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Segre, D.

Publications and source records attributed to Segre, D..

At least 37 records · Page 2Linked to original sources
bioRxiv2022

Growth instabilities shape morphology and genetic diversity of cellular aggregates

Cellular aggregates assume an incredible variety of shapes ranging from circular molds to irregular tumors. While we understand many of the mechanisms responsible for these spatial patterns, little is known about how the shape of an aggregate influences its ecology and evolution. Here, we investigate this relationship in the context of microbial colonies grown on hard agar plates. This a well-studied system that exhibits a transition from smooth circular disks to more irregular and rugged shapes as either the nutrient concentration or cellular motility is decreased. Starting from a mechanistic model of colony growth, we identify two dimensionless quantities that determine how morphology and genetic diversity of the population depend on the model parameters. Our simulations further reveal that population dynamics cannot be accurately described by the commonly-used surface growth models. Instead, one has to explicitly account for the emergent growth instabilities and demographic fluctuations. Overall, our work links together environmental conditions, colony morphology, and evolution. This link is essential for a rational design of concrete, biophysical perturbations to steer evolution in the desired direction.

biophysics↗
bioRxiv2022

Metabolic phenotyping of marine heterotrophs on refactored media reveals diverse metabolic adaptations and lifestyle strategies

Microbial communities, through their metabolism, drive carbon cycling in marine environments. These complex communities are composed of many different microorganisms including heterotrophic bacteria, each with its own nutritional needs and metabolic capabilities. Yet, models of ecosystem processes typically treat heterotrophic bacteria as a "black box", which does not resolve metabolic heterogeneity nor address ecologically important processes such as the successive modification of different types of organic matter. Here we directly address the heterogeneity of metabolism by characterizing the carbon source utilization preferences of 63 heterotrophic bacteria representative of several major marine clades. By systematically growing these bacteria on 10 media containing specific subsets of carbon sources found in marine biomass, we obtained a phenotypic fingerprint that we used to explore the relationship between metabolic preferences and phylogenetic or genomic features. At the class level, these bacteria display broadly conserved patterns of preference for different carbon sources. Despite these broad taxonomic trends, growth profiles correlate poorly with phylogenetic distance or genome-wide gene content. However, metabolic preferences are strongly predicted by a handful of key enzymes that preferentially belong to a few enriched metabolic pathways, such as those involved in glyoxylate metabolism and biofilm formation. We find that enriched pathways point to enzymes directly involved in the metabolism of the corresponding carbon source and suggest potential associations between metabolic preferences and other ecologically-relevant traits. The availability of systematic phenotypes across multiple synthetic media constitutes a valuable resource for future quantitative modeling efforts and systematic studies of inter-species interactions. ImportanceHalf of the Earths annual primary production is carried out by phytoplankton in the surface ocean. However, this metabolic activity is heavily impacted by heterotrophic bacteria, which dominate the transformation of organic matter released from phytoplankton. Here, we characterize the diversity of metabolic preferences across many representative heterotrophs by systematically growing them on different fractions of dissolved organic carbon (DOC). Our analysis suggests that different clades of bacteria have substantially distinct preferences for specific carbon sources, in a way that cannot be simply mapped onto phylogeny. These preferences are associated with the presence of specific genes and pathways, reflecting an association between metabolic capabilities and ecological lifestyles. In addition to helping understand the importance of heterotrophs under different conditions, the phenotypic fingerprint we obtained can help build higher resolution quantitative models of global microbial activity and biogeochemical cycles in the oceans.

microbiology↗
bioRxiv2021

Optimality of extracellular enzyme production and activity in dynamic flux balance modeling

