Biology subjects

Maslov, S.

Publications and source records attributed to Maslov, S..

36 records · Page 2Linked to original sources

bioRxiv2022

Functional universality in slow-growing microbial communities arises from thermodynamic constraints

The dynamics of microbial communities is complex, determined by competition for metabolic substrates and cross-feeding of byproducts. Species in the community grow by harvesting energy from chemical reactions that transform substrates to products. In many anoxic environments, these reactions are close to thermodynamic equilibrium and growth is slow. To understand the community structure in these energy-limited environments, we developed a microbial community consumer-resource model incorporating energetic and thermodynamic constraints on an intercon-nected metabolic network. The central element of the model is product inhibition, meaning that microbial growth may be limited not only by depletion of metabolic substrates but also by accu-mulation of products. We demonstrate that these additional constraints on microbial growth cause a convergence in the structure and function of the community metabolic network--independent of species composition and biochemical details--providing a possible explanation for convergence of community function despite taxonomic variation observed in many natural and industrial en-vironments. Furthermore, we discovered that the structure of community metabolic network is governed by the thermodynamic principle of maximum free energy dissipation. Our results predict the decrease of functional convergence in faster growing communities, which we validate by ana-lyzing experimental data from anaerobic digesters. Overall, the work demonstrates how universal thermodynamic principles may constrain community metabolism and explain observed functional convergence in microbial communities.

ecology↗

bioRxiv2021

Complementary resource preferences spontaneously emerge in diauxic microbial communities

Many microbes grow diauxically, utilizing the available resources one at a time rather than simultaneously. The properties of communities of microbes growing diauxically remain poorly understood, largely due to a lack of theory and models of such communities. Here, we develop and study a minimal model of diauxic microbial communities assembling in a serially diluted culture. We find that unlike co-utilizing communities, diauxic community assembly repeatably and spontaneously leads to communities with complementary resource preferences, i.e., communities where species prefer different resources as their top choice. Simulations and theory explain that the emergence of complementarity is driven by the disproportionate contribution of the top choice resource to the growth of a diauxic species. Additionally, we develop a geometric approach for analyzing serially diluted communities, with or without diauxie, which intuitively explains several additional emergent community properties, such as the apparent lack of species which grow fastest on a resource other than their most preferred resource. Overall, our work provides testable predictions for the assembly of natural as well as synthetic communities of diauxically shifting microbes.

ecology↗

bioRxiv2020

Ligation of random oligomers leads to emergence of autocatalytic sequence network

The emergence of longer information-carrying and functional nucleotide polymers from random short strands was a major stepping stone at the dawn of life. But the formation of those polymers under temperature oscillation required some form of selection. A plausible mechanism is template-based ligation where theoretical work already suggested a reduction in information entropy. Here, we show how nontrivial sequence patterns emerge in a system of random 12mer DNA sequences subject to enzyme-based templated ligation reaction and temperature cycling. The strands acted both as a template and substrates of the reaction and thereby formed longer oligomers. The selection for templating sequences leads to the development of a multiscale ligation landscape. A position-dependent sequence pattern emerged with a segregation into mutually complementary pools of A-rich and T-rich sequences. Even without selection for function, the base pairing of DNA with ligation showed a dynamics resembling Darwinian evolution.

biophysics↗

bioRxiv2020

Transforming the Language of Life: Transformer Neural Networks for Protein Prediction Tasks

AO_SCPLOWBSTRACTC_SCPLOWThe scientific community is rapidly generating protein sequence information, but only a fraction of these proteins can be experimentally characterized. While promising deep learning approaches for protein prediction tasks have emerged, they have computational limitations or are designed to solve a specific task. We present a Transformer neural network that pre-trains task-agnostic sequence representations. This model is fine-tuned to solve two different protein prediction tasks: protein family classification and protein interaction prediction. Our method is comparable to existing state-of-the art approaches for protein family classification, while being much more general than other architectures. Further, our method outperforms all other approaches for protein interaction prediction. These results offer a promising framework for fine-tuning the pre-trained sequence representations for other protein prediction tasks.

