Interfacing Machine Learning and Microbial Omics: A Promising Means to Address Environmental Challenges

Abstract

Microbial communities are ubiquitous and carry an exceptionally broad metabolic capability. Upon environmental perturbation, microbes are also amongst the first natural responsive elements with perturbation-specific cues and markers. These communities are thereby uniquely positioned to inform on the status of environmental conditions. The advent of microbial omics has led to an unprecedented volume of complex microbiological data sets. Importantly, these data sets are rich in biological information with potential for predictive environmental classification and forecasting. However, the patterns in this information are often hidden amongst the inherent complexity of the data. There has been a continued rise in the development and adoption of machine learning (ML) and deep learning architectures for solving research challenges of this sort. Indeed, the interface between molecular microbial ecology and artificial intelligence (AI) appears to show considerable potential for significantly advancing environmental monitoring and management practices through their application. Here, we provide a primer for ML, highlight the notion of retaining biological sample information for supervised ML, discuss workflow considerations, and review the state of the art of the exciting, yet nascent, interdisciplinary field of ML-driven microbial ecology. Current limitations in this sphere of research are also addressed to frame a forward-looking perspective toward the realization of what we anticipate will become a pivotal toolkit for addressing environmental monitoring and management challenges in the years ahead.

Microbially mediated fossil concretions and their characterization by the latest methodologies: a review

“…By using these approaches, researchers can gain insights into the diversity and complexity of microbial ecosystems and understand the roles of different microorganisms in these environments, their metabolic pathways, and their interactions with other organisms and their environment. Omic techniques have broad applications in microbiology, ecology, and biotechnology, and are helping to drive discoveries in fields such as environmental science and bioremediation ( Garnatje et al, 2017 ; Gutleben et al, 2018 ; Zhang et al, 2018 ; Laczi et al, 2020 ; McElhinney et al, 2022 ). In recent years, there has been increasing interest in applying omic techniques to the study of fossils and the processes involved in their formation and preservation ( Dong et al, 2019 ; Janssen et al, 2022 ).…”

Toward a Predictive Understanding of Cyanobacterial Harmful Algal Blooms through AI Integration of Physical, Chemical, and Biological Data

“…66 The usefulness of LRM data for ML with respect to lake ecology remains undetermined, although successful applications have been demonstrated with predictions of soil productivity. 67,68 We therefore suggest greater adoption of LRM data usage in ML of HAB dynamics will be beneficial in the near future, while in the distant future, affordable HRM and MT capabilities will increase the predictive power of ML models for HABs. 1).…”

Environmental microbiology going computational—Predictive ecology and unpredicted discoveries

“…The field of computational biology, being the intersection of computer science and biology, is rapidly expanding and developing new methods for this purpose. Artificial intelligence (AI), including machine learning (ML) and to some extent also deep learning (DL) methods are promising for dealing with big data in microbial ecology and environmental microbiology (Ghannam & Techtmann, 2021 ; McElhinney et al, 2022 ). Especially ML approaches are increasingly adopted by ecologists and many of these methods will soon become routine tools for analyses of complex microbial omics data.…”

“…This type of approach can potentially assist in the microbiome engineering of important crops. However, with sequencing costs being relatively cheap, there is an increasing interest in using AI and microbiome data for microbiome‐based diagnostics as a means to address environmental challenges and advance management practices (McElhinney et al, 2022 ). Two recent examples of the latter are the use of soil microbiome data to predict the propensity for specific plant diseases in agriculture (Yuan et al, 2020 ) and soil health metrics (Wilhelm et al, 2022 ), which can be laborious and expensive to measure.…”