About this Research Topic
The advent of Artificial Intelligence (AI), machine learning (ML), and OMICs has paved the way for new insights into complex biological systems, advancing biotechnologies in biomedical sciences, planetary protection, and space medicine. The space environment presents a unique set of stress factors, leading to distinct molecular changes in microbes compared to those on Earth. This altered environment significantly impacts microbial behavior, phenotype, and outcomes of their interactions with other microbial species and humans. Notably, in space conditions, microbial behavior has been observed to be more virulent compared to their terrestrial counterparts. Utilizing Earth-analog systems, that simulate space stressors, provide a valuable platform for modelling these microbial behaviors and the outcomes of their interactions with other life forms. By using human-derived cell lines, animal models and conducting experiments under simulated space conditions, researchers are able to gain critical insights into predictable impact of microbes on human health and disease.
As humanity ventures further into space exploration, understanding the behavior of microbial communities becomes crucial for ensuring the success of long-duration space missions and the sustainability of life support systems. This Research Topic aims to explore the intersection of AI, Omics and microbiome, specifically focusing on the modelling of molecular and phenotypic signatures of microbial communities in different environmental conditions, including spaceflight and simulated space conditions. Studies conducted in spaceflights and earth based analog systems to evaluate the impact of these stressors are welcome.
The scope of this Research Topic is not limited to space-relevance but also open to Original Research and Review articles highlighting the use of AI and ML methods to predict and model the functional aspects (virulence, biofilm, resistance and biotechnological applications) of microbes and microbial communities in context to systems and human health on earth.
This Research Topic will encompass a diverse range of subjects, including:
1. AI-Driven ML-models for planetary protection and space medicine:
• Cutting-edge approaches in utilizing AI techniques such as ML, deep learning, and data analytics to analyze and model dynamics, interactions, and responses of microbial communities to space stressors, sterilization approaches and various environments by employing models of closed systems and human diseases.
2. Biotechnological Applications in Space:
• Highlight AI-driven innovations in harnessing microbial communities for biotechnological applications, including waste recycling, food production, space-medicines, and nutrition.
• Integration of new sterilization techniques, probiotics, postbiotics and nutraceuticals for mitigating the contamination risks, risk of pathogenic microbes in the context of planetary protection and space missions targeted to closed systems like International Space Station.
3. Data Integration and Interdisciplinary Approaches:
• Studies utilizing a range of omics or multi-omics data including genomics, metagenomics, metabolomics, transcriptomics, proteomics, to provide a holistic understanding for predictive and functional behavior of microbes and their interaction with hosts.
• Development of Omics based risk prediction models for human diseases on earth and space by evaluating the responses of human subjects and diseases models to microbe/microbiome signatures, space stressors and other environmental risk factors.
4. Ethical Considerations and Biosecurity:
• Address ethical implications and biosecurity concerns associated with the interaction of AI and microbial systems, particularly in the context of space missions. Insights from AI and ML modeling can enhance biosecurity measures by predicting potential outbreaks or contaminations in space habitats, ensuring the safety of space missions and Earth's biosphere upon return.
By advancing our understanding of microbial communities in space through AI and ML, this Research Topic not only aims to enhance the feasibility and safety of future space missions but also to drive innovations in biotechnology, biomedicines and healthcare on Earth. The potential applications and impacts underscore the importance of interdisciplinary collaboration, merging computational sciences with microbiology and space research to tackle some of the most pressing challenges in science and exploration.
This Research Topic has been coordinated and developed by Dr. Atul Munish Chander.
As humanity ventures further into space exploration, understanding the behavior of microbial communities becomes crucial for ensuring the success of long-duration space missions and the sustainability of life support systems. This Research Topic aims to explore the intersection of AI, Omics and microbiome, specifically focusing on the modelling of molecular and phenotypic signatures of microbial communities in different environmental conditions, including spaceflight and simulated space conditions. Studies conducted in spaceflights and earth based analog systems to evaluate the impact of these stressors are welcome.
The scope of this Research Topic is not limited to space-relevance but also open to Original Research and Review articles highlighting the use of AI and ML methods to predict and model the functional aspects (virulence, biofilm, resistance and biotechnological applications) of microbes and microbial communities in context to systems and human health on earth.
This Research Topic will encompass a diverse range of subjects, including:
1. AI-Driven ML-models for planetary protection and space medicine:
• Cutting-edge approaches in utilizing AI techniques such as ML, deep learning, and data analytics to analyze and model dynamics, interactions, and responses of microbial communities to space stressors, sterilization approaches and various environments by employing models of closed systems and human diseases.
2. Biotechnological Applications in Space:
• Highlight AI-driven innovations in harnessing microbial communities for biotechnological applications, including waste recycling, food production, space-medicines, and nutrition.
• Integration of new sterilization techniques, probiotics, postbiotics and nutraceuticals for mitigating the contamination risks, risk of pathogenic microbes in the context of planetary protection and space missions targeted to closed systems like International Space Station.
3. Data Integration and Interdisciplinary Approaches:
• Studies utilizing a range of omics or multi-omics data including genomics, metagenomics, metabolomics, transcriptomics, proteomics, to provide a holistic understanding for predictive and functional behavior of microbes and their interaction with hosts.
• Development of Omics based risk prediction models for human diseases on earth and space by evaluating the responses of human subjects and diseases models to microbe/microbiome signatures, space stressors and other environmental risk factors.
4. Ethical Considerations and Biosecurity:
• Address ethical implications and biosecurity concerns associated with the interaction of AI and microbial systems, particularly in the context of space missions. Insights from AI and ML modeling can enhance biosecurity measures by predicting potential outbreaks or contaminations in space habitats, ensuring the safety of space missions and Earth's biosphere upon return.
By advancing our understanding of microbial communities in space through AI and ML, this Research Topic not only aims to enhance the feasibility and safety of future space missions but also to drive innovations in biotechnology, biomedicines and healthcare on Earth. The potential applications and impacts underscore the importance of interdisciplinary collaboration, merging computational sciences with microbiology and space research to tackle some of the most pressing challenges in science and exploration.
This Research Topic has been coordinated and developed by Dr. Atul Munish Chander.
Keywords: Machine Learning, Microbes, Space Health, Stressors, Space Radiation, Microgravity, AI, Artificial Intelligence
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
