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The primary application of a computational genetic network inference tool is to study the genes underlying a complex disease in a systematic way. Our study has identified the genetic predispositions and cellular basis of a number of heritable spinal curves in the mouse, a model organism that allows for powerful genetics tools. Due to the central role of epigenetics in the postnatal development of the limb, the genetic cause of the described diseases can be pursued to identify any environmental factors that may play a role in this process. It has been previously discovered that mTORC1 has a crucial role in controlling joint formation [382]. By using mathematical modeling and bioinformatics approaches, it was determined that increased mTORC1 activity can explain the spinal component and increased length of the limb in certain genetic mouse models. This study also identified the genetic mutation that caused the increase in mTORC1 activity and the protein kinase that regulates mTORC1. The goal of this study was to create a computational network inference tool as a pipeline of using micrograph and gene expression data, manual curation, known phenotypes, and mathematical modeling to replace labor-intensive and time consuming biological experimentation. d2c66b5586