In microbial communities, many vital metabolic functions, including the degradation of cellulose, proteins and other complex macromolecules, are carried out by costly, extracellularly secreted enzymes. While significant effort has been dedicated to analyzing genome-scale metabolic networks for individual microbes and communities, little is known about the interplay between global allocation of metabolic resources in the cell and extracellular enzyme secretion and activity. Here we introduce a method for modeling the secretion and catalytic functions of extracellular enzymes using dynamic flux balance analysis. This new addition, implemented within COMETS (Computation Of Microbial Ecosystems in Time and Space), simulates the costly production and secretion of enzymes and their diffusion and activity throughout the environment, independent of the producing organism. After tuning our model based on data for a Saccharomyces cerevisiae strain engineered to produce exogenous cellulases, we explored the dynamics of the system at different cellulose concentrations and enzyme production rates. We found that there are distinct rates of constitutive enzyme secretion which maximize either growth rate or biomass yield. These optimal rates are strongly dependent on enzyme kinetic properties and environmental conditions, including the amount of cellulose substrate available. Our framework will facilitate the development of more realistic simulations of microbial community dynamics within environments rich in complex macromolecules, with applications in the study of soil and plant-associated ecosystems, and other natural and engineered microbiomes. ImportanceMany organisms - including soil, marine and human-associated bacteria and fungi - perform part of their metabolic functions outside of the boundary of the cell, through the secretion of extracellular enzymes that can diffuse and facilitate reactions independently of the organism that produced them. In order to better understand and predict microbial ecosystems, it would be helpful to create mathematical models incorporating these extracellular reactions within simulations of metabolism at the whole-cell level. In this paper we demonstrate the implementation of such a methodology and apply it to study a cellulase-secreting yeast. This work will be useful for a number of microbial ecology applications, including modeling of microbiome dynamics, engineering of bioproducts (e.g. biofuels) from plant biomass through synthetic communities or modified organisms, and testing of basic ecological hypotheses about the balance between cost and benefits of the production of common goods in microbial communities.

systems biology↗
bioRxiv2021

Thermodynamics drives coenzyme redundancy in metabolism

Coenzymes distribute a variety of chemical moieties throughout cellular metabolism, participating in group (e.g., phosphate, acyl) and electron transfer. For a variety of reactions requiring acceptors or donors of specific resources, there often exist degenerate sets of molecules (e.g., NAD(H) and NADP(H)) that carry out similar functions. Although the physiological roles of various coenzyme systems are well established, it is unclear what selective pressures may have driven the emergence of coenzyme redundancy. Here we use genome-wide metabolic modeling approaches to decompose the selective pressures driving enzymatic specificity for either NAD(H) or NADP(H) in the metabolic network of Escherichia coli. We found that few enzymes are thermodynamically constrained to using a single coenzyme, and in principle, a metabolic network relying on only NAD(H) is feasible. However, structural and sequence analyses revealed widespread conservation of residues that retain selectivity for either NAD(H) or NADP(H), suggesting that additional forces may shape specificity. Using a model accounting for the cost of oxidoreductase enzyme expression, we found that coenzyme redundancy universally reduces the minimal amount of protein required to catalyze coenzyme-coupled reactions, inducing individual reactions to strongly prefer one coenzyme over another when reactions are near thermodynamic equilibrium. We propose that protein minimization generically promotes coenzyme redundancy, and that coenzymes typically thought to exist in a single pool (e.g., CoA) may exist in more than one form (e.g., dephospho-CoA). Significance statementMetabolism relies on a small class of molecules (coenzymes) that serve as universal donors and acceptors of key chemical groups and electrons. Although metabolic networks crucially depend on structurally redundant coenzymes (e.g., NAD(H) and NADP(H)) associated with different enzymes, the criteria that led to the emergence of this redundancy remain poorly understood. Our combination of modeling, and structural and sequence analysis indicates that coenzyme redundancy is not essential for metabolism, but rather an evolved strategy promoting efficient usage of enzymes when biochemical reactions are near equilibrium. Our work suggests that early metabolism may have operated with fewer coenzymes, and that adaptation for metabolic efficiency may have driven the rise of coenzyme diversity in living systems.