bioinformatics↗

bioRxiv2020

Ecology-guided prediction of cross-feeding interactions in the human gut microbiome

Understanding a complex microbial ecosystem such as the human gut microbiome requires information about both microbial species and the metabolites they produce and secrete. These metabolites are exchanged via a large network of cross-feeding interactions, and are crucial for predicting the functional state of the microbiome. However, till date, we only have information for a part of this network, limited by experimental throughput. Here, we propose an ecology-based computational method, GutCP, using which we predict hundreds of new experimentally untested cross-feeding interactions in the human gut microbiome. GutCP utilizes a mechanistic model of the gut microbiome with the explicit exchange of metabolites and their effects on the growth of microbial species. To build GutCP, we combined metagenomic and metabolomic measurements from the gut microbiome with optimization techniques from machine learning. Close to 65% of the cross-feeding interactions predicted by GutCP are supported by evidence from genome annotation; we provide these predictions for experimentally testing. Our method has the potential to greatly improve existing models of the human gut microbiome, as well as our ability to predict the metabolic profile of the gut.

ecology↗

bioRxiv2020

Modeling microbial cross-feeding at intermediate scale portrays community dynamics and species coexistence

Social interaction between microbes can be described at many levels of details: from the biochemistry of cell-cell interactions to the ecological dynamics of populations. Choosing an appropriate level to model microbial communities without losing generality remains a challenge. Here we show that modeling cross-feeding interactions at an intermediate level between genome-scale metabolic models of individual species and consumer-resource models of ecosystems is suitable to experimental data. We applied our modeling framework to three published examples of multi-strain Escherichia coli communities with increasing complexity: uni-, bi-, and multi-directional cross-feeding of either substitutable metabolic byproducts or essential nutrients. The intermediate-scale model accurately fit empirical data and quantified metabolic exchange rates that are hard to measure experimentally, even for a complex community of 14 amino acid auxotrophies. By studying the conditions of species coexistence, the ecological outcomes of cross-feeding interactions, and each communitys robustness to perturbations, we extracted new quantitative insights from these three published experimental datasets. Our analysis provides a foundation to quantify cross-feeding interactions from experimental data, and highlights the importance of metabolic exchanges in the dynamics and stability of microbial communities. Author summaryThe behavior of microbial communities such as the human microbiome is hard to predict by its species composition alone. Our efforts to engineer microbiomes--for example to improve human health--would benefit from mathematical models that accurately describe how microbes exchange metabolites with each other and how their environment shapes these exchanges. But what is an appropriate level of details for those models? We propose an intermediate level to model metabolic exchanges between microbes. We show that these models can accurately describe population dynamics in three laboratory communities and predicts their stability in response to perturbations such as changes in the nutrients available in the medium that they grow on. Our work suggests that a highly detailed metabolic network model is unnecessary for extracting ecological insights from experimental data and improves mathematical models so that one day we may be able to predict the behavior of real-world communities such as the human microbiome.

ecology↗

bioRxiv2019

The network structure and eco-evolutionary dynamics of CRISPR-induced immune diversification

As a heritable sequence-specific adaptive immune system, CRISPR-Cas is a powerful force shaping strain diversity in host-virus systems. While the diversity of CRISPR alleles has been explored, the associated structure and dynamics of host-virus interactions have not. We explore the role of CRISPR in mediating the interplay between host-virus interaction structure and eco-evolutionary dynamics in a computational model and compare results with three empirical datasets from natural systems. We show that the structure of the networks describing who infects whom and the degree to which strains are immune, are respectively modular (containing groups of hosts and viruses that interact strongly) and weighted-nested (specialist hosts are more susceptible to subsets of viruses that in turn also infect the more generalist hosts with many spacers matching many viruses). The dynamic interplay between these networks influences transitions between dynamical regimes of virus diversification and host control. The three empirical systems exhibit weighted-nested protection networks, a pattern our theory shows is indicative of hosts able to suppress virus diversification. Previously missing from studies of microbial host-pathogen systems, the protection network plays a key role in the coevolutionary dynamics.

