# GraphGDel: Constructing and Learning Graph Representations of Genome-Scale Metabolic Models for Growth-Coupled Gene Deletion Prediction

arXiv: https://arxiv.org/abs/2504.06316 · HTML: https://mlsanity.com/paper/2504.06316/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 48 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 3 of 3)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)

## Abstract (arXiv)

In genome-scale constraint-based metabolic models, gene deletion strategies are essential for achieving growth-coupled production, where cell growth and target metabolite synthesis occur simultaneously. Despite the inherently networked nature of genome-scale metabolic models, existing computational approaches rely primarily on sequential data and lack graph representations that capture their complex relationships, as both well-defined graph constructions and learning frameworks capable of exploiting them remain largely unexplored. To address this gap, we present a twofold solution. First, we introduce a systematic pipeline for constructing graph representations from constraint-based metabolic models. Second, we develop a deep learning framework that integrates these graph representations with gene and metabolite sequence data to predict growth-coupled gene deletion strategies. Across three metabolic models, our approach consistently outperforms established baselines, with improvements in overall accuracy of 14.04%, 16.26%, and 13.18% over a deep feedforward neural network baseline, 6.17%, 4.96%, and 5.31% over a sequence-learning baseline, and 5.10%, 4.36%, and 4.70% over a topology-aware graph aggregation baseline on the same metabolite graph, respectively. The source code and example datasets are available at: https://github.com/MetNetComp/GraphGDel.