systems biology↗
bioRxiv2021

A co-culture microplate for real-time measurement of microbial interactions

The dynamic structures of microbial communities emerge from the complex network of interactions between their constituent microbial organisms. Quantitative measurements of these microbial interactions are important for understanding and engineering microbial community structure. Here, we present the development and application of the BioMe plate, a redesigned microplate device in which pairs of wells are separated by porous membranes. BioMe facilitates the measurement of dynamic microbial interactions and integrates easily with standard laboratory equipment. We first applied BioMe to recapitulate recently characterized, natural symbiotic interactions between bacteria isolated from the D. melanogaster gut microbiome. Specifically, the BioMe plate allowed us to observe the benefit provided by two Lactobacilli strains to an Acetobacter strain. We next explored the use of BioMe to gain quantitative insight into the engineered obligate syntrophic interaction between a pair of E. coli amino acid auxotrophs. We integrated experimental observations with a mechanistic computational model to quantify key parameters associated with this syntrophic interaction, including metabolite secretion and diffusion rates. This model also allowed us to explain the slow growth observed for auxotrophs growing in adjacent wells, by demonstrating that under the relevant range of parameters, local exchange between auxotrophs is essential for efficient growth. The BioMe plate provides a scalable and flexible approach for the study of dynamic microbial interactions.

bioengineering↗
bioRxiv2020

An evolutionary algorithm for designing microbial communities via environmental modification

Despite a growing understanding of how environmental composition affects microbial community properties, it remains difficult to apply this knowledge to the rational design of synthetic multispecies consortia. This is because natural microbial communities can harbor thousands of different organisms and environmental substrates, making up a vast combinatorial space that precludes exhaustive experimental testing and computational prediction. Here, we present a method based on the combination of machine learning and dynamic flux balance analysis (dFBA) that selects optimal environmental compositions to produce target community phenotypes. In this framework, dFBA is used to model the growth of a community in candidate environments. A genetic algorithm is then used to evaluate the behavior of the community relative to a target phenotype, and subsequently adjust the environment to allow the organisms to more closely approach this target. We apply this iterative process to in silico communities of varying sizes, showing how it can rapidly identify environments that yield desired phenotypes. Moreover, this novel combination of approaches produces testable predictions for the in vivo assembly of microbial communities with specific properties, and can facilitate rational environmental design processes for complex microbiomes.

bioinformatics↗
bioRxiv2020

Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation

Microbes tend to organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these microbial communities. These snapshots, when collected from multiple samples, have the potential to reveal which microbes co-occur, providing a glimpse into the network of associations in these communities. The inference of networks from 16S data is prone to statistical artifacts. There are many tools for performing each step of the 16S analysis workflow, but the extent to which these steps affect the final network is still unclear. In this study, we perform a meticulous analysis of each step of a pipeline that can convert 16S sequencing data into a network of microbial associations. Through this process, we map how different choices of algorithms and parameters affect the co-occurrence network and estimate steps that contribute most significantly to the variance. We further determine the tools and parameters that generate the most accurate and robust co-occurrence networks based on comparison with mock and synthetic datasets. Ultimately, we develop a standardized pipeline (available at https://github.com/segrelab/MiCoNE) that follows these default tools and parameters, but that can also help explore the outcome of any other combination of choices. We envisage that this pipeline could be used for integrating multiple data-sets, and for generating comparative analyses and consensus networks that can help understand and control microbial community assembly in different biomes. ImportanceTo understand and control the mechanisms that determine the structure and function of microbial communities, it is important to map the interrelationships between its constituent microbial species. The surge in the high-throughput sequencing of microbial communities has led to the creation of thousands of datasets containing information about microbial abundances. These abundances can be transformed into networks of co-occurrences across multiple samples, providing a glimpse into the structure of microbiomes. However, processing these datasets to obtain co-occurrence information relies on several complex steps, each of which involves multiple choices of tools and corresponding parameters. These multiple options pose questions about the accuracy and uniqueness of the inferred networks. In this study, we address this workflow and provide a systematic analysis of how these choices of tools and parameters affect the final network, and on how to select those that are most appropriate for a particular dataset.

