Limits of entrainment of circadian neuronal networks.

Circadian rhythmicity lies at the center of various important physiological and behavioral processes in mammals, such as sleep, metabolism, homeostasis, mood changes, and more. Misalignment of intrinsic neuronal oscillations with the external day-night cycle can disrupt such processes and lead to numerous disorders. In this work, we computationally determine the limits of circadian synchronization to external light signals of different frequency, duty cycle, and simulated amplitude. Instead of modeling circadian dynamics with generic oscillator models (e.g., Kuramoto-type), we use a detailed computational neuroscience model, which integrates biomolecular dynamics, neuronal electrophysiology, and network effects. This allows us to investigate the effect of small drug molecules, such as Longdaysin, and connect our results with experimental findings. To combat the high dimensionality of such a detailed model, we employ a matrix-free approach, while our entire algorithmic pipeline enables numerical continuation and construction of bifurcation diagrams using only direct simulation. We, thus, computationally explore the effect of heterogeneity in the circadian neuronal network, as well as the effect of the corrective therapeutic intervention of Longdaysin. Last, we employ unsupervised learning to construct a data-driven embedding space for representing neuronal heterogeneity.

Computational modeling of the gut microbiota reveals putative metabolic mechanisms of recurrent Clostridioides difficile infection.

Approximately 30% of patients who have Clostridioides difficile infection (CDI) will suffer at least one incident of reinfection. While the underlying causes of CDI recurrence are poorly understood, interactions between C. difficile and commensal gut bacteria are thought to play an important role. In this study, an in silico pipeline was used to process 16S rRNA gene amplicon sequence data of 225 stool samples from 93 CDI patients into sample-specific models of bacterial community metabolism. Clustered metabolite production rates generated from post-diagnosis samples generated a high Enterobacteriaceae abundance cluster containing disproportionately large numbers of recurrent samples and patients. This cluster was predicted to have significantly reduced capabilities for secondary bile acid synthesis but elevated capabilities for aromatic amino acid catabolism. When applied to 16S sequence data of 40 samples from fecal microbiota transplantation (FMT) patients suffering from recurrent CDI and their stool donors, the community modeling method generated a high Enterobacteriaceae abundance cluster with a disproportionate large number of pre-FMT samples. This cluster also was predicted to exhibit reduced secondary bile acid synthesis and elevated aromatic amino acid catabolism. Collectively, these in silico predictions suggest that Enterobacteriaceae may create a gut environment favorable for C. difficile spore germination and/or toxin synthesis.

Incorporating hydrodynamics into spatiotemporal metabolic models of bubble column gas fermentation.

Gas fermentation has emerged as a technologically and economically attractive option for producing renewable fuels and chemicals from carbon monoxide (CO) rich waste streams. LanzaTech has developed a proprietary strain of the gas fermentating acetogen Clostridium autoethanogenum as a microbial platform for synthesizing ethanol, 2,3-butanediol, and other chemicals. Bubble column reactor technology is being developed for the large-scale production, motivating the investigation of multiphase reactor hydrodynamics. In this study, we combined hydrodynamics with a genome-scale reconstruction of C. autoethanogenum metabolism and multiphase convection-dispersion equations to compare the performance of bubble column reactors with and without liquid recycle. For both reactor configurations, hydrodynamics was predicted to diminish bubble column performance with respect to CO conversion, biomass production, and ethanol production when compared with bubble column models in which the gas phase was modeled as ideal plug flow plus axial dispersion. Liquid recycle was predicted to be advantageous by increasing CO conversion, biomass production, and ethanol and 2,3-butanediol production compared with the non-recycle reactor configuration. Parametric studies performed for the liquid recycle configuration with two-phase hydrodynamics showed that increased CO feed flow rates (more gas supply), smaller CO gas bubbles (more gas-liquid mass transfer), and shorter column heights (more gas per volume of liquid per time) favored ethanol production over acetate production. Our computational results demonstrate the power of combining cellular metabolic models and two-phase hydrodynamics for simulating and optimizing gas fermentation reactors.

Suboptimal community growth mediated through metabolite crossfeeding promotes species diversity in the gut microbiota.

