The interactions between cells and the extracellular matrix are vital for the self-organisation of tissues. In this paper we present proof-of-concept to use machine learning tools to predict the role of this mechanobiology in the self-organisation of cell-laden hydrogels grown in tethered moulds. We develop a process for the automated generation of mould designs with and without key symmetries. We create a large training set with $N=6500$ cases by running detailed biophysical simulations of cell-matrix interactions using the contractile network dipole orientation (CONDOR) model for the self-organisation of cellular hydrogels within these moulds. These are used to train an implementation of the \texttt{pix2pix} deep learning model, reserving $740$ cases that were unseen in the training of the neural network for training and validation. Comparison between the predictions of the machine learning technique and the reserved predictions from the biophysical algorithm show that the machine learning algorithm makes excellent predictions. The machine learning algorithm is significantly faster than the biophysical method, opening the possibility of very high throughput rational design of moulds for pharmaceutical testing, regenerative medicine and fundamental studies of biology. Future extensions for scaffolds and 3D bioprinting will open additional applications.

翻译：细胞与细胞外基质间的相互作用对组织的自组织至关重要。本文提出概念验证，利用机器学习工具预测这种力学生物学在约束模具中培养的细胞负载水凝胶自组织过程中的作用。我们开发了自动生成含关键对称性及不含对称性模具设计的流程。通过使用收缩性网络偶极取向（CONDOR）模型对细胞-基质相互作用进行详细生物物理模拟，构建了包含$N=6500$个案例的大型训练集（该模型用于模拟这些模具中细胞水凝胶的自组织）。这些数据被用于训练\texttt{pix2pix}深度学习模型的实现，并预留$740$个神经网络训练中未见过的案例进行验证。机器学习技术预测结果与生物物理算法预留预测结果的对比表明，机器学习算法具有卓越的预测能力。该机器学习算法较生物物理方法显著提速，为药物测试、再生医学及基础生物学研究中模具的高通量理性设计开辟可能。未来向支架与3D生物打印的扩展将进一步开拓更多应用场景。