Background: Addressing the Resolution Limitations of Existing Single-Omics Analyses and Data Bottlenecks in Improving Nitrogen Metabolism Efficiency in Crop Biotechnology R&D

Conventional plant systems biology research has been biased towards static, single-omics-centric analyses, creating blind spots in tracking complex, multi-dimensional feedback loops. In particular, optimizing nitrogen use efficiency (NUE) in maize R&D is directly linked to macroeconomic demands for reducing chemical fertilizer use and carbon emissions. However, inter-species differences and noise from the loss of cellular heterogeneity have hindered successful genetic modeling. Static baselines that fail to reflect the dynamic metabolic fluxes of cellular microenvironments have led to failures in controlling nitrogen flow after genetic modification and in field trials, creating a critical barrier to ensuring effective establishment rates. Furthermore, the inability to precisely predict feedback loops of downstream metabolites in silico has exacerbated molecular-level bottlenecks.

Discovery: Implementation of a Multi-Dimensional Multi-Omics Tensor Synchronization Algorithm and Demonstration of the ZmAVT1A-1 Molecular Transport Mechanism at a Population Scale

This study implemented a multi-omics tensor synchronization algorithm to stereoscopically synchronize genomic, transcriptomic, and metabolomic matrices from a large-scale maize population of 1,404 progeny derived from 24 diverse founder lines. This clearly demonstrated the molecular biological role of the ZmAVT1A-1 gene, a key gateway for amino acid transport, in re-orchestrating nitrogen metabolism pathways. By calculating entropic binding free energies and predicting rate constants in silico, we achieved transport efficiency that significantly surpasses conventional one-dimensional genome-wide association studies. Computational processing was used to remove batch effects from large-scale sequencing and to track the topological variation curves of downstream transcriptomic networks, fully demonstrating that ZmAVT1A-1 acts as a key switch for intracellular nitrogen distribution.

Establishment of a Precision Stratification Model for Amino Acid Transport Pathway Regulation and Reversible Nitrogen Metabolism Homeostasis

We established an operational model for precise stratification of molecular phenotypes for each line based on fluid metabolic matrices. We designed a control backbone that allows for artificial up- and down-regulation of rate-limiting step constants to enable crops to maintain reversible homeostasis even in soil with nutrient stress. This established a molecular ecological stratification model that can proactively control the grain filling stage of maize by maximizing nitrogen conservation homeostasis even in unstable external environments.

Prospects: Establishing a Standard for Programmable Crop Systems Biology and Implementing Next-Generation Digital Governance

This discovery shifts agricultural R&D governance from a post-hoc analysis approach to an AI-based, computational, multi-dimensional tensor programmable infrastructure. In the pipeline expansion process of global multinational pharmaceutical and agricultural biotechnology companies, it provides a computational moat that eliminates batch-to-batch variation by linking to genetic gradient correction coefficients in the high-throughput screening (HTS) stage. By transplanting digital healthcare-grade companion diagnostics (CDx) standards into crop biomarker validation, it will contribute as a key asset to disruptively shorten the safety validation and IND approval timelines for next-generation gene-edited crops.