Get help from the best in academic writing.

Computer Simulation of Action Potential in Squid Axon

Please use this proforma to record your data. You should aim to complete the experimental part and answer all the questions before you leave.
Please show your completed pro-forma to one of the demonstrators before leaving so that they can check with you that you can answer all the questions.
This will form also be available as electronically on studynet. You can use the electronic form to produce a final version of your report for submission online.
Introduction In 1952, Hodgkin and Huxley published a series of four papers in the Journal of Physiology (London) reporting their experiments to investigate the underlying events of the action potential. In their final paper, they derived a series of equations that describe the relationship between sodium conductance (gNa ), potassium conductance (gK ) and the membrane potential in a squid axon following electrical stimulation. Hodgkin and Huxley were awarded the Nobel Prize for this work.
In this practical, you will use a computer program based on the Hodgkin and Huxley equations to show what is happening to the membrane potential, gNa and gK during and after electrical stimulation. An example of the output from the program is illustrated in figure 1. It can be seen that the electrical stimulation depolarises the membrane. Once a depolarisation of 30mV has occurred, the conductance to sodium ions increases rapidly and the membrane potential rises to 20mV. The rise in gK is slower in onset and lasts for longer than the increase in gNa . The fall in gNa and the associated rise in gK returns the membrane potential towards the resting value.
Methods and Results Run the Squid Giant Axon simulation from the Start menu, HHX.
Experiments using a single electrical stimulus In the first series of experiments, you will use a single electrical stimulus to initiate an action potential. Run a simulation with the following parameters:

Q1 and 2. Investigate the effects of varying stimulus amplitude and duration by running all the simulations shown in the matrix below in Table 1: Enter a ‘X’ in the Table 1 matrix for experiments that produce an action potential, and record the peak height, amplitude, latency and threshold of any action potentials in Table 2 overleaf. For experiments that fail to elicit an action potential, enter a ‘O’ in the matrix below, and record a value of ¥ (infinity) for the latency and – for the other parameters in the table overleaf.
Q3. Plot two graphs to show the relationship between: (i) Stimulus strength and latency and (ii) Stimulus duration and latency.
How these graphs should be plotted is not immediately obvious, and information on how to complete this task will not be explicitly given! The optimal solution to the problem is for you to find, but the following points are provided for guidance:
It is not legitimate to plot infinity on graphs
It is not appropriate to extrapolate beyond data points
It is not legitimate to plot average latencies. The graphs must be plotted so that every value of latency (except ¥) is represented.
Use the blank sheet on the proforma, there is no need to use graph paper.
Graph 1: Stimulus strength and latency
Graph 2: Stimulus Duration and Latency
Experiments with dual stimuli
Q4. Run a simulation with the following parameters to demonstrate the absolute refractory period:
Stimulation A shows a normal single, action potential, with the second action potential not being produced as the stimulation doesn’t depolarise the membrane fully, it only caused a minor depolarisation of -92mV. The reason there isn’t a second action potential is due to the fact that there is a lack of repolarisation, this is called the absolute refractory period. During this period, a second action potential cannot be initiated, no matter how large the stimulus is being applied.
Stimulation B still only produces one action potential, which peaks at 17mV. Again a second is not produced because the stimulus is not large enough to create an action potential, however the second depolarisation is slightly larger than the one from stimulation A, as its peak is -81mV. This shows that this time, the neurone is again in absolute refractory period.
Q5. Repeat the simulations, but with a longer delay between stimuli:
Stimulation C again produces a single action potential; however the second peak of depolarisation is larger than both those found in stimulation A and B. The peak of the action potential is 17mV. The second depolarisation reaches -81mV, which is higher than stimulation A or B.
Stimulation D produces two action potentials. The first action potential is larger than the second. Stimulation D shows a period of relative refractory period as when stimulus 2 is increased to 100 mV and the duration is increased to 7ms.
Discussion Q6. Briefly justify why a latency of ¥ was recorded if an action potential was not produced.
As there is no action potential, so this means it will never occur, so the latency will never be reached. This is the same for infinity, we can never reach it, so this is an appropriate number.
