G2Φnet: Relating Genotype and Biomechanical Phenotype of Tissues with Deep Learning
This work addresses the need to correlate genotype and biomechanical phenotype in soft tissues, which is crucial for understanding genetic diseases affecting tissue function, though it appears incremental as it builds on existing deep learning methods.
The authors tackled the problem of integrating genetic and biomechanical data to understand tissue health, presenting G2Φnet, a neural network that inferred nonlinear constitutive behavior of aortas in mouse models with genetic defects, achieving correct genotype ascription using limited, noisy data.
Many genetic mutations adversely affect the structure and function of load-bearing soft tissues, with clinical sequelae often responsible for disability or death. Parallel advances in genetics and histomechanical characterization provide significant insight into these conditions, but there remains a pressing need to integrate such information. We present a novel genotype-to-biomechanical-phenotype neural network (G2Φnet) for characterizing and classifying biomechanical properties of soft tissues, which serve as important functional readouts of tissue health or disease. We illustrate the utility of our approach by inferring the nonlinear, genotype-dependent constitutive behavior of the aorta for four mouse models involving defects or deficiencies in extracellular constituents. We show that G2Φnet can infer the biomechanical response while simultaneously ascribing the associated genotype correctly by utilizing limited, noisy, and unstructured experimental data. More broadly, G2Φnet provides a powerful method and a paradigm shift for correlating genotype and biomechanical phenotype quantitatively, promising a better understanding of their interplay in biological tissues.