evolutionary biology↗

bioRxiv2019

Regime shifts in a phage-bacterial ecosystem and strategies for its control

The competition between bacteria often involves both nutrients and phage predators and may give rise to abrupt regime shifts between the alternative stable states characterized by different species compositions. While such transitions have been previously studied in the context of competition for nutrients, the case of phage-induced bistability between competing bacterial species has not been considered yet. Here we demonstrate a possibility of regime shifts in well-mixed phage-bacterial ecosystems. In one of the bistable states the fast-growing bacteria competitively exclude the slow-growing ones by depleting their common nutrient. Conversely, in the second state the slow-growing bacteria with a large burst size generate such a large phage population that the other species cannot survive. This type of bistability can be realized as the competition between a strain of bacteria protected from phage by abortive infection and another strain with partial resistance to phage. It is often desirable to reliably control the state of microbial ecosystems, yet bistability significantly complicates this task. We discuss successes and limitations of one control strategy in which one adds short pulses to populations of individual species. Our study proposes a new type of phage therapy, where introduction of the phage is supplemented by addition of a partially resistant host bacteria.\n\nIMPORTANCEPhage-microbial communities play an important role in human health as well as natural and industrial environments. Here we show that these communities can assume several alternative species compositions separated by abrupt regime shifts. Our model predicts these regime shifts in the competition between bacterial strains protected by two different phage defense mechanisms: abortive infection/CRISPR and partial resistance. The history dependence caused by regime shifts greatly complicates the task of manipulation and control of a community. We propose and study a successful control strategy via short population pulses aimed at inducing the desired regime shifts. In particular, we predict that a fast-growing pathogen could be eliminated by a combination of its phage and a slower-growing susceptible host.

microbiology↗

bioRxiv2019

Multistability and regime shifts in microbial communities explained by competition for essential nutrients

Microbial communities routinely have several possible species compositions or community states observed for the same environmental parameters. Changes in these parameters can trigger abrupt and persistent transitions (regime shifts) between such community states. Yet little is known about the main determinants and mechanisms of multistability in microbial communities. Here we introduce and study a resource-explicit model in which microbes compete for two types of essential nutrients. We adapt game-theoretical methods of the stable matching problem to identify all possible species compositions of a microbial community. We then classify them by their resilience against three types of perturbations: fluctuations in nutrient supply, invasions by new species, and small changes of abundances of existing ones. We observe multistability and explore an intricate network of regime shifts between stable states in our model. Our results suggest that multistability requires microbial species to have different stoichiometries of essential nutrients. We also find that balanced nutrient supply promote multistability and species diversity yet make individual community states less stable.

ecology↗

bioRxiv2019

Evidence for a multi-level trophic organization of the human gut microbiome

The human gut microbiome is a complex ecosystem, in which hundreds of microbial species and metabolites coexist, in part due to an extensive network of cross-feeding interactions. However, both the large-scale trophic organization of this ecosystem, and its effects on the underlying metabolic flow, remain unexplored. Here, using a simplified model, we provide quantitative support for a multi-level trophic organization of the human gut microbiome, where microbes consume and secrete metabolites in multiple iterative steps. Using a manually-curated set of metabolic interactions between microbes, our model suggests about four trophic levels, each characterized by a high level-to-level metabolic transfer of byproducts. It also quantitatively predicts the typical metabolic environment of the gut (fecal metabolome) in approximate agreement with the real data. To understand the consequences of this trophic organization, we quantify the metabolic flow and biomass distribution, and explore patterns of microbial and metabolic diversity in different levels. The hierarchical trophic organization suggested by our model can help mechanistically establish causal links between the abundances of microbes and metabolites in the human gut.

microbiology↗

bioRxiv2019

Phages associated with horses provide new insights into the dominance of lateral gene transfer in virulent bacteriophages evolution in natural systems

