomic origin of contigs [START_REF] Lind | Accurate and sensitive detection of microbial eukaryotes from whole metagenome shotgun sequencing[END_REF].

Recent methodological developments tackle this challenge, through the identification of eukaryotic contigs and genes [START_REF] Pronk | Whokaryote: Distinguishing eukaryotic and prokaryotic contigs in metagenomes based on gene structure[END_REF], Karlicki et al., 2022, Stanke et al., 2004] and their subsequent annotation, for instance with MetaEuk [START_REF] Karin | MetaEuk-sensitive, high-throughput gene discovery, and annotation for large-scale eukaryotic metagenomics[END_REF]. A few examples of eukaryotic MAGs have been reported, but these relied either on metagenomes with a higher fraction of eukaryotes present (such as e.g. [START_REF] Duncan | Metagenome-assembled genomes of phytoplankton microbiomes from the arctic and atlantic oceans[END_REF]), or on human gut samples that were clearly dysbiotic and had an overgrowth of a single fungal species [START_REF] Olm | Genomeresolved metagenomics of eukaryotic populations during early colonization of premature infants and in hospital rooms[END_REF]. These examples demonstrate some of the difficulties associated to investigating eukaryotic metagenomes and that methodological challenges remain. Nonetheless, the recovery of eukaryotic genomes in microbiomes has a major ecological interest and it will be facilitated by TGS.

Importance of data reuse and repurposing in metagenomics and metabolic modelling

The data pipeline from the reads to the model encompasses a long list of steps, tools and parameters that scientists may use and combine in the process. This raises important questions related to the reproducibility of the analysis outcomes, and the traceability of the data and processes involved. We provide below an overview of the importance of these aspects in the context of the topics treated in the review.

Characterising microbial communities requires data, data are acquired in context, and context provides key clues for deciphering the function of a gene or an organism. Metadata are descriptions of, and facts about, data and other research outputs1 . Metadata for genomic and metagenomic data from environmental samples can describe the context of the source material providing the data, including space, time, habitat, characteristics of the environment, biotic relationships; as well as sequencing and assembly methods [START_REF] Yilmaz | The genomic standards consortium: Bringing standards to life for microbial ecology[END_REF]. For metabolic modelling, metadata can include references to genomes, traces of curation and tools used for reconstruction [START_REF] Aite | Traceability, reproducibility and wiki-exploration for "à-la-carte" reconstructions of genome-scale metabolic models[END_REF]. Indeed, the computational processes that produce research outputs are also performed in a context, which provides key information for reproducing, evaluating, and reusing those research outputs. Metadata can therefore also describe computational processes across an analytic stack consisting of input data, tools, reports, pipelines, and publications [START_REF] Leipzig | The role of metadata in reproducible computational research[END_REF].

Data and reporting standards must be used in order to guarantee interoperability, reuse, and repurposing. Reuse of publicly available raw sequences of metagenomes, or metagenomic assemblies is very common thanks to dedicated resources such as MGnify [START_REF] Richardson | MGnify: The microbiome sequence data analysis resource in 2023[END_REF], and MAGs as well are made available in databases. The Genomic Standards Consortium (GSC) has formalised minimum reporting standards for genomes, metagenomes-Minimum Information about Metagenomic Sequence (MIMS)-and marker gene sequences with these purposes in mind [START_REF] Yilmaz | The genomic standards consortium: Bringing standards to life for microbial ecology[END_REF][START_REF] Ten Hoopen | The metagenomic data life-cycle: Standards and best practices[END_REF]. In particular,

the Minimum Information about a Metagenome-Assembled Genome (MIMAG) [START_REF] Bowers | Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea[END_REF]] is a community-developed standard for reporting bacterial and archaeal genome MAGs.

