Metabolic modelling approaches for describing and engineering microbial communities

Bioprocess industries aim to meet the increasing demand for product complexity by designing enhanced cellular and metabolic capabilities for the host. Monocultures, standard biomanufacturing workhorses, are often restricted in their capability to meet these demands, and the solution often involves the genetic modification of the host. Synthetic microbial communities are a promising alternative to monocultures because they exhibit division of labor, enabling efficient resource utilization and pathway modularity. This specialization minimizes metabolic burden and enhances robustness to perturbations, providing a competitive advantage. Despite this potential, their utilization in biotechnological or bioprocessing applications remains limited. The recent emergence of new and innovative community design tools and strategies, particularly those harnessing the division of labor, holds promise to change this outlook. Understanding the microbial interactions governing natural microbial communities can be used to identify complementary partners, informing synthetic community design. Therefore, we particularly consider engineering division of labor in synthetic microbial communities as a viable solution to accelerate progress in the field. This review presents the current understanding of how microbial interactions enable division of labor and how this information can be used to design synthetic microbial communities to perform tasks otherwise unfeasible to individual organisms. We then evaluate laboratory and formal modeling approaches specifically developed to: elucidate microbial community physiology, guide experimental design, and improve our understanding of complex community interactions assisting synthetic community design. By synthesizing these insights, we aim to present a comprehensive framework that advances the use of microbial communities in biomanufacturing applications.

Synthetic microbial communities have become a focus of biotechnological research since they can overcome several of the limitations of single-specie cultures. A paradigmatic example is Clostridium cellulovorans DSM 743B, which can decompose lignocellulose but cannot produce butanol. Clostridium beijerinckii NCIMB 8052 however, is unable to use lignocellulose but can produce high amounts of butanol from simple sugars. In our previous studies, both organisms were cocultured to produce butanol by consolidated bioprocessing. However, such consolidated bioprocessing implementation strongly depends on pH regulation. Since low pH (pH 4.5-5.5) is required for butanol fermentation, C. cellulovorans cannot grow well and saccharify sufficient lignocellulose to feed both strains at a pH below 6.4. To overcome this bottleneck, this study engineered C. cellulovorans by adaptive laboratory evolution, inactivating cell wall lyases genes (Clocel_0798 and Clocel_2169), and overexpressing agmatine deiminase genes (augA, encoded by Cbei_1922) from C. beijerinckii NCIMB 8052. The generated strain WZQ36: 743B*6.0*3△lyt0798△lyt2169-(pXY1-Pthl -augA) can tolerate a pH of 5.5. Finally, the alcohol aldehyde dehydrogenase gene adhE1 from Clostridium acetobutylicum ATCC 824 was introduced into the strain to enable butanol production at low pH, in coordination with solvent fermentation of C. beijerinckii in consortium. The engineered consortium produced 3.94 g/L butanol without pH control within 83 hr, which is more than5-fold of the level achieved by wild consortia under the same conditions. This exploration represents a proof of concept on how to combine metabolic and evolutionary engineering to coordinate coculture of a synthetic microbial community.

The effect and mechanism of Debaryomyces nepalensis and six strains of winter jujube epiphytic bacteria in building a synthetic community to control post-harvest black spot disease of winter jujube

Synthetic microbial communities (SynComs) of the human gut: design, assembly, and applications

There have been many studies on single strains in wastewater treatment and a new synthetic microbial community was prepared in this study, which provides a reference for the application of heterotrophic nitrification-aerobic denitrification in actual wastewater treatment. The growth period distribution of the composite bacteria was determined by plotting growth curves with different sole nitrogen sources, and the influence of the carbon source, carbon to nitrogen ratio (C/N) ratio, pH, and temperature on ammonia removal by the composite heterotrophic nitrifying-aerobic denitrifying strain was investigated. The optimal conditions for the heterotrophic nitrification process were sodium citrate as the carbon source, a C/N ratio of 10, a pH of 7, and a temperature of 30 °C, and only trace amounts of nitrate and nitrite were observed during the process. When the sequencing batch reactor (SBR) of a pig farm wastewater treatment plant was inoculated with the synthetic microbial community, the average removals of the chemical oxygen demand (COD) and ammonia nitrogen in the effluent were 92.61% and 20.56%, respectively. From the results, the synthetic microbial community was able to simultaneously perform heterotrophic nitrification-aerobic denitrification indicating great potential for full-scale applications.

