computational model

Investigating an effective controller to shift hippocampal epileptic periodicity to normal chaotic behavior will be new hope for epilepsy treatment. Astrocytes nourish and protect neurons and maintain synaptic transmission and network activity. Therefore, this study explored the ameliorating effect of the astrocyte computational model on epileptic periodicity. 

Modified Morris-Lecar equations were used to model the hippocampal CA3 network. Network inhibitory parameters were employed to generate oscillation-induced epileptiform periodicity. The astrocyte controller was based on a functional dynamic mathematical model of brain astrocytic cells.

Results demonstrated that the synchronization of two neural networks shifted the brain’s chaotic state to periodicity. Applying an astrocytic controller to the synchronized networks returned the system to the desynchronized chaotic state.

It is concluded that astrocytes are probably a good model for controlling epileptic periodicity. However, more research is needed to delineate this effect.

Drugs of abuse, such as cocaine, affect different brain regions and lead to pathological memories. These abnormal memories may occur due to changes in synaptic transmissions or variations in synaptic properties of neurons. It has been shown that cocaine inhibits delayed rectifying potassium currents in affected brain regions and can create pathological memories.This study investigates how the change in the conductance of delayed rectifying potassium channels can affect the produced action potentials using a computational model. 

We present a computational model with different channels and receptors, including sodium, potassium, calcium, NMDARs, and AMPARs, which can produce burst-type action potentials. In the simulations, by changing the delayed rectifying potassium conductance bifurcation diagram is calculated.

By decreasing the potassium current for a fixed stimulatory signal, burst-type action potentials can be generated. In the following and with a further reduction of potassium conductance, produced action potentials exhibit non-linear and even chaotic behaviors.

Results show that for a specific range of potassium conductance, a chaotic regime emerges in produced action potentials. These chaotic oscillations may play a role in inducing abnormal memories.

In a new approach, computational methods are used to design and evaluate the vaccine. The aim of the current study was to develop a computational tool to predict epitope candidate vaccines to be tested in experimental models.

This study was conducted in the School of Allied Medical Sciences, and Center for Research and Training in Skin Diseases and Leprosy, Tehran University of Medical Sciences, Tehran, Iran in 2018. The random forest which is a classifier method was used to design computer-based tool to predict immunogenic peptides. Data was used to check the collected information from the IEDB, UniProt, and AAindex database. Overall, 1,264 collected data were used and divided into three parts; 70% of the data was used to train, 15% to validate and 15% to test the model. Five-fold cross-validation was used to find optimal hyper parameters of the model. Common performance metrics were used to evaluate the developed model.

Twenty seven features were identified as more important using RF predictor model and were used to predict the class of peptides. The RF model improves the performance of predictor model in comparison with the other predictor models (AUC±SE: 0.925±0.029). Using the developed RF model helps to identify the most likely epitopes for further experimental studies.

The current developed random forest model is able to more accurately predict the immunogenic peptides of intracellular parasites.