Towards new model of neuronal growth: Comparison of models and tools for neuronal growth in vitro
Riikka Havela (Department of Signal Processing, Tampere University of Technology), Jugoslava Aćimović (Department of Signal Processing, Tampere University of Technology), Tuomo Mäki-Marttunen (Department of Signal Processing, Tampere University of Technology), Marja-Leena Linne (Department of Signal Processing, Tampere University of Technology)
Previously, we compared two tools, Netmorph  and Cx3D , for modeling growth and structural changes in neuronal networks in vitro . We concluded that both simulators can reproduce typical experimental values for network growth when phenomenological model of growth and graph theoretic analysis measures are used. The main difference between the tools is that NETMORPH implements computationally inexpensive models and is therefore more useful in theoretical studies. The advantage of Cx3D simulator is its flexibility. Cx3D is valuable when modeling a small number of neurons equipped with intracellular and extracellular chemical species. It may as well be useful for constructing multilevel models that incorporate cellular and network levels.
In this work, we propose a slightly modified model of neuronal growth with carefully assessed morphologies. The effects of different model components and parameters will be assessed using Sholl analysis to characterize the growth of axons and dendrites. The model is simulated using both Cx3D and its recently published parallelized version, Cx3Dp. We apply standard graph theoretic measures and Sholl analysis (see Fig. 1) to analyze and quantitatively compare the obtained morphologies and network structures. We also use analysis methods for weighted networks to assess the effects of synapse numbers. Our future aim is to present generic models of neuronal growth with relevant features of both in vitro and in vivo experiments. Such models, when incorporated with neuronal activity and known homeostatic mechanisms such as those provided by astrocytes, will help to decipher the role of network structure in the development of activity.
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