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Effect of Temperature on Enzyme Activity | Experiment

Rationale:
Enzymes are proteins that greatly speed up the rate of reaction and lower the activation energy required for the reaction to occur (Evans, B., Ladgies, P., McKenzie, J. and Spencely, M. 2004). Since they are proteins, enzymes can be denatured by an increase in heat. Though limited in rage of effectiveness, an increase in heat causes an increase in molecular motion, the reactants begin to move around more quickly and become more likely to collide into each other, causing a reaction (Kuipers, K., Leillor, P., Sharpe, P., Bloomfield, C. and Silvester, H. 2019). The temperature that causes the most activity is called the optimum temperature (Bliss, C., Fesuk, S. and Jacobs, J.). If the temperature is decreased and increased, the efficiency of the enzyme decreases and there is a decrease in reaction rate. This is because the temperature has the ability to change the structure of enzymes and make them become denatured or inactivated (Borger, P., Grant, P., Munro, L. and Wright, J. 2019).
The enzyme rennin if found in the gastric juice of young mammals and has the ability to react with milk proteins. It can turn the protein caseinogen found in milk, into clumps of insoluble curds of casein. This process brings out the proteins of the milk so that they are more readily available to the digestive system. Rennin performs best at the body temperature of 37oC.
Junket is a dessert that is based off this enzymatic reaction and is composed of the commercially available substance rennet.
Research question:
Does increasing the amount of junket solution applied, produce more defined results in showing that the milk at a higher temperature will allow the rennin to perform more efficiently because the heightened temperature will cause the enzymes to increase their activity?
Original experiment:
The methodology used was Experiment 7.2A: The effects of temperature on the rate of reaction of an enzyme, which was composed and developed by Oxford University Press Australia and New Zealand.
The original experiment had milk set to three different temperatures; Less than 15oC, 37oC and 60oC. Each had a set time of 10 minutes to allow the milk to properly adjust in temperature. 100mL of junket solution was made up with the concentration of one junket tablet. 6 drops of the junket solution were then applied to the three different temperature beakers of milk. Each sample was left for 10 minutes in the correct temperature to allow the reaction to take place and then were taken out and the results were recorded. Each sample was examined and its qualitative properties were recorded on whether they were liquid, solid or a state in between.
Modifications to the experiment:
To ensure that accurate and reliable data was collected, the methodology was much the same as the original with only a single change made. The change was instead of applying 6 drops of junket solution to the milk samples, 9 drops would be applied. This change to the original experiment was made in the pursuit to produce more defined results in showing how the enzyme rennin performs in correlation to temperature. In order to determine whether a higher temperature will allow the rennin to perform more efficiently, more precise results were needed. To produce these needed results, the amount of junket solution applied to the milk was increased with the expectation that some of the milk samples would set and prove the hypothesis that a higher temperature will allow the rennin to perform more efficiently.
Safety considerations:
Safety and some of the risks were taken into consideration by completing a full risk assessment
(see Appendix #1)
Processed Data:
The data recorded was collected as qualitative data. Each sample was put into a table and given a number to represent their state and degree of setting:
liquid
lumpy liquid
Very softy set, does not maintain shape when gently wobbled
Maintains shape when gently wobbled
Maintains shape when vigorously wobbled
The milk samples were poured from the test tubes into beakers and the degree of setting was recorded. The first experiment where 6 drops of junket solution are applied was repeated three times and the results are shown in the table below:
Original Experiment
Tube
Degree of setting
<15oC
1
1
1
37oC
2
2
2
60oC
1
2
1

Every sample had no change in state and all remained liquids. The experiment that was modified to have 9 drops of junket solution applied was also repeated three times. The results are shown in the table below:
Modified Experiment
Tube
Degree of setting
<15oC
1
1
1
37oC
1
1
1
60oC
1
1
1
The concept that increasing the number of drops of junket solution applied would provide more definitive and defined results was in no way supported by this data. By increasing the number of drops, instead of a more defined result, the results were much more obscure and vague making is even more difficult to evaluate whether rennin performs more efficiently in higher temperatures.
By increasing the amount of junket solution applied the results were in no way improved, thus meaning the best data collected to make an evaluation on the correlation of temperature and the efficiency of enzymes is the data from the original experiment.
Going off the data collected from the original experiment, the evaluation can be made that the optimum temperature for rennin is not higher temperatures such as 60oC but is closer to 37oC. The data showed that cold temperatures cause the rennin enzymes to inactivate as the samples that were <15oC showed no degrees of setting. The data also showed that higher temperature causes the enzymes to denature as the samples that were 60oC showed inconsistent degrees of setting. Finally, it was found that normal or warm temperature is the optimum temperature for enzymes as the most degrees of setting occurred at this temperature.
Drawing these conclusions from the results from the original experiment is limited by the fact that the experiment did not provide definitive results. With the range of setting being so finite that the samples that did set, only had minuscule lumps in them. This makes the evaluation that warm temperature is the optimum temperature for rennin an imprecise and uncertain.

