Bridging Sequence and Kinetics: Utilizing Multi-scale Representations for Genome-Scale Metabolic Models

Rana Ahmed Barghout, Lya Chinas Serrano, Zhiqing Xu, Benjamin Manuel Sanchez, Radhakrishnan Mahadevan

Track: Full Paper Track

Keywords: Metabolic modeling, kinetic parameter prediction, enzyme design, genome-scale models, metabolic optimization, deep learning, metabolic engineering, machine learning, simulated annealing, multi-scale modeling, biological representations

TL;DR: This research enhances biological modeling by integrating foundation models for enzyme kinetics into genome-scale metabolic models, enabling interpretable, multi-scale insights into metabolic function and genetic perturbations.

Abstract: The construction of accurate enzyme-constrained genome-scale models (ecGEMs) remains a critical challenge in systems biology, limited by sparse kinetic data and the need for biologically meaningful representations. This work presents an integrated framework combining CPI-Pred, a deep learning model to predict kinetic parameters ($k_{cat}$, $K_M$, $K_I$, and $k_{cat}$/$K_M$) from sequence and compound embeddings, with kinGEMs, a pipeline to incorporate these parameters into ecGEMs for metabolic optimization. By leveraging representations at multiple scales, the approach captures sequence, structure, and kinetic data to enhance model generalizability and accuracy. Rigorous benchmarking demonstrates the framework's capability to predict growth rates and fluxes that are consistent with experimental observations, reduce median flux variability by 3 fold, and enable better-defined predictive and interpretable metabolic models. These innovations open new avenues for metabolic engineering and synthetic biology, offering robust tools to explore biological perturbations and guiding experimental designs.

Attendance: Rana Ahmed Barghout

Submission Number: 68