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Open Access

Review

Applications of marine microbial community models in the nature-based economy

Abstract

Marine microorganisms are central to global ecological and biogeochemical systems, with their intricate interactions shaping community dynamics. While meta-omics data sets have revolutionized marine microbial ecology, they often provide fragmented insights, underscoring the need for advanced integrative modeling frameworks. In this review, we highlight the potential that community genome-scale metabolic models (cGEMs), in combination with meta-omics and environmental data sets, offer in advancing marine microbial ecology. We explore 3 key applications: quantifying marine ecosystem services, guiding bioremediation strategies for environmental challenges, and enhancing climate and biogeochemical models. Furthermore, we propose novel indices derived from cGEMs to assess microbial contributions to ecosystem functions, potentially informing economic valuation strategies for marine conservation. This interdisciplinary approach paves the way for innovative strategies in biotechnology, environmental restoration, and the development of nature-aligned economic systems, ultimately contributing to the preservation and sustainable use of marine ecosystems.

Citation: Robaina-Estévez S, Gutiérrez J (2024) Applications of marine microbial community models in the nature-based economy. PLOS Sustain Transform 3(11): e0000145. https://doi.org/10.1371/journal.pstr.0000145

Editor: Jose Carlos Báez, Spanish Institute of Oceanography: Instituto Espanol de Oceanografia, SPAIN

Published: November 27, 2024

Copyright: © 2024 Robaina-Estévez, Gutiérrez. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Marine microorganisms form intricate interaction networks that play pivotal roles in global ecological systems and biogeochemical cycles (terms defined in Box 1) [1]. This metabolic cross-feeding shapes microbial community structure, biogeography, and diversification. However, challenges in cultivating marine microorganisms and discerning the vast array of interchanged molecules limit our understanding of these interactions [2].

Box 1. Glossary

Biogeochemical cycles: The pathways by which chemicals or elements move through both the biotic (biosphere) and abiotic (lithosphere, atmosphere, and hydrosphere) components of the Earth.

Biome: A large naturally occurring community of flora and fauna occupying a major habitat, e.g., forest or tundra.

Bioremediation: The use of living organisms, typically microorganisms, to remove or neutralize pollutants from a contaminated site.

Biostimulation: A remediation technique that enhances the growth of existing microorganisms in an environment to improve the degradation of contaminants.

Constraint-based metabolic modeling: A method to predict the metabolic fluxes and behavior of a cell or a community of cells under different environmental and genetic conditions, based on stoichiometric and physicochemical constraints.

Ecological firewalls: Specialized strategies designed to regulate and contain the activities of engineered organisms within a specific ecological or microbial community.

Ecosystem services: Benefits provided by ecosystems to humans, including provisioning (e.g., food), regulating (e.g., climate regulation), and supporting (e.g., nutrient cycles) services.

Generalized Lotka–Volterra model: This model is a mathematical framework used in ecology to study interactions within microbial communities, including predator–prey and competitive dynamics.

Genome-scale metabolic models (GEMs): Detailed representations of an organism’s metabolic network, based on its genomic information.

Marine microbial ecology: The study of microorganisms in marine environments, focusing on their roles in ecological and biogeochemical cycles.

Marine protected areas (MPAs): Regions of seas, oceans, or estuaries that restrict human activity for conservation purposes, to protect natural or cultural resources.

Metagenomic-assembled genomes (MAGs): Genomes assembled from metagenomic data, which is the collective genetic material obtained from environmental samples.

Meta-omics data sets: Collections of data derived from various “omics” technologies, such as metagenomics, metatranscriptomics, metaproteomics, and meta-metabolomics, that provide insights into the collective genetic and metabolic potential of microbial communities.

Nature-based economy: An economic system that integrates ecological and environmental values and services into economic decision-making.

The advent of meta-omics data sets has revolutionized marine microbial ecology, enabling precise annotation of sequences from environmental samples and reconstruction of metagenomic-assembled genomes (MAGs). While MAGs offer valuable insights into the genomic and metabolic footprints of marine microorganisms, they often provide a fragmented view of community metabolism [3]. This limitation obscures metabolic interactions and ecological roles within communities, revealing a significant gap in marine microbial ecology. Understanding the dynamics of community composition in relation to environmental parameters and internal feedback mechanisms requires integrative approaches beyond omics data sets [4]. Combining mathematical models with meta-omics data offers a promising way to address these complexities. This integrated approach can provide a more comprehensive framework for analyzing microbial community relationships and their environmental interactions.

