Volume 1, Issue 1

Journal of FDR Science

December 2014 Volume 1 Issue 1

Welcome

ScienceFACULTY&STAFF

by Gilles Buck, Editor- in- Chief Welcome to this first issue of the Journal of FDR Science. The aim of this journal is to share and showcase some of the high quality student science that is occurring here at Colegio Roosevelt in Lima, Peru. I encourage you to note, as you read through the studies in this journal, that these studies represent authentic work that is original. Students devised their own question and created experiments to attempt to answer their questions. Work found here was conducted within science classes as well for Extended Essays.

This first edition of this journal would not be possible without the hard work of our teachers and lab assistants who spend time and energy coaching and advising our students. They help prepare materials, devise strategies and teach the scientific method and scientific writing process. They inspire and provide opportunities for experiential learning, discovery and inquiry. They edit and encourage students through the cycle of improvement. Please join me in thanking our Science department teachers and laboratory assistants.

- Grade 6 & 7: Nikki Ellwood and Rae Merrigan - Grade 8 & 9: Rocio Malatesta and Amy Rebancos - Grade 10: Keith Herold, Leigh Petty, Gilles Buck and Sam Bourke - IB Chemistry: Leigh Petty - IB Biology: Sam Bourke and Gilles Buck - IB Physics: Keith Herold - IB ESS: Peter Martin and Allana Rumble - Laboratory Assistants: Pati Moritani and Tabata Molina

Cover Photo The Garden Snail, Helix aspersa, a test subject in senior Sil via Chero?s study in this Journal is captured by 11th Grade student Gabriel Barret o.

Photos in the Journal All other images of Helix aspersa taken by Gabriel Barret o. All other images are the work of each student author for the exception of the stock images used of maca, pouring milk bacterial colonies and molecular structure of caffeine. These images are used under the Creative Commons CC0 1.0 Universal Public Domain Dedication

celebratingstudent success Please join me in recognizing the efforts of our students whose work is showcased in this journal. They produced high quality work for their studies and then engaged in the cycle of editing and review that is necessary to have their work published. I would like to thank them for their effort and perseverance.

Great job and congratulations.

Featured Student Work by the following seniors -

Silvia Chero Clemencia Pinasco Dolores Sanchez- Carrion Catarina Niccolini Mengxin Wan Gisella Silva

Gilles Buck Subject Area Leader Sciences IB Biology MYP Grade 10

A Colegio F.D. Roosevelt Science Department Publication December 2014

TABLEO FCO NTENTS The Effect of Temperature on respiration of Helix aspersa.

by Sil via Chero The Effect of Maca on respiration of yeast

by Cl emencia Pinasco How does the calcium content vary within different types of milk and is it affected by changes in temperature?

by Mengxin Wan

Effect of Temperature on respiration of Helix aspersa

The Effect of changing carbohydrate sources on the growth of DH52 Escherichia coli.

by Dol ores Sanchez-Carrion The relationship between the quantity of active dry yeast in bread dough and the rate of fermentation

by Gisel l a Sil va The Effect of Caffeine on respiration of yeast

by Cat erina Nicol ini

Effect of Lepidium meyenii (Maca) on respiration of Saccharomyces cerevisiae (common yeast)

How does t he cal cium cont ent vary wit hin dif f erent t ypes of mil k and is it af f ect ed by changes in t emperat ure?

The Ef f ect of changing carbohydrat e sources on t he growt h of DH52 Escherichia 39 coli

Article Title Measuring t he rel at ionship bet ween t he quant it y of act ive dry 39 Effect of Caffeine on respiration of yeast yeast in bread dough and t he rat e of f erment at ion

39

Effect of TemperatureonHelixAspersarespiration How an increase in temperature leads to an increase in the rate of cell respiration per gram of Helix aspersa

By Silvia Chero, Grade 12 Biology

Abst ract The aim of this experiment was to investigate how a change in temperature affected the rate of cell respiration per gram of Helix aspersa. The hypothesis predicted that if the temperature to which Helix aspersa was exposed increased, then the rate of cell respiration per gram of Helix aspersa would also increase due to greater oxygen consumption by the organism. A total of five Helix aspersa were used in the experiment and exposed to temperatures ranging from 6.0 to 37.0 ± 0.5°C. The initial pressure levels inside the vial in which they were placed, was subtracted from the pressure levels after 4 minutes in order to calculate the amount of oxygen consumed by Helix aspersa. The results showed that when Helix aspersa were exposed to the maximum temperature of 37.0 ± 0.5°C, their rate of cell respiration per gram was highest; this way concluding that as temperature increases, the rate of cell respiration in ectothermic organisms such as Helix aspersa increases, until it reaches a temperature that the metabolism cannot tolerate, where it decreases significantly due to enzyme denaturation.