Evaluation:
The modified experiment possessed a main problem that caused limitations to the experiment as a whole which in turn produced results that were limited in their communication of enzyme efficiency.
The main source of limitation was found in the lack of range in the number of drops of junket solution applied to the milk samples. The application of only 6 and 9 drops created a very limited array of tests and provided even more so results. Instead of only changing the number of drops to a set number, the number should have been changed to an array to provide more extensive samples and results.
Sources of error:
Both the original and the modified experiment exhibited a number of sources of error that could have changed the reliability and validity of the results collected.
One source of error was found in the use of the water baths and the management of their temperature. Each water bath was set to a temperature and was required to maintain that temperature throughout the whole experiment. It was found that by placing such a large number of milk samples into the water baths, they fluctuated in temperature. This could have caused an indifference in the data collected due to the changes in temperature and them not being what they were meant to be consistently set too.
Another source of error was found in the creation and application of the junket solution. Once the junket tablet was placed into the beaker of 100mL of water, it never fully dissolved. It was found that the junket only partially dissolved and the rest created a cloudy mixture till it settled at the bottom of the beaker. This is a large source of error as the concentration of the junket solution applied to the milk samples would have been inconsistent and produced inconsistent results.
Suggested improvements

Can a Thermoregulatory Response Model Predict How the Human Body Will React in Its Environment?

Can A Thermoregulatory Response Model Predict How The Human Body Will React In Its Environment?

Rationale
The human body consists of eleven major organ systems, including the nervous system. The human nervous system is made up of the brain and spinal cord, controlling our body and the way we think. For this specific report, the brain will be the main part of the system that will be looked at. This includes the thermoregulatory centre, which is located in the hypothalamus. The hypothalamus functions a section of our brains which links the nervous system to the endocrine system.
The thermoregulatory centre is known for regulating body temperature, separating the word into separate sections, we are able to recognise thermal and regulatory or regulate. In this case, thermal relates to heat and regulate refers to control or maintain. The thermoregulatory centre maintains and controls body temperature, regulating and changing its temperature depending on the environment the body is in or faced with.
The purpose of this report is to validate and endorse the claim ‘’models of a human thermoregulatory responses can predict how the body will react in nature’’. The term model used in this report signifies a paper model/equation created to predict and identify the temperature the body may reach in its environment. In this case, predict means to guess or estimate (something) that is to happen in the near or far future, as a result of an action.

This study will analyse and interpret data relevant to the thermoregulatory centre, and claim – stated above.

Research Question
This investigation will conduct a breakdown and understanding of the following research question:
Can a thermoregulatory response model predict how the human body will react in its environment?

Introduction

The average body temperature of the human body, can range between 36.5-37.5°C. If and when our body is sweating, it means that the thermoregulatory (temperature-regulatory) system is activated. To keep the body cool, water or sweat evaporates from the sweat glands, cooling the body and keeping it’s temperature in the correct range. Commonly, the body is known to ‘shiver’ when cold, shivering allows the body to heat itself as it’s allowing muscles to move within the body. People that suffer with hyperthyroidism tend to feel hot or receive hot flushes every once in a while. Whereas if you are cold, almost on a daily basis, you could have anaemia or diabetes.
The human organism maintaining its body temperature is just one of the many specialties it has. But what are the chances of predicting the response of the human thermoregulatory system, is it possible to even do so? An article written in 2015, states that humans can detect and predict temperature in many ways. The most complex of these—likely a uniquely human trait—is the ability to predict changes in ambient temperature long before such changes could potentially impact temperature in the body’s core. This claim is stating that the human body itself is able to predict changes in temperature.
Depending on environment, the temperature your body can tolerate essentially depends on your age, gender and medical history. A common reaction to cold environments is shivering and even hypothermia. If you are healthy, physiological systems such as the thermoregulatory system can prevent hypothermia from occurring. It is also important to note that feeling hot or cold is different to being hot or cold. You may feel cold, but your body core temperature can remain the same. Typically, relating to gender, women are more likely to feel colder than men due to the lower metabolic rate, producing less heat than men, which causes the feeling of being cold.
The chances of a thermoregulatory model being able to predict how the body will react in its environment, seem quite high – depending on how much you know on thermoregulatory models. The evidence that will be displayed in this paper may use equations such as:
S = M – (± W,) ± E ± R ± C ± K. [W/m^2]

This equation describes heat balance.

where:
M = metabolic rate.
W = measurable external work.
R = heat exchange to and from (±) the environment by radiation (R).
C = heat exchange to and from (±) the environment by convection.
K = heat exchange to and from (±) the environment by conduction.
E = heat exchange between the body and the environment by evaporation. (±)

W/m^2 = watts per square metre.
The sum of these processes is heat storage (S), which represents heat gain by the body if positive or heat loss from the body if negative.