microbiology↗
bioRxiv2020

Stoichiometric Modelling of String Chemistries

Uncovering the general principles that govern the architecture of metabolic networks is key to understanding the emergence and evolution of living systems. Artificial chemistries, in silico representations of chemical reaction networks arising from a defined set of mathematical rules, can help address this challenge by enabling the exploration of alternative chemical universes and the possible metabolic networks that could emerge within them. Here we focus on artificial chemistries in which strings of characters represent simplified molecules, and string concatenation and splitting represent possible chemical reactions. We study string chemistries using tools borrowed from the field of stoichiometric constraint-based modeling of organismal metabolic networks, through a novel Python package, ARtificial CHemistry NEtwork Toolbox (ARCHNET). In addition to exploring the complexity and connectivity properties of different string chemistries, we developed a network-pruning algorithm that can generate minimal metabolic networks capable of producing a specified set of biomass precursors from a given assortment of environmental molecules within the string chemistry framework. We found that the identities of the metabolites in the biomass reaction wield much more influence over the structure of the minimal metabolic networks than the identities of the nutrient metabolites -- a notion that could help us better understand the rise and evolution of biochemical organization. Our work provides a bridge between artificial chemistries and stoichiometric modeling, which can help address a broad range of open questions, from the spontaneous emergence of an organized metabolism to the structure of microbial communities.

bioinformatics↗
bioRxiv2020

Dynamic allocation of carbon storage and nutrient-dependent exudation in a revised genome-scale model of Prochlorococcus

Microbial life in the oceans impacts the entire marine ecosystem, global biogeochemistry and climate. The marine cyanobacterium Prochlorococcus, an abundant component of this ecosystem, releases a significant fraction of the carbon fixed through photosynthesis, but the amount, timing and molecular composition of released carbon are still poorly understood. These depend on several factors, including nutrient availability, light intensity and glycogen storage. Here we combine multiple computational approaches to provide insight into carbon storage and exudation in Prochlorococcus. First, with the aid of a new algorithm for recursive filling of metabolic gaps (ReFill), and through substantial manual curation, we extended an existing genome-scale metabolic model of Prochlorococcus MED4. In this revised model (iSO595), we decoupled glycogen biosynthesis/degradation from growth, thus enabling dynamic allocation of carbon storage. In contrast to standard implementations of flux balance modeling, we made use of forced influx of carbon and light into the cell, to recapitulate overflow metabolism due to the decoupling of photosynthesis and carbon fixation from growth during nutrient limitation. By using random sampling in the ensuing flux space, we found that storage of glycogen or exudation of organic acids are favored when the growth is nitrogen limited, while exudation of amino acids becomes more likely when phosphate is the limiting resource. We next used COMETS to simulate day-night cycles and found that the model displays dynamic glycogen allocation and exudation of organic acids. The switch from photosynthesis and glycogen storage to glycogen depletion is associated with a redistribution of fluxes from the Entner-Doudoroff to the Pentose Phosphate pathway. Finally, we show that specific gene knockouts in iSO595 exhibit dynamic anomalies compatible with experimental observations, further demonstrating the value of this model as a tool to probe the metabolic dynamic of Prochlorococcus.

microbiology↗
bioRxiv2020

Comparative whole-genome approach to identify traits underlying microbial interactions

Microbial interactions shape the structure and function of microbial communities with profound consequences for biogeochemical cycles and ecosystem health. Yet, most interaction mechanisms are studied only in model systems and their prevalence is unknown. To systematically explore the functional and interaction potential of sequenced marine bacteria, we developed a new trait-based approach, and applied it to 473 complete genomes (248 genera), representing a significant fraction of marine microbial communities. We identified genome functional clusters (GFCs) which condense bacterial diversity into groups with potentially common ecology and life history. Most GFCs revealed unique combinations of interaction traits, some widely distributed (e.g., antimicrobial activity), others less common (e.g., production of siderophores and phytohormones, biosynthesis and export of specific B vitamins). Specific GFCs, comprising Alpha- and Gammaproteobacteria, are predicted to preferentially interact synergistically and/or antagonistically with bacteria and phytoplankton. Moreover, linked trait clusters (LTCs) identify traits that may have evolved together and point to specific modes of interactions. Our approach translates multidimensional genomic information into an atlas of marine bacteria and their ecosystem functions, relevant for understanding the fundamental rules that govern community assembly and dynamics.