Mathematical models of microbial communities serve as frameworks for hypothesis testing and mechanistic insights. Traditionally, dynamic models, such as those based on generalized Lotka–Volterra equations, capture microbial dynamics including predator–prey and competitive interactions [5]. However, these models face significant limitations. First, they often require detailed knowledge of structure and parameters, which is not always available. Additionally, these models typically focus on interactions between pairs of species, overlooking more complex relationships involving multiple community members. Furthermore, effectively incorporating environmental data or meta-omics data sets remains challenging [6]. These shortcomings highlight the need for new modeling approaches to provide a more comprehensive picture of microbial communities.

Genome-scale metabolic models (GEMs) and constraint-based metabolic modeling offer solutions to these challenges, transforming our understanding of microbial metabolism. GEMs address many limitations of traditional models. Using stoichiometric, biochemical, physicochemical, and environmental constraints like available nutrients, they enable genome-scale predictions of metabolic fluxes, encompassing growth, metabolic capacities, and potential metabolite exchanges for individual organisms (Fig 1). By mapping genes to proteins and enzyme-catalyzed reactions, GEMs serve as integrative frameworks for multi-omics data sets, offering insights into metabolic capabilities and environmental interactions [7]. Moreover, GEMs can be reconstructed semi-automatically from MAGs extracted from environmental DNA, providing predictions tailored to specific marine ecosystems [8,9]. Thus, GEMs bridge the gap between meta-omics data and the mechanistic understanding of microbial metabolism in marine environments, addressing many of the shortcomings of earlier modeling approaches.

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Fig 1. Elements of cGEMs and constraint-based modeling.

(A) Marine microbial communities, composed of diverse taxa, interact through trophic exchanges. (B) Metagenomic approaches facilitate the construction of MAGs for each community member. (C) These MAGs, supplemented with biochemical data, are used to reconstruct cGEMs, capturing both individual and collective metabolic processes, inclusive of trophic interactions. (D) Information on the relative abundances of community members and available environmental nutrients informs the application of constraint-based methods (E) such as FBA, refining flux simulations for specific marine contexts. Panel (F) illustrates the results of cGEM simulations, emphasizing the exchange fluxes between community members on a simplified metabolic network, with salmon-colored hexagons denoting enzymes or transporters facilitating these exchanges. The inclusion of gene-protein-reaction associations for each enzyme in cGEMs permits the integration of metatranscriptomic or metaproteomic data, enhancing the model’s predictive accuracy. This comprehensive approach yields insights into the metabolic interactions within marine microbial communities. cGEM, community genome-scale metabolic model; FBA, flux balance analysis; MAG, metagenomic-assembled genome.

https://doi.org/10.1371/journal.pstr.0000145.g001

Building upon individual GEMs, community genome-scale metabolic models (cGEMs) integrate individual models with environmental data sets to capture the collective metabolism and trophic interactions of diverse taxa within specific ecosystems [10–12]. cGEMs provide detailed insights into microbial community metabolism, uncovering aspects not evident from meta-omics data alone. They are useful for predicting cross-feeding patterns, elucidating metabolic dependencies, and identifying keystone species vital for ecosystem robustness [13]. However, despite their potential, the application of cGEMs in marine microbial ecology remains underexplored, representing a significant research gap that this review aims to address.

Moreover, cGEMs’ adaptability allows for seamless integration with other modeling approaches and data-driven tools, enhancing their predictive power. For example, [3] combined ecological interaction networks from Tara Oceans meta-omics data with cGEMs, revealing significant amino acid and B vitamins cross-feeding in the euphotic zone. Recent methodologies also enable cGEMs to model temporal and spatial variations within microbial communities, moving beyond static assumptions [14–17]. This comprehensive approach makes cGEMs valuable for hypothesis-driven research and experimental validation, potentially guiding the bioengineering of microbial communities for commercial compound synthesis or bioremediation [18]. Furthermore, integrating cGEMs into broader biogeochemical or climate models provides detailed insights into metabolic interactions among planktonic organisms, an aspect often oversimplified in current models [19]. This integration enhances predictive accuracy, advancing scientific knowledge with significant implications for environmental management and biotechnology.

