A graph-based neuroscience platform for integrating multi-modal data with embedded optimization and simulation workflows
C. elegans with its complete connectome, molecularly identifiable neurons, single-cell transcriptome, neuropeptide-receptor distribution, and an amenability to whole-brain neural imaging and behavior presents a unique opportunity for a multiscale circuit level understanding of how neural circuits generate behavior. However, there is absence of a unifying framework to integrate these diverse datasets, which limits our ability to connect network structure and attributes with function. For this reason, I have been building CeDNe (C.elegans Dynamical Network), an open-source computational framework that integrates anatomical, molecular, and imaging datasets into a unified graph-based representation. CeDNe enables multimodal data analysis by cross-referencing different omics layers in a single computational environment and provides modular tools for visualizing and analyzing network connectivity, motif distribution, and circuit paths. Further, it also incorporates a data-driven modeling framework that simulates neural dynamics and optimizes network models to bridge structural connectivity with neural activity.