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Computational approaches which emulate in-vivo nervous system are needed toinvestigate mechanisms of the brain to orchestrate behavior. Such approachesmust integrate a series of biophysical models encompassing the nervous system,muscles, biomechanics to allow observing the system in its entirety whilesupporting model variations. Here we develop modWorm: a modular modelingframework for the nematode C. elegans. modWorm allows for construction of amodel as an integrated series of configurable, exchangeable modules eachdescribing specific biophysical processes across different modalities.Utilizing modWorm, we propose a base neuro-mechanical model for C. elegansbuilt upon the complete connectome. The model integrates a series of 7 modules:i) intra-cellular dynamics, ii) electrical and iii) chemical extra-cellularneural dynamics, iv) translation of neural activity to muscle calcium dynamics,v) muscle calcium dynamics to muscle forces, vi) muscle forces to body posturesand vii) proprioceptive feedback. We validate the base model by in-silicoinjection of constant currents into neurons known to be associated withlocomotion behaviors and by applying external forces to the body. Applicationsof in-silico neural stimuli experimentally known to modulate locomotion showthat the model can recapitulate natural behavioral responses such as forwardand backward locomotion as well as mid-locomotion responses such as avoidanceand turns. Furthermore, through in-silico ablation surveys, the model can infernovel neural circuits involved in sensorimotor behaviors. To further dissectmechanisms of locomotion, we utilize modWorm to introduce empirical based modelvariations and model optimizations to elucidate their effects on simulatedlocomotion. Our results show that modWorm can be utilized to identify neuralcircuits which control, mediate and generate natural behavior.

Modular integration of neural connectomics, dynamics and biomechanics  for identification of behavioral sensorimotor pathways in Caenorhabditis  elegans