%0 Journal Article
%T Modeling of Neuronal Growth In Vitro: Comparison of Simulation Tools NETMORPH and CX3D
%A J A£¿imovi£¿
%A T M£¿ki-Marttunen
%A R Havela
%A H Teppola
%A M-L Linne
%J EURASIP Journal on Bioinformatics and Systems Biology
%D 2011
%I BioMed Central
%R 10.1155/2011/616382
%X Development of computational tools has been one of the central topics in the computational neuroscience community. Several simulators of bioelectrical activity are publicly available and considered well-established tools. Both the cellular mechanisms behind this activity and the communication between cells, through the exchange of activity, can be modeled and analyzed using these tools [1]. In addition to the bioelectrical activity, the morphological structure of neurons and neuronal networks can be reconstructed by methods based on the experimentally verified morphological constraints [2]. Recently, two simulators were proposed, aiming to reproduce the morphological and structural changes of neuronal networks during growth [3, 4]. These two tools reproduce the morphological characteristics of neurons in each step of growth and not only in its final phase. Both provide a set of components that can be combined in a user-defined model, including functions defining axonal and dendritic growth, morphology of different types of neurons, or environmental constraints. They can reproduce growth in planar and three-dimensional space. Currently, they simulate solely the morphological aspects of neuronal circuits, but they will likely be extended, in the near future, to include the development of bioelectrical activity.Various aspects of growth in neuronal systems can be analyzed using models [5]. Some models concentrate on details of biophysical processes related to one phenomenon, while others describe several processes with less details [6, 7]. Examples of analyzed phenomena are initialization of dendritic and axonal arbors [5], dynamics of intracellular chemicals involved in axonal and dendritic outgrowth [6], and selection of axon growth direction following guidance cues in the environment [8, 9]. The framework for phenomenological modeling of growth is proposed in [3, 10, 11]. Here, the statistics of morphological changes, including branching and elongation, are computed
%U http://bsb.eurasipjournals.com/content/2011/1/616382