Metabolic modelling is not exempt from considerations related to traceability. Given the variety of tools suitable for GENRE generation, it is important to promote their sharing and standardisation. The use of standardised nomenclatures for GENREs reconstructed in different labs, such as in the BiGG database [START_REF] Norsigian | BiGG Models 2020: Multi-strain genome-scale models and expansion across the phylogenetic tree[END_REF], is crucial for GENRE comparison and construction of multi-species GEMs. Indeed, a recurring problem lies in the reconciliation of identifiers across databases, for which MetaNetX provides identifier mapping resources [START_REF] Moretti | MetaNetX/MNXrefreconciliation of metabolites and biochemical reactions to bring together genome-scale metabolic networks[END_REF]. A standard format for the representation and exchange of GENREs but also cell signaling pathways and other systems biology models is the Systems Biology Markup Language [START_REF] Hucka | The systems biology markup language (SBML): Language specification for level 3 version 2 core[END_REF]. This declarative format based on XML (eXtensible Markup Language) not only permits the representation of the metabolic network, but also includes mathematical parameters used for its simulation. However, the use of this format does not guarantee by itself the quality of the model contents, its reusability with other tools, or the compatibility of the identifiers with different databases. MEMOTE is a community-based test suite for the assessment of GENRE quality, representing a valuable effort in the direction of standardisation, reproducibility and interoperability [START_REF] Lieven | MEMOTE for standardized genome-scale metabolic model testing[END_REF].

Widespread adoption of FAIR guidelines [START_REF] Wilkinson | The FAIR Guiding Principles for scientific data management and stewardship[END_REF] is a further driver of the formalisation of data and metadata, since the latter are essential for finding and reusing the former. Going beyond this basic requirement, a key opportunity is to promote reuse and repurposing of research objects [START_REF] Bechhofer | Why linked data is not enough for scientists[END_REF]. Capturing the computational processes that produce research objects, is critical for reproducibility; [START_REF] Cohen-Boulakia | Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities[END_REF][START_REF] Leipzig | The role of metadata in reproducible computational research[END_REF] identify the needs-and challenges-of improved reproducibility and define criteria for "reproducibility-friendly" scientific workflow systems.

Conclusion and future prospects

Improvements in genome reconstruction from metagenomic data have made it possible to build datainformed metabolic models for microbial communities. These mechanistic models describe the phenomenological behaviour of the community, but furthermore capture the metabolic functions of under-represented species and strain-specific metabolic variations that play an important role in the community disproportionate to their abundance. Third-generation sequencing has proved to be a key technology for answering challenges in metagenomics, providing better recovery of eukaryotic genomes, allowing strain-level analysis and pangenomics, and meeting algorithmic challenges in genome delineation. Improved metagenomics leads to improved metabolic models that more fully describe communal functions as well as the added value of individual species and strains. The translation of the latest technologies into routine experimental design will likely take years, however, so algorithmic developments addressing these same questions for NGS short read metagenomics are still needed.

Metagenomics is the -omics that contributes the most to community-wide metabolic models, in the same way that genomics contributes to models of individual species. Integrating transcriptomics and metabolomics to build integrative models, following a systems biology and systems ecology approach, is necessary to fully comprehend the (eco)system. Even if these data are not obtained systematically, they can be valuable for calibrating metabolic models.

Depending on the complexity of the ecosystem and despite the use of TGS, the number of MAGs retrieved from environmental samples is today subpar, and the proportion of raw reads they make use of can remain limited [START_REF] Pessi | In-depth characterization of denitrifier communities across different soil ecosystems in the tundra[END_REF]. Those limitations may prevent MAG-derived metabolic models from accurately reflecting the complete functional diversity of the microbiome. Continuing efforts in developing efficient algorithms for MAG reconstruction must be synergetic with sequencing technique improvements.

Throughout this review we contrasted phenomenological and mechanistic models as complementary ways of describing the complex behaviour of microbial communities. As alluded to in section 5, hybrid models are a promising avenue for reconciling both approaches. They could combine coarse-grained but highly-scalable phenomenological modelling with limited fine-grained modelling of selected systems, such as the often-overlooked secondary metabolism. Developing a hybrid paradigm would combine the advantages of the two approaches and greatly contribute to building complete understanding of natural microbial communities.

Figure 1 .