Tailed bacteriophages (Caudovirales order) are omnipresent on our planet. Their impressive ecological and evolutionary success largely relies on the bacteriophage potential to adapt to great variety of the environmental conditions found in the Biosphere. It is believed that the adaptation of bacteriophages, including short time scale adaptation, is achieved almost exclusively via the (micro)evolution processes. In order to analyze the major mechanisms driving adaptation of phage genomes in a natural habitat we used comparative genomics of G7C-like coliphage isolates obtained during 7 years period from the feces of the horses belonging to a local population. The data suggest that even at this relatively short time scale the impact of various recombination events overwhelms the impact of the accumulation of point mutations. The access to the large pool of the genes of a complex microbial and viral community of the animal gut had major effect on the evolutionary trajectories of these phages. Thus the "real world" bacteriophage evolution mechanisms may differ significantly from those observed in the simplified laboratory model systems.

evolutionary biology↗

bioRxiv2018

Alternative stable states in a model of microbial community limited by multiple essential nutrients

Microbial communities routinely have several alternative stable states observed for the same environmental parameters. Sudden and irreversible transitions between these states make external manipulation of these systems more complicated. To better understand the mechanisms and origins of multistability in microbial communities, we introduce and study a model of a microbial ecosystem colonized by multiple specialist species selected from a fixed pool. Growth of each species can be limited by essential nutrients of two types, e.g. carbon and nitrogen, each represented in the environment by multiple metabolites. We demonstrate that our model has an exponentially large number of potential stable states realized for different environmental parameters. Using game theoretical methods adapted from the stable marriage problem we predict all of these states based only on ranked lists of competitive abilities of species for each of the nutrients. We show that for every set of nutrient influxes, several mutually uninvadable stable states are generally feasible and we distinguish them based upon their dynamic stability. We further explore an intricate network of discontinuous transitions (regime shifts) between these alternative states both in the course of community assembly, or upon changes of nutrient influxes.

ecology↗

bioRxiv2018

Hitchhiking, collapse and contingency in phage infections of migrating bacterial populations

Natural bacterial populations are subject to constant predation pressure by phages. Bacteria use a variety of well-studied molecular mechanisms to defend themselves from phage predation. However, since phage are non-motile, perhaps the simplest defense against phage would be for bacteria to outrun their predators. In particular, chemotaxis, the active migration of bacteria up attractant gradients, may help the bacteria escape slowly diffusing phages. Here we study phage infection dynamics in migrating bacterial populations driven by chemotaxis through low viscosity agar plates. We find that expanding phage-bacteria populations support two migrating fronts, an outermost \"bacterial\" front driven by nutrient uptake and chemotaxis and an inner \"phage\" front at which bacterial population collapses due to phage predation. We show that with increasing adsorption rate and initial phage population, the rate of migration of the phage front increases, eventually overtaking the bacterial front and driving the system across a \"phage transition\" from a regime where bacteria outrun a phage infection to one where they must evolve phage resistance to survive. We confirm experimentally that this process requires phages to \"surf\" the bacterial front by repeatedly reinfecting the fastest moving bacteria. A deterministic model recapitulates the transition. Macroscopic fluctuations in bacterial densities at the phage front suggest that a feedback mechanism, possibly due to growth rate dependent phage infection rates, drives millimeter scale spatial structure in phage-bacteria populations. Our work opens a new, spatiotemporal, line of investigation into the eco-evolutionary struggle between bacteria and their phage predators.\n\nSignificance StatementThe infection of bacteria by phage requires physical contact. This fact means that motile bacteria may avoid non-motile phage by simply running away. By this mechanism bacterial chemotaxis may help bacteria to escape phages. Here we show that when phage infect bacteria moving in soft agar plates, high phage populations or infectivity rates result in phages stopping and killing all bacteria. Conversely, when initial phage numbers or infectivity rates are low, bacteria are able to migrate away from phage successfully, despite phage ability to \"surf\" bacterial fronts for more than 24 hours. Between these regimes we document a \"phage transition\" where bacterial physiology and contingency in phage infection manifest through large-scale fluctuations in spatio-temporal dynamics.

