Metabolism in dense microbial colonies: 13C metabolic flux analysis of E. coli grown on agar identifies two distinct cell populations with acetate cross-feeding

Model validation and selection in metabolic flux analysis and flux balance analysis

13C-Metabolic Flux Analysis (13C-MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both methods use metabolic reaction network models of metabolism operating at steady state so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. These fluxes can shed light on basic biology and have been successfully used to inform metabolic engineering strategies. Several approaches have been taken to test the reliability of estimates and predictions from constraint-based methods and to compare alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, such as the quantification of flux estimate uncertainty, validation and model selection methods have been underappreciated and underexplored. We review the history and state-of-the-art in constraint-based metabolic model validation and model selection. Applications and limitations of the χ2 -test of goodness-of-fit, the most widely used quantitative validation and selection approach in 13C-MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C-MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how adopting robust validation and selection procedures can enhance confidence in constraint-based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology.

Toward standardized solid medium cultivations: Online microbial monitoring based on respiration activity

Cultivating microorganisms on solid agar media is a fundamental technique in microbiology and other related disciplines. For the evaluation, most often, a subjective visual examination is performed. Crucial information, such as metabolic activity, is not assessed. Thus, time-resolved monitoring of the respiration activity in agar cultivations is presented to provide additional insightful data on the metabolism. A modified version of the Respiration Activity MOnitoring System (RAMOS) was used to determine area-specific oxygen and carbon dioxide transfer rates and the resulting respiratory quotients of agar cultivations. Therewith, information on growth, substrate consumption, and product formation was obtained. The validity of the presented method was tested for different prokaryotic and eukaryotic organisms on agar, such as Escherichia coli BL21, Pseudomonas putida KT2440, Streptomyces coelicolor A3(2), Saccharomyces cerevisiae WT, Pichia pastoris WT, and Trichoderma reesei RUT-C30. Furthermore, it is showcased that several potential applications, including the determination of colony forming units, antibiotic diffusion tests, quality control for spore production or for pre-cultures and media optimization, can be quantitatively evaluated by interpretation of the respiration activity.

Metabolic heterogeneity and cross-feeding within isogenic yeast populations captured by DILAC

Genetically identical cells are known to differ in many physiological parameters such as growth rate and drug tolerance. Metabolic specialization is believed to be a cause of such phenotypic heterogeneity, but detection of metabolically divergent subpopulations remains technically challenging. We developed a proteomics-based technology, termed differential isotope labelling by amino acids (DILAC), that can detect producer and consumer subpopulations of a particular amino acid within an isogenic cell population by monitoring peptides with multiple occurrences of the amino acid. We reveal that young, morphologically undifferentiated yeast colonies contain subpopulations of lysine producers and consumers that emerge due to nutrient gradients. Deconvoluting their proteomes using DILAC, we find evidence for in situ cross-feeding where rapidly growing cells ferment and provide the more slowly growing, respiring cells with ethanol. Finally, by combining DILAC with fluorescence-activated cell sorting, we show that the metabolic subpopulations diverge phenotypically, as exemplified by a different tolerance to the antifungal drug amphotericin B. Overall, DILAC captures previously unnoticed metabolic heterogeneity and provides experimental evidence for the role of metabolic specialization and cross-feeding interactions as a source of phenotypic heterogeneity in isogenic cell populations.

Establishing a growth-coupled mechanism for high-yield production of β-arbutin from glycerol in Escherichia coli

Biodiesel production has increased significantly in recent years, leading to an increase in the production of crude glycerol. In this study, a novel growth-coupled erythrose 4-phosphate (E4P) formation strategy that can be used to produce high levels of β-arbutin using engineered Escherichia coli and glycerol as the carbon source was developed. In the strategy, E4P formation was coupled with cell growth, and a growth-driving force made the E4P formation efficient. By applying this strategy, efficient microbial synthesis of β-arbutin was achieved, with 7.91 g/L β-arbutin produced in shaking flask, and 28.1 g/L produced in a fed batch fermentation with a yield of 0.20 g/g glycerol and a productivity of 0.39 g/L/h. This is the highest β-arbutin production through microbial fermentation ever reported to date. This study may have significant implications in the large-scale production of β-arbutin as well as other aromatic compounds of importance.

