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Computational Model Provides Insight into the Distinct Responses of Neurons to Chemical and Topographical Cues

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Abstract

Neuronal cell polarization (i.e., establishment of an axon) and axon guidance are mediated and controlled by mechanical and chemical signals from the environment. Unfortunately, an integrated approach to study cell–substrate interactions in a unified framework incorporating structural and chemical effects of the substrate has been lacking. In this paper, we present a new model combining experimental and computational methods to better understand the distinct behavior of E18 hippocampal neurons in response to topographical vs. immobilized chemical cues. We present results from our coarse-grain physiological computational model that correctly describes previously observed phenomena and predicts behavior that was subsequently tested through new experiments. The model differentiates topographical from chemical cues via a difference in cue spacing in these two substrates. Using the feature size spacing for topographical cues and a minimum step size, governed by the physics of filopodia protrusion, for chemical cues, the model successfully mimics the trend observed in experimental polarization probability for four different topographical feature sizes and constant chemical cue spacing. Our results not only show good agreement with experiments, but also provide novel suggestions for development of substrates for finer control of neuronal cell polarization.

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Acknowledgments

The authors would like to thank the assistance of Tianyi Yang, Natalia Gomez, and members of the Schmidt and Zaman labs for numerous enlightening discussions. The resources of the NST and MRC at the University of Texas at Austin are gratefully acknowledged. This work was supported by faculty development startup funds from the Cockrell School of Engineering to MHZ and by an NIH Grant (R21 EB003416) to CES.

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Authors and Affiliations

  1. Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA

    Leandro Forciniti & Christine E. Schmidt

  2. Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA

    Christine E. Schmidt & Muhammad H. Zaman

  3. Institute of Theoretical Chemistry, The University of Texas at Austin, Austin, TX, 78712, USA

    Muhammad H. Zaman

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  1. Leandro Forciniti
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  2. Christine E. Schmidt
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Correspondence to Christine E. Schmidt or Muhammad H. Zaman.

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Forciniti, L., Schmidt, C.E. & Zaman, M.H. Computational Model Provides Insight into the Distinct Responses of Neurons to Chemical and Topographical Cues. Ann Biomed Eng 37, 363–374 (2009). https://doi.org/10.1007/s10439-008-9613-x

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  • DOI: https://doi.org/10.1007/s10439-008-9613-x

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