Finally, cGEMs offer valuable applications beyond basic ecological understanding, particularly in optimizing bioremediation processes and supporting a nature-based economy through marine ecosystem management. In bioremediation, these models can predict how microbial communities metabolize pollutants, allowing for the design of more effective cleanup strategies in marine environments [20]. This application extends to enhancing sustainable aquaculture and driving blue biotechnology innovations, where cGEMs can model cross-feeding interactions to maintain ecosystem health amid environmental changes. In the context of a nature-based economy, cGEMs enable the development of metrics to quantify ecosystem services in marine protected areas (MPAs). These metrics could form the basis for economic valuation and the creation of financial instruments, providing a novel approach to sustaining MPAs and preserving vital marine ecosystems. This integration of microbial ecology with economic frameworks not only informs sustainable practices but also demonstrates how scientific insights can directly contribute to nature-based economic models, highlighting the value of interdisciplinary approaches in marine conservation and resource management.

In this review, we synthesize current developments in applying cGEMs to marine microbial ecology, addressing challenges and exploring their potential within the nature-based economy. We highlight research gaps and the transformative potential of cGEMs, providing insights to guide future research and inform sustainable management practices.

Engineering microbial communities: From specialized metabolic activities to environmental interventions

Building on the capabilities of cGEMs to unravel the complexities of marine microbial metabolism, these models also serve as powerful tools for engineering microbial communities with specific functional objectives. By integrating genomic data with metabolic modeling, cGEMs enable the design and optimization of microbial consortia for targeted applications. In this section, we delve into how cGEMs facilitate the bioengineering of microbial consortia for specialized metabolic activities and guide bioremediation strategies in marine environments, bridging the gap between theoretical understanding and practical ecological interventions.

Bioengineering microbial consortia

cGEMs are instrumental in designing microbial consortia tailored to specific goals. These models help conceptualize and cultivate minimal microbial communities optimized for tasks such as specialized compound synthesis, making bioengineering efforts more precise. Complementary computational approaches further support this process. Some approaches use combinatorial strategies to link metabolic reactions with the community members responsible for producing target compounds [21,22]. Others apply constraint-based methods to optimize metabolic fluxes and growth rates, arranging communities for maximum efficiency [23–25].

In this context, OptCom [24], a framework for designing synthetic microbial communities, was utilized by Zuñiga and colleagues [26] in conjunction with cGEMs to pair the phototrophic Synechococcus elongatus with heterotrophic bacterial strains. Their models elucidated key metabolic exchanges, sustaining heterotrophs without external organic carbon and highlighted the S. elongatus–Escherichia coli K-12 consortium for its metabolic complementarity. These model-derived insights were experimentally validated, underscoring the pivotal role of cGEMs and community optimization tools in guiding synthetic microbial community design for biotechnological applications.

Bioremediation strategies guided by cGEMs

Beyond bioengineering, cGEMs can inform intervention strategies to mitigate environmental hazards, such as marine oil spills [27]. For illustration purposes, we present a case study showcasing the utility of cGEMs in simulating bioremediation strategies for oil-contaminated marine environments (Fig 2 and S1 Notebook). Specifically, we modeled a community of 5 bacterial genera known to degrade toluene—a toxic component of crude oil—in a marine setting [28]. The analysis revealed a complex trophic network (Fig 2A) and, through sensitivity analysis, identified key nutrients influencing degradation rates (Fig 2B). Simulating biostimulation with specific nutrients led to a marked increase in toluene degradation (Fig 2C), demonstrating how cGEMs can guide targeted interventions that balance desired outcomes with potential ecological impacts.

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Fig 2. Designing a biostimulation strategy for a coastal oil spill.

cGEMs together with environmental data sets can be employed to design a bioremediation strategy for a coastal oil spill. This figure summarizes a cGEM-informed biostimulation strategy demonstrated in the accompanying S1 Notebook, in which the capacity to uptake toluene from polluted waters by a community is increased through biostimulation. (A) A cGEM and constraint-based methods are employed to analyze the trophic interactions that support toluene uptake and degradation by the community, identifying Pseudomonas as the main genus uptaking toluene. (B) A sensitivity analysis is conducted to identify which environmental nutrients show the largest positive effects on toluene uptake by the community. (C) Community flux simulations are run at baseline and in a biostimulation scenario, where key nutrients for the enhancement of toluene uptake are added to the medium, resulting in an increase of the predicted toluene uptake rate by Pseudomonas. This case study demonstrates the potential of cGEMs for guiding intervention strategies, leveraging natural processes for environmental restoration. The full runnable example can be found in S1 Notebook and in the GitHub repo: https://github.com/Robaina/oil_spill_example. cGEM, community genome-scale metabolic model.

