Digital twin platform for plant gene expression using transcription factors and environmental data

Overview

This research proposes an advanced machine learning approach for predicting plant gene expression by integrating multiple biological data sources into a digital twin framework. The platform combines transcription factor protein interaction networks, genomic sequence data, and environmental covariates to predict gene expression at specific growth stages. This addresses key limitations of existing models that rely primarily on DNA sequence data and do not account for environmental conditions or developmental timing. The approach has significant implications for crop improvement, precision breeding, and understanding gene regulation in plants.

Technical specifications

Core methodology:

• Integration of transcription factor protein interaction networks with gene sequence information
• Incorporation of environmental covariates and growth stage data alongside genomic markers
• Application of transformer-based machine learning architectures for prediction tasks
• Modeling of time-series environmental data summarized into usable input features
• Relational modeling approaches for capturing biological interactions

Technical advantages:

• Improved prediction accuracy compared to sequence-only models such as Enformer, DNABERT, and FloraBERT
• Growth stage-specific expression predictions rather than generic tissue-level estimates
• Robustness across new varieties and unseen environmental conditions
• Compatibility with other computational models for building larger plant-level predictive systems

Technology readiness level

The research team has demonstrated prior success in related machine learning applications for plant biology, including environmental data integration for crop trait prediction, transformer-based phenotype prediction for barley disease resistance, and metabolic pathway prediction using comparative genomics. The current project begins with a six-month data identification and preliminary model development phase, followed by model refinement in collaboration with industry partners over the subsequent six to twelve months. The platform is at an early research and development stage, with initial proof-of-concept validation underway and full model development planned as the primary deliverable.