Intra-colony channel morphology in Escherichia coli biofilms is governed by nutrient availability and substrate stiffness

Nutrient-transporting channels have been recently discovered in mature Escherichia coli biofilms, however the relationship between intra-colony channel structure and the surrounding environmental conditions is poorly understood. Using a combination of fluorescence mesoscopy and a purpose-designed open-source quantitative image analysis pipeline, we show that growth substrate composition and nutrient availability have a profound effect on the morphology of intra-colony channels in mature E. coli biofilms. Under all nutrient conditions, intra-colony channel width was observed to increase non-linearly with radial distance from the centre of the biofilm. Notably, the channels were around 25% wider at the centre of carbon-limited biofilms compared to nitrogen-limited biofilms. Channel density also differed in colonies grown on rich and minimal media, with the former creating a network of tightly packed channels and the latter leading to well-separated, wider channels with defined edges. Our approach paves the way for measurement of internal patterns in a wide range of biofilms, offering the potential for new insights into infection and pathogenicity.

Metabolic flux analysis: a comprehensive review on sample preparation, analytical techniques, data analysis, computational modelling, and main application areas

Metabolic flux analysis (MFA) quantitatively describes cellular fluxes to understand metabolic phenotypes and functional behaviour after environmental and/or genetic perturbations. In the last decade, the application of stable isotopes became extremely important to determine and integrate in vivo measurements of metabolic reactions in systems biology. 13C-MFA is one of the most informative methods used to study central metabolism of biological systems. This review aims to outline the current experimental procedure adopted in 13C-MFA, starting from the preparation of cell cultures and labelled tracers to the quenching and extraction of metabolites and their subsequent analysis performed with very powerful software. Here, the limitations and advantages of nuclear magnetic resonance spectroscopy and mass spectrometry techniques used in carbon labelled experiments are elucidated by reviewing the most recent published papers. Furthermore, we summarise the most successful approaches used for computational modelling in flux analysis and the main application areas with a particular focus in metabolic engineering.

Selective drivers of simple multicellularity

In order to understand the evolution of multicellularity, we must understand how and why selection favors the first steps in this process: the evolution of simple multicellular groups. Multicellularity has evolved many times in independent lineages with fundamentally different ecologies, yet no work has yet systematically examined these diverse selective drivers. Here we review recent developments in systematics, comparative biology, paleontology, synthetic biology, theory, and experimental evolution, highlighting ten selective drivers of simple multicellularity. Our survey highlights the many ecological opportunities available for simple multicellularity, and stresses the need for additional work examining how these first steps impact the subsequent evolution of complex multicellularity.

Gradients and consequences of heterogeneity in biofilms

Historically, appreciation for the roles of resource gradients in biology has fluctuated inversely to the popularity of genetic mechanisms. Nevertheless, in microbiology specifically, widespread recognition of the multicellular lifestyle has recently brought new emphasis to the importance of resource gradients. Most microorganisms grow in assemblages such as biofilms or spatially constrained communities with gradients that influence, and are influenced by, metabolism. In this Review, we discuss examples of gradient formation and physiological differentiation in microbial assemblages growing in diverse settings. We highlight consequences of physiological heterogeneity in microbial assemblages, including division of labour and increased resistance to stress. Our impressions of microbial behaviour in various ecosystems are not complete without complementary maps of the chemical and physical geographies that influence cellular activities. A holistic view, incorporating these geographies and the genetically encoded functions that operate within them, will be essential for understanding microbial assemblages in their many roles and potential applications.

Spatial alanine metabolism determines local growth dynamics of Escherichia coli colonies

Bacteria commonly live in spatially structured biofilm assemblages, which are encased by an extracellular matrix. Metabolic activity of the cells inside biofilms causes gradients in local environmental conditions, which leads to the emergence of physiologically differentiated subpopulations. Information about the properties and spatial arrangement of such metabolic subpopulations, as well as their interaction strength and interaction length scales are lacking, even for model systems like Escherichia coli colony biofilms grown on agar-solidified media. Here, we use an unbiased approach, based on temporal and spatial transcriptome and metabolome data acquired during E. coli colony biofilm growth, to study the spatial organization of metabolism. We discovered that alanine displays a unique pattern among amino acids and that alanine metabolism is spatially and temporally heterogeneous. At the anoxic base of the colony, where carbon and nitrogen sources are abundant, cells secrete alanine via the transporter AlaE. In contrast, cells utilize alanine as a carbon and nitrogen source in the oxic nutrient-deprived region at the colony mid-height, via the enzymes DadA and DadX. This spatially structured alanine cross-feeding influences cellular viability and growth in the cross-feeding-dependent region, which shapes the overall colony morphology. More generally, our results on this precisely controllable biofilm model system demonstrate a remarkable spatiotemporal complexity of metabolism in biofilms. A better characterization of the spatiotemporal metabolic heterogeneities and dependencies is essential for understanding the physiology, architecture, and function of biofilms.

