Quotient space approach improves genome model repair diversity and success
Beyond Action Entropy: Quotient-Space Exploration for Genome-Scale Metabolic Model Repair
Machine Learning
Summary
Fixing large biological models to match experimental results is tricky because many different changes can explain the same outcome. The authors introduce a new way to explore possible fixes by grouping together equivalent solutions that represent the same biological idea. This method helps find a wider variety of meaningful solutions and improves the chances of correctly fixing the models. Tests show that their approach finds more successful repairs than previous methods under the same effort.
What this means in practice
- •For bioinformatics engineers: Improve automated correction of genome-scale metabolic models by discovering diverse and biologically distinct repair mechanisms efficiently.
- •For biotech algorithm developers: Develop better tools for metabolic model refinement by incorporating quotient-space exploration to avoid redundant equivalent solutions.
Authors
Xuan Gong, Hanbo Huang, Wenbin Dai, Jing Wang, Lei Bai, Xiang Xiao, Weishu Zhao, Shiyu Liang
Abstract
Repairing scientific models from functional observations differs fundamentally from supervised prediction: feedback may certify a solution without revealing which structural correction is responsible. We study this setting for genome-scale metabolic model (GEM) repair, where multiple reaction edits can explain the same phenotypes and many apparently distinct edits correspond to the same biological mechanism. This many-to-one structure creates a hidden failure mode for conventional exploration: diversity in the output space need not translate into diversity of scientific hypotheses. We introduce QuotientPO, which collapses equivalent repairs into canonical mechanisms and optimizes exploration directly over the resulting quotient space. To make quotient exploration informative under finite rollouts, we derive a kernelized Rényi estimator that resolves graded crowding among distinct repair cores beyond coarse exact-match counts. On 2,212 held-out GEMs, QuotientPO improves Success@32 from 17.93% to 20.10% (+12.1% relative) while consistently increasing distinct successful-core discovery under the same sampling budget. These results establish quotient-space exploration as a principled approach to mechanism-level discovery under verifier-induced equivalence.