Q7. What evidence from your results suggests that action potentials are threshold phenomena?
As we can see, action potential depends on the threshold voltage being reached. The threshold voltages are all around the same value, about -70mV. From this we can see that once this threshold is reached then the action potential will occur.
Q8. Comment briefly on the amplitude of the action potentials generated in these experiments.
All the amplitudes from the action potential are around the same value. This is due to the all or nothing principle. Once an action potential is fired, it is always the same strength.
Q9. From Graph 1, describe the effect of increasing stimulus strength on the latency of the action potential.
Overall, the trend of the graph is that as you increase stimulus strength, the latency of the action potential reduces. On average, most of the stimulus durations follow the same pattern. Duration
0.5 ms doesn’t have enough data to create a curve. The point 20µA/cm2, duration 0.5 ms, is an anomaly, as it doesn’t match in with the rest of the graph. It is nearly 0.5 ms too high. Also, the graph has an unusual peak at 10 µA/cm2, duration 1 ms. This is about 2 ms above the rest of the graph.
Q10. From Graph 2, describe the effect of increasing stimulus duration on the latency of the action potential.
Overall, the trend of the graph is that as you increase stimulus duration, the latency remains constant. After 2 ms, all of the latencies remain steady, except from stimulus strength 7µA/cm2. This has a slight reduced latency, making a parabola shaped graph. However, all the latencies before 1 ms, are all increased compared to the points at 2 ms.
Q11. Draw a simple flow diagram to illustrate the positive feedback cycle that results in the rapid depolarizing phase of the action potential.
Q12. What event at the ion channel level terminates the above cycle?
The potassium ions move out of the cell and the sodium ion channels shut.
Q13. What physiological mechanism is responsible for the absolute refractory period?
Absolute refractory period is caused by sodium ion channels being open even after an action potential has occurred. This means you cannot generate another action potential, until the membrane hyperpolarises. Once the channels close, they activate again, so an action potential can be generated again.
Q14. Explain your observations to simulations C and D in the Methods and Results section.
Stimulation C only has one action potential; this is due to the fact that the second amplitude is during the relative refractory period. This is shown as the second stimulation produces a slight depolarisation of the membrane; however it isn’t large enough to produce an action potential. However, the depolarisation is larger than that in stimulations A or B, and this is due to the delay of 7 ms.
Stimulation D has two action potentials. This is due to the fact that the second stimulation is large enough to create a large depolarisation during the relative refractory period. This large depolarisation then causes the second action potential. Also, the delay of 7 ms allows the second action potential.
Q15. Briefly summarise two effects that refractory periods impose on the behaviour of neurones (N.B. restatement of the definitions of refractory periods is not what is asked here)
The refractory periods have two main effects on the behaviour of neurones. These are frequency coding and unidirectional propagation of action potentials. Frequency coding is the stimulus intensity of the action potential; this determines the number of action potentials that occur per specific time period. A stimulus with a longer duration will produce more than one action potential, as the time period for a second action potential to occur is longer. This means that it can overcome the relative refractory period. Unidirectional propagation of action potential makes sure that action potentials only travel in one direction. This makes sure that the second action potential doesn’t occur in the wrong direction.
Questions to answer after the practical. Q 16 . Most Local anaesthetics are Sodium channel blockers. Describe how these compounds work, the side-effects and what their main clinical uses are. ( max 300 words).
Local anaesthetics work by inhibiting the voltage dependant sodium channels located in the neurones. By inhibiting these channels, depolarisation doesn’t occur. This will lead onto action potentials not being produced in the neurone. If this occurs in the sensory neurones, this will prevent action potentials being fired towards the central nervous system. This means communication has broken down, so no pain will be felt during clinical procedures.
The side effects of local anaesthetics are that the effect is total. All neurones will not be able to fire any action potentials, so all feelings and movement in the area is lost. Other side effects include confusion, respiratory depression and convulsions, hypotension and bradycardia, which could lead onto a cardiac arrest. Also, hypersensitivity has also been reported.
The main clinical use for local anaesthetics is during dental procedures and during minor surgery on a small part of the body, often performed by a GP or a surgeon.

