Predicting time-varying flux and balance in metabolic systems using structured neural-ODE processes

TL;DR

This paper introduces SNODEP, a neural ODE-based framework that predicts flux and balance in metabolic systems from gene-expression data, avoiding complex domain-specific modeling.

Contribution

The paper presents a structured neural ODE process that improves modeling of metabolic dynamics and generalizes well to unseen conditions and irregular data sampling.

Findings

SNODEP accurately predicts flux and balance in real-world gene-expression data.

SNODEP generalizes to unseen knockout configurations.

SNODEP outperforms standard neural ODE models in modeling metabolic systems.

Abstract

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}.