microbiology↗
bioRxiv2019

BowSaw: inferring higher-order trait interactions associated with complex biological phenotypes

Machine learning is helping the interpretation of biological complexity by enabling the inference and classification of cellular, organismal and ecological phenotypes based on large datasets, e.g. from genomic, transcriptomic and metagenomic analyses. A number of available algorithms can help search these datasets to uncover patterns associated with specific traits, including disease-related attributes. While, in many instances, treating an algorithm as a black box is sufficient, it is interesting to pursue an enhanced understanding of how system variables end up contributing to a specific output, as an avenue towards new mechanistic insight. Here we address this challenge through a suite of algorithms, named BowSaw, which takes advantage of the structure of a trained random forest algorithm to identify combinations of variables ("rules") frequently used for classification. We first apply BowSaw to a simulated dataset, and show that the algorithm can accurately recover the sets of variables used to generate the phenotypes through complex Boolean rules, even under challenging noise levels. We next apply our method to data from the integrative Human Microbiome Project and find previously unreported high-order combinations of microbial taxa putatively associated with Crohns disease. By leveraging the structure of trees within a random forest, BowSaw provides a new way of using decision trees to generate testable biological hypotheses.

systems biology↗
bioRxiv2018

Boundary conditions for early life converge to an organo-sulfur metabolism

It has been suggested that a deep memory of early life is hidden in the architecture of metabolic networks, whose reactions could have been catalyzed by small molecules or minerals prior to genetically encoded enzymes (1-6). A major challenge in unraveling these early steps is assessing the plausibility of a connected, thermodynamically consistent proto-metabolism under different geochemical conditions, which are still surrounded by high uncertainty. Here we combine network-based algorithms (9, 10) with physicochemical constraints on chemical reaction networks to systematically show how different combinations of parameters (temperature, pH, redox potential and availability of molecular precursors) could have affected the evolution of a proto-metabolism. Our analysis of possible trajectories indicates that a subset of boundary conditions converges to an organo-sulfur-based proto-metabolic network fueled by a thioester- and redox-driven variant of the reductive TCA cycle, capable of producing lipids and keto acids. Surprisingly, environmental sources of fixed nitrogen and low-potential electron donors seem not to be necessary for the earliest phases of biochemical evolution. We use one of these networks to build a steady-state dynamical metabolic model of a proto-cell, and find that different combinations of carbon sources and electron acceptors can support the continuous production of a minimal ancient \"biomass\" composed of putative early biopolymers and fatty acids.

systems biology↗
bioRxiv2018

Experiments and simulations on short chain fatty acid production in a colonic bacterial community

Understanding how production of specific metabolites by gut microbes is modulated by interactions with surrounding species and by environmental nutrient availability is an important open challenge in microbiome research. As part of this endeavor, this work explores interactions between F. prausnitzii, a major butyrate producer, and B. thetaiotaomicron, an acetate producer, under three different in vitro media conditions in monoculture and coculture. In silico Genome-scale dynamic flux balance analysis (dFBA) models of metabolism in the system using COMETS (Computation of Microbial Ecosystems in Time and Space) are also tested for explanatory, predictive and inferential power. Experimental findings indicate enhancement of butyrate production in coculture relative to F. prausnitzii monoculture but defy a simple model of monotonic increases in butyrate production as a function of acetate availability in the medium. Simulations recapitulate biomass production curves for monocultures and accurately predict the growth curve of coculture total biomass, using parameters learned from monocultures, suggesting that the model captures some aspects of how the two bacteria interact. However, a comparison of data and simulations for environmental acetate and butyrate changes suggest that the organisms adopt one of many possible metabolic strategies equivalent in terms of growth efficiency. Furthermore, the model seems not to capture subsequent shifts in metabolic activities observed experimentally under low-nutrient regimes. Some discrepancies can be explained by the multiplicity of possible fermentative states for F. prausnitzii. In general, these results provide valuable guidelines for design of future experiments aimed at better determining the mechanisms leading to enhanced butyrate in this ecosystem.\n\nImportanceStudies associating butyrate levels with human colonic health have inspired research on therapeutic microbiota consortia that would optimize butyrate production if implanted in the human colon. Faecalibacterium prausnitzii is commonly observed in human fecal samples and produces butyrate as a product of fermentation. Previous studies indicate that Bacteroides thetaiotaomicron, also commonly found in human fecal samples, may enhance butyrate production in F. prausnitzi when the two species are co-localized. This possibility is investigated here under different environmental conditions using experimental methods paired with computer simulations of the whole metabolism of bacterial cells. Initial findings indicate that interactions between these two species result in enhanced butyrate production. However, results also paint a nuanced picture, suggesting the existence of a multiplicity of equivalently efficient metabolic strategies and complex interactions between acetate and butyrate production in these species that appear highly dependent on specific environmental conditions.