The gut microbiota represent a highly complex ecosystem comprised of approximately 1000 species that forms a mutualistic relationship with the human host. A critical attribute of the microbiota is high species diversity, which provides system robustness through overlapping and redundant metabolic capabilities. The gradual loss of bacterial diversity has been associated with a broad array of gut pathologies and diseases including malnutrition, obesity, diabetes and inflammatory bowel disease. We formulated an in silico community model of the gut microbiota by combining genome-scale metabolic reconstructions of 28 representative species to explore the relationship between species diversity and community growth. While the individual species offered a broad range of metabolic capabilities, communities optimized for maximal growth on simulated Western and high-fiber diets had low diversities and imbalances in short-chain fatty acid (SCFA) synthesis characterized by acetate overproduction. Community flux variability analysis performed with the 28-species model and a reduced 20-species model suggested that enhanced species diversity and more balanced SCFA production were achievable at suboptimal growth rates. We developed a simple method for constraining species abundances to sample the growth-diversity tradeoff and used the 20-species model to show that tradeoff curves for Western and high-fiber diets resembled Pareto-optimal surfaces. Compared to maximal growth solutions, suboptimal growth solutions were characterized by higher species diversity, more balanced SCFA synthesis and lower exchange rates of crossfed metabolites between more species. We hypothesized that modulation of crossfeeding relationships through host-microbiota interactions could be an important means for maintaining species diversity and suggest that community metabolic modeling approaches that allow multiobjective optimization of growth and diversity are needed for more realistic simulation of complex communities.

Metabolic modeling of bacterial co-culture systems predicts enhanced carbon monoxide-to-butyrate conversion compared to monoculture systems.

We used metabolic modeling to computationally investigate the potential of bacterial coculture system designs for CO conversion to the platform chemical butyrate. By taking advantage of the native capabilities of wild-type strains, we developed two anaerobic coculture designs by combining Clostridium autoethanogenum for CO-to-acetate conversion with bacterial strains that offer high acetate-to-butyrate conversion capabilities: the environmental bacterium the human gut bacteriumEubacterium rectale. When grown in continuous stirred tank reactor on a 70/0/30 CO/H2/N2 gas mixture, the C. autoethanogenum-C Kluyveri co-culture was predicted to offer no mprovement in butyrate volumetric productivity compared to an engineered C. autoethanogenum monoculture despite utilizing vinyl acetate as a secondary carbon source for C. kluyveri growth enhancement. A coculture consisting of C. autoethanogenum and C. kluyveri engineered in silico to eliminate hexanoate synthesis was predicted to enhance both butyrate productivity and titer. The C. autoethanogenum-E. rectale coculture offered similar improvements in butyrate productivity without the need for metabolic engineering when glucose was provided as a secondary carbon source to enhance E. rectale growth. A bubble column model developed to assess the potential for large-scale butyrate production of the C. autoethanogenum-E. rectale design predicted that a 40/30/30 CO/H2/N2 gas mixture and a 5 m column length would be preferred to enhance C. autoethanogenum growth and counteract CO inhibitory effects on E. rectale.

Predicting the performance of pressure filtration processes by coupling computational fluid dynamics and discrete element methods.

To obtain a fundamental understanding of the various factors affecting pressure filtration performance, we developed a coupled computational fluid dynamics (CFD) and discrete element method (DEM) model for simulating the effect of solvent flow through the solid particle cake. The model was validated using data collected by filtering mixtures of spherical glass beads and deionized water through a dead-end cell over a range of applied pressures. Numerical experiments were performed to study the effects of particle properties, liquid properties and operating conditions on filtration performance. The model predicted that the filtrate flow rate could be strongly affected by the mean size of the particles, the presence of small particles (i.e. fines) in the particle distribution, the viscosity of the liquid, and particle deformation leading to cake compression. Our study demonstrated that CFD-DEM modeling is a powerful approach for understanding cake filtration processes and predicting filtration performance.

Competitive resource allocation to metabolic pathways contributes to overflow metabolisms and emergent properties in cross-feeding microbial consortia.

Resource scarcity is a common stress in nature and has a major impact on microbial physiology. This review highlights microbial acclimations to resource scarcity, focusing on resource investment strategies for chemoheterotrophs from the molecular level to the pathway level. Competitive resource allocation strategies often lead to a phenotype known as overflow metabolism; the resulting overflow byproducts can stabilize cooperative interactions in microbial communities and can lead to cross-feeding consortia. These consortia can exhibit emergent properties such as enhanced resource usage and biomass productivity. The literature distilled here draws parallels between in silico and laboratory studies and ties them together with ecological theories to better understand microbial stress responses and mutualistic consortia functioning.

In silico metabolic engineering of Clostridium ljungdahlii for synthesis gas fermentation.

