Open Access

Investigation of cellular and network dynamics in the brain by means of modeling and simulation has evolved into a highly interdisciplinary field, that uses sophisticated modeling and simulation approaches to understand distinct areas of brain function. Depending on the underlying complexity, these models vary in their level of detail, in order to cope with the attached computational cost. Hence for large network simulations, single neurons are typically reduced to time-dependent signal processors, dismissing the spatial aspect of each cell. For single cell or networks with relatively small numbers of neurons, general purpose simulators allow for space and time-dependent simulations of electrical signal processing, based on the cable equation theory. An emerging field in Computational Neuroscience encompasses a new level of detail by incorporating the full three-dimensional morphology of cells and organelles into three-dimensional, space and time-dependent, simulations. While every approach has its advantages and limitations, such as computational cost, integrated and methods-spanning simulation approaches, depending on the network size could establish new ways to investigate the brain. In this paper we present a hybrid simulation approach, that makes use of reduced 1D-models using e.g., the NEURON simulator—which couples to fully resolved models for simulating cellular and sub-cellular dynamics, including the detailed three-dimensional morphology of neurons and organelles. In order to couple 1D- and 3D-simulations, we present a geometry-, membrane potential- and intracellular concentration mapping framework, with which graph- based morphologies, e.g., in the swc- or hoc-format, are mapped to full surface and volume representations of the neuron and computational data from 1D-simulations can be used as boundary conditions for full 3D simulations and vice versa. Thus, established models and data, based on general purpose 1D-simulators, can be directly coupled to the emerging field of fully resolved, highly detailed 3D-modeling approaches. We present the developed general framework for 1D/3D hybrid modeling and apply it to investigate electrically active neurons and their intracellular spatio-temporal calcium dynamics.

Background: Targeted therapies have improved therapeutic options of treating renal cell carcinoma (RCC). However, drug response is temporary due to resistance development. Methods: Functional and molecular changes in RCC Caki-1 cells, after acquired resistance to the mammalian target of rapamycin (mTOR)-inhibitor everolimus (Cakires), were investigated with and without additional application of the histone deacetylase (HDAC)-inhibitor valproic acid (VPA). Cell growth was evaluated by MTT assay, cell cycle progression and apoptosis by flow cytometry. Target molecules of everolimus and VPA, apoptotic and cell cycle regulating proteins were investigated by western blotting. siRNA blockade was performed to evaluate the functional relevance of the proteins. Results: Everolimus resistance was accompanied by significant increases in the percentage of G2/M-phase cells and in the IC50. Akt and p70S6K, targets of everolimus, were activated in Cakires compared to drug sensitive cells. The most prominent change in Cakires cells was an increase in the cell cycle activating proteins cdk2 and cyclin A. Knock-down of cdk2 and cyclin A caused significant growth inhibition in the Cakires cells. The HDAC-inhibitor, VPA, counteracted everolimus resistance in Cakires, evidenced by a significant decrease in tumor growth and cdk2/cyclin A. Conclusion: It is concluded that non-response to everolimus is characterized by increased cdk2/cyclin A, driving RCC cells into the G2/M-phase. VPA hinders everolimus non-response by diminishing cdk2/cyclin A. Therefore, treatment with HDAC-inhibitors might be an option for patients with advanced renal cell carcinoma and acquired everolimus resistance.