• microbial ecology
• biofilms

• Biofilms/growth & development
• Microbiota
• Feedback, Physiological
• Bacteria/classification
• Bacteria/genetics
• Bacteria/growth & development

• 180575 Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
• 180575 Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
• 180575 Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
• 180575 Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
• PZ00P3_202186 Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
• 542395 Simons Foundation

• FBA
• GEMs
• Mathematical modeling
• Microbial community
• Microbial ecology
• Synthetic microbial consortia

• microbial ecology
• computational models
• ecological networks

• Algorithms
• Microbial Interactions
• Microbiota
• Poisson Distribution
• Bacteria/genetics
• Bacteria/metabolism

• Plant Phenotyping and Imaging Research Centre (P2IRC) Canada Foundation for Research Excellence Fund (CFREF)
• Plant Phenotyping and Imaging Research Centre (P2IRC) Canada Foundation for Research Excellence Fund (CFREF)
• Plant Phenotyping and Imaging Research Centre (P2IRC) Canada Foundation for Research Excellence Fund (CFREF)
• Plant Phenotyping and Imaging Research Centre (P2IRC) Canada Foundation for Research Excellence Fund (CFREF)
• Plant Phenotyping and Imaging Research Centre (P2IRC) Canada Foundation for Research Excellence Fund (CFREF)

• community modeling
• host–microbe interactions
• machine learning
• microbiome-associated phenotype
• plant microbiome
• rhizosphere engineering

• Soil Microbiology
• Microbiota/physiology
• Rhizosphere
• Phenotype
• Crops, Agricultural/microbiology
• Agriculture
• Bacteria/genetics
• Bacteria/metabolism
• Bacteria/classification
• Plants/microbiology
• Plant Development
• Plant Roots/microbiology
• Soil/chemistry

• Habitat Condition Assessment System (HCAS)
• bacterial and fungal distributions
• human impacts
• land use
• microbial biogeography
• topsoil

• Humans
• Mycobiome
• Anthropogenic Effects
• Australia
• Bacteria
• Proteobacteria
• Soil
• Microbiota

• Australian Government
• DP200102542 Australian Research Council

• bacterial production
• community structure
• microbial ecological function
• random forest regression

• Antarctic Regions
• Bacteria/classification
• Bacteria/genetics
• Bacteria/isolation & purification
• Microbiota
• California
• Ecosystem
• Seawater/microbiology
• Oceans and Seas

• 3779 Gordon and Betty Moore Foundation
• NSF-OCE-1737409 National Science Foundation
• 549943 Simons Foundation
• NSF OPP-1846837 National Science Foundation Office of Polar Programs

• Biohydrogen
• Machine learning
• Microbial shift
• Optimization
• Reinforced machine learning

• Reproducibility of Results
• Fermentation
• Hydrogen
• Machine Learning
• Biomass

Microorganisms play an important role in natural material and elemental cycles. Many common and general biology research techniques rely on microorganisms. Machine learning has been gradually integrated with multiple fields of study. Machine learning, including deep learning, aims to use mathematical insights to optimize variational functions to aid microbiology using various types of available data to help humans organize and apply collective knowledge of various research objects in a systematic and scaled manner. Classification and prediction have become the main achievements in the development of microbial community research in the direction of computational biology. This review summarizes the application and development of machine learning and deep learning in the field of microbiology and shows and compares the advantages and disadvantages of different algorithm tools in four fields: microbiome and taxonomy, microbial ecology, pathogen and epidemiology, and drug discovery.

• China Postdoctoral Science Foundation

The dynamics of microbial processes are difficult to study in natural soil, owing to the small spatial scales on which microorganisms operate and to the opacity and chemical complexity of the soil habitat. To circumvent these challenges, we have created a 3D-bioprinted habitat that mimics aspects of natural soil aggregates while providing a chemically defined and translucent alternative culturing method for soil microorganisms. Our Synthetic Soil Aggregates (SSAs) retain the porosity, permeability, and patchy resource distribution of natural soil aggregates—parameters that are expected to influence emergent microbial community interactions. We demonstrate the printability and viability of several different microorganisms within SSAs and show how the SSAs can be integrated into a multi-omics workflow for single SSA resolution genomics, metabolomics, proteomics, lipidomics, and biogeochemical assays. We study the impact of the structured habitat on the distribution of a model co-culture microbial community and find that it is significantly different from the spatial organization of the same community in liquid culture, indicating a potential for SSAs to reproduce naturally occurring emergent community phenotypes. The SSAs have the potential as a tool to help researchers quantify microbial scale processes in situ and achieve high-resolution data from the interplay between environmental properties and microbial ecology.