Int roduct ion Cell respiration is a sequence of enzyme catalyzed reactions in which organic compounds containing energy such as carbohydrates, lipids and fats, are broken down to release energy that can be used within the cell (?Effects of Temperature on Cell Respiration?). The by product of cell respiration is carbon dioxide and water and it can be summarized by the following equation: C6 H12 O6 + 6 O2 ? 6 CO2 + 6H2 O+ Energy The process of aerobic cell respiration can be divided in three stages: glycolysis, the Krebs cycle and oxidative phosphorylation, the final one being the main source of energy production and involving a transport of electrons from one protein to another (Allot, Mindorff 88-91). In order for this flow of electrons to continue, which allows the phosphorylation of ADP into ATP energy, electrons must be transferred to the final electron acceptor: oxygen. So due to its electronegative chemistry, oxygen must be consumed in order for aerobic cell respiration to occur (Allot, Mindorff 91).

Helix aspersa are ectothermic organisms found in ?marshes, woodlands, pond margins and backyard wildlife habitats? (?All about land snails?). They can tolerate freezing conditions for a few hours (Ansart, Vernon and Daguzan 2001) and warm conditions around 36°C (Perea et al. 2005).

The temperature at which Helix aspersa is exposed to will be manipulated in order to see how this affects the rate of cell respiration per gram of Helix aspersa. This will be measured by calculating the difference in pressure levels inside the vial where Helix aspersa will be placed, after 4 minutes, because this indicates oxygen consumption by Helix aspersa. The greater the difference in pressure levels inside the vial after 4 minutes, the greater the oxygen consumed in that amount of time, which would indicate a faster rate of cell respiration per gram of Helix aspersa.

Quest ion: How will a change in temperature (6.0, 15.0, 20.5, 30.0, 37.0± 0.5°C) measured by a thermometer, affect the rate of cell respiration per gram of Helix aspersa, measured using a Vernier pressure sensor probe and by subtracting the initial pressure (KPa) inside the vial minus the pressure level inside the vial after 4 minutes?

Hypot hesis If the temperature to which Helix aspersa is exposed to increases, then the difference in pressure level after 4 minutes will be greater, indicating greater oxygen consumption by Helix aspersa and an increase in the rate of cell respiration per gram of Helix aspersa. This is because particles inside the cell will have more kinetic energy, so enzymes and substrate molecules involved in the process of cell respiration will move faster and collide with each other more easily and therefore substrates will attach faster to the enzyme's active site (Allot, Mindorff 74). Helix aspersa will thus require and breathe more oxygen when temperature increases, causing the pressure levels inside the vial to decrease faster.

Mat erial s and Met hods Five Helix aspersa that could physically fit and move inside respirometer vials, were collected from San Andres terrain in Lima, Peru. A test trial was conducted in order to determine the maximum amount of time Helix aspersa could respire without the carbon dioxide that it exhaled as by-product affecting the pressure level inside the vial. Two respirometer vials were placed in a test tube rack and one Vernier pressure sensor probe was connected to the Vernier Lab Quest. One Helix aspersa was placed inside the vial, the other one remained empty and served as a control. Two pressure sensors were connected to the two vials. The initial pressure inside the vials was recorded and the Ultrak Seiko timer was started. When the pressure levels appeared to settle within a specific range, the timer was stopped and that time (4 minutes) was determined to be used in the Figure 1. One vial experiment in all conditions.

Afterwards, the real experiment was conducted. Each Helix aspersa was weighed independently using a weight scale and its mass (in grams) was recorded.