Cerebral Autoregulation Mechanism | Report

From: Biose Ifechukwude Joachim
Cerebral autoregulation (CA) is the multifactorial vascular mechanism that maintains a constant cerebral blood supply in spite of fluctuations in the cerebral perfusion pressure (CPP) (Lassen, 1959; Tiecks et al., 1995). This mechanism thrives for CPP values within the range of 50-150 mmHg (Lassen, 1959; Paulson, Strandgaard and Edvinsson, 1990; Panerai, 1998) (Fig. 1).
The vascular response involved in CA is rapid and so robust that hypertension (Eames et al., 2003; Serrador et al., 2005; Zhang et al., 2007) and aging (Eames et al., 2003; Fisher et al., 2008; Liu et al., 2013; Oudegeest-Sander et al., 2014) does not alter its physiological role.
However, CA is compromised following pathologic conditions such as traumatic brain injury, intracerebral haemorrhage, stroke, hyper-perfusion syndrome, and subarachnoid haemorrhage (Diedler et al., 2009; Atkins et al., 2010; Budohoski et al., 2012; Saeed et al., 2013; Buczek et al., 2013).

Fig. 1. Cerebral autoreglation in relation to vascular response. Within the upper and lower boundaries of the autoregulatory range (dotted lines), blood flow remains constant (blue line with beads). As Pressure falls below the lower limit, vascular smooth muscle relaxes to allow dilatation, while constriction of vessels (red circles) ensues to reduce blood flow as pressure approximates the upper limit. Adapted from Pires et al., 2013.
Based on factors affecting cerebral blood flow (CBF), CA can be classified into two categories, metabolic autoregulation (MA) and pressure autoregulation (PA).
Mainly due to changes in brain tissue pH (Cotev and Severinghaus, 1969; Betz and Heuser, 1967; Raichle, Posner and Plum, 1970), MA is the principal regulatory mechanism of CBF according to metabolic demand. This implies that MA responds to local or global ischemia and hypoxia which increases pH by increasing CBF via vasodilatation (Ekstrom-Jodal et al., 1971; Raichle and Stone, 1971).While PA is the vascular response to maintain blood flow following changes in perfusion pressure, achieved by varying the degree of vasoconstriction or vasodilatation of the cerebral vasculature.
In adults and under normal conditions, provided CPP falls within the boundary of 50-150 mmHg, CBF is preserved at approximately 50 mL per 100 g of brain tissue per minute (McHenry et al., 1974; Strandgaard et al., 1976; Paulson, Strandgaard and Edvinsson, 1990). Outside this range of CPP, CA is impaired and CBF becomes directly dependent on mean arterial pressure (MacKenzie et al., 1976; Heistad and Kontos, 1979; Baumbach and Heistad, 1985; Paulson et al., 1990). More so, should CPP falls below the lower boundary of CA, blood flow reduces and ischemia sets in (Hossmann, 2006).
The precise mechanism of CA is currently elusive; however, it is believed to be subject to the interaction of neurogenic, metabolic and myogenic factors (Czosnyka et al., 2009; Novak and Hajjar, 2010).
Intrinsic innervation is touted to be directly involved in the mechanisms of CA (Goadsby and Edvinsson, 2002) and extrinsic pathway is implausible, since CA is unimpaired following sympathetic and parasympathetic denervation in experimental animals (Busija and Heistad, 1984). The perikarya within the subcortical region of the brain, precisely those from the nucleus basalis, locus ceruleus and raphe nucleus project to cortical microvessels for the control of local blood flow by release of neurotransmitters (ACH, norepinephrine and 5HT) (Hamel, 2006). These released neurotransmitter substances interact with the receptors on smooth muscle, endothelium, or astrocytes to cause constriction or dilation, thus regulating blood supply according to the metabolic demand (Iadecola, 2004; Hamel, 2006; Drake and Iadecola, 2007).
Also, metabolic by-products released by the brain during CBF decrease are important for CA (Paulson, Strandgaar and Edvinsson, 1990). These substances, potassium, adenosine, and hydrogen ion triggers vasodilatation.