https://doi.org/10.1371/journal.pstr.0000145.g002

Additionally, the principles demonstrated in this oil spill remediation model extend to other marine microbial systems, such as harmful algal blooms (HABs). HABs cause substantial threats to marine ecosystems and economies, with estimated annual global losses at USD 8 billion [29]. Applying cGEMs to HAB-forming species and their associated microbial communities could elucidate critical metabolic pathways involved in toxin production and bloom formation. This enhanced understanding would facilitate early warning systems and inform mitigation strategies by identifying key pathways to disrupt or pinpointing bloom-inhibiting microbial taxa, ultimately leading to more effective prediction and management of HABs.

Community metabolic models to improve ecosystem and climate predictions

Integrating microbial community models with broader ecosystem and climate models presents a promising frontier in marine microbial ecology, aiming to decipher the complex interactions that define marine ecosystems and their interplay with the Earth system. Previous studies have employed diverse methodologies to connect the genetic underpinnings of microbial communities with broader ecosystem processes, ranging from gene-centric approaches to metabolic flux modeling of single-species to multispecies systems [30]. While these approaches have provided valuable insights, they have also revealed the need for more comprehensive modeling techniques to fully capture the intricacies of marine microbial systems.

This need for more sophisticated models extends to Earth System Models (ESMs), which are crucial for predicting climate change impacts on ocean health [19,31]. Traditional ocean models initially focused on physical processes, then gradually incorporated biogeochemical cycles and limited phytoplankton types. However, these models are often insufficient to represent the full spectrum of microbial dynamics and environmental responses. While omics technologies provide detailed insights into microbial functioning, many climate models still rely on bulk biological indicators, overlooking these rich data sets. This gap between data complexity and model simplicity necessitates more integrative approaches.

Addressing this gap, Régimbeau and colleagues [32] introduced a novel method to address this disconnect by combining GEMs with ESMs. Specifically, they embedded GEMs within the NEMO-PISCES biogeochemical model. This approach connects molecular-level data to large-scale climate models, allowing for more nuanced representations of microbial communities. The method enables predictions of growth rates, metabolite production, and cellular composition across ocean environments, capturing details previously overlooked. This integration improves the representation of microbial acclimation strategies and carbon storage mechanisms, addressing key limitations of traditional modeling approaches.

Building upon this groundwork, integrating cGEMs into climate and ecological models holds significant promise. As previously discussed, these models offer a granular perspective on species growth predictions and mechanistic interactions through shared compounds among community members and environmental influences—many of which play pivotal roles in global climate systems. Incorporating cGEMs allows us to better account for microbial interactions that affect the production and consumption of key climate-active substances. This enhancement can improve the accuracy of climate models, leading to more precise predictions and effective environmental strategies. For example, certain marine microbes emit dimethyl sulfide (DMS) into the atmosphere, a sulfur-containing gas that significantly influences the Earth’s radiation budget. DMS emissions from marine sources contribute to marine aerosol mass, affecting cloud condensation nuclei over remote oceanic regions [33]. Furthermore, cGEMs offer a powerful method to predict metabolic fluxes, which are key to understanding biogeochemical cycles and global climate patterns, such as those associated with carbon fixation and organic carbon utilization. Additionally, integrating satellite-derived data on phytoplankton biomass, quantified through chlorophyll measurements, with cGEMs can enhance the accuracy of these predictions [19], offering a more precise understanding of phytoplankton dynamics and their contribution to carbon cycles.

Toward a quantitative assessment of marine ecosystem services

The nature-based economy emphasizes nature’s intrinsic value, advocating for its conservation and regeneration as essential to sustainable economic growth. This paradigm challenges traditional economic models that prioritize growth at nature’s expense, recognizing the interdependence between economic prosperity and healthy ecosystems. By assigning economic value to ecosystem services, it seeks a harmonious balance between human development and environmental conservation, ensuring shared prosperity within ecological boundaries [34]. As discussed in Box 2, financial products anchored in marine ecosystem services—such as carbon credits, water quality credits, and insurance products based on ecosystem health—exemplify how the nature-based economy can operationalize this valuation of natural capital.

