Topographical strategies to control neural outgrowth

In this work a synergistic approach is used to investigate how directional anisotropic surfaces (i.e., nanogratings) control the alignment of PC12 neurites. Finite Element models were used to assess the distribution of stresses in non-spread growth cones and filopodia. The stress field was assumed to be the main triggering cause fostering the increase and stabilization of filopodia, so the local stress maxima were directly related to the neuritic orientation. Moreover, a computational framework was implemented within an open source Java environment (CX3D), and in silico simulations were carried out to reproduce and predict biological experiments. No significant differences were found between biological experiments and in silico simulations (alignment angle, p = 0.4685; tortuosity, p = 0.9075) with a standard level of confidence (95%).

[1]  Silvestro Micera,et al.  A hybrid computational model to predict chemotactic guidance of growth cones , 2015, Scientific Reports.

[2]  Christian Cipriani,et al.  The SmartHand transradial prosthesis , 2011, Journal of NeuroEngineering and Rehabilitation.

[3]  Silvestro Micera,et al.  A hybrid approach for the control of axonal outgrowth: preliminary simulation results , 2007, 2007 3rd International IEEE/EMBS Conference on Neural Engineering.

[4]  S. Micera,et al.  Nanotopographic control of neuronal polarity. , 2011, Nano letters.

[5]  Rodney J. Douglas,et al.  Frontiers in Computational Neuroscience , 2022 .

[6]  G. Ciofani,et al.  Deterministic control of mean alignment and elongation of neuron-like cells by grating geometry: a computational approach. , 2015, Integrative biology : quantitative biosciences from nano to macro.

[7]  M. Cecchini,et al.  Neuronal polarity selection by topography-induced focal adhesion control. , 2010, Biomaterials.

[8]  E. Pasquale,et al.  Eph receptors inactivate R-Ras through different mechanisms to achieve cell repulsion , 2006, Journal of Cell Science.

[9]  G. Goodhill,et al.  A new chemotaxis assay shows the extreme sensitivity of axons to molecular gradients , 2004, Nature Neuroscience.

[10]  L. Greene,et al.  Rapid regulation of neuronal growth cone shape and surface morphology by nerve growth factor , 1987, Neurochemical Research.

[11]  Roman Borisyuk,et al.  A Developmental Approach to Predicting Neuronal Connectivity from Small Biological Datasets: A Gradient-Based Neuron Growth Model , 2014, PloS one.

[12]  Geoffrey J. Goodhill,et al.  Predicting Axonal Response to Molecular Gradients with a Computational Model of Filopodial Dynamics , 2004, Neural Computation.

[13]  Eshel Ben-Jacob,et al.  Generic modeling of chemotactic based self-wiring of neural networks , 1998, Neural Networks.

[14]  S. Micera,et al.  Cell Guidance on Nanogratings: A Computational Model of the Interplay between PC12 Growth Cones and Nanostructures , 2013, PloS one.

[15]  Silvestro Micera,et al.  Design of a cybernetic hand for perception and action , 2006, Biological Cybernetics.

[16]  Kozo Kaibuchi,et al.  Neuronal polarity: from extracellular signals to intracellular mechanisms , 2007, Nature Reviews Neuroscience.

[17]  Silvestro Micera,et al.  A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems , 2005, Journal of the peripheral nervous system : JPNS.