ecology↗

bioRxiv2017

Microbial community structure predicted by the stable marriage problem

Experimental studies of microbial communities routinely reveal that they have multiple stable states. While each of these states is generally resilient, certain perturbations such as antibiotics, probiotics and diet shifts, result in transitions to other states. Can we reliably both predict such stable states as well as direct and control transitions between them? Here we present a new conceptual model -- inspired by the stable marriage problem in game theory and economics -- in which microbial communities naturally exhibit multiple stable states, each state with a different species abundance profile. Our models core ingredient is that microbes utilize nutrients one at a time while competing with each other. Using only two ranked tables, one with microbes nutrient preferences and one with their competitive abilities, we can determine all possible stable states as well as predict inter-state transitions, triggered by the removal or addition of a specific nutrient or microbe. Further, using an example of 7 Bacteroides species common to the human gut utilizing 9 polysaccharides, we predict that mutual complementarity in nutrient preferences enables these species to coexist at high abundances.

ecology↗

bioRxiv2017

Diversity, stability, and reproducibility in stochastically assembled microbial ecosystems

Microbial ecosystems are remarkably diverse, stable, and often consist of a balanced mixture of core and peripheral species. Here we propose a conceptual model exhibiting all these emergent properties in quantitative agreement with real ecosystem data, specifically species abundance and prevalence distributions. Resource competition and metabolic commensalism drive stochastic ecosystem assembly in our model. We demonstrate that even when supplied with just one resource, ecosystems can exhibit high diversity, increasing stability, and partial reproducibility between samples.

ecology↗

bioRxiv2017

Onset of natural selection in auto-catalytic heteropolymers

Reduction of information entropy along with ever-increasing complexity are among the key signatures of living matter. Understanding the onset of such behavior in early prebiotic world is essential for solving the problem of origins of life. To elucidate this transition, we study a theoretical model of information-storing heteropolymers capable of template-assisted ligation and subjected to cyclic non-equilibrium driving forces. We discover that this simple physical system undergoes a spontaneous reduction of the information entropy due to the competition of chains for constituent monomers. This natural-selection-like process ultimately results in the survival of a limited subset of polymer sequences. Importantly, the number of surviving sequences remains exponentially large, thus opening up the possibility of further increase in complexity due to Darwinian evolution. We also propose potential experimental implementations of our model using either biopolymers or artificial nano-structures.

evolutionary biology↗

bioRxiv2017

Severe population collapses and species extinctions in multi-host epidemic dynamics

Most infectious diseases including more than half of known human pathogens are not restricted to just one host, yet much of the mathematical modeling of infections has been limited to a single species. We investigate consequences of a single epidemic propagating in multiple species and compare and contrast it with the endemic steady state of the disease. We use the two-species Susceptible-Infected-Recovered (SIR) model to calculate the severity of post-epidemic collapses in populations of two host species as a function of their initial population sizes, the times individuals remain infectious, and the matrix of infection rates. We derive the criteria for a very large, extinction-level, population collapse in one or both of the species. The main conclusion of our study is that a single epidemic could drive a species with high mortality rate to local or even global extinction provided that it is co-infected with an abundant species. Such collapse-driven extinctions depend on factors different than those in the endemic steady state of the disease.

epidemiology↗

bioRxiv2016

The DOE Systems Biology Knowledgebase (KBase)

The U.S. Department of Energy Systems Biology Knowledgebase (KBase) is an open-source software and data platform designed to meet the grand challenge of systems biology -- predicting and designing biological function from the biomolecular (small scale) to the ecological (large scale). KBase is available for anyone to use, and enables researchers to collaboratively generate, test, compare, and share hypotheses about biological functions; perform large-scale analyses on scalable computing infrastructure; and combine experimental evidence and conclusions that lead to accurate models of plant and microbial physiology and community dynamics. The KBase platform has (1) extensible analytical capabilities that currently include genome assembly, annotation, ontology assignment, comparative genomics, transcriptomics, and metabolic modeling; (2) a web-browser-based user interface that supports building, sharing, and publishing reproducible and well-annotated analyses with integrated data; (3) access to extensive computational resources; and (4) a software development kit allowing the community to add functionality to the system.

bioinformatics↗