A gap-filling algorithm for prediction of metabolic interactions in microbial communities

The study of microbial communities and their interactions has attracted the interest of the scientific community, because of their potential for applications in biotechnology, ecology and medicine. The complexity of interspecies interactions, which are key for the macroscopic behavior of microbial communities, cannot be studied easily experimentally. For this reason, the modeling of microbial communities has begun to leverage the knowledge of established constraint-based methods, which have long been used for studying and analyzing the microbial metabolism of individual species based on genome-scale metabolic reconstructions of microorganisms. A main problem of genome-scale metabolic reconstructions is that they usually contain metabolic gaps due to genome misannotations and unknown enzyme functions. This problem is traditionally solved by using gap-filling algorithms that add biochemical reactions from external databases to the metabolic reconstruction, in order to restore model growth. However, gap-filling algorithms could evolve by taking into account metabolic interactions among species that coexist in microbial communities. In this work, a gap-filling method that resolves metabolic gaps at the community level was developed. The efficacy of the algorithm was tested by analyzing its ability to resolve metabolic gaps on a synthetic community of auxotrophic Escherichia coli strains. Subsequently, the algorithm was applied to resolve metabolic gaps and predict metabolic interactions in a community of Bifidobacterium adolescentis and Faecalibacterium prausnitzii, two species present in the human gut microbiota, and in an experimentally studied community of Dehalobacter and Bacteroidales species of the ACT-3 community. The community gap-filling method can facilitate the improvement of metabolic models and the identification of metabolic interactions that are difficult to identify experimentally in microbial communities.

Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution

Capturing the heterogeneous phenotypes of microbial populations at relevant spatiotemporal scales is highly challenging. Here, we present par-seqFISH (parallel sequential fluorescence in situ hybridization), a transcriptome-imaging approach that records gene expression and spatial context within microscale assemblies at a single-cell and molecule resolution. We applied this approach to the opportunistic pathogen Pseudomonas aeruginosa, analyzing about 600,000 individuals across dozens of conditions in planktonic and biofilm cultures. We identified numerous metabolic- and virulence-related transcriptional states that emerged dynamically during planktonic growth, as well as highly spatially resolved metabolic heterogeneity in sessile populations. Our data reveal that distinct physiological states can coexist within the same biofilm just several micrometers away, underscoring the importance of the microenvironment. Our results illustrate the complex dynamics of microbial populations and present a new way of studying them at high resolution.

High-Throughput and Continuous Chaotic Bioprinting of Spatially Controlled Bacterial Microcosms

Microorganisms do not work alone but instead function as collaborative microsocieties. The spatial distribution of different bacterial strains (micro-biogeography) in a shared volumetric space and their degree of intimacy greatly influences their societal behavior. Current microbiological techniques are commonly focused on the culture of well-mixed bacterial communities and fail to reproduce the micro-biogeography of polybacterial societies. Here, we bioprinted fine-scale bacterial microcosms using chaotic flows induced by a printhead containing a static mixer. This straightforward approach (i.e., continuous chaotic bacterial bioprinting) enables the fabrication of hydrogel constructs with intercalated layers of bacterial strains. These multilayered constructs are used to analyze how the spatial distributions of bacteria affect their social behavior. For example, we show that bacteria within these biological microsystems engage in either cooperation or competition, depending on the degree of shared interface. The extent of inhibition in predator-prey scenarios (i.e., probiotic-pathogen bacteria) increases when bacteria are in greater intimacy. Furthermore, two Escherichia coli strains exhibit competitive behavior in well-mixed microenvironments, whereas stable coexistence prevails for longer times in spatially structured communities. We anticipate that chaotic bioprinting will contribute to the development of a greater complexity of polybacterial microsystems, tissue-microbiota models, and biomanufactured materials.