 1 Figure 1. From mono-organism to community function-based model of microbial populations. Monoculture model (upper panel): In monoculture, a GENRE is obtained from the annotated genome, with semi-automatic tools (e.g. gap-filling) and manual curation. A matricial representation of the metabolic network, the reaction matrix, is an input for flux prediction models. Metabolic fluxes can be computed iteratively to estimate the time evolution of a dynamical model of the population. Community model (lower panel):In a microbial community, the metagenome makes it possible to obtain MAGs and GENREs for members of the community. GENREs may share some common functions and metabolites (vertical gray vertices in the GENRE batch). A first paradigm for building a community-wide metabolic model is a bag-of-genome model: a set of independent GENREs interacting through common external metabolites. The corresponding reaction matrix is the concatenation of the individual matrices, and the corresponding dynamical model is the sum of the fluxes computed for each unique genome. A second paradigm, termed bag-of-gene model consists in merging all the GENREs through their common functions and metabolites, resulting in a global metabolic network for the whole community. The corresponding reaction matrix is the concatenation of the individual matrices after merging or removal of common reactions (columns) and metabolites (rows), resulting in a lower dimension matrix and a dynamical system with a unique equation. With this paradigm analysis can be performed at the scale of a community, but the contribution of each taxon is fuzzier.

Figure 2 .

 2 Figure2. Approaches for numerical simulation and reasoning on community metabolism. Methods and tools are sorted according to the functional characterisation of the microbial community metabolism they can provide: predicting small-scale community metabolism (upper left panel), assessing community dynamics (lower left panel), deciphering cooperation and competition potentials (upper right panel) and characterising large-scale community metabolism (lower right panel). For each class, the main methods and dedicated software (gray italic names) are recapitulated. The main "pros" (+) and "cons" (-) are listed on the lateral box associated to each panel.

  Microbial community models other than GEMs have recently been divided into 4 types: (1) consumerresource, (2) generalised Lotka-Volterra (gLV), (3) trait based models and (4) individual based model (IBM) [van den Berg et al., 2022]. Models (1) to (3) are population-dynamic models based on a system of ordinary differential equations (ODE), describing the rate of change of the population density depending on other species ((2) directly, (1) indirectly), available resources (1), and environmental factors (3). Model type (4) describes the fate of individual cells, whose juxtaposition models the population. IBMs have been used on a wide range of ecosystems in order to gain knowledge of the underlying ecology and for decision making [DeAngelis and Grimm, 2014]. Models of type (4) are not opposed to models (1)-(3),

Figure 3 .

 3 Figure 3. Dynamic models of metabolism: opportunities beyond GENRE-based models. Different formalisms can be used to capture microbial population dynamics (upper panel). These models can be placed on a gradient, from phenomenological (left) to mechanistic models (right) of the metabolic capabilities of the community. Each formalism can be supplemented by additional spatio-temporal mechanisms (lower panel) that can be represented at the population (PDE) or individual scale (IBM). The addition of spatio-temporal features in the community dynamics induces simplifications in the metabolic models to keep the model computationally tractable.

  -40B reads, up to 3 Tb, ≥ 85% of bases higher than Q30 -Taxonomic and functional profiling, -Genome assembly and binning; -High throughput, low cost for production-

Figure 4 .

 4 Figure 4. Metagenomic sequencing and data processing. After quality control, shotgun metagenomic reads can enter an assembly pipeline or an assembly-independent profiling pipeline. The assembly pipeline is highlighted as it permits the reference-free reconstruction of MAGs and subsequently GENRE reconstruction that directly survey the functions present in the ecosystem. Examples of tools associated to each step are indicated next to the box. Tools or steps related specifically to TGS, eukaryotic metagenomics and strain resolution are highlighted with dotted boxes.

Table 1 .

 1 Main generalist and specialised functional annotation databases and resources of existing metabolic network reconstructions

	Category	Name	Content

Table 2 .

 2 Main methods and corresponding framework to solve community FBA and dFBA models

	Framework Method	Description

An inner optimisation is made for each individual GENRE and coupled to an outer community-wide optimisation: trade-off between individual and total biomass growth

Table 3 . Main short read and long read metagenomic sequencing technologies.

 3

	Abbrev. PacBio, Pacific

Table 4 . Main sequence analysis methods.

 4

	OTU clustering §6.1
	QIIME 2	[

https://schema.datacite.org

Acknowledgements

Conflicts of interest

The authors declare no conflict of interest.

Copyright

This research was funded, in whole or in part, by the European Research Council. A CC-BY public copyright license has been applied by the authors to the present document and will be applied to all subsequent versions up to the Author Accepted Manuscript arising from this submission, in accordance with the grant's open access conditions.