Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes

TL;DR

This paper reviews scalable methods for inferring parameters and structures of large-scale biochemical ODE models, addressing computational challenges in analyzing highly multiplexed datasets.

Contribution

It provides an overview of state-of-the-art scalable inference techniques specifically designed for large-scale biochemical ODE models.

Findings

Highlights the computational intractability of traditional methods for large models.

Summarizes recent scalable algorithms that enable efficient inference.

Emphasizes the importance of scalable methods for analyzing multiplexed biochemical data.

Abstract

Ordinary differential equation models have become a standard tool for the mechanistic description of biochemical processes. If parameters are inferred from experimental data, such mechanistic models can provide accurate predictions about the behavior of latent variables or the process under new experimental conditions. Complementarily, inference of model structure can be used to identify the most plausible model structure from a set of candidates, and thus gain novel biological insight. Several toolboxes can infer model parameters and structure for small- to medium-scale mechanistic models out of the box. However, models for highly multiplexed datasets can require hundreds to thousands of state variables and parameters. For the analysis of such large-scale models, most algorithms require intractably high computation times. This chapter provides an overview of state-of-the-art methods for parameter and model inference, with an emphasis on scalability.