  Combining experiments with artificial intelligence algorithms, we propose a
new machine learning based approach to extract the cellular force distributions
from the microscope images. The full process can be divided into three steps.
First, we culture the cells on a special substrate allowing to measure both the
cellular traction force on the substrate and the corresponding substrate
wrinkles simultaneously. The cellular forces are obtained using the traction
force microscopy (TFM), at the same time that cell-generated contractile forces
wrinkle their underlying substrate. Second, the wrinkle positions are extracted
from the microscope images. Third, we train the machine learning system with
GAN (generative adversarial network) by using sets of corresponding two images,
the traction field and the input images (raw microscope images or extracted
wrinkle images), as the training data. The network understands the way to
convert the input images of the substrate wrinkles to the traction distribution
from the training. After sufficient training, the network is utilized to
predict the cellular forces just from the input images. Our system provides a
powerful tool to evaluate the cellular forces efficiently because the forces
can be predicted just by observing the cells under the microscope, which is a
way simpler method compared to the TFM experiment. Additionally, the machine
learning based approach presented here has the profound potential for being
applied to diverse cellular assays for studying mechanobiology of cells.
