Predicting time-varying flux and balance in metabolic systems using structured neural-ODE processes
This work addresses the challenge of scalable metabolic pathway analysis for researchers, offering an incremental improvement by enhancing neural ODE processes with structure for better modeling dynamics.
The authors tackled the problem of predicting time-varying flux and balance in metabolic systems by developing a structured neural ODE process (SNODEP) model, which bypasses the need for domain knowledge and manual optimization in dynamic flux balance analysis, achieving accurate predictions on real-world gene-expression data and generalizing to unseen knockout configurations and irregular sampling scenarios.
We develop a novel data-driven framework as an alternative to dynamic flux balance analysis, bypassing the demand for deep domain knowledge and manual efforts to formulate the optimization problem. The proposed framework is end-to-end, which trains a structured neural ODE process (SNODEP) model to estimate flux and balance samples using gene-expression time-series data. SNODEP is designed to circumvent the limitations of the standard neural ODE process model, including restricting the latent and decoder sampling distributions to be normal and lacking structure between context points for calculating the latent, thus more suitable for modeling the underlying dynamics of a metabolic system. Through comprehensive experiments ($156$ in total), we demonstrate that SNODEP not only predicts the unseen time points of real-world gene-expression data and the flux and balance estimates well but can even generalize to more challenging unseen knockout configurations and irregular data sampling scenarios, all essential for metabolic pathway analysis. We hope our work can serve as a catalyst for building more scalable and powerful models for genome-scale metabolic analysis. Our code is available at: \url{https://github.com/TrustMLRG/SNODEP}.