Abstract：
BACKGROUND:A key point in developmental biology is to understand how gene expression influences the morphological and dynamical patterns that are observed in living beings. NEW METHOD:In this work we propose a methodology capable of addressing this problem that is based on estimating the mutual information and Pearson correlation between the intensity of gene expression and measurements of several morphological properties of the cells. A similar approach is applied in order to identify effects of gene expression over the system dynamics. Neuronal networks were artificially grown over a lattice by considering a reference model used to generate artificial neurons. The input parameters of the artificial neurons were determined according to two distinct patterns of gene expression and the dynamical response was assessed by considering the integrate-and-fire model. RESULTS:As far as single gene dependence is concerned, we found that the interaction between the gene expression and the network topology, as well as between the former and the dynamics response, is strongly affected by the gene expression pattern. In addition, we observed a high correlation between the gene expression and some topological measurements of the neuronal network for particular patterns of gene expression. COMPARISON WITH EXISTING METHODS:To our best understanding, there are no similar analyses to compare with. CONCLUSIONS:A proper understanding of gene expression influence requires jointly studying the morphology, topology, and dynamics of neurons. The proposed framework represents a first step towards predicting gene expression patterns from morphology and connectivity.
journal_name
J Neurosci Methodsjournal_title
Journal of neuroscience methodsauthors
de Arruda HF,Comin CH,Miazaki M,Viana MP,Costa Lda Fdoi
10.1016/j.jneumeth.2015.02.019subject
Has Abstractpub_date
2015-04-30 00:00:00pages
1-14eissn
0165-0270issn
1872-678Xpii
S0165-0270(15)00071-0journal_volume
245pub_type
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