Cooperative metabolic resource allocation in spatially-structured systems

Natural selection has shaped the evolution of cells and multi-cellular organisms such that social cooperation can often be preferred over an individualistic approach to metabolic regulation. This paper extends a framework for dynamic metabolic resource allocation based on the maximum entropy principle to spatiotemporal models of metabolism with cooperation. Much like the maximum entropy principle encapsulates 'bet-hedging' behaviour displayed by organisms dealing with future uncertainty in a fluctuating environment, its cooperative extension describes how individuals adapt their metabolic resource allocation strategy to further accommodate limited knowledge about the welfare of others within a community. The resulting theory explains why local regulation of metabolic cross-feeding can fulfil a community-wide metabolic objective if individuals take into consideration an ensemble measure of total population performance as the only form of global information. The latter is likely supplied by quorum sensing in microbial systems or signalling molecules such as hormones in multi-cellular eukaryotic organisms.

Metabolic modelling approaches for describing and engineering microbial communities

Microbes do not live in isolation but in microbial communities. The relevance of microbial communities is increasing due to growing awareness of their influence on a huge number of environmental, health and industrial processes. Hence, being able to control and engineer the output of both natural and synthetic communities would be of great interest. However, most of the available methods and biotechnological applications involving microorganisms, both in vivo and in silico, have been developed in the context of isolated microbes. In vivo microbial consortia development is extremely difficult and costly because it implies replicating suitable environments in the wet-lab. Computational approaches are thus a good, cost-effective alternative to study microbial communities, mainly via descriptive modelling, but also via engineering modelling. In this review we provide a detailed compilation of examples of engineered microbial communities and a comprehensive, historical revision of available computational metabolic modelling methods to better understand, and rationally engineer wild and synthetic microbial communities.

Microbial model communities: To understand complexity, harness the power of simplicity

Natural microbial communities are complex ecosystems with myriads of interactions. To deal with this complexity, we can apply lessons learned from the study of model organisms and try to find simpler systems that can shed light on the same questions. Here, microbial model communities are essential, as they can allow us to learn about the metabolic interactions, genetic mechanisms and ecological principles governing and structuring communities. A variety of microbial model communities of varying complexity have already been developed, representing different purposes, environments and phenomena. However, choosing a suitable model community for one's research question is no easy task. This review aims to be a guide in the selection process, which can help the researcher to select a sufficiently well-studied model community that also fulfills other relevant criteria. For example, a good model community should consist of species that are easy to grow, have been evaluated for community behaviors, provide simple readouts and - in some cases - be of relevance for natural ecosystems. Finally, there is a need to standardize growth conditions for microbial model communities and agree on definitions of community-specific phenomena and frameworks for community interactions. Such developments would be the key to harnessing the power of simplicity to start disentangling complex community interactions.

Multicellular and unicellular responses of microbial biofilms to stress

Biofilms are a ubiquitous mode of microbial life and display an increased tolerance to different stresses. Inside biofilms, cells may experience both externally applied stresses and internal stresses that emerge as a result of growth in spatially structured communities. In this review, we discuss the spatial scales of different stresses in the context of biofilms, and if cells in biofilms respond to these stresses as a collection of individual cells, or if there are multicellular properties associated with the response. Understanding the organizational level of stress responses in microbial communities can help to clarify multicellular functions of biofilms.

A guide to metabolic flux analysis in metabolic engineering: Methods, tools and applications

The field of metabolic engineering is primarily concerned with improving the biological production of value-added chemicals, fuels and pharmaceuticals through the design, construction and optimization of metabolic pathways, redirection of intracellular fluxes, and refinement of cellular properties relevant for industrial bioprocess implementation. Metabolic network models and metabolic fluxes are central concepts in metabolic engineering, as was emphasized in the first paper published in this journal, "Metabolic fluxes and metabolic engineering" (Metabolic Engineering, 1: 1-11, 1999). In the past two decades, a wide range of computational, analytical and experimental approaches have been developed to interrogate the capabilities of biological systems through analysis of metabolic network models using techniques such as flux balance analysis (FBA), and quantify metabolic fluxes using constrained-based modeling approaches such as metabolic flux analysis (MFA) and more advanced experimental techniques based on the use of stable-isotope tracers, i.e. ¹³C-metabolic flux analysis (¹³C-MFA). In this review, we describe the basic principles of metabolic flux analysis, discuss current best practices in flux quantification, highlight potential pitfalls and alternative approaches in the application of these tools, and give a broad overview of pragmatic applications of flux analysis in metabolic engineering practice.