Afterwards, five different water baths were set up. One was set up so water could heat up to 30°C and another one was set up so it could heat up to 37°C. For the colder water baths, two water tubs were half filled with water and ice was gradually poured inside until they reached the temperatures of 6°C and 15°C respectively. The fifth water bath was set up by filling a tub with tap water at room temperature. Two empty vials were placed inside the 6°C water bath for 5 minutes so that they could acclimate and obtain the same temperature conditions of the water. Meanwhile, 2 pressure sensors were connected to the labquest and one Helix aspersa was placed in one vial. One pressure sensor was connected to the empty vial (control) and the other one was connected to the vial containing the Helix aspersa. The initial pressure levels inside the vials were recorded and the timer was started; after 4 minutes, the pressure level inside the vial containing the Helix aspersa (final pressure level) was recorded. The process was repeated with the 4 other Helix aspersa at the same temperature of 6°C and then repeated 4 more times but using the rest of the water baths containing water at 15°C, 20.5°C, 30°C and 37°C ± 0.5 respectively.

Figure 3. Two Vernier pressure sensors that were connected to the vials and the Vernier LabQuest indicated the pressure levels inside the two vials.

contained the Helix aspersa and the other one served as a control.F

Figure 2. Each Helix aspersa was weighed independently.

Figure 4. The Helix aspersa were exposed to a maximum temperature of 37? +/ -0.5

Results

The shape of the trend line in graph 1 suggests that there's a very strong positive correlation between these two variables, and the R² value of 0.921 confirms this because this number is very close to +1, the perfect positive fit. What this means is that the relationship between the independent and the dependent variable is such that as temperature increases, respiration rate also increases.

The error bars that represent the standard deviation of the respiration rates of the 5 different Helix aspersa, are short except for the data point of (37,1.11). This means that in general, there´ s very low variation from the mean and that the values are therefore more reliable.

Discussion The results that the data presents show that as the temperature to which Helix aspersa were exposed to increases, the rate of cell respiration per gram of Helix aspersa also increases, suggesting that at higher temperatures, enzymes involved in the process of cell respiration catalyze the reactions faster and since oxygen is required as the final electron acceptor in oxidative phosphorylation, Helix aspersa breathed more oxygen when exposed to higher temperatures.

However, this positive correlation between temperature and rate of cell respiration per gram of Helix aspersa would not constantly continue forever, even when this is what the data suggests, because at higher temperatures the enzymes catalyzing the reactions in the cells of Helix aspersa would denature. This is something that cannot be seen in the tables or graph because due to ethical reasons Helix aspersa weren?t exposed to higher temperatures to discover what its optimum temperature was; but research on enzymatic activity has shown that the positive correlation between temperature and enzymatic activity stops at a certain optimum temperature and from that point onwards there´ s a negative correlation between these two variables because the enzyme denatures and enzymatic activity therefore decreases (Allot, Mindorff 74) .

Furthermore, this correlation between temperature and rate of cell respiration could be seen because Helix aspersa are ectothermic organisms that ?have no metabolic method of regulating their body temperature? (?Ectotherms and Endotherms?); meaning that their body temperature is similar to that of the environment (Grigg). ?Ectotherms use behavior to control their body temperatures? (?Ectotherms and Endotherms?): if they are exposed to cold temperatures they must move into a warmer place, but since the Helix aspersa used in this experiment were trapped inside the vial, they couldn´ t move to warmer or colder environments and therefore their body adopted the water´ s temperature and their metabolism (enzymatic activity) was affected. On the other hand, if endotherms would have been used, the results would have probably been different because they maintain their ?body temperature the same no matter what the temperature of the environment is? (C. Grigg) meaning that exposure to higher or lower temperatures would have not affected enzymatic activity as it did with Helix aspersa.

The difference in pressure level was used to measure oxygen consumption by Helix aspersa and this was a valid way of measuring it because pressure is the force exerted against an object, meaning that less oxygen exerting force against the vial would create less pressure inside it. So when Helix aspersa breathed oxygen, oxygen levels inside the vial decreased, exerting less force against it and decreasing the pressure level. The more oxygen Helix aspersa breathes in (caused by exposure to warmer temperatures), the less oxygen available in the vial exerting force against it and therefore creating a larger decrease in pressure level.

