SOPSim: A cell vertex-based application for simulating sensory bristle patterning with dynamic filopodia
Repeating patterns, such as the bristles on the thorax of fruit flies developed from sensory organ precursor (SOP) cells, play important roles in the lives of flies as they are connected to the central nervous system and help detect environmental cues and threats. Notch signaling is known to regulate the formation of these patterns. Primarily, Notch signaling involves local communication between neighboring cells in direct contact (apical or junctional interaction). However, this short-range interaction alone cannot fully explain the complex biological patterns observed. Further studies have revealed long-range communication between cells through actin-based protrusions known as filopodia. The precise understanding of how the dynamics of filopodia, such as protrusion and retraction, lead to Notch-Delta activation remains unclear, which limits our ability to fully comprehend the mechanisms underlying cell fate decisions and the emergence of spatially organized patterns during tissue development and regeneration. In this study, we extend the modeling framework for Notch signaling by incorporating a filopodia dynamics model driven by myosin-mediated contraction. We address existing gaps by defining criteria under which any given pair of filopodia will physically intersect. We also introduce the local sensory area (LSA), a metric for quantifying local SOP spacing, and use experimental data to calibrate a Notch threshold that distinguishes SOP from epithelial cells. Through numerical simulations, we confirm that long-range communication via filopodia is essential for reproducing the refined patterns observed in vivo. Additionally, we explore how filopodia length, lifetime, and orientation influence the spatial organization of SOP cells. To this end, a Python library has been developed which allows users to perform simulations of Notch Delta signaling via a (1) Graphical User Interface/website, local to the user's computer, (2) via commands in the command line interface, or (3) by calling the simulation using a function via a Python API. This library allows users without computational expertise to easily specify a small number of input parameters and generate simulations of the model.