Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models

TL;DR

This paper introduces RAPTOR, a machine learning method using GANs trained on biophysical simulations to rapidly predict tissue organization in engineered corneal, glial, and fibroblast tissues, aiding tissue design.

Contribution

The paper presents RAPTOR, a novel GAN-based approach trained on biophysical model data for fast, accurate prediction of tissue organization in tissue engineering applications.

Findings

RAPTOR accurately predicts tissue organization with good agreement to experimental data.

The method significantly reduces prediction time compared to traditional simulations.

It can estimate tissue properties like cell density and tension effectively.

Abstract

We present a machine learning approach for predicting the organisation of corneal, glial and fibroblast cells in 3D cultures used for tissue engineering. Our machine-learning-based method uses a powerful generative adversarial network architecture called pix2pix, which we train using results from biophysical contractile network dipole orientation (CONDOR) simulations. In the following, we refer to the machine learning method as the RAPTOR (RApid Prediction of Tissue ORganisation) approach. A training data set containing a range of CONDOR simulations is created, covering a range of underlying model parameters. Validation of the trained neural network is carried out by comparing predictions with cultured glial, corneal, and fibroblast tissues, with good agreements for both CONDOR and RAPTOR approaches. An approach is developed to determine CONDOR model parameters for specific tissues using a fit to tissue properties. RAPTOR outputs a variety of tissue properties, including cell densities of cell alignments and tension. Since it is fast, it could be valuable for the design of tethered moulds for tissue growth.