It could be argued that since one of the by-products of cell respiration is carbon dioxide, the intake of oxygen which results in less force exerted against the vial,

would be counterbalanced by the exhalation of CO2 . However, this was controlled for before conducting the experiment, since a practice round was conducted to determine the maximum amount of time Helix aspersa could respire without the carbon dioxide that it exhaled as by-product affecting the pressure level inside the vial. Apparently, carbon dioxide doesn?t exert as much force against the vial until it starts to accumulate, which happens after 4 minutes approximately because it was around that time that the pressure level stopped decreasing due to the balance of both gases. This happens because according to research by P. Artacho and R. Nespolo (2008), ?H.aspersa exhibits a relatively lower than expected VCo2 [rate of elimination of carbon dioxide].

In some cases, the Helix aspersa would slide to the very top of the vial were water did not touch it, which could be a problem since the temperature in that part of the vial could have been different than the water´ s temperature for the same reason that it was not touching it. Since Helix aspersa are ectotherms they have to move to colder or hotter places to control their body temperature, so it is possible that they were trying to achieve this by moving to the top of the vial were the temperature was more suitable for them. This could have been a source of error in some cases, resulting in outliers, because by being exposed to different temperatures, the amount of oxygen consumed by those that moved to the top could have been different than those who stayed at the bottom.

Some Helix aspersa were moving inside the vials while others remained inside their shells, and this could be another source of error because those that were moving were expending more energy and thus had a greater rate of cell respiration. There are some data points that indicate that this could be a possibility. At 15°C we can see that Helix aspersa number 4 was the only active one in that condition and it was the one that had the highest rate of respiration per gram. Similarly, for temperature 30°C, the three Helix aspersa that were active as seen in table 3, had the highest respiration rates per gram as seen in table 4. For the outlier at temperature 37°C, Helix aspersa number one was not active and it had a very low rate of respiration compared to the other ones.

To conclude, the rate of cell respiration per gram of Helix aspersa increases constantly as temperature increases until it reaches a certain optimum temperature (at which Helix aspersa was not exposed to due to ethical reasons).

This occurs because Helix aspersa are ectotherms meaning that their body and metabolism adopt the temperature of the environment. A further increase in temperature from that point on would cause the rate of cell respiration per gram of Helix aspersa to decrease because enzymes would start to denature and they would not be able to catalyze reactions in cell respiration anymore.

The results are significant because it gives us insight on how the metabolism of other organisms works, allowing us to compare it against ours. A future experiment could be comparing the rate of cell respiration between passive organisms (like Helix aspersa ) and active organisms (like crickets) to test the hypothesis that since organisms with greater movement expend more energy, they will have greater rates of cell respiration.

References "All about Land Snails." Welcome Wildlife. Skyle Tech Inc., n.d. Web. 7 Oct. 2014. .

Allot, Andrew, and David Mindorff.

Biology. New York: Oxford, 2007.

Print.

Ansart, Vernon, and Daguzan.

"Freezing tolerance versus freezing susceptibility in the land snail Helix aspersa (Gastropoda: Helicidae)."

PubMed.gov. US National Library of Medicine, 2001. Web. 7 Oct. 2014. .

Artacho, Paulina, and Roberto Nespolo. "Intrapopulation Variation in the Standard Metabolism of a Terrestrial Mollusc: Repeatability of the CO2 Production in the Land snail Helix aspersa." Chicago Journals (2009): n. pag. Print.

"Ectotherms and Endotherms."

A- level Biology Pages. N.p., n.d. Web. 20 Sept. 2014. .

"Effects of Temperature on Cell Respiration." Modern Biology Inc. N.p., 2013. Web. 23 Sept. 2014. .

Grigg, Cindy.

"Endotherm or Ectotherm?" Ed Helper. N.p., n.d.

Web. 20 Sept. 2014. .

Minard, Anne. "How Does the Human Body Maintain Homeostasis?" Ehow.

Demand Media, n.d. Web. 7 Oct. 2014. .

Perea, et al. Selection of the habitat in the rest phase of the Helix aspersa under laboratorial conditions. N.p.: n.p., 2005. Print.

Acknowledgements The author wishes to express her gratitude to her supervisor, Mr. Buck, who has offered invaluable assistance, support and guidance in the completion of this article.