Another important component of the CA mechanism is the myogenic response of the cerebrovascular smooth muscle in regulating vascular tone. Constriction of the cerebral vasculature due to smooth muscle contraction ensues during pressure fluctuations at the upper boundary of the autoregulatory range of CPP, thus blood flow is not excessive (Fig. 1). Conversely, fluctuations at the lower limit of CPP is followed by vasodilatation (Fig.1) (Kontos, 1978,Busija and Heistad, 1984; Mellander, 1989; Osol et al., 2002).
Furthermore, the direct contact between astrocytes and the parenchymal arterioles of the brain have been shown to play a role in CA (Rennels and Nelson, 1975; Cohen, Molinatti and Hamel, 1997; Iadecola, 2004; Hamel, 2006; Drake and Iadecola, 2007; Zlokovic, 2008). Most microvessels at the subcortical level have astrocytic end-feet at the interface between them and neurons (Kulik et al., 2008), thus, under the direct influence of the vasoactive factors released by astrocytes (Murphy et al., 1994).
Interestingly, the type of cerebral vasculature may also contribute to CA in an unexpected manner, with respect to their response to blood flow changes. While basilar artery dilates in response to increased blood flow, MCA constricts Koller and Toth, (2012).
Under Anaesthesia
Anaesthesia puts the brain in a state of reduced neuronal activity, as a result CBF decreases in light of neurovascular coupling (Attwell et al., 2010). Also, in their studies in rats, Jones et al., (2002) reported that anaesthesia reduces the CCP levels below the lower limit of CA.
More importantly, anaesthetics have significant impact on CA as they affect the vasculature of the brain, directly or indirectly. Under the influence of volatile anaesthetics, calcium entry via voltage gated Ca2 channels on vascular smooth muscle cells is reduced significantly, causing the vasculature to dilate (Bosnjak et al. 1992), thereby, directly overriding CA. Also, anaesthetics cause profound respiratory depression in spontaneously breathing animals, consequently PaCO2 increased.
Given that the vasculature of the brain is highly sensitive to changes in CO2, an increase value of PaCO2 stimulates cerebral vasodilatation (Kuschinsky, 1997; Willie et al., 2014); correspondingly CBF increases (Figure 2). These effects of anaesthetics lead ultimately to the failure of CA in mammals.
However, certain anaesthetics for example Ethomidate, preserves CA (Wang et al., 2010). This is mainly due to their ability to keep PaCO2 nearly constant within the nomal range without artificial ventilation (Lacombe et al. 2005; Joutel et al., 2010).

Fig. 2. Cerebral blood flow with respect to arterial pressure of CO2.
CBF increases as PaCO2 level increases beyond the level of 25 mmHg. However, at 80 mmHg blood vessels are maximally dilated and CBF remains constant with a further increase in PaCO2 values. Adapted from Adapted from Hill and Gwinnutt, no date.
During arterial occlusion, as in the case of ischaemic stroke, local cerebral perfusion pressure falls below the normal CA range while MAP does not change. With persistent occlusion, autoregulation fails (Reinhard et al., 2008; Reinhard et al., 2012; Immink et al., 2005; Atkins et al., 2010) and regional CBF further decreases. For this reason, blood pressure changes, high or low, results in poor outcome (Castillo et al, 2004; Aslanyan et al., 2003; Sandset et al., 2012). However, this is not entirely due to the failed autoregulatory capacity of the vessels during ischemia, but perhaps their normal vasodilatory capacity has reached a maximal limit (Petersen et al., 2015).
The impaired autoregulatory response following acute stroke has been observed both in the affected and contralateral hemispheres (Cupini et al., 2001; Dawson et al., 2000; Dawson, Panerai and Potter, 2003; Fieschi et al., 1988; Gelmers, 1982; Lisk et al., 1993; Hakim et al., 1989).
Aslanyan S, Fazekas F, Weir CJ, Horner S and Lees KR (2003). GAIN International Steering Committee and Investigators: Effect of blood pressure during the acute period of ischemic stroke on stroke outcome: a tertiary analysis of the GAIN International Trial. Stroke. 34: 2420–2425.