A guide to deciphering microbial interactions and metabolic fluxes in microbiome communities

Microbiomes occupy nearly all environments on Earth. These communities of interacting microorganisms are highly complex, dynamic biological systems that impact and reshape the molecular composition of their habitats by performing complex biochemical transformations. The structure and function of microbiomes are influenced by local environmental stimuli and spatiotemporal changes. In order to control the dynamics and ultimately the function of microbiomes, we need to develop a mechanistic and quantitative understanding of the ecological, molecular, and evolutionary driving forces that govern these systems. Here, we describe recent advances in developing computational and experimental approaches that can promote a more fundamental understanding of microbial communities through comprehensive model-based analysis of heterogeneous data types across multiple scales, from intracellular metabolism, to metabolite cross-feeding interactions, to the emergent macroscopic behaviors. Ultimately, harnessing the full potential of microbiomes for practical applications will require developing new predictive modeling approaches and better tools to manipulate microbiome interactions.

Metabolic engineering of Escherichia coli for production of chemicals derived from the shikimate pathway

The shikimate pathway is indispensable for the biosynthesis of natural products with aromatic moieties. These products have wide current and potential applications in food, cosmetics and medicine, and consequently have great commercial value. However, compounds extracted from various plants or synthesized from petrochemicals no longer satisfy the requirements of contemporary industries. As a result, an increasing number of studies has focused on this pathway to enable the biotechnological manufacture of natural products, especially in E. coli. Furthermore, the development of synthetic biology, systems metabolic engineering and high flux screening techniques has also contributed to improving the biosynthesis of high-value compounds based on the shikimate pathway. Here, we review approaches based on a combination of traditional and new metabolic engineering strategies to increase the metabolic flux of the shikimate pathway. In addition, applications of this optimized pathway to produce aromatic amino acids and a range of natural products is also elaborated. Finally, this review sums up the opportunities and challenges facing this field.

Staring into the void: demystifying microbial metabolomics

Metabolites give us a window into the chemistry of microbes and are split into two subclasses: primary and secondary. Primary metabolites are required for life whereas secondary metabolites have historically been classified as those appearing after exponential growth and are not necessarily needed for survival. Many microbial species are estimated to produce hundreds of metabolites and can be affected by differing nutrients. Using various analytical techniques, metabolites can be directly detected in order to elucidate their biological significance. Currently, a single experiment can produce anywhere from megabytes to terabytes of data. This big data has motivated scientists to develop informatics tools to help target specific metabolites or sets of metabolites. Broadly, it is imperative to identify clear biological questions before embarking on a study of metabolites (metabolomics). For instance, studying the effect of a transposon insertion on phenazine biosynthesis in Pseudomonas is a very different from asking what molecules are present in a specific banana-derived strain of Pseudomonas. This review is meant to serve as a primer for a 'choose your own adventure' approach for microbiologists with limited mass spectrometry expertise, with a strong focus on liquid chromatography mass spectrometry based workflows developed or optimized within the past five years.

Metabolic Heterogeneity and Cross-Feeding in Bacterial Multicellular Systems

Cells in assemblages differentiate and perform distinct roles. Though many pathways of differentiation are understood at the molecular level in multicellular eukaryotes, the elucidation of similar processes in bacterial assemblages is recent and ongoing. Here, we discuss examples of bacterial differentiation, focusing on cases in which distinct metabolisms coexist and those that exhibit cross-feeding, with one subpopulation producing substrates that are metabolized by a second subpopulation. We describe several studies of single-species systems, then segue to studies of multispecies metabolic heterogeneity and cross-feeding in the clinical setting. Many of the studies described exemplify the application of new techniques and modeling approaches that provide insights into metabolic interactions relevant for bacterial growth outside the laboratory.

Emergent microscale gradients give rise to metabolic cross-feeding and antibiotic tolerance in clonal bacterial populations

Bacteria often live in spatially structured groups such as biofilms. In these groups, cells can collectively generate gradients through the uptake and release of compounds. In turn, individual cells adapt their activities to the environment shaped by the whole group. Here, we studied how these processes can generate phenotypic variation in clonal populations and how this variation contributes to the resilience of the population to antibiotics. We grew two-dimensional populations of Escherichia coli in microfluidic chambers where limiting amounts of glucose were supplied from one side. We found that the collective metabolic activity of cells created microscale gradients where nutrient concentration varied over a few cell lengths. As a result, growth rates and gene expression levels varied strongly between neighbouring cells. Furthermore, we found evidence for a metabolic cross-feeding interaction between glucose-fermenting and acetate-respiring subpopulations. Finally, we found that subpopulations of cells were able to survive an antibiotic pulse that was lethal in well-mixed conditions, likely due to the presence of a slow-growing subpopulation. Our work shows that emergent metabolic gradients can have important consequences for the functionality of bacterial populations as they create opportunities for metabolic interactions and increase the populations' tolerance to environmental stressors. This article is part of a discussion meeting issue 'Single cell ecology'.