Box 2: Financial products anchored in marine ecosystem services

Economic incentives for marine conservation

In the evolving landscape of a nature-based economy, financial products tied to ecosystem services are becoming increasingly crucial for marine conservation, aligning economic and environmental interests (Chami and colleagues).

Carbon credits in marine protected areas (MPAs): The development of carbon credits based on the carbon sequestration capacities of MPAs exemplifies how ecosystem services can be monetized. Such initiatives offer economic incentives for preserving these vital ecosystems (Dasgupta Review, 2020).

Water quality credits: Metrics for nutrient cycling efficiency or bioremediation capacity in marine environments can be translated into water quality credits, incentivizing practices that maintain or enhance water quality (Handbook for Nature-related Financial Risks, CISL).

Insurance products based on ecosystem health: Evaluating the robustness of ecosystem services through cGEMs can inform the creation of insurance products that reflect the health of marine ecosystems, directing investments toward their conservation (World Economic Forum, 2020).

Innovative financial products: The potential for novel products, such as insurance-linked securities tied to marine ecosystem health, illustrates the innovative fusion of ecological science with financial markets (Directive of the European Parliament and of the Council on Corporate Sustainability Due Diligence, 2021).

Biocredit market as a future development: The concept of a biocredit market, wherein companies could buy credits to offset their impacts on biodiversity, carbon, and ecosystem services, represents a promising future development. Emerging law proposals that require large companies to compensate for their environmental footprint through bioremediation, carbon, biodiversity, or ecosystem service offsets could be a catalyst for this market [46,47]. Such legislation would not only enforce corporate responsibility for environmental impacts but also foster a market-driven approach to conservation and restoration efforts.

These financial instruments, from carbon credits to innovative insurance products, align economic interests with marine conservation. Local and indigenous community involvement ensures responsible management and reinvestment of ecosystem service revenues (Chami and colleagues). They provide sustainable financing for MPAs and other conservation efforts, bridging ecosystem preservation and economic growth. In the transition to a nature-based economy, these financial products are crucial for promoting marine ecosystem stewardship, safeguarding both intrinsic and economic values. The evolution of these instruments, particularly the potential biocredit market, represents a promising frontier integrating ecological science, financial innovation, and corporate responsibility.

Quantifying marine ecosystem services is crucial for integrating ecological considerations into economic decision-making. Various frameworks and methods—including ecological indicators, valuation techniques, and modeling approaches—have been developed to assess these services [35–37]. These aim to capture the multifaceted contributions of marine ecosystems, such as nutrient cycling, carbon sequestration, and biodiversity, to inform sustainable management and conservation strategies. However, quantifying the contributions of microbial communities remains challenging. Given the critical role of the ocean microbiome in global ecological and biogeochemical processes, there is a pressing need to develop methods to effectively integrate microbial contributions into ecosystem service assessments [38].

cGEMs offer a promising framework to address this gap by simulating metabolic fluxes under specific environmental conditions, thereby assessing the impacts of microbial communities on key metabolic processes. To demonstrate this potential, we propose illustrative indices that quantify microbial ecosystem services (Fig 3; see S1 File for complete definitions). The maximum flux capacity (MFC) index quantifies the maximum potential of specific metabolic functions across a microbial community, providing insights into capacities like carbon sequestration or pollutant degradation by estimating the upper limits of key reactions. The Metabolic Functional Robustness Index (MFRI) evaluates the resilience of essential metabolic reactions against environmental perturbations, reflecting the stability of services like nutrient cycling under fluctuating conditions by computing the average elasticity of these reactions in response to changes in nutrient uptake rates. The Metabolic Flux Shift Index (MFSI) measures shifts in the metabolic state of a community between scenarios—such as before and after an environmental intervention—highlighting how interventions may impact microbial functions by assessing changes in flux values over selected key reactions. These proposed indices exemplify how cGEMs can provide valuable insights into microbial ecosystem service capacities, capturing the dynamic interplay between microbial communities and their environment—information essential for effective ecosystem management and conservation strategies.

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Fig 3. Examples of cGEM-derived indices quantifying ecosystem services.