Synthesis gas fermentation is one of the most promising routes to convert synthesis gas (syngas; mainly comprised of H2 and CO) to renewable liquid fuels and chemicals by specialized bacteria. The most commonly studied syngas fermenting bacterium is Clostridium ljungdahlii, which produces acetate and ethanol as its primary metabolic byproducts. Engineering of C. ljungdahlii metabolism to overproduce ethanol, enhance the synthesize of the native byproducts lactate and 2,3-butanediol, and introduce the synthesis of non-native products such as butanol and butyrate has substantial commercial value. We performed in silico metabolic engineering studies using a genome-scale reconstruction of C. ljungdahlii metabolism and the OptKnock computational framework to identify gene knockouts that were predicted to enhance the synthesis of these native products and non-native products, introduced through insertion of the necessary heterologous pathways. The OptKnock derived strategies were often difficult to assess because increase product synthesis was invariably accompanied by decreased growth. Therefore, the OptKnock strategies were further evaluated using a spatiotemporal metabolic model of a syngas bubble column reactor, a popular technology for large-scale gas fermentation. Unlike flux balance analysis, the bubble column model accounted for the complex tradeoffs between increased product synthesis and reduced growth rates of engineered mutants within the spatially varying column environment. The two-stage methodology for deriving and evaluating metabolic engineering strategies was shown to yield new C. ljungdahlii gene targets that offer the potential for increased product synthesis under realistic syngas fermentation conditions.

Inhibitory and excitatory networks balance cell coupling in the suprachiasmatic nucleus: A modeling approach.

Neuronal coupling contributes to circadian rhythms formation in the suprachiasmatic nucleus (SCN). While the neurotransmitter vasoactive intestinal polypeptide (VIP) is considered essential for synchronizing the oscillations of individual neurons, γ-aminobutyric acid (GABA) does not have a clear functional role despite being highly concentrated in the SCN. While most studies have examined the role of either GABA or VIP, our mathematical modeling approach explored their interplay on networks of SCN neurons. Tuning the parameters that control the release of GABA and VIP enabled us to optimize network synchrony, which was achieved at a peak firing rate during the subjective day of about 7Hz. Furthermore, VIP and GABA modulation could adjust network rhythm amplitude and period without sacrificing synchrony. We also performed simulations of SCN networks to phase shifts during 12h:12h light-dark cycles and showed that GABA networks reduced the average time for the SCN model to re-synchronize. We hypothesized that VIP and GABA balance cell coupling in the SCN to promote synchronization of heterogeneous oscillators while allowing flexibility for adjustment to environmental changes.

Genome-scale modelling of microbial metabolism with temporal and spatial resolution.

Most natural microbial systems have evolved to function in environments with temporal and spatial variations. A major limitation to understanding such complex systems is the lack of mathematical modelling frameworks that connect the genomes of individual species and temporal and spatial variations in the environment to system behaviour. The goal of this review is to introduce the emerging field of spatiotemporal metabolic modelling based on genome-scale reconstructions of microbial metabolism. The extension of flux balance analysis (FBA) to account for both temporal and spatial variations in the environment is termed spatiotemporal FBA (SFBA). Following a brief overview of FBA and its established dynamic extension, the SFBA problem is introduced and recent progress is described. Three case studies are reviewed to illustrate the current state-of-the-art and possible future research directions are outlined. The author posits that SFBA is the next frontier for microbial metabolic modelling and a rapid increase in methods development and system applications is anticipated.

Dynamic model-based analysis of furfural and HMF detoxification by pure and mixed batch cultures of S. cerevisiae and S. stipitis.

Inhibitory compounds that result from biomass hydrolysis are an obstacle to the efficient production of second-generation biofuels. Fermentative microorganisms can reduce compounds such as furfural and 5-hydroxymethyl furfural (HMF), but detoxification is accompanied by reduced growth rates and ethanol yields. In this study, we assess the effects of these furan aldehydes on pure and mixed yeast cultures consisting of a respiratory deficient mutant of Saccharomyces cerevisiae and wild-type Scheffersomyces stipitis using dynamic flux balance analysis. Uptake kinetics and stoichiometric equations for the intracellular reduction reactions associated with each inhibitor were added to genome-scale metabolic reconstructions of the two yeasts. Further modification of the S. cerevisiae metabolic network was necessary to satisfactorily predict the amount of acetate synthesized during HMF reduction. Inhibitory terms that captured the adverse effects of the furan aldehydes and their corresponding alcohols on cell growth and ethanol production were added to attain qualitative agreement with batch experiments conducted for model development and validation. When the two yeasts were co-cultured in the presence of the furan aldehydes, inoculums that reduced the synthesis of highly toxic acetate produced by S. cerevisiae yielded the highest ethanol productivities. The model described here can be used to generate optimal fermentation strategies for the simultaneous detoxification and fermentation of lignocellulosic hydrolysates by S. cerevisiae and/or S. stipitis.

