Fig. 10: Workflow of mitochondrial network analysis and simulation.

a Workflow of the mitochondrial network model simulation and fitting of fission/fusion rates. An agent-based mitochondrial network model simulating two types of fission and fusion behaviors using nodes, edges, and degrees is used to represent and describe the mitochondrial network. Edges are considered the basic units that represent small segments of mitochondria, and nodes with different degrees are regarded as characteristic measurements of the mitochondrial network structure. b Fitting fission/fusion rates of mitochondria under different glucose concentrations and chemical treatments. The network parameters <k> (average degree of mitochondrial nodes), Ng1/N (number of nodes or edges of the largest cluster/total nodes or edges), and Ng2/N (number of nodes or edges of the second largest cluster/total nodes or edges) were extracted from fluorescence images of INS-1 and used as features for genetic algorithm (GA) fittings. By minimizing the distance between the distribution of network parameters (D(<k > , Ng1/N, Ng2/N)) extracted from fluorescence image analysis and the network model, the optimized C1 (ratio of the rate constants of tip-to-tip fusion to tip-to-tip fission) and C2 (ratio of the rate constants of tip-to-side fusion to tip-to-side fission) were obtained by a random search of the GA. Kernel density estimation was used to estimate the probability density function of the network parameters from confocal microscopy images of mitochondria, and Kullback-Leibler divergence was used to minimize the difference between two distributions calculated from KDE. c Agent-based model for visualization of simulated mitochondrial networks with C1/C2 ratios obtained from GA. d Ten and fifteen repeats of fitting for glucose and toxicity 2D data, respectively. e Tracking indicator “average degree” of the simulated networks during the iterations. f Images of the mitochondrial network in the agent-based model.