In silico encounters: harnessing metabolic modelling to understand plant–microbe interactions

Mutualistic interactions have great importance in ecology, with genetic information that takes shape through interactions within the symbiotic partners and between the partners and the environment. It is known that variation of the host-associated microbiome contributes to buffer adaptation challenges of the host’s physiology when facing varying environmental conditions. In agriculture, pivotal examples are symbiotic nitrogen-fixing rhizobia, known to contribute greatly to host (legume plants) adaptation and host productivity. A holistic view of increasing crop yield and resistance to biotic and abiotic stresses is that of microbiome engineering, the exploitation of a host-associated microbiome through its rationally designed manipulation with synthetic microbial communities. However, several studies highlighted that the expression of the desired phenotype in the host resides in species-specific, even genotype-specific interactions between the symbiotic partners. Consequently, there is a need to dissect such an intimate level of interaction, aiming to identify the main genetic components in both partners playing a role in symbiotic differences/host preferences. In the present paper, while briefly reviewing the knowledge and the challenges in plant–microbe interaction and rhizobial studies, we aim to promote research on genotype x genotype interaction between rhizobia and host plants for a rational design of synthetic symbiotic nitrogen-fixing microbial communities to be used for sustainably improving leguminous plants yield.

Metabolic modelling approaches for describing and engineering microbial communities

The genome-scale metabolic model (GEM), a mathematical representation of whole-cell metabolism, enables in silico high-throughput simulations of metabolic flux distribution under different conditions. As a mechanistic and quantitative framework at the system level, GEMs have been applied to investigate microbial interactions and provide guidelines for the rational design of synthetic microbial communities that can perform the desired functions and provide novel strategies for bioremediation. Here, we review different types of microbial interactions and their research progress in environmental remediation. The detailed process of GEM reconstruction and the current main automatic reconstruction programs as well as knowledge databases are summarized. Exciting recent application studies combining metabolic models, omics, and machine learning approaches are also presented. The combination of these methods improves the predictive ability and broadens the applications of GEMs. However, extensive work is still needed to deeply understand and arbitrarily use GEMs to describe the metabolic interactions of microbial communities and apply them in pollutant biodegradation. Finally, an in-depth discussion of the current challenges and limitations of metabolic models provides us with an outlook for their future development in environmental science, especially synthetic microbiology.

Living organisms in analogy with chemical factories use simple molecules such as sugars to produce a variety of compounds which are necessary for sustaining life and some of which are also commercially valuable. The metabolisms of simple (such as bacteria) and higher organisms (such as plants) alike can be exploited to convert low value inputs into high value outputs. Unlike conventional chemical factories, microbial production chassis are not necessarily tuned for a single product overproduction. Despite the same end goal, metabolic and industrial engineers rely on different techniques for achieving productivity goals. Metabolic engineers cannot affect reaction rates by manipulating pressure and temperature, instead they have at their disposal a range of enzymes and transcriptional and translational processes to optimize accordingly. In this review, we first highlight how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed in systems and control engineering. Specifically, how algorithmic concepts derived in operations research can help explain the structure and organization of metabolic networks. Finally, we consider the future directions and challenges faced by the field of metabolic network modeling and the possible contributions of concepts drawn from the classical fields of chemical and control engineering. The aim of the review is to offer a current perspective of metabolic engineering and all that it entails without requiring specialized knowledge of bioinformatics or systems biology.

The systematic collection, integration and modelling of high-throughput molecular data (multi-omics) allows the detailed characterisation of microbiomes in situ. Through metabolic trait inference, metabolic network reconstruction and modelling, we are now able to define ecological interactions based on metabolic exchanges, identify keystone genes, functions and species, and resolve ecological niches of constituent microbial populations. The resulting knowledge provides detailed information on ecosystem functioning. However, as microbial communities are dynamic in nature the field needs to move towards the integration of time- and space-resolved multi-omic data along with detailed environmental information to fully harness the power of community- and population-level metabolic network modelling. Such approaches will be fundamental for future targeted management strategies with wide-ranging applications in biotechnology and biomedicine.

Abiotic stresses are important limiting factors that limit the yields of crops. Plants need to develop such mechanisms that help in dealing with the harsh environment and soil health. Medicinal plants are important due to their medicinal, aesthetic, and bioactive phytochemical characteristics. Microorganisms display metabolic abilities to reduce stress in diverse environments. The beneficial microbes reside outside or inside plant tissues and perform numerous activities that control phytopathogens. They also include plant growth-promoting bacteria, such as endophytes present in the rhizosphere and phyllosphere. As microbes are the natural partners of plants, they ameliorate the effect of a harsh environment. Plant–microbe interaction is essential for the ecosystem. Plant–microbe interaction encompasses the complex mechanism inside the cell. Physiological, biochemical, and molecular studies help understand the complicated and integrated processes of plant–microbe interaction. Due to environmental changes and incessant stress, it is very important to distinguish and access information on plant–microbe interaction concerning defense against abiotic stresses. Multi-omics studies about plant–microbe interaction and their environment provide a deeper insight and provide multi-layered information that has a substantial chance for practical execution. In this chapter, we review the structure and function of microbiota, the physiological and molecular response of plant and microbiota against stress, and microbe-mediated mitigation of abiotic stresses. Additionally, how plant root exudates attract microbes that play a role in biological control has also been reviewed.