Synthetic microbial communities (SynComs) are powerful tools for investigating microbial interactions and community assembly by focusing on minimal yet functionally representative members. Here, we will highlight key principles for designing SynComs, specifically emphasizing the anaerobic digestion (AD) microbiome for waste treatment and upcycling. The AD process has traditionally been used to reduce organic waste volume while producing biogas as a renewable energy source. Its microbiome features well-defined trophic layers and metabolic groups. There has been growing interest in repurposing the AD process to produce value-added products and chemical precursors, contributing to sustainable waste management and the goals of a circular economy. Optimizing the AD process requires a better understanding of microbial interactions and the influence of both biotic and abiotic parameters, where SynComs offer great promise. Focusing on AD microbiomes, we review the principles of SynComs' design, including keystone taxa and function, cross-feeding interactions, and metabolic redundancy, as well as how modeling approaches could guide SynComs design. Furthermore, we address practical considerations for working with AD SynComs and examine constructed SynComs designed for anaerobic waste digestion. Finally, we discuss the challenges associated with designing and applying SynComs to enhance our understanding of the AD process. This review aims to explore the use of synthetic communities in studying anaerobic digestion and highlights their potential for developing innovative biotechnological processes.

High throughput design of defined microbial consortia for crop protection.

Predictability is a fundamental requirement in biological engineering. As we move to building coordinated multicellular systems, the potential for such systems to display chaotic behaviour becomes a concern. Therefore understanding which systems show chaos is an important design consideration. We developed a methodology to explore the potential for chaotic dynamics in small microbial communities governed by resource competition, intercellular communication and competitive bacteriocin interactions. Our model selection pipeline uses Approximate Bayesian Computation to first identify oscillatory behaviours as a route to finding chaotic behaviour. We have shown that we can expect to find chaotic states in relatively small synthetic microbial systems, understand the governing dynamics and provide insights into how to control such systems. This work is the first to query the existence of chaotic behaviour in synthetic microbial communities and has important ramifications for the fields of biotechnology, bioprocessing and synthetic biology.

The complexity of natural microbial communities poses significant challenges for predictive manipulation, driving the emergence of Synthetic Microbial Communities (SynComs) as tractable models for functional optimization in environmental, agricultural, and biomedical applications. While SynComs provide enhanced controllability, their rational design faces persistent challenges in achieving both functional precision and ecological stability. Here, we present a theoretical and methodological framework for engineering SynComs through the strategic integration of ecological principles, evolutionary theory, and computational innovation. By (i) ecological interaction engineering for dynamic equilibrium of cooperative and competitive relationships, (ii) hierarchical species orchestration ensuring structural integrity through keystone species governance, helper-mediated adaptation, and rare taxa preservation, (iii) evolution-guided artificial selection overcoming functional-stability trade-offs, and (iv) modular metabolic stratification for efficient resource partitioning, we demonstrate how SynComs can be programmed for predictable functionality. We further identify critical frontiers for SynCom construction and application, including: mechanistic decoding of microbial interaction networks, high-throughput culturomics for strain discovery, artificial intelligence-enabled exploitation of microbial dark matter, automated platform-assisted consortium assembly, predictive modelling of long-term community dynamics, and the development of standardized frameworks and shared databases. The theory-technology integrated paradigm establishes SynComs as programmable ecotechnologies capable of addressing global sustainability challenges through engineered ecological resilience. This synthesis provides both a conceptual roadmap and a practical toolkit for transitioning from empirical community construction to predictive ecosystem engineering.

The design and development of EcoBiomes: Multi-species synthetic microbial consortia inspired by natural desert microbiome to enhance the resilience of climate-sensitive ecosystems

Synthetic microbial community mimicking kefir for investigating community dynamics and interspecies interactions.

