ABSTRACT
There has been a considerable growth in datasets with respect to multicellular imaging in embryogenesis. The dataset mainly keeps track of the trajectories, shapes, and patterns of cells during cell growth and proliferation. Thus, if the changes in the generated dataset are tracked, the complications leading to birth defects can be detected and more importantly, the reason behind the defect can be interpreted. The technological advancements in imaging techniques like microscopy, ultrasound, CT, and MRI can be used in conjunction to generate accurate images of the developing embryo. The challenge here lies in storing these enormous datasets in a structured way. XML-based mark-up languages like CellML and SBML can be used to store these data in a structured format, and can also be used to process, quantify, and analyze the datasets, thus describing the interaction networks of genes, proteins, and metabolites. Ongoing projects and platforms like BioEmergences and CompuCell 3D primarily aim to reconstruct the embryonic development in silico, with the help of mathematical models. In the near future, accurate simulation of the cell lineages will eventually help us figure out the reasons behind any abnormal growth during fetal development.
