Bioscience Method 2024, Vol.15, No.2, 76-88 http://bioscipublisher.com/index.php/bm 83 technologies, leading to computerized fluorescence microscopy (CFM). CFM allows for both subjective visualization and objective quantitative analysis of microscopic data, enabling detailed study of the localization and dynamics of intracellular processes beyond the diffraction limit of light microscopy (Puchkov, 2021). Another advanced technique is light sheet fluorescence microscopy, which provides rapid acquisition of three-dimensional images over large fields of view, making it ideal for studying complex microbial communities like biofilms and gut microbiota (Parthasarathy, 2018). Correlative cryo-fluorescence microscopy combined with cryo-scanning electron microscopy offers another powerful approach by enabling near-to-nanometer resolution imaging of microbial interactions in their natural state, minimizing artifacts typically caused by chemical fixation (Strnad et al., 2015). Additionally, helium ion microscopy (HIM) and 3D structured illumination microscopy (3D-SIM) have been used to study the interactions of bacterial membranes with nanotextured surfaces, providing high-resolution imaging crucial for understanding bactericidal mechanisms (Bandara et al., 2020). These advanced microscopy techniques collectively enhance our ability to visualize and quantify microbial interactions, offering deep insights into microbial behavior and their interactions with the environment. 5.2 Omics approaches for decoding interactions Omics technologies, encompassing genomics, transcriptomics, proteomics, and metabolomics, have revolutionized the study of microbial interactions by providing comprehensive insights into the molecular underpinnings of these interactions. High-throughput sequencing and multi-omics approaches allow for a detailed understanding of the functional roles and dynamic activities within microbial communities. For instance, integrating genomics with transcriptomics, proteomics, and metabolomics has revealed the complex interplay between microbial genes, their expression products, and metabolic processes. Such integrative omics approaches have been crucial in understanding the biosynthesis of secondary metabolites and the regulatory networks controlling microbial interactions (Palazzotto and Weber, 2018). Metagenomics and metaproteomics provide strain-level resolution and functional profiling of microbial communities, facilitating the discovery of novel interactions and potential therapeutic targets in the human gut microbiome (Zhang et al., 2019). Systems biology and multi-omics integration have been employed to model the metabolic interactions within microbial communities, offering insights into the collective metabolic capabilities and interactions that drive community dynamics (Pinu et al., 2019). 5.3 Computational modeling and simulation of microbial interactions Computational modeling and simulation are essential tools for predicting and understanding microbial interactions. These approaches leverage mathematical models and high-performance computing to simulate complex microbial behaviors and interactions within communities. Metabolic network modeling, for example, uses stoichiometric models to characterize metabolic interactions and optimize microbial production processes in environmental and industrial biotechnology (Perez-Garcia et al., 2016). Genome-scale models (GEMs) of metabolism allow for the detailed simulation of metabolic interactions within microbial communities, enabling the prediction of community dynamics under various environmental conditions (Colarusso et al., 2021). Individual-based modeling (IbM) approaches, such as NUFEB, simulate the 3D dynamics of microbial communities at the single-cell level, offering insights into population behaviors emerging from individual interactions (Li et al., 2019). Advanced computational methods also include the inference of microbial interaction networks from high-dimensional microbiome data, using techniques like network information theory and probabilistic graphical models to accurately predict direct microbial interactions (Tackmann et al., 2018). 6 Challenges and Limitations 6.1 Technical challenges in engineering and studying SynComs The engineering and study of synthetic microbial communities (SynComs) face several technical challenges. One significant challenge is ensuring the stability and functionality of SynComs over time. Synthetic communities are prone to changes due to horizontal gene transfer and retained mutations, which can alter their composition and
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