Illustration of 3 quantitative indices derived from cGEMs and constraint-based approaches to assess different aspects of microbial ecosystem services. (A) Depiction of the MFC index, which quantifies the maximum potential of a specific metabolic function across a microbial community. Here, blue rectangles represent the target reaction whose flux is maximized across the community. (B) The MFRI quantifies the resilience of key metabolic reactions for ecosystem services against perturbations in nutrient exchange rates. To this end, it computes the average elasticity value across all perturbed nutrient uptake rates. Here, the flux through a key reaction is represented in blue, while the perturbed nutrient uptake rates are represented in gray. (C) The MFSI measures the shift in metabolic state of a community from a baseline to an intervention scenario. To this end, it minimizes the sum of absolute differences in flux values, v, w, between both conditions over a set of selected key reactions, K. Here, key reaction fluxes between the 2 conditions are highlighted in blue and red, respectively. Quantitative indices such as the ones displayed in this figure could inform the sustainable management and economic valuation strategies in MPAs, ultimately facilitating the integration of ecological function into economic decision-making frameworks. A more detailed description of these indices can be found in S1 File. cGEM, community genome-scale metabolic model; MFRI, Metabolic Functional Robustness Index; MFSI, Metabolic Flux Shift Index; MPA, marine protected area.

https://doi.org/10.1371/journal.pstr.0000145.g003

MPAs could greatly benefit from this system-level approach. cGEM-informed metrics, such as the proposed indices, have the potential to contribute to financial instruments or market-based solutions that recognize and quantify the value of key marine ecosystem functions. For example, an MFC-based metric quantifying carbon sequestration capacity could inform the development of blue carbon credits [39,40]. Similarly, indices like the MFRI, which measure the resilience of nutrient cycling processes, could facilitate the creation of water quality credits by providing quantitative assessments of nutrient cycling efficiency or bioremediation capacity. An MFSI-like index might be employed to evaluate the impact of conservation interventions, supporting adaptive management strategies within MPAs. This scientific approach to valuing natural capital supports the sustainability of MPAs and aligns with the nature-based economy, ensuring that conservation efforts are both ecologically effective and economically viable [34,41,42].

Beyond financial instruments like blue carbon and water quality credits, cGEM-derived metrics have significant implications for environmental risk assessment and the insurance market. Sensitivity analyses of cGEMs can evaluate the robustness of microbial ecosystem services to perturbations [11], leading to the development of indices that quantify metabolic resilience—a crucial factor in assessing the likelihood and impact of ecological disturbances such as pollution or climate-induced changes [43,44]. These insights enable the creation of precise, risk-reflective policies and innovative financial products, such as insurance-linked securities tied to marine ecosystem health, effectively merging ecological science with financial markets.

Integrating advanced, data-driven modeling tools, such as cGEMs, with economic innovation allows us to translate ecological insights into tangible economic value, promoting a nature-aligned economic system that recognizes and incorporates marine ecosystem services into blue natural capital markets. This approach offers new avenues for marine conservation and viable options for sustaining MPAs, engaging local and Indigenous communities as key stakeholders. Responsible management and reinvestment of financial benefits from ecosystem services are crucial, fostering a development model that is ecologically responsible, economically beneficial, and inclusive [45]. As we transition towards a nature-based economy, this synergy between scientific tools and economic strategies supports environmental stewardship, preserving marine ecosystems and underpinning the prosperity upon which all societies depend.

Challenges and future directions

Implementing cGEMs in marine microbial ecology faces several challenges. A major issue is the uncertainty in predictions due to incomplete microbial genome information. This is particularly problematic in MAGs, which often contain incomplete gene sets and functional annotations. Another challenge stems from limitations in existing biochemical databases—required to map genes to reactions—since marine microbes are underrepresented in curated biochemical databases, enriched in cultured organisms. This underrepresentation introduces additional uncertainty into cGEMs. The model structure is especially affected, as it depends on the specific reconstruction approaches and databases employed. However, recent efforts to develop consensus GEMs from various drafts and to unify biochemical databases have shown potential in reducing these structural uncertainties and improving the reliability of cGEMs in marine microbial ecology [48–50].