"The rate of cell respiration per gram of Helix aspersa increases constantly as temperature increases until it reaches the optimum. A further increase in temperature from that point on would cause the rate to decrease because enzymes would start to denature. "

Effect of MACAonRESPIRA TIO Nof Yeast Effect of Lepidium meyenii on the rate of cellular respiration of Saccharomyces cerevisiae

By Clemencia Pinasco, Grade 12 Biology

Abstract This experiment aimed to analyze the effect of Lepidium meyenii, commonly known as Maca, on the rate of cellular respiration of Saccharomyces cerevisiae, yeast. This was accomplished by answering the following scientific question: What is the effect of Lepidium meyenii (in concentrations of 0.000, 0.025, 0.050, 0.075, 0.100 g/ ml of maca on the rate of cellular respiration of Saccharomyces cerevisiae (in a stock solution ? 100g/ L yeast and 50 g/ L of sucrose) measured through gas pressure when controlling water temperature and the stock solution? The experiment showed that there was a positive correlation between the concentration of maca and the rate of respiration that occurred although the data showed large standard deviations from the mean.

Int roduct ion This experiment will look at the effect of Lepidium meyenii, commonly known as maca, on cellular respiration of Saccharomyces cerevisiae, yeast.

Maca, part of the Brassicaceae family, is a plant that has been historically grown in the Peruvian Andes for the nutritional value of the root. During the Spanish colonization it was discovered that the root had fertility-enhancing and aphrodisiac characteristics. These properties have been verified in experiments conducted by Universdad Cayetano Heredia ? the leading biology and medicine university in Peru ? using rats and humans as test subjects (Gonzales, Gustavo F, et al). In more recent years it has been hypothesized that maca may have cancer-fighting properties and that it serves as an antioxidant and energizer (Sandoval, Manuel, et al ). Research in the properties of maca is limited, thus none of the assumptions above are verified. A study conducted by the Agrarian University in Peru showed that maca contains 18 amino acids that include: Glutamic Acid, Aspartic Acid, Serine, Threonine. (?La Maca?).

Some of the amino acids can be catalyzed into products that are used for cellular respiration.

Furthermore, maca is also an antioxidant. A study about the antioxidant effects proved that maca ?protected cells against oxidative stress? (Sandoval, Manuel, et al ).

This experiment will aim to identify if there is any relationship between maca and cellular respiration of yeast, to see if its assumed energizing properties affect the rate of cellular respiration, measured through gas pressure.

Quest ion

Met hod Trials

What is t he ef f ect of Lepidium meyenii (in concent rat ions of 0.000, 0.025, 0.050, 0.075, 0.100 g/ ml of maca on t he rat e of cel l ul ar respirat ion of Saccharomyces cerevisiae (in a st ock sol ut ion ? 100g/ L yeast and 50 g/ L of sucrose) measured t hrough gas pressure when cont rol l ing wat er t emperat ure and t he st ock sol ut ion?

Five trials of the experiment were conducted. Each used a different concentration of maca with the stock solution. For each trial 5 test tubes were placed on a rack, each test tube had 10 ml of the maca and water mixture, measured with a graduated cylinder. If the trial was for a concentration of 5g of maca/ ml of distilled water, 10 ml of mixture at the concentration were placed in each test tuve. Maca measurements were done using an electronic balance. There were also 5 Vernier Pressure Probes with rubber stoppers available. To begin the experiment, 10 ml of the activated yeast (in the stock solution) was measured in a graduated cylinder and added to each of the test tubes. The rubber stoppers were immediately placed on the test tubes and the rate of respiration was recored for 600 seconds.

Stock Solution to activate yeast: The standard solution used to activate the yeast. For 100 ml of stock solution: 10 g of yeast, 5 g of sucrose and 100 ml of distilled water at 4o C were mixed in an Erlenmeyer flask. Yeast was activated in about 2 minutes.

Results

General Trend: The trendline in the graph shows that there is a positive relationship between the amount of maca root powder in a yeast solution and the rate of cellular respiration of the yeast. As the amount of maca powder increases, the faster the rate of cellular respiration. The error bars show a large standard deviation, meaning that the data collected was not consistent throughout the trials.

The error bars may negate the trend shown because they could show that maca does not have a direct effect on cellular respiration.

Discussion My hypothesis states that as there is more maca root powder in the yeast reaction, there will be an increase in the rate of cellular respiration. The data collected partially supports my hypothesis.

Although the trendline shows an increase in cellular respiration as the concentration of maca increases, the large error bars cause the data to be unreliable because the gas pressure measurements were too different.