Atkins ER, Brodie FG, Rafelt SE, Panerai RB and Robinson TG (2010). Dynamic cerebral autoregulation is compromised acutely following mild ischaemic stroke but not transient ischaemic attack. Cerebrovasc. Dis. 29: 228–235.
Attwell D, Buchan AM, Charpak S et al. (2010). Glial and neuronal control of brain blood flow. Nature. 468: 232–43.
Baumbach GL and Heistad DD (1989). Remodeling of cerebral arterioles in chronic hypertension. Hypertension. 13: 968–972.
Betz E and Heuser D (1967). Cerebral cortical blood flow during changes of acid-base equilibrium the brain. J. Appl. Physiol. 23: 726-733.
Bosnjak ZJ, Aggarwal A, Turner LA, Kampine JM and Kampine JP (1992). Differential effects of halothane, enflurane, and isofluurane on Ca2 transients and papillary muscle tension in guinea pigs. Anesthesiology. 76: 123–131
Buczek J, Karlin´ski M, Kobayashi A, BiaÅ‚ek P and CzÅ‚onkowska A (2013). Hyperperfusion syndrome after carotid endarterectomy and carotid stenting. Cerebrovasc. Dis. 35: 531–7.
Budohoski KP, Czosnyka M, Smielewski P, Kasprowicz M, Helmy A, Bulters D et al. (2012). Impairment of cerebral autoregulation predicts delayed cerebral ischemia after subarachnoid hemorrhage: a prospective observational study. Stroke. 43: 3230–3237.
Busija DW and Heistad DD (1984). Factors involved in the physiological regulation of the cerebral circulation. Rev. Physiol. Biochem. Parmacol. 101: 161–211.
Castillo J, Leira R, García MM, Serena J, Blanco M and Dávalos A (2004). Blood pressure decrease during the acute phase of ischemic stroke is associated with brain injury and poor stroke outcome. Stroke. 35: 520–526.
Cohen Z, Molinatti G and Hamel E (1997). Astroglial and vascular interactions of noradrenaline terminals in the rat cerebral cortex. J. Cereb. Blood Flow Metab. 17: 894–904.
Cotev S and Severinghaus JW (1969). Role of cerebrospinal fluid pH in management of respiratory problems. Anesth. Analg. 48: 42-47.
Cupini LM, Diomedi M, Placidi F, Silvestrini M and Giacomini P (2001). Cerebrovascular reactivity and subcortical infarctions. Arch. Neurol. 58: 577–581.
Czosnyka M, Brady K, Reinhard M, Smielewski P and Steiner LA (2009). Monitoring of cerebrovascular autoregulation: facts, myths, and missing links. Neurocritical Care. 10: 373–86.
Dawson SL, Blake MJ, Panerai RB and Potter JF (2000). Dynamic but not static cerebral autoregulation is impaired in acute ischaemic stroke. Cerebrovasc. Dis.10:126–132.
Dawson SL, Panerai RB and Potter JF (2003). Serial changes in static and dynamic cerebral autoregulation after acute ischaemic stroke. Cerebrovasc. Dis. 16:69–75.
Diedler J, Sykora M, Rupp A et al. (2009). Impaired cerebral vasomotor activity in spontaneous intracerebral hemorrhage. Stroke. 40: 815–9.
Drake CT and Iadecola C (2007). The role of neuronal signalling in controlling cerebral blood flow. Brain Lang. 102: 141–152.
Ekstrom-Jodal B, Haggendal E, Linder LE and Nilsson NJ (1971). Cerebral blood flow autoregulation at high arterial pressures and different levels of carbon dioxide tension in dogs. Eur. Neurol. 6:6-10.
Fieschi C, Argentino C, Toni D and Pozzilli C (1988). Calcium antagonists in ischemic stroke. J. Cardiovasc. Pharmacol. 12(6): 83–85.
Fisher JP, Ogoh S, Young CN, Raven PB and Fadel PJ (2008). Regulation of middle cerebral artery blood velocity during dynamic exercise in humans: influence of aging. J. Appl. Physiol. 105: 266–273.
Goadsby PJ and Edvinsson L (2002). Neurovascular control of the cerebral circulation, Lippincott Williams