Evidence

A study published in 2013, conducted research titled Prediction of human core body temperature using non-invasive measurement methods. This study was not conducted with a thermoregulation model, but a principle component analysis was conducted to extract independent factors, which were then used in a linear regression model. A linear regression model is used for estimating the relationships amongst variables. (Y=a BX) Where: Y = dependent variable
X = independent variable
B = the slope of the line
A = y-intercept
This specific study was to define relevant non-invasive measures to predict core body temperatures under various conditions. Their conclusion state the following: results from this study illustrate that multiple physiological parameters (e.g. skin temperature and skin heat fluxes) are needed to predict core body temperature. Experiments presented in this study were human subject studies with different experimental protocols.
To be able to predict core body temperature in different environmental and working conditions, skin temperature, heart rate and particularly skin heat flux have to be considered for a reliable prediction. Stated again in the conclusion of this report, they recommend measuring parameters close to the skin rather than distant from the human surface, where the influence of the measurement results becomes less controlled.
The information gathered from the study is a closer step towards our claim, models of a human thermoregulatory responses can predict how the body will react in nature. The only problem with this study is that they were able to predict the body’s reaction, but the model used was not what is being looked for.
Continuing research, an analysis on the thermoregulatory model was published in 2014. Throughout this analysis they state various thermoregulatory models have been developed to describe regulation of body core temperature about a set-point.
Further readings of this article provide an insight to modulation.

Figure 1.
There are a number of nonthermal factors that can modulate thermoeffector activity. Taking a look at figure 1, it’s stated that a nonthermal impairment in the body’s physiological capacity to dissipate heat. For example, dehydration, aging, chronic diseases or poor fitness. (View dashed lines, figure 1). One way this can result is from the onset threshold of the response being shifted to the right, such that a greater change in mean body temperature is required to initiate the activation of the heat loss response. It has been suggested that a parallel shift in the onset threshold of both effector responses must occur to be representative of a central modulation.

So how does this go back to the research question? Truthfully it doesn’t. After reading further studies on modulation of thermoregulatory response, there are no ways to predict body temperature unless tested physically.

Evaluation
Through this paper, two main experimental researches were completed with precise approaches. There are no faults with either experiments. Experiment A, (Prediction of human core body temperature using non-invasive measurement methods) however claimed to have tested non-invasive measures; this is half-true. Reading this experiment, there are graphs that include invasive measure, that were tested. This may have been to test accuracy of testing the non-invasive measures, which came out to be quite different in measures. (See Figure 2).

Experiment A:
Invasive measurement method.
Figure 2

Article B, was research on physiological adaptations in response to heat or cold. This research showed no faults in thoughts of honesty and references. However the full chapter for this article was unable to be obtained, which limited research information. All other information obtained was via medical websites and online books and articles, all which deem to be trustworthy.
In future, further research should be conducted before accessing or creating a report.
Conclusion
The research evidence that was analysed and obtained from these studies, did not provide enough evidence to support the claim of models of a human thermoregulatory responses can predict how the body will react in nature. However, it was able to be identified that there are several ways to predict core-body temperature without invasive measures. These predictions were not what was to be looked for, but it gave a greater insight to this study.
It is clear that the chances of predicting core-body temperature with a replicated thermoregulatory model is close to unresearched. The data collected was in-fact trustworthy and accurate, but not the answer that needed to be answered. The near future always holds open opportunities for experiments to be conducted to answer this question.
Unfortunately no answer was able to be obtained.

References:

Rossi, R. (2012). Prediction of human core body temperature using non-invasive measurement methods. Retrieved September 4, 2019, from: https://www.researchgate.net/publication/237199505_Prediction_of_human_core_body_temperature_using_non-invasive_measurement_methods#pf6
Mitchell, D. (2015). Health Check: why do some people feel the cold more than others?. Retrieved September 3, 2019, from https://theconversation.com/health-check-why-do-some-people-feel-the-cold-more-than-others-37805
Kenny, G.P, Flouris, A.D, (2014). The human thermoregulatory system and its response to thermal stress. Retrieved September 4, 2019, from: https://www.sciencedirect.com/science/article/pii/B9781782420323500132 and https://www.sciencedirect.com/topics/engineering/thermoregulatory-model
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