High-resolution 13C metabolic flux analysis

Precise quantification of metabolic pathway fluxes in biological systems is of major importance in guiding efforts in metabolic engineering, biotechnology, microbiology, human health, and cell culture. ¹³C metabolic flux analysis (¹³C-MFA) is the predominant technique used for determining intracellular fluxes. Here, we present a protocol for ¹³C-MFA that incorporates recent advances in parallel labeling experiments, isotopic labeling measurements, and statistical analysis, as well as best practices developed through decades of experience. Experimental design to ensure that fluxes are estimated with the highest precision is an integral part of the protocol. The protocol is based on growing microbes in two (or more) parallel cultures with ¹³C-labeled glucose tracers, followed by gas chromatography-mass spectrometry (GC-MS) measurements of isotopic labeling of protein-bound amino acids, glycogen-bound glucose, and RNA-bound ribose. Fluxes are then estimated using software for ¹³C-MFA, such as Metran, followed by comprehensive statistical analysis to determine the goodness of fit and calculate confidence intervals of fluxes. The presented protocol can be completed in 4 d and quantifies metabolic fluxes with a standard deviation of ≤2%, a substantial improvement over previous implementations. The presented protocol is exemplified using an Escherichia coli ΔtpiA case study with full supporting data, providing a hands-on opportunity to step through a complex troubleshooting scenario. Although applications to prokaryotic microbial systems are emphasized, this protocol can be easily adjusted for application to eukaryotic organisms.

Metabolic activity affects the response of single cells to a nutrient switch in structured populations

Microbes live in ever-changing environments where they need to adapt their metabolism to different nutrient conditions. Many studies have characterized the response of genetically identical cells to nutrient switches in homogeneous cultures; however, in nature, microbes often live in spatially structured groups such as biofilms where cells can create metabolic gradients by consuming and releasing nutrients. Consequently, cells experience different local microenvironments and vary in their phenotype. How does this phenotypic variation affect the ability of cells to cope with nutrient switches? Here, we address this question by growing dense populations of Escherichia coli in microfluidic chambers and studying a switch from glucose to acetate at the single-cell level. Before the switch, cells vary in their metabolic activity: some grow on glucose, while others cross-feed on acetate. After the switch, only few cells can resume growth after a period of lag. The probability to resume growth depends on a cells' phenotype prior to the switch: it is highest for cells cross-feeding on acetate, while it depends in a non-monotonic way on the growth rate for cells growing on glucose. Our results suggest that the strong phenotypic variation in spatially structured populations might enhance their ability to cope with fluctuating environments.

Engineering microbial consortia by division of labor

During microbial applications, metabolic burdens can lead to a significant drop in cell performance. Novel synthetic biology tools or multi-step bioprocessing (e.g., fermentation followed by chemical conversions) are therefore needed to avoid compromised biochemical productivity from over-burdened cells. A possible solution to address metabolic burden is Division of Labor (DoL) via natural and synthetic microbial consortia. In particular, consolidated bioprocesses and metabolic cooperation for detoxification or cross feeding (e.g., vitamin C fermentation) have shown numerous successes in industrial level applications. However, distributing a metabolic pathway among proper hosts remains an engineering conundrum due to several challenges: complex subpopulation dynamics/interactions with a short time-window for stable production, suboptimal cultivation of microbial communities, proliferation of cheaters or low-producers, intermediate metabolite dilution, transport barriers between species, and breaks in metabolite channeling through biosynthesis pathways. To develop stable consortia, optimization of strain inoculations, nutritional divergence and crossing feeding, evolution of mutualistic growth, cell immobilization, and biosensors may potentially be used to control cell populations. Another opportunity is direct integration of non-bioprocesses (e.g., microbial electrosynthesis) to power cell metabolism and improve carbon efficiency. Additionally, metabolic modeling and ¹³C-metabolic flux analysis of mixed culture metabolism and cross-feeding offers a computational approach to complement experimental research for improved consortia performance.