Assuming that there is a positive relationship between maca concentration and rate of cellular respiration my hypothesis would

also be partially supported. a higher average rate of reaction Looking at the data on Table 4 we that the control trial. This occurs can see that the difference in rate with all the other trials, the one of reaction between the control with the lowest rate of reaction is trials and the trial with the the control. highest concentration is Maca has properties that may significant. The rate of reaction both increase and decrease the when there was 1 g of maca is rate of reaction of cellular 0.102 kPa/ s faster than the respiration, it has several amino control. Referring to Diagram 1 acids that may be catalyzed as a the difference is clearly visible. nutrient source for cellular This clearly indicates that there is respiration and it also has a positive relationship between anti-oxidant properties that may maca slow down concentration different "Maca has propert ies and cellular processes in t hat may bot h increase cellular respiration.

However, when and decrease t he rat e of respiration the (Sandoval, react ion of cel l ul ar concentration Manuel, et al ) respirat ion" of maca was (?La Maca?).

0.5mg/ 10ml of This may be an water there explanation for was a significant decrease in rate the high standard deviation in the of reaction from the previous data. The standard deviation concentration. It went from an itself may be an indication that average rate of reaction of 0.988 there is little or no relationship kPa/ s to 0. 720 kPa/ s. This between maca and yeast solution. decrease in rate of reaction may The unreliable data may also be have not been caused by the caused by the poor control of maca itself, but by other issues variables, such as the amount of that were not controlled time the yeast was open or the correctly. Nevertheless, even tightness of the rubber stopper with a significant decrease in the on the test tubes. rate of reaction that trial still had

References Dini, A., et al. "Chemical composition of< i> Lepidium meyenii</ i>." Food chemistry 49.4 (1994): 347- 349.

Gonzales, Gustavo F., et al. "Effect of Lepidium meyenii (maca) roots on spermatogenesis of male rats."

Asian J Androl 3.3 (2001): 231- 3.

"La Maca" Programa De Investigación Y Proyección Social En Raíces Y Tuberosas. Universidad Nacional La Agraria La Molina, n.d.

Web. León, Jorge. "The ?Maca?(Lepidium meyenii), a little known food plant of Peru."Economic Botany 18.2 (1964): 122- 127.

Sandoval, Manuel, et al. "Antioxidant activity of the cruciferous vegetable Maca (< i> Lepidium meyenii</ i>)." Food Chemistry 79.2 (2002): 207- 213.

Acknowledgements I would like to thank Mr. Gilles

Buck for his support during this experiment. I would also like to thank Clemencia Ferreyros for helping me gather the materials necessary for the data collection.

Calciumcontentinm ilk How does the calcium content vary within different types of milk and is it affected by changes in temperature? By Mengxin Wan, Grade 12 Chemistry

Abstract How does the calcium content vary within different types of milk (whole milk, lactose- free milk, fat- free milk) from a specific commercial brand and is it affected by changes in temperature? Due to the fact that many milk factories are producing lactose- free milk and fat- free milk in response to different customer needs, I wondered whether the calcium content in milk varies significantly due to the processing for different types of milk ? heat treatment especially. In order to answer the research question, complexometric titrations were performed to determine the amount of calcium in milk. EDTA was used as a complexometric titrant, and PR indicator was used to indicate the presence of calcium. The calcium concentration in milk was determined by finding the moles of EDTA needed to titrate a known amount of milk to a blue endpoint. The amount of calcium in different types of milk at different temperatures was then compared. Those experimental values were also compared to the value on the store- bought milk to calculate the percentage error in the experiment. However, the results obtained opposed what was understood in the initial research.

By complexometric titration, whole milk was shown to have the highest concentration of calcium ions, followed by lactose- free milk and then fat- free milk. The experiment revealed that the milk at room temperature had decent amounts of calcium that can be absorbed by the body whereas milk at high temperatures showed denatured features of protein and possible less calcium content. This suggests that high temperature may cause protein denaturation and affect the calcium content in milk.

