Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations

TL;DR

This paper reviews how machine learning techniques are applied to improve realistic cellular simulations, focusing on structure reconstruction, generation, and simulation data analysis, and discusses future challenges and opportunities.

Contribution

It provides a comprehensive overview of machine learning applications across the cellular simulation pipeline, highlighting current methods and future research directions.

Findings

ML enhances cellular structure reconstruction and segmentation.

ML-driven generation improves cellular models' realism.

The paper identifies key challenges and future opportunities in ML-enabled cellular simulations.

Abstract

In this perspective, we examine three key aspects of an end-to-end pipeline for realistic cellular simulations: reconstruction and segmentation of cellular structures; generation of cellular structures; and mesh generation, simulation, and data analysis. We highlight some of the relevant prior work in these distinct but overlapping areas, with a particular emphasis on current use of machine learning technologies, as well as on future opportunities.