A population balance equation model of aggregation dynamics in Taxus suspension cell cultures.

The nature of plant cells to grow as multicellular aggregates in suspension culture has profound effects on bioprocess performance. Recent advances in the measurement of plant cell aggregate size allow for routine process monitoring of this property. We have exploited this capability to develop a conceptual model to describe changes in the aggregate size distribution that are observed over the course of a Taxus cell suspension batch culture. We utilized the population balance equation framework to describe plant cell aggregates as a particulate system, accounting for the relevant phenomenological processes underlying aggregation, such as growth and breakage. We compared model predictions to experimental data to select appropriate kernel functions, and found that larger aggregates had a higher breakage rate, biomass was partitioned asymmetrically following a breakage event, and aggregates grew exponentially. Our model was then validated against several datasets with different initial aggregate size distributions and was able to quantitatively predict changes in total biomass and mean aggregate size, as well as actual size distributions. We proposed a breakage mechanism where a fraction of biomass was lost upon each breakage event, and demonstrated that even though smaller aggregates have been shown to produce more paclitaxel, an optimum breakage rate was predicted for maximum paclitaxel accumulation. We believe this is the first model to use a segregated, corpuscular approach to describe changes in the size distribution of plant cell aggregates, and represents an important first step in the design of rational strategies to control aggregation and optimize process performance.

Dynamic flux balance modeling of S. cerevisiae and E. coli co-cultures for efficient consumption of glucose/xylose mixtures.

Current researches into the production of biochemicals from lignocellulosic feedstocks are focused on the identification and engineering of individual microbes that utilize complex sugar mixtures. Microbial consortia represent an alternative approach that has the potential to better exploit individual species capabilities for substrate uptake and biochemical production. In this work, we construct and experimentally validate a dynamic flux balance model of a Saccharomyces cerevisiae and Escherichia coli co-culture designed for efficient aerobic consumption of glucose/xylose mixtures. Each microbe is a substrate specialist, with wild-type S. cerevisiae consuming only glucose and engineered E. coli strain ZSC113 consuming only xylose, to avoid diauxic growth commonly observed in individual microbes. Following experimental identification of a common pH and temperature for optimal co-culture batch growth, we demonstrate that pure culture models developed for optimal growth conditions can be adapted to the suboptimal, common growth condition by adjustment of the non-growth associated ATP maintenance of each microbe. By comparing pure culture model predictions to co-culture experimental data, the inhibitory effect of ethanol produced by S. cerevisiae on E. coli growth was found to be the only interaction necessary to include in the co-culture model to generate accurate batch profile predictions. Co-culture model utility was demonstrated by predicting initial cell concentrations that yield simultaneous glucose and xylose exhaustion for different sugar mixtures. Successful experimental validation of the model predictions demonstrated that steady-state metabolic reconstructions developed for individual microbes can be adapted to develop dynamic flux balance models of microbial consortia for the production of renewable chemicals.

Multicellular model for intercellular synchronization in circadian neural networks.

We developed a multicellular model characterized by a high degree of heterogeneity to investigate possible mechanisms that underlie circadian network synchronization and rhythmicity in the suprachiasmatic nucleus (SCN). We populated a two-dimensional grid with 400 model neurons coupled via γ-aminobutyric acid (GABA) and vasoactive intestinal polypeptide (VIP) neurotransmitters through a putative Ca(2+) mediated signaling cascade to investigate their roles in gene expression and electrical firing activity of cell populations. As observed experimentally, our model predicted that GABA would affect the amplitude of circadian oscillations but not synchrony among individual oscillators. Our model recapitulated experimental findings of decreased synchrony and average periods, loss of rhythmicity, and reduced circadian amplitudes as VIP signaling was eliminated. In addition, simulated increases of VIP reduced periodicity and synchrony. We therefore postulated a physiological range of VIP within which the system is able to produce sustained and synchronized oscillations. Our model recapitulated experimental findings of diminished amplitudes and periodicity with decreasing intracellular Ca(2+) concentrations, suggesting that such behavior could be due to simultaneous decrease of individual oscillation amplitudes and population synchrony. Simulated increases in Cl(-) levels resulted in increased Cl(-) influx into the cytosol, a decrease of inhibitory postsynaptic currents, and ultimately a shift of GABA-elicited responses from inhibitory to excitatory. The simultaneous reduction of IPSCs and increase in membrane resting potential produced GABA dose-dependent increases in firing rates across the population, as has been observed experimentally. By integrating circadian gene regulation and electrophysiology with intracellular and intercellular signaling, we were able to develop the first (to our knowledge) multicellular model that allows the effects of clock genes, electrical firing, Ca(2+), GABA, and VIP on circadian system behavior to be predicted.