Introduction Calcium is an essential macronutrient, required in relatively large amounts by the body (Neuss 341). In humans, more than 99% of total body calcium is stored in bones and teeth, where it maintains their structure and function (Ross et al.). According to the National Institutes of Health, the average requirement for daily calcium intake is 1000 mg per day, but recent statistics report that about 80% of teenagers do not meet the calcium intake requirement in their daily diet (Romano). From this, it can be deducted that calcium content in food is wort hy of invest igat ion because calcium is beneficial for human health and should be gained from our daily diet. I live in Peru, a developing country in South America, where 7.4% women between ages of 45 and 60 suffer from ost eoporosis while 5.5% in men ("En El PerĂş MĂĄs").

Osteoporosis is a reduction in bone density caused by calcium deficiency, a lack of calcium in the body, which results in mobilization of bones (Nordin).

My interest in chemistry led me to questioning the calcium intake in our daily diet. This further led to the calcium content in milk, an ?excellent? source of calcium that we consume in large amounts on a daily bases. Many milk factories are producing lactose-free milk and fat-free milk in respond to different customer needs. I wonder

whether the calcium content in milk vary significantly due to the processing for different types of milk. In addition, it is common to include milk in delicious recipes, but does temperature have any effect on the calcium content in milk? In order to f ocus on these questions, my research question has narrowed down to:

Calcium exists in milk as calcium salt, calcium caseinate (Spurlock). The isoel ect ric point of this protein complex, calcium caseinate, is pH 4.6 at which the positive and negative charges are balanced as seen in Fig. 1 (Neuss 330). This means that at a pH higher than 4.6, it will dissolve in the solution. Milk normally has a pH around 6.6, so How does t he cal cium cont ent calcium caseinate has less Hvary wit hin dif f erent t ypes of ions at this pH and is soluble as mil k (whol e mil k, l act ose-f ree a salt, where Ca2+ ions are free mil k, f at -f ree mil k) f rom a and can be absorbed by the specif ic commercial brand and body (Spurlock).

Inf l uence of is it af f ect ed by changes in The Temperat ure on Casein t emperat ure?

High

Caseinate is the most soluble The amount of calcium in milk form of casein ? the main was detected by complexometric titration with EDTA solution using PR indicator. The purpose of the experiment was to conclude how much Fig. 1 Isoelectric point (?Biochem & Science Notes?) calcium is in different types of milk at different temperatures, and to protein in milk, which carries compare it to the value on the Ca2+ ions. Casein is one of the store-bought milk. It is important heat-stable proteins according to understand how factors like to Dr. Ullah, which means high thermal processing may influence temperature treatments can the calcium intake in our daily affect its functional but not the diet since it is arguable whether nutritional properties of casein. heating reduces the nutritional In addition, according to properties of milk. biochemistry experts, protein

Background Knowledge The Form of Cal cium in Mil k ?

Cal cium Caseinat e

denaturation only changes the secondary and tertiary structures of protein, but usually does not affect the sequence of

amino acid composition (Ullah et al.). This means that the cal cium cont ent in mil k shoul d not change signif icant l y at high t emperat ure, indicating milk can be processed without decreasing the calcium content.

Cal cium Cont ent in Dif f erent Types of Mil k Lactose-free Milk: Some people are lactose-intolerant because they are unable to produce lactase enzymes to digest the milk sugar lactose. Lactose-free milk does not contain lactose, so it is suitable for lactose-intolerant people. During the production of lactose-free milk, the milk passes through a tube containing immobilize lactase on a surface, which breaks down the milk sugar lactose into simple sugars ? galactose and glucose (Rosario).

Therefore, the process of reducing lactose does not reduce calcium, meaning that lactose-free milk should have similar calcium content as whole milk.

Fat-free Milk: Reducing the fat content in milk slightly brings up its calcium content. This is because the fat in milk does not contain calcium. While the fat is removed, the watery proportion of the milk that contains casein goes up, slightly increases the calcium concentration in milk ("Which Milk Has the Most").

Consequently, fat-free milk should contain the highest calcium concentration among different types of milk.

