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Understanding pain, virtually

Read time: 5 mins
Understanding pain, virtually

A computer based model of neurons in the urinary bladder

One of the hardest things to do is resisting the urge to urinate. Our brain receives signals from the bladder that it is full, and directs our action to empty the bladder. While science tells us that holding a full bladder is unhealthy, we still wait to relieve ourselves. However, individuals suffering from a range of bladder disorders, like bladder inflammation or overactivity, are prone to accidental urine leakage as the stimuli from the bladder are amiss. In a recent study, Dr Darshan Mandge and Prof Rohit Manchanda from the Indian Institute of Technology Bombay (IIT Bombay), have made a computer based model of the workings of sensory neurons supplying the urinary bladder, to understand the neurological processes. They have identified the mechanisms that govern the repetitive firing of neurons during abnormal conditions such as inflammation and spinal cord injury. 

The urinary bladder has sensory neurons called dorsal root ganglion neurons (DRG Neurons), which transmit the signals related to bladder pressure, volume and temperature, to the brain. The smaller DRG neurons also act as pain receptors, sensing any discomfort in the bladder and sending it to the brain. Since these neurons are tightly packed in the bladder and challenging to separate, studying their function, while still inside the body, is not possible. Here's where computational models of the working of the neurons play a significant role.

In the current study, published in the journal PLOS Computational Biology, the researchers have developed a computational model of the small dorsal root ganglion. They have verified the authenticity of the model based on experimental data. Using the model, they have tried to understand the different responses generated by this neuron to stimuli like heat, bladder volume and pressure, and analyse what goes wrong during various pathological conditions of the bladder.

A typical neuron consists of a cell body called the soma, and a long cylindrical structure called the axon conducts the electrical signals. At the end of the axon are tree-like terminals that receive electrical signals from the neighbouring neurons. Specific molecules bind to receptors on the surface and activate a signal. There are also specific ion channels present on the membrane surface, which allows the movement of ions that generate a small voltage, allowing information to pass from one neuron to the next, and ultimately to the brain.

The computational model developed by the researchers of the current study encompassed the function of every component of the soma of the neuron, including receptors, ion channels and signals. It also captures the change in potential resulting from the movement of ions. In all, there are 22 cellular mechanisms incorporated into it. The researchers have also checked the working of every aspect of the model against experimental data to make it accurate.

"Tuning multiple parameters of each ionic mechanism to validate them experimentally was one of the biggest challenges we faced while creating the model," says Dr Darshan Mandge. "Each channel is represented by multiple differential equations and parameters. With 22 different mechanisms modelled, the number of parameters was close to 100," he recollects.

The activation of ion channels holds the key to how neural signals are generated and transmitted. For example, a calcium-activated potassium channel called SKCa is activated based on the concentration of calcium ions in the cell. When there is a difference, a small electric current is generated due to the movement of ions. It activates for both positive potential and negative potential. Experimental data shows that the current conducted for a positive potential is always smaller than the one at the negative potential, indicating that any change affects the firing rate or the number of signals sent by the cell. But one question hounded science—how does the difference in the potential influence the cell's firing rate?

The researchers of the current study hypothesised that the favourable inward movement via the SKCa ion channels might affect the repetitive firing of the neuron. They tested this hypothesis by incorporating this property into the model and measuring the outcome. They found that a relatively smaller ionic movement flow through the channels to the outside the cell promotes its spiking activity which is similar to that seen experimentally in sensory neurons from pathological conditions such as bladder overactivity.

The researchers have added to the model, six different types of potassium channels that were reported experimentally earlier, including the SKCa and A-type channels. Another hypothesis the researchers proposed was that the A-type potassium ion channel was responsible for bringing back the potential in the cell to the resting state. They believed that there could be different parts of the channel responsible for bringing down the current. When this hypothesis was tested with the model, they found that two phases of current—a fast phase and a slow phase—were caused by a combination of proteins in the two parts of the channel.

"By modelling the sensory neurons and connecting them to other components of the neural reflex pathway, one can understand the neural control of bladder during normal conditions. We can predict what could go wrong in pathological conditions such as bladder overactivity where there is repetitive firing," says Dr Mandge.

The researchers plan to improvise their model to include the functions of the axon and axon terminals and validate it against experimental data.

This article has been run past the researchers, whose work is covered, to ensure accuracy.