Microbial community structure is influenced by the environment and in turn exerts control on many environmental parameters. We applied this concept in a bioreactor study to test whether microbial community structure contains information sufficient to predict the concentration of H₂S as the product of sulfate reduction. Microbial sulfate reduction is a major source of H₂S in many industrial and environmental systems and is often influenced by the existing physicochemical conditions. Production of H₂S in industrial systems leads to occupational hazards and adversely affects the quality of products. A long-term (148 days) experiment was conducted in upflow bioreactors to mimic sulfidogenesis, followed by inhibition with nitrate salts and a resumption of H₂S generation when inhibition was released. We determined microbial community structure in 731 samples across 20 bioreactors using 16S rRNA gene sequencing and applied a random forest algorithm to successfully predict different phases of sulfidogenesis and mitigation (accuracy = 93.17%) and sessile and effluent microbial communities (accuracy = 100%). Similarly derived regression models that also included cell abundances were able to predict H₂S concentration with remarkably high fidelity (R² > 0.82). Metabolic profiles based on microbial community structure were also found to be reliable predictors for H₂S concentration (R² = 0.78). These results suggest that microbial community structure contains information sufficient to predict sulfidogenesis in a closed system, with anticipated applications to microbially driven processes in open environments.

IMPORTANCE Microbial communities control many biogeochemical processes. Many of these processes are impractical or expensive to measure directly. Because the taxonomic structure of the microbial community is indicative of its function, it encodes information that can be used to predict biogeochemistry. Here, we demonstrate how a machine learning technique can be used to predict sulfidogenesis, a key biogeochemical process in a model system. A distinction of this research was the ability to predict H₂S production in a bioreactor from the effluent bacterial community structure without direct observations of the sessile community or other environmental conditions. This study establishes the ability to use machine learning approaches in predicting sulfide concentrations in a closed system, which can be further developed as a valuable tool for predicting biogeochemical processes in open environments. As machine learning algorithms continue to improve, we anticipate increased applications of microbial community structure to predict key environmental and industrial processes.

• biogeochemical state
• machine learning
• microbial community analysis
• random forest
• sulfidogenesis potential

• Erythrocytes/parasitology
• Hemeproteins
• Humans
• Image Processing, Computer-Assisted/methods
• Leukocyte Count/methods
• Leukocytes/cytology
• Machine Learning
• Neural Networks, Computer
• Parasite Load
• Plasmodium falciparum/cytology
• Plasmodium falciparum/isolation & purification

• RF1 NS113287 NINDS NIH HHS

• Deep learning
• Fecal microbiota transplantation
• Gut microbiome
• Microbial metabolites
• Next-generation probiotics
• Obesity

• Dysbiosis
• Gastrointestinal Microbiome
• Humans
• Microbiota
• Prebiotics
• Synbiotics

Agent-based modeling (ABM) is a powerful simulation technique which describes a complex dynamic system based on its interacting constituent entities. While the flexibility of ABM enables broad application, the complexity of real-world models demands intensive computing resources and computational time; however, a metamodel may be constructed to gain insight at less computational expense. Here, we developed a model in NetLogo to describe the growth of a microbial population consisting of Pantoea. We applied 13 parameters that defined the model and actively changed seven of the parameters to modulate the evolution of the population curve in response to these changes. We efficiently performed more than 3,000 simulations using a Python wrapper, NL4Py. Upon evaluation of the correlation between the active parameters and outputs by random forest regression, we found that the parameters which define the depth of medium and glucose concentration affect the population curves significantly. Subsequently, we constructed a metamodel, a dense neural network, to predict the simulation outputs from the active parameters and found that it achieves high prediction accuracy, reaching an R² coefficient of determination value up to 0.92. Our approach of using a combination of ABM with random forest regression and neural network reduces the number of required ABM simulations. The simplified and refined metamodels may provide insights into the complex dynamic system before their transition to more sophisticated models that run on high-performance computing systems. The ultimate goal is to build a bridge between simulation and experiment, allowing model validation by comparing the simulated data to experimental data in microbiology.

• Pantoea
• agent-based model
• machine learning
• neural network
• random forest regression

• Degrading bacterial isolates
• In situ biodegradation
• Interaction mechanisms
• Molecular mechanisms
• Phthalic acid esters
• Synthetic microbial ecology

• Bacteria
• Biodegradation, Environmental
• Esters
• Humans
• Phthalic Acids
• Prospective Studies

• microbial habitat
• microbial scale
• multi-scale
• soil microbial ecology

Imaging across scales reveals disease mechanisms in organisms, tissues, and cells. Yet, particular infection phenotypes, such as virus-induced cell lysis, have remained difficult to study. Here, we developed imaging modalities and deep learning procedures to identify herpesvirus and adenovirus (AdV) infected cells without virus-specific stainings. Fluorescence microscopy of vital DNA-dyes and live-cell imaging revealed learnable virus-specific nuclear patterns transferable to related viruses of the same family. Deep learning predicted two major AdV infection outcomes, non-lytic (nonspreading) and lytic (spreading) infections, up to about 20 hr prior to cell lysis. Using these predictive algorithms, lytic and non-lytic nuclei had the same levels of green fluorescent protein (GFP)-tagged virion proteins but lytic nuclei enriched the virion proteins faster, and collapsed more extensively upon laser-rupture than non-lytic nuclei, revealing impaired mechanical properties of lytic nuclei. Our algorithms may be used to infer infection phenotypes of emerging viruses, enhance single cell biology, and facilitate differential diagnosis of non-lytic and lytic infections.

•

Artificial intelligence identifies HSV- and AdV-infected cells without specific probes.

• Machine learning
• Virology