Dynamic flux balance modeling of microbial co-cultures for efficient batch fermentation of glucose and xylose mixtures.

Sequential uptake of pentose and hexose sugars that compose lignocellulosic biomass limits the ability of pure microbial cultures to efficiently produce value-added bioproducts. In this work, we used dynamic flux balance modeling to examine the capability of mixed cultures of substrate-selective microbes to improve the utilization of glucose/xylose mixtures and to convert these mixed substrates into products. Co-culture simulations of Escherichia coli strains ALS1008 and ZSC113, engineered for glucose and xylose only uptake respectively, indicated that improvements in batch substrate consumption observed in previous experimental studies resulted primarily from an increase in ZSC113 xylose uptake relative to wild-type E. coli. The E. coli strain ZSC113 engineered for the elimination of glucose uptake was computationally co-cultured with wild-type Saccharomyces cerevisiae, which can only metabolize glucose, to determine if the co-culture was capable of enhanced ethanol production compared to pure cultures of wild-type E. coli and the S. cerevisiae strain RWB218 engineered for combined glucose and xylose uptake. Under the simplifying assumption that both microbes grow optimally under common environmental conditions, optimization of the strain inoculum and the aerobic to anaerobic switching time produced an almost twofold increase in ethanol productivity over the pure cultures. To examine the effect of reduced strain growth rates at non-optimal pH and temperature values, a break even analysis was performed to determine possible reductions in individual strain substrate uptake rates that resulted in the same predicted ethanol productivity as the best pure culture.

A multicellular model for differential regulation of circadian signals in the core and shell regions of the suprachiasmatic nucleus.

We developed a multicellular model of the mammalian circadian clock characterized by a high degree of heterogeneity with respect to single cell periodicity and behavior (intrinsic and driven oscillators), neurotransmitter release (VIP, GABA and glutamate synthesis) and spatial organization (core and shell regions), mimicking structural patterns within the suprachiasmatic nucleus (SCN) associated with distinct circadian functions. We simulated the SCN core and shell separately utilizing experimentally derived connectivity schemes for the two subdivisions as observed within the rat SCN. The core was modeled via a small world network characterized by VIP and GABA co-localization, whereas the shell was simulated as a nearest neighbor network promoting local GABAergic connections. To study the function of the axonal plexus extending from the densely innervated ventrolateral region to distal areas across the dorsomedial SCN, directed long range links from the core to the shell were gradually introduced via a probability p(cs) that ranged from 0 to 1. A probability value of 0 excluded core-shell interactions, whereas p(cs)=1 achieved maximal connectivity between the two regions. Our model exhibited a threshold in the number of core-to-shell links required for sufficient cell-to-cell coordination to maintain periodicity and rhythmic behavior across the entire model network (including both shell and core populations) in constant darkness as well as 12:12h light-dark cycles. By contrast, constant light was shown to increase phase synchronization across the shell while core populations remained poorly synchronized, suggesting differential light response across the two SCN compartments. We further simulated increasing percentages of intrinsic oscillators and demonstrated a negative correlation between the number of intrinsic oscillators distributed across the SCN and the ability of the system to produce synchronized signals. Simulations that differed with respect to the placement of intrinsic oscillators supported the hypothesis that improved synchronization is achieved with networks characterized by localized intrinsic oscillators placed exclusively within the shell versus networks containing uniformly distributed intrinsic oscillators in both SCN compartments. This study has successfully reproduced a number of spatiotemporal and behavioral attributes of the SCN, providing a useful computational tool to correlate observed circadian phenotypes with distinct chemoarchitectural properties of spatially localized neural populations.

A multiscale model to investigate circadian rhythmicity of pacemaker neurons in the suprachiasmatic nucleus.