The choice of objective function and constraints during GEM optimization significantly influences predictions. This is particularly challenging for uncultured organisms, where biomass component stoichiometry cannot be measured directly, requiring alternative objective functions and generic biomass pseudo-reactions [49]. Furthermore, the reconstruction of cGEMs faces the additional challenge of accurately modeling metabolite exchange and community composition effects. The COMMIT approach addresses these issues by introducing a gap-filling procedure that considers both metabolite permeability and community composition [51]. This method reduces the number of reactions needed to fill gaps in cGEMs while preserving genomic support, improving functional predictions, and elucidating interspecies dependencies, especially in complex and uncultured microbial consortia.

Model validation in cGEMs is particularly challenging due to scarce experimental data and the complex interdependencies within microbial communities. To enhance validation, techniques like co-occurrence or co-activity networks can compare cGEM-predicted structures with actual abundances from metabarcoding or metagenomic data sets, providing valuable insights into predicted metabolic interactions [52]. Controlled experiments, such as those by Zuñiga and colleagues [26], and mesocosm experiments that simulate natural environments are especially useful for assessing the viability and ecological impact of engineered communities. Additionally, the application of engineered microbial communities in natural ecosystems poses potential environmental risks, which becomes a more pressing concern as research advances toward real-world implementation. On this front, ecological firewalls offer a promising approach for containing engineered microbial consortia, leveraging specific interaction patterns within ecological networks to create self-regulating systems that perform targeted functions while limiting their spread [53].

Conclusions

This review underscores the transformative potential of cGEMs in advancing marine microbial ecology and supporting the transition to a nature-based economy. By integrating meta-omics data with environmental variables, cGEMs offer unprecedented insights into microbial community dynamics, metabolic cross-feeding, and ecosystem stability.

Our analysis reveals 3 key applications of cGEMs. First, they enable the quantification of ecosystem services in MPAs, potentially informing novel financial instruments for conservation. Second, cGEMs guide bioremediation strategies for environmental challenges, such as oil spills and harmful algal blooms. Third, they enhance biogeochemical models to improve predictions of global ecological dynamics. The case study on toluene degradation demonstrates how cGEMs can inform targeted interventions in microbial systems, balancing desired outcomes with ecological considerations.

Despite these advances, challenges remain in applying cGEMs to marine ecosystems, including data limitations and computational complexities. Addressing these will require interdisciplinary collaboration among ecologists, bioinformaticians, and economists. Looking ahead, we envision data-driven modeling approaches, such as cGEMs, playing a pivotal role in developing quantitative indices for marine ecosystem services, informing sustainable management practices in MPAs, and advancing blue biotechnology innovations.

The ongoing development and application of cGEMs show promise in helping to align economic frameworks with the services provided by marine ecosystems. This approach offers opportunities to deepen our scientific understanding and can provide valuable insights for sustainable marine resource management and conservation strategies. Future research should focus on improving the accuracy and predictive power of cGEMs through enhanced data integration and validation techniques. Additionally, efforts to translate cGEM insights into practical conservation and economic policies will be essential for realizing the full potential of this approach in marine ecosystem management.

In conclusion, cGEMs represent a powerful tool at the intersection of microbial ecology, environmental science, economics, and biotechnology. Their continued development and application promise to revolutionize our understanding of marine ecosystems and our ability to manage them sustainably. Furthermore, cGEMs are poised to play a crucial role in opening innovative avenues for integrating biological processes into sustainable industrial and environmental strategies. This interdisciplinary approach paves the way for a more harmonious relationship between economic development, biotechnological innovation, and environmental conservation in the world’s oceans.

Supporting information

S1 File. Quantitative indices for microbial ecosystem services using community Genome-scale Metabolic Models.

Three novel quantitative indices are proposed: Maximum Flux Capacity (MFC) to measure metabolic potential, Metabolic Flux Shift Index (MFSI) to assess community responses to environmental changes, and Metabolic Functional Robustness Index (MFRI) to evaluate resilience against nutrient fluctuations. Detailed mathematical definitions of these indices are provided, as well as their significance in the context of the nature-based economy.

https://doi.org/10.1371/journal.pstr.0000145.s001

(PDF)

S1 Notebook. Analysis of oil spill bioremediation using community metabolic modeling.

This Jupyter notebook demonstrates how to construct and analyze a five-member bacterial community model for coastal oil spill bioremediation. Using MICOM and constraint-based modeling approaches, it provides a computational framework for predicting optimal biostimulation strategies for toluene degradation.

https://doi.org/10.1371/journal.pstr.0000145.s002

(IPYNB)

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