'Bioimmobilization' of redox-sensitive metals and radionuclides is being investigated as a way to remediate contaminated groundwater and sediments. In this approach, growth-limiting substrates are added to stimulate the activity of targeted groups of indigenous microorganisms and create conditions favorable for the microbially-mediated precipitation ('bioimmobilization') of targeted contaminants. This project investigated a fundamentally new approach for modeling this process that couples thermodynamic descriptions for microbial growth with associated geochemical reactions. In this approach, a synthetic microbial community is defined as a collection of defined microbial groups; each with a growth equation derived from bioenergetic principles. The growth equations and standard-state free energy yields are appended to a thermodynamic database for geochemical reactions and the combined equations are solved simultaneously to predict the effect of added substrates on microbial biomass, community composition, and system geochemistry. This approach, with a single set of thermodynamic parameters (one for each growth equation), was used to predict the results of laboratory and field bioimmobilization experiments at two geochemically diverse research sites. Predicted effects of ethanol or acetate addition on uranium and technetium solubility, major ion geochemistry, mineralogy, microbial biomass and community composition were in general agreement with experimental observations although the available experimental data precluded rigorous model testing. Model simulations provide insight into the long-standing difficulty in transferring experimental results from the laboratory to the field and from one field site to the next, especially if the form, concentration, or delivery of growth substrate is varied from one experiment to the next. Although originally developed for use in better understanding bioimmobilization of uranium and technetium via reductive precipitation, the modeling approach is potentially useful for exploring the coupling of microbial growth and geochemical reactions in a variety of basic and applied biotechnology research settings.

SummaryTo mass‐produce nutritional kombucha with quality consistency, it is necessary to design synthetic microbial community (SMC) starters with clear bacterial compositions. In this study, a three‐species SMC, including Acetobacter pasteurianus, Gluconacetobacter xylinus and Zygosaccharomyces bailii, was designed. Acetic acid bacteria (AAB) in SMC produced a higher concentration of organic acids synergistically compared to each strain inoculated alone. Gluconic acid was the most abundant organic acid in kombucha fermented by SMC, which contributed to the pleasant sour taste of the beverage. AAB could also oxidise the alcohol produced by Z. bailii, making kombucha a soft beverage. Total phenol and flavonoid concentrations increased during fermentation, which enhanced the health benefits of kombucha. The sensory evaluation demonstrated tsshat sensory attributes of SMC fermented kombucha were quite similar to those fermented by the symbiotic culture of bacteria and yeast (SCOBY). Therefore, SMC could be utilised in the industrial mass production of kombucha for better quality control and product consistency.

When plants grow in soil, their roots release carbon molecules that attract microbes. These microbes colonize the roots and form a complex system known as the root microbiome. Similar to the human gut microbiome, a balanced and diverse root microbiome offers numerous benefits to its host. For plants, these benefits include enhanced immunity, suppression of pathogens, improved nutrient uptake, and greater tolerance to environmental stresses. To harness these beneficial interactions and make crops more resilient, it is crucial to understand how the root microbiome is established and how it functions as beneficial microbial activities, such as pathogen suppression, depend on the microbes successfully establishing themselves in the root microbiome. To study this, I conducted synthetic community (SynCom) inoculation experiments. SynComs are communities of microbes that were originally extracted from plant roots when grown in natural soil. In SynCom experiments, these microbes are recultivated and mixed into controlled microbial communities, which are subsequently introduced to plants in sterile conditions to examine how microbes interact with each other and interact with the plant to form root communities. In my thesis, I analyzed both experimental and publicly available SynCom datasets to explore root microbiome assembly and functioning. First, I discuss the challenges of root microbiome research and how SynCom experiments help overcome these challenges (Chapter 1). In Chapter 2, I uncover key principles of root microbiome assembly, including how plants prioritize bacterial functions over genera, how the selection of bacterial functions occurs at the family level, how hosts exhibit unique patterns in selecting bacterial functions, and how functionally diverse bacteria are more likely to successfully colonize roots. In Chapter 3, a metagenome-wide association study highlights that key traits, such as the ability to metabolize diverse compounds, motility, immune evasion strategies, and the capacity to inhibit other microbes, are crucial for competitive bacterial colonization of plant roots. In Chapter 4, I developed a tool to identify and quantify SynCom isolates in complex SynCom datasets, achieving better performance than existing benchmark tools. In Chapter 5, I analyze publicly available SynCom datasets and reveal that microbial communities often exist in distinct functional states, influenced by the presence or absence of Bacillus bacteria, highlighting the ecological importance of this species in the root microbiome. Finally, in Chapter 6, I discuss the role of artificial intelligence and machine learning in modeling the root microbiome, how the experimental findings in the other chapters contribute to this effort, and the challenges involved in building predictive models for the complex interactions within the root microbiome. Conclusively, I demonstrate in this thesis how combining complex SynCom experiments with advanced bioinformatics can unravel the complexities of the root microbiome. My findings identify key principles of root microbiome assembly and the essential microbial functions required for root colonization. In the long term, this research may help develop bioinoculants by designing microbial consortia that enhance the effective establishment of bioinoculants in the root microbiome. This, in turn, could improve crop resilience to stresses, addressing challenges intensified by climate change.

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