About this Research Topic
Anthropogenic activities are drastically altering the nitrogen (N) and carbon (C) cycles – as evidenced by major changes in patterns of N and C utilization in managed and unmanaged ecosystems. Many elemental nutrient cycling processes in earth systems are mediated by microorganisms. C and N cycling in soil and aquatic systems depends on microbes functioning over a wide range of environmental conditions. Global temperature variability is of particular interest since microbial rates of activity change with temperature. The resilience of environmental systems is governed by how microorganisms respond to environmental extremes. While environmental change predictions and climate models have focused on plant effects and C sequestration, the evaluation of environmental change on microbial physiology has lagged behind.
Our goal is to facilitate the prediction of emergent microbial system responses to extreme environmental change as it affects C and N cycling in the aquatic and soil components of managed and unmanaged ecosystems. Modelling linkages between the C and N cycle form the fundamental basis of nutrient pool dynamics and sub-model parameterizations in current versions of many grid-based Earth Systems Models and are a source of model uncertainty for large systems models. Explicit treatment of C and N assimilation and separation among the various forms of C and N will result in a more robust sub-model architecture. Modelling studies that would elucidate or predict the potential effects of extreme conditions on microbial processes in the environment would be particularly useful in this context.
This collection encompasses the development of modelling approaches, including treatments for model uncertainty, for enhancing the basic understanding of microbial physiology transformations in engineered and natural systems across the range of environmental conditions related to climate change.
• Application of genome-scale modelling of cell metabolism and constraint-based reconstruction and analysis (including flux balance analysis) to environmental systems;
• Application of deterministic and stochastic modelling tools, particularly those from machine learning to large microbial environmental data sets;
• Application of modelling tools from systems biology, bioinformatics, multi-omic data analysis, and microbial cell analysis to microbial function in ecological systems;
• Application of modelling tools to provide insights into microbial roles in the circular economy and achieving carbon and energy neutrality in various industrial sectors.
• Application of modelling tools to provide insights into microbial adaptations that might occur under extreme environmental conditions
• Other modelling applications relevant to microbial processes in natural and engineered systems.
Keywords: nutrient cycling, resilience, systems biology, climate change, environment
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