Elucidate growth control mechanisms using reconstructed spatiotemporal distributions of signaling events

A fundamental biological question is how diverse and complex signaling and patterning is controlled correctly to generate distinct tissues, organs, and body plans, but incorrectly in diseased cells and tissues. Signaling pathways important for growth control have been identified, but to identify the mechanisms their transient and context-dependent interactions encode is more difficult. Currently computational systems biology aims to infer the control mechanisms by investigating quantitative changes of gene expression and protein concentrations, but this inference is difficult in nature. We propose it is desirable to explicitly simulate events and orders of gene regulation and protein interactions, which better elucidate control mechanisms, and report a method and tool with three examples. The Drosophila wing model includes Wnt, PCP, and Hippo pathways and mechanical force, incorporates well-confirmed experimental findings, and generates novel results. The other two examples illustrate the building of three-dimensional and large-scale models. These examples support that reconstructed spatiotemporal distributions of key signaling events help elucidate growth control mechanisms. As biologists pay increasing attention to disordered signaling in diseased cells, to develop new modeling methods and tools for conducting new computational studies is important.