Research On The Structure And Function Of Biological Neural Network From The Perspective Of Graph Theory And Dynamics

A major challenge in systems biology is how to understand the function of complex biological networks from the structure.In recent years,complex network theory has been widely used in biological complex network research.However,it is impossible to deeply understand the regulation mechanism of information transmission in the network only from a statistical perspective.Therefore,designing an algorithm for the analysis of biological complex networks is helpful to better understand the regulation mechanism of networks,and is of great significance to the construction and transformation of biological networks.With the help of complex network theory and the modular idea of HVD algorithm,we designed an algorithm that can explore different functional modules of biological neural network and display the signal transmission process in layers.By changing the input of the algorithm,namely the sensory neurons and motor neurons of signal transmission,the sub-networks of specific physiological functions can be found in the complete neural network.In order to test the effectiveness of the algorithm,we applied the algorithm to the C.elegans neural network.We changed the input settings of the algorithm according to different physiological functions and explored the functional modules of the neural network.We focused on three physiological functions,namely the egg-laying function,chemosensation and mechanosensation of C.elegans.We found that the algorithm can discover important neurons in the network and reveal the mechanism of signal transduction.After the simulation of the neural network,we found that the dynamics of sub-networks obtained by the algorithm were similar to the complete network,which confirmed the effectiveness of the algorithm and provided a certain theoretical basis for the further exploration of biological neural networks.