Recent Advances in Modeling Cells, Cellular Compartments and Signaling Networks as a Tool to Understand the Nervous System
In the last two decades, the progress in molecular biology and associated areas promoted a large accumulation of structural and molecular data about the nervous system. Almost simultaneously with that, fields such as Computational Neuroscience and Systems Biology have emerged with the aim to obtain a system-level understanding about the nervous systems through the development of computational models based on experimental data. Today, both fields are consolidated areas of research and have given valuable contributions to our knowledge of processes such as electrical integration, calcium dynamics, synaptic plasticity, sensory transduction, pathologies, among many others. In this special volume, we want to highlight some of the recent contributions of both computational neuroscience and systems biology to Neuroscience especially through the development and analysis of computational models of cells, cellular compartments and signaling networks. These types of models are powerful tools to investigate the physiological and molecular dynamics of neurons with a large range of spatial and temporal resolutions. Therefore, in this special volume, our intent is to select bleeding edge contributions focused on the gather works that will cover simulations of signaling networks and signal transduction solved deterministically or stochastically; the interaction between signaling networks and the regulation of ionic channels through covalent modifications such as phosphorylation; the impact of neuronal geometries on signaling networks and on cellular electrical integration. Thus, we have the expectation that this special volume will be of interest to both the people working in the fields on Computational Neuroscience and Systems Biology, and to experimental biophysicists that will have the opportunity to get an overall view of the main topics that have been investigated with computational models of cells and signaling networks.