systems biology↗
bioRxiv2018

Designing metabolic division of labor in microbial communities

Microbes face a tradeoff between being metabolically independent and relying on neighboring organisms for the supply of some essential metabolites. This balance of conflicting strategies affects microbial community structure and dynamics, with important implications for microbiome research and synthetic ecology. A \"gedanken experiment\" to investigate this tradeoff would involve monitoring the rise of mutual dependence as the number of metabolic reactions allowed in an organism is increasingly constrained. The expectation is that below a certain number of reactions, no individual organism would be able to grow in isolation, and cross-feeding partnerships and division of labor would emerge. We implemented this idealized experiment using in silico genome-scale models. In particular, we used mixed integer linear programming to identify tradeoff solutions in communities of Escherichia coli strains. The strategies we found reveal a large space of nuanced and nonintuitive metabolic division of labor opportunities, including, for example, splitting the TCA cycle into two separate halves. The systematic computation of possible division of labor solutions for 1-, 2-, and 3-strain consortia resulted in a rich and complex landscape. This landscape displays a nonlinear boundary, indicating that the loss of an intracellular reaction is not necessarily compensated by a single imported metabolite. Different regions in this landscape are associated with specific solutions and patterns of exchanged metabolites. Our approach also predicts the existence of regions in this landscape where independent bacteria are viable, but outcompeted by cross-feeding pairs, providing a possible incentive for the rise of division of labor.

systems biology↗
bioRxiv2018

Quantifying biosynthetic network robustness across the human oral microbiome

Metabolic interactions, such as cross-feeding, play a prominent role in microbial communitystructure. For example, they may underlie the ubiquity of uncultivated microorganisms. We investigated this phenomenon in the human oral microbiome, by analyzing microbial metabolic networks derived from sequenced genomes. Specifically, we devised a probabilistic biosynthetic network robustness metric that describes the chance that an organism could produce a given metabolite, and used it to assemble a comprehensive atlas of biosynthetic capabilities for 88 metabolites across 456 human oral microbiome strains. A cluster of organisms characterized by reduced biosynthetic capabilities stood out within this atlas. This cluster included several uncultivated taxa and three recently co-cultured Saccharibacteria (TM7) phylum species. Comparison across strains also allowed us to systematically identify specific putative metabolic interdependences between organisms. Our method, which provides a new way of converting annotated genomes into metabolic predictions, is easily extendible to other microbial communities and metabolic products.

systems biology↗
bioRxiv2018

Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems

Metabolic exchange can mediate beneficial interactions among microbes, helping explain diversity in microbial communities. These interactions are often assumed to involve a fitness cost, prompting questions on how cooperative phenotypes can be stable and withstand the emergence of cheaters. Here we use genome-scale models of metabolism to investigate whether a radically different scenario, the pervasive release of \"costless\" metabolites (i.e. those that cause no fitness cost to the producing organism), can serve as a prominent mechanism for inter-microbial interactions. By carrying out over 1 million pairwise growth simulations for 14 microbial species in a combinatorial assortment of environmental conditions, we find that there is indeed a large space of metabolites that can be secreted at no cost, which can generate ample cross-feeding opportunities. In addition to providing an atlas of putative costless interdependencies, our modeling also demonstrates that oxygen availability significantly enhances mutualistic interactions by providing more opportunities for metabolic exchange through costless metabolites, resulting in an over-representation of specific ecological network motifs. In addition to helping explain natural diversity, we show how the exchange of costless metabolites can facilitate the engineering of stable synthetic microbial consortia.

