CeDNe

CeDNe integrates anatomical, molecular, and imaging datasets into a unified graph-based representation that enables multimodal data analysis by cross-referencing different omics layers in a single computational environment for modeling structure-function relationships in the C. elegans nervous system.

Key Features:

• Multi-omic data integration: CeDNe combines anatomical, molecular, and imaging datasets including connectome, single-cell transcriptome, and neuropeptide-receptor distribution data.
• Graph-based object model: CeDNe represents neuronal data using a unified graph-based representation for network structure and attributes.
• Network connectivity analysis: CeDNe provides modular tools for visualizing and analyzing network connectivity.
• Motif distribution analysis: CeDNe includes tools for analyzing motif distribution in neural networks.
• Circuit path analysis: CeDNe provides tools for analyzing circuit paths within the network.
• Neural dynamics simulation: CeDNe incorporates a computational framework that simulates neural dynamics to bridge structural connectivity with neural activity.
• Network model optimization: CeDNe optimizes network models to fit structural connectivity with neural activity data.

Scientific Applications:

• Computational connectomics: CeDNe facilitates computational connectomics in C. elegans by integrating structural connectome data with other omics layers.
• Multimodal neural data analysis: CeDNe enables cross-referencing of different omics layers including transcriptomic and peptidergic data with anatomical and functional data in a single computational environment.
• Structure-function relationship modeling: CeDNe bridges structural connectivity with neural activity to model how neural circuits generate behavior.
• Cross-species neural network investigation: CeDNe serves as a generalizable framework for investigating structure-function relationships in neural networks of other organisms beyond C. elegans.

Methodology:

CeDNe uses a graph-based object model to represent, combine, analyze, and simulate complex neuronal data. The framework includes modular tools for network analysis and a computational framework for neural dynamics simulation and network model optimization.