Computational NeuroscienceA framework for analyzing the relationship between gene expression and morphological, topological, and dynamical patterns in neuronal networks

- Measuring gene expression influence on structure and dynamics.
- A first step towards predicting gene expression from morphology and connectivity.
- Different patterns of gene expression yield specific topology and dynamics.

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.