Umibato: estimation of time-varying microbial interaction using continuous-time regression hidden Markov model

Hosoda, Shion; Fukunaga, Tsukasa; Hamada, Michiaki

doi:10.1093/bioinformatics/btab287

Cited by 4 publications

(2 citation statements)

References 41 publications

(48 reference statements)

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“…Data obtained were described using a set of ODE or Boolean functions which contained variables attributed to all factors that could play a role in mapping the interactions. Similarly, high throughput data obtained from qualitative laboratory culture techniques can be described with these equations [ 70 ]. Dynamic models are often used to infer synthetic microbial consortia.…”

Section: Quantitative Network Models For Assessment Of Microbial Inte...mentioning

confidence: 99%

Modeling Microbial Community Networks: Methods and Tools for Studying Microbial Interactions

Srinivasan,

Jnana,

Murali

2024

Microb Ecol

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Microbial interactions function as a fundamental unit in complex ecosystems. By characterizing the type of interaction (positive, negative, neutral) occurring in these dynamic systems, one can begin to unravel the role played by the microbial species. Towards this, various methods have been developed to decipher the function of the microbial communities. The current review focuses on the various qualitative and quantitative methods that currently exist to study microbial interactions. Qualitative methods such as co-culturing experiments are visualized using microscopy-based techniques and are combined with data obtained from multi-omics technologies (metagenomics, metabolomics, metatranscriptomics). Quantitative methods include the construction of networks and network inference, computational models, and development of synthetic microbial consortia. These methods provide a valuable clue on various roles played by interacting partners, as well as possible solutions to overcome pathogenic microbes that can cause life-threatening infections in susceptible hosts. Studying the microbial interactions will further our understanding of complex less-studied ecosystems and enable design of effective frameworks for treatment of infectious diseases.

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Section: Quantitative Network Models For Assessment Of Microbial Inte...mentioning

confidence: 99%

Modeling Microbial Community Networks: Methods and Tools for Studying Microbial Interactions

Srinivasan,

Jnana,

Murali

2024

Microb Ecol

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“…Among them, hidden Markov models (HMMs), Kalman filters, and dynamic Bayesian networks (DBNs) are widely employed in microbiome research. For example, Umibato, an unsupervised learning-based inference for microbial interactions, combined HMMs with Gaussian process regression to estimate growth rates and interaction networks ( 90 ). HMMs are generally suitable for discrete latent status representation.…”

Section: Network Modeling Bridges the Gap Between Mechanistic Modelin...mentioning

confidence: 99%

Progress on network modeling and analysis of gut microecology: a review

Luo,

Zhu,

Jia

et al. 2024

Appl Environ Microbiol

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The gut microecological network is a complex microbial community within the human body that plays a key role in linking dietary nutrition and host physiology. To understand the complex relationships among microbes and their functions within this community, network analysis has emerged as a powerful tool. By representing the interactions between microbes and their associated omics data as a network, we can gain a comprehensive understanding of the ecological mechanisms that drive the human gut microbiota. In addition, the network-based approach provides a more intuitive analysis of the gut microbiota, simplifying the study of its complex dynamics and interdependencies. This review provides a comprehensive overview of the methods used to construct and analyze networks in the context of gut microecological background. We discuss various types of network modeling approaches, including co-occurrence networks, causal networks, dynamic networks, and multi-omics networks, and describe the analytical techniques used to identify important network properties. We also highlight the challenges and limitations of network modeling in this area, such as data scarcity and heterogeneity, and provide future research directions to overcome these limitations. By exploring these network-based methods, researchers can gain valuable insights into the intricate relationships and functional roles of microbial communities within the gut, ultimately advancing our understanding of the gut microbiota’s impact on human health.

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Conceptual strategies for characterizing interactions in microbial communities

Kodera

Gilbert

et al. 2022

iScience

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Summary Understanding the sets of inter- and intraspecies interactions in microbial communities is a fundamental goal of microbial ecology. However, the study and quantification of microbial interactions pose several challenges owing to their complexity, dynamic nature, and the sheer number of unique interactions within a typical community. To overcome such challenges, microbial ecologists must rely on various approaches to distill the system of study to a functional and conceptualizable level, allowing for a practical understanding of microbial interactions in both simplified and complex systems. This review broadly addresses the role of several conceptual approaches available for the microbial ecologist’s arsenal, examines specific tools used to accomplish such approaches, and describes how the assumptions, expectations, and philosophies underlying these tools change across scales of complexity.

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Umibato: estimation of time-varying microbial interaction using continuous-time regression hidden Markov model

Cited by 4 publications

References 41 publications

Modeling Microbial Community Networks: Methods and Tools for Studying Microbial Interactions

Modeling Microbial Community Networks: Methods and Tools for Studying Microbial Interactions

Progress on network modeling and analysis of gut microecology: a review

Conceptual strategies for characterizing interactions in microbial communities

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