systems biology↗
bioRxiv2018

Emergent sub-population behavior uncovered with a community dynamic metabolic model of Escherichia coli diauxic growth

Microbial communities have adapted to greatly variable environments in order to survive both short-term perturbations and permanent changes. The diauxic shift of Escherichia coli, growing first on glucose and, after it is exhausted, on acetate, is still today actively studied. As a fundamental example of metabolic adaptation, we are interested in understanding if diauxie in monocultures of E. coli is a coordinated and uniform metabolic shift, or rather the observable emergent result of individual sub-populations behavior. To do so we first develop a modeling framework that integrates dynamic models (ordinary differential equation systems) with structural models (metabolic networks), providing an open source modeling framework that is suitable to investigate the dynamics of microbial communities. We apply our methods to model E. coli either as having an average, unique metabolic state or as being the combination of two E. coli populations adapted to one of the two carbon sources. Our results are in strong agreement with previously published data and suggest that rather than a coordinated metabolic shift, diauxie could be the emergent pattern resulting from a survival strategy where individual cells differentiate for optimal growth on different substrates in view of environmental fluctuations. This work offers a new perspective on how to use dynamic metabolic modeling to investigate population dynamics, as the proposed approach can be easily transfered to studies on other multi-species communities as well as single cells.\n\nImportanceEscherichia coli diauxie is a fundamental example of metabolic adaptation that has not yet been completely understood, and to this aim approaches integrating experimental and theoretical biology are needed. We present a novel dynamic metabolic modeling approach that captures diauxie as an emergent property of sub-population dynamics rather than a homogeneous metabolic shift in E. coli monocultures. Without fine tuning of the parameters of E. coli core genome-scale model we obtain good agreement with published data. Our results suggest a change of paradigm in using single organism metabolic models, which can only to a certain approximation represent the average population metabolic state. We finally provide an open source modeling framework that can be applied to model multi-organism dynamics in variable environments.

systems biology↗
bioRxiv2018

Machine learning reveals missing edges and putative interaction mechanisms in microbial ecosystem networks

Microbes affect each others growth in multiple, often elusive ways. The ensuing interdependencies form complex networks, believed to influence taxonomic composition, as well as community-level functional properties and dynamics. Elucidation of these networks is often pursued by measuring pairwise interaction in co-culture experiments. However, combinatorial complexity precludes the exhaustive experimental analysis of pairwise interactions even for moderately sized microbial communities. Here, we use a machine-learning random forest approach to address this challenge. In particular, we show how partial knowledge of a microbial interaction network, combined with trait-level representations of individual microbial species, can provide accurate inference of missing edges in the network and putative mechanisms underlying interactions. We applied our algorithm to two case studies: an experimentally mapped network of interactions between auxotrophic E. coli strains, and a large in silico network of metabolic interdependencies between 100 human gut-associated bacteria. For this last case, 5% of the network is enough to predict the remaining 95% with 80% accuracy, and mechanistic hypotheses produced by the algorithm accurately reflect known metabolic exchanges. Our approach, broadly applicable to any microbial or other ecological network, can drive the discovery of new interactions and new molecular mechanisms, both for therapeutic interventions involving natural communities and for the rational design of synthetic consortia.\n\nImportanceDifferent organisms in a microbial community may drastically affect each others growth phenotype, significantly affecting the community dynamics, with important implications for human and environmental health. Novel culturing methods and decreasing costs of sequencing will gradually enable high-throughput measurements of pairwise interactions in systematic co-culturing studies. However, a thorough characterization of all interactions that occur within a microbial community is greatly limited both by the combinatorial complexity of possible assortments, and by the limited biological insight that interaction measurements typically provide without laborious specific follow-ups. Here we show how a simple and flexible formal representation of microbial pairs can be used for classification of interactions with machine learning. The approach we propose predicts with high accuracy the outcome of yet to be performed experiments, and generates testable hypotheses about the mechanisms of specific interactions.

bioinformatics↗