Microbial coexistence in the rhizosphere and the promotion of plant stress resistance: A review

Introduction to a Virtual Special Issue on cell biology at the plant-microbe interface.

Systems biology is an interdisciplinary field that applies engineering principles and computational modeling to understand complex biological systems at multiple scales. This paper examines how systems biology evolved from reductionist approaches to integrative, system-level methodologies that leverage high-throughput technologies and mathematical frameworks. Core topics include the history of systems thinking in biology, computational modeling of metabolic and signaling networks, data integration techniques, and applications in medicine, agriculture, and synthetic biology. With the advent of omics technologies and bioinformatics tools, researchers can now simulate, predict, and design biological functions with increasing precision. However, despite its promise, systems biology faces significant challenges, including data standardization, model validation, and ethical implications. By blending systems engineering with molecular biology, this field aims not only to advance fundamental science but also to offer innovative solutions to pressing societal needs such as disease treatment, food security, and environmental sustainability. Keywords: Systems Biology, Computational Modeling, Biological Networks, Data Integration, Synthetic Biology, Engineering Principles, Omics Technologies.

Soil-borne diseases are a growing threat to global agricultural production, a challenge further intensified by climate change. While chemical pesticides and fertilizers can offer short-term control over pathogen populations, their excessive use accelerates soil degradation, biodiversity loss, and environmental pollution. Due to these negative effects, there is an increasing demand for more sustainable alternatives. Breeding disease-resistant varieties is a widely recognized solution, and significant efforts have been made to develop, for instance, disease-resistant banana varieties. However, beyond the plant innate immune system encoded in the genome, increasing evidence has highlighted the plant microbiome as a functional extension of plant immunity. The rhizosphere microbiome, often described as a plant’s “second genome,” plays a central role in plant health and offers promising opportunities for sustainable disease suppression. However, optimizing plant-microbe interactions for disease resistance remains challenging due to the intricate interplay among environmental factors, plant traits, and microbiome dynamics. Therefore, this research investigates how disease-resistant plants shape rhizosphere microbial communities to suppress pathogens, using banana varieties with varying resistance to Fusarium wilt as a model. The study revealed that resistant banana varieties selectively enriched specific fungal taxa, particularly Trichoderma and Penicillium, which contributed to pathogen suppression. These beneficial fungi were stimulated by plant-secreted metabolites such as shikimic acid, D-(-)-ribofuranose, and propylene glycol. Applying these metabolites to soil enhanced the pathogen resistance of otherwise susceptible varieties, underscoring the role of metabolite-mediated recruitment in microbiome assembly. Further analysis demonstrated that the resistance of highly resistant variety was driven by the combined action of both bacteria and fungi. Synthetic cross-kingdom microbial communities, designed based on the natural recruitment patterns of highly resistant variety, significantly inhibited pathogen growth. Their suppressive effects were amplified when combined with metabolites like stearic acid and shikimic acid. The work also showed that transferring rhizosphere microbiomes from resistant to susceptible varieties effectively reduced pathogen densities, providing direct evidence that microbiome composition is a determinant of disease resistance. Moreover, soils previously cultivated with resistant varieties harbored beneficial microbial legacies that improved the resistance of subsequent susceptible plants, highlighting the potential for long-term microbiome-based disease management strategies. Overall, the findings demonstrate that a substantial component of plant disease resistance derives from the ability to recruit functional microbiomes. This shifts the breeding paradigm toward targeting microbiome recruitment traits as a means of improving plant health. By integrating ecological principles with plant–microbe interaction insights, this research lays a foundation for sustainable, microbiome-informed agricultural practices capable of enhancing resilience against soil-borne diseases.

Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer.

It is well known that plant–microbe interaction can be affected by global climate change. Many abiotic and biotic factors have great influence on the generation and maintenance of microbial diversity in the rhizosphere. On the other hand, the interaction between plants and beneficial plant microbe aids plants to develop resistance against biotic or abiotic stresses and raise crop yield. In this chapter, we focus on plant natural systems; artificial systems (microbial-mediated transformation of plants); plant–microbes interaction and plant immune system; plant–microbe interaction; and genomics and genome-scale modelling at metabolic level.