The suprachiasmatic nucleus (SCN) of the hypothalamus is a multicellular system that drives daily rhythms in mammalian behavior and physiology. Although the gene regulatory network that produces daily oscillations within individual neurons is well characterized, less is known about the electrophysiology of the SCN cells and how firing rate correlates with circadian gene expression. We developed a firing rate code model to incorporate known electrophysiological properties of SCN pacemaker cells, including circadian dependent changes in membrane voltage and ion conductances. Calcium dynamics were included in the model as the putative link between electrical firing and gene expression. Individual ion currents exhibited oscillatory patterns matching experimental data both in current levels and phase relationships. VIP and GABA neurotransmitters, which encode synaptic signals across the SCN, were found to play critical roles in daily oscillations of membrane excitability and gene expression. Blocking various mechanisms of intracellular calcium accumulation by simulated pharmacological agents (nimodipine, IP3- and ryanodine-blockers) reproduced experimentally observed trends in firing rate dynamics and core-clock gene transcription. The intracellular calcium concentration was shown to regulate diverse circadian processes such as firing frequency, gene expression and system periodicity. The model predicted a direct relationship between firing frequency and gene expression amplitudes, demonstrated the importance of intracellular pathways for single cell behavior and provided a novel multiscale framework which captured characteristics of the SCN at both the electrophysiological and gene regulatory levels.

Interrogation of the perturbed gut microbiota in gouty arthritis patients through in silico metabolic modeling.

Recent studies have shown perturbed gut microbiota associated with gouty arthritis, a metabolic disease characterized by an imbalance between uric acid production and excretion. To mechanistically investigate altered microbiota metabolism associated with gout disease, 16S rRNA gene amplicon sequence data from stool samples of gout patients and healthy controls were computationally analyzed through bacterial community metabolic models. Patient-specific community models constructed with the metagenomics modeling pipeline, mgPipe, were used to perform k-means clustering of samples according to their metabolic capabilities. The clustering analysis generated statistically significant partitioning of samples into a Bacteroides-dominated, high gout cluster and a Faecalibacterium-elevated, low gout cluster. The high gout cluster was predicted to allow elevated synthesis of the amino acids D-alanine and L-alanine and byproducts of branched-chain amino acid catabolism, while the low gout cluster allowed higher production of butyrate, the sulfur-containing amino acids L-cysteine and L-methionine, and the L-cysteine catabolic product H2S. By expanding the capabilities of mgPipe to provide taxa-level resolution of metabolite exchange rates, acetate, D-lactate and succinate exchanged from Bacteroides to Faecalibacterium were predicted to enhance butyrate production in the low gout cluster. Model predictions suggested that sulfur-containing amino acid metabolism generally and H2S more specifically could be novel gout disease markers.

Metabolic Modeling to Interrogate Microbial Disease: A Tale for Experimentalists.

The explosion of microbiome analyses has helped identify individual microorganisms and microbial communities driving human health and disease, but how these communities function is still an open question. For example, the role for the incredibly complex metabolic interactions among microbial species cannot easily be resolved by current experimental approaches such as 16S rRNA gene sequencing, metagenomics and/or metabolomics. Resolving such metabolic interactions is particularly challenging in the context of polymicrobial communities where metabolite exchange has been reported to impact key bacterial traits such as virulence and antibiotic treatment efficacy. As novel approaches are needed to pinpoint microbial determinants responsible for impacting community function in the context of human health and to facilitate the development of novel anti-infective and antimicrobial drugs, here we review, from the viewpoint of experimentalists, the latest advances in metabolic modeling, a computational method capable of predicting metabolic capabilities and interactions from individual microorganisms to complex ecological systems. We use selected examples from the literature to illustrate how metabolic modeling has been utilized, in combination with experiments, to better understand microbial community function. Finally, we propose how such combined, cross-disciplinary efforts can be utilized to drive laboratory work and drug discovery moving forward.

One versus Many: Polymicrobial Communities and the Cystic Fibrosis Airway.

Culture-independent studies have revealed that chronic lung infections in persons with cystic fibrosis (pwCF) are rarely limited to one microbial species. Interactions among bacterial members of these polymicrobial communities in the airways of pwCF have been reported to modulate clinically relevant phenotypes. Furthermore, it is clear that a single polymicrobial community in the context of CF airway infections cannot explain the diversity of clinical outcomes. While large 16S rRNA gene-based studies have allowed us to gain insight into the microbial composition and predicted functional capacities of communities found in the CF lung, here we argue that in silico approaches can help build clinically relevant in vitro models of polymicrobial communities that can in turn be used to experimentally test and validate computationally generated hypotheses. Furthermore, we posit that combining computational and experimental approaches will enhance our understanding of mechanisms that drive microbial community function and identify new therapeutics to target polymicrobial infections.