Design of a causal metabolic metastructural framework based on the integration of multi-omics data, high-throughput phenotyping, and hyperspectral remote sensing for engineering synthetic microbial consortia and multi-objective Bayesian optimization of the intelligent bioconversion of agri-food wastes into next-generation functional proteins in climate-adaptive precision agriculture systems.

Simultaneous increases in agri-food waste, pressure on conventional protein resources, and the intensification of climate change have highlighted the urgent need to develop intelligent, sustainable, and scalable solutions for the bioconversion of low-value biomass into high-value-added products. In this context, the present study proposes a causal-metabolic metastructural framework for engineering synthetic microbial consortia aimed at the intelligent conversion of agri-food wastes into next-generation functional proteins. By integrating multi-omics data, high-throughput phenotyping, and hyperspectral remote sensing, this framework enables the identification of causal relationships among feedstock characteristics, consortium dynamics, metabolic pathways, and bioconversion efficiency. Furthermore, through the application of multi-objective Bayesian optimization, the consortium composition and process conditions are simultaneously optimized based on indicators such as protein yield, nutritional quality, biological stability, process cost, and climate resilience. The principal innovation of this approach lies in moving beyond trial-and-error strategies toward the data-driven, interpretable, and predictive design of biological systems. This framework can provide a novel foundation for the development of precise, sustainable, and climate-adaptive bioprocesses within the bio-circular economy and future food security.

کلیدواژه ها:

Synthetic microbial consortia ، multi-omics data integration ، hyperspectral remote sensing ، multi-objective Bayesian optimization ، bioconversion of agri-food wastes