Soil constitutes a wide range of microorganisms, both beneficial and harmful. Although the plant and microbial community have distinct differences with regard to their characteristics, their interaction may hold benefits for the agro-ecological system. This chapter's topic of discussion sheds light on the various plant-microbe interactions in the rhizosphere and phyllosphere, as well as in the soil environment. This mayenhance crop production in an indirect manner by inducing or enhancing various factors of crop production. Despite the fact that plant-microbe interaction may lead to both beneficial and harmful effects, still this interaction contributeshugely to a healthy and sustainable agroecosystem. The role of plant-microbe interaction in the fixation of nutrients, nutrient dynamics, and crop growth, as well as resistance to biotic and abiotic stresses are inevitable to have detailed research and study on their different aspects. This chapter will help the reader to have a brief and compiled idea on the interaction between plants and microbes.

The abundance of methane in shale gas and of other gases such as carbon monoxide, hydrogen, and carbon dioxide as chemical process byproducts has motivated the use of gas fermentation for bioproduction. Recent advances in metabolic engineering and synthetic biology allow for engineering of microbes metabolizing a variety of chemicals including gaseous feeds into a number of biorenewables and transportation liquid fuels. New computational tools enable the systematic exploration of all feasible conversion alternatives. Here we computationally assessed all thermodynamically feasible ways of co-utilizing CH4, CO, and CO2 using ferric as terminal electron acceptor for the production of all key precursor metabolites. We identified the thermodynamically feasible co-utilization ratio ranges of CH4, CO, and CO2 toward production of the target metabolite(s) as a function of ferric uptake. A revised version of the iMAC868 genome-scale metabolic model of Methanosarcina acetivorans was chosen to assess co-utilization of CH4, CO, and CO2 and their conversion into selected target products using the optStoic pathway design tool. This revised version contains the latest information on electron flow mechanisms by the methanogen while supplied with methane as the sole carbon source. The interplay between different gas co-utilization ratios and the energetics of reverse methanogenesis were also analyzed using the same metabolic model.

Pichia pastoris metabolic modelSohn et al., Biotechnol. J. 2010, 5, 705–715The methylotrophic yeast Pichia pastoris become a platform for producing heterologous recombinant proteins of pharmaceutical importance, thanks to its ability to reproduce post‐translational modification similar to higher eukaryotes. With the recent release of the full genome sequence for P. pastoris, in‐depth study of its functions has become feasible. The groups of Sang Yup Lee (Daejeon, Korea) and Diethard Mattanovich (Vienna, Austria) present the first reconstruction of a genome‐scale metabolic model of the eukaryote P. pastoris type strain DSMZ 70382, PpaMBEL1254. It consists of 1254 metabolic reactions and 1147 metabolites compartmentalized into eight different regions which represent organelles. They incorporated equations describing the production of two heterologous proteins, human serum albumin and human superoxide dismutase, and analyzed for the impact of oxygen limitation on protein production.A blueprint of lifeHenry et al., Biotechnol. J. 2010, 5, 695–704The de novo synthesis and implantation of an entire prokaryotic genome to create a living synthetic cell represents a breakthrough in the field of systems and synthetic biology. The challenge is to construct a blueprint for the first truly synthetic organism containing only a minimal genome. Chris Henry, Ross Overbeek and Rick L. Stevens review the significant progress made in the design and creation of a minimal organism. They discuss how comparative genomes, gene essentiality data, naturally small genomes and metabolic modeling are being applied to produce a catalogue of the biological functions essential for life. Minimal gene sets from three published sources with functions identified in 13 existing gene essentiality datasets are compared. They also examine how genome‐scale metabolic models can been applied to design a minimal metabolism for growth in simple and complex media.Exoenzymes cooperate or cheatSchuster et al., Biotechnol. J. 2010, 5, 751–758Extracellular enzymes (exoenzymes) are needed for many biotechnologically relevant processes, like secreted cellulases for biofuel production. Productivity is often reduced by “cheater” mutants, which are deficient in exoenzyme production and benefit from the product provided by the “cooperating” cells. Anja Schroeter and colleagues from Jena (Germany) and Tel Aviv (Israel) present a game‐theoretical model to analyze the population structure and exoenzyme productivity for biotechnological applications. They predict three scenarios: i) when the metabolic effort for exoenzyme production is low all cells cooperate; ii) at intermediate metabolic costs cooperators and cheaters coexist; and iii) at high costs all cells use the cheating strategy. These three regimes correspond to the harmony game, snowdrift game, and prisoner's dilemma, respectively. While also taking into account cell density, the model predicts that engineered microbial strains play the “harmony game” when the conditions are appropriate.