Investigation EDTA as Tit rant

a

Compl exomet ric

EDTA (ethylenediaminetetraacetic acid, C10H16N2O8) is a large molecule that has six coordination sites ("Determination of Calcium Ion"), acting as a ligand. These sites include four carboxyl groups and two amine groups as shown in Fig. 2. Those coordination sites act as

compl exomet ric t it rat ion to indicate the endpoint of a titration by observing the color change in the samples (?Complexometric Titration of Zn(II)?). In this experiment, Pat t on and Reeder?s (PR) indicat or (C21 H14 N2 O7 S) was used to detect the endpoint of EDTA titration for Ca2+ ions in milk. PR indictor forms pink

Fig 2. Metal-EDTA complex ("EDTA stabilizing a metal")

electron pair donors by donating its six lone pairs of electrons to form coordination compounds with metallic ions like Ca2+ (Kirschner et al). When a metallic ion like Ca2+ is present, EDTA bonds with the ion through the six coordination site as shown in Fig.3. Then, the Ca2+ ion and EDTA combine in 1:1 mol e rat io to form a large calcium ion-EDTA complex as [Ca-EDTA]2- (Zumdahl et al.).

Pat t on and Reeder?s indicat or

Fig 3. Complex formed by EDTA and calcium ion ("Determination of calcium ion")

Ca-PR complex with calcium ions, and turns bl ue when the Ca-PR complex is completely replaced by the [Ca-EDTA] 2complex ("Determination of Calcium Ion").

The blue endpoint indicates that the calcium ions have been used up or removed from the solution by EDTA. Purple is formed when both the Ca-PR complex and [Ca-EDTA] 2- complex are present because pink and blue makes purple.

A typical complexometric indicator displays a definite color change when specific metal ions Ca-PR + EDTA4- - PR + [Ca-EDTA] 2are present in the sample. This (Ca-PR complex is pink and PR itself is blue.) type of indicator is used in

This is because the Ca-PR complex is less stable than the [Ca-EDTA] 2- ion complex. As a result, when Ca-PR complex is titrated with EDTA, the Ca2+ ion reacts with EDTA to form a more stable complex with stronger bonding interactions ("Determination of Calcium Ion").

Henceforth, PR indicator was used in this experiment because it forms an ion complex with Ca2+ ions in milk. In addition, in this experiment the PR indicator used was undiluted powder and thus very strong, so only small portion was required.

Method

determine the amount of calcium in milk by complexometric titration.

The calcium concentration in milk was determined by detecting the moles of EDTA needed to titrate a known volume of milk to a blue endpoint. Then, using the moles of EDTA, the moles of calcium in milk can be determined since Ca2+ ion and EDTA combine in 1:1 mole ratio. The method I used was modified from several sources.

The primary resource was a lab project called Determination of Calcium Ion Concentration from

preparing the milk samples and titrating the samples with EDTA, which allowed me to calculate the calcium concentration in each milk sample and thus compare. Lastly, the most time consuming part ? measuring the calcium content at different temperatures.

Preparat ion of Sol ut ions Sodium hydroxide sol ut ion (8.0 mol - dm -3 ) Due to the fact that the PR indicator only works in a basic system, I prepared an 8.0 mol-dm-3 sodium hydroxide solution to ensure the pH of the milk samples were sufficiently high.

From a chemist perspective, when NaOH was added to milk, the negative charges on the outer surface of the casein micelles remained negative in milk (Spurlock). As a result, calcium caseinate persisted in milk where Ca2+ ions were still attached to the

Measuring the calcium content in the University of Canterbury. To milk was a challenge because all measure the calcium content in methods were complicated and milk required three main difficult to understand.

Calcium was Safety Notes not simply Sodium hydroxide is a strong base, and it is very Ca2+ ions but caustic and corrosive even in small amounts. Thus, the rather than in concentrated (8.0 mol-dm-3) sodium hydroxide a complex ion solution was prepared and handled with extra care.

Ca+2 Caseinat e + PR - Ca-PR + Caseinat e form formed Cleaning up spills of NaOH solution with vinegar and from a large amounts of water was needed. A universal casein. This prevented the indicator was used to inspect if the lab desk was protein formation of Ca(OH)2 , which completely clean and would cause no further complex. allowed PR indicator to form concerns. The PR indicator and EDTA are considered After Ca-PR complex with the Ca2+ irritants, so it was important to avoid direct contact researching, I with skin and inhaling the dust. To protect the hands ions, making the solution pink. found the from chemicals, gloves had to be worn at all times in The NaOH solution was most the laboratory. In addition, wearing safety goggles was prepared from solid sodium commonly necessary in order to avoid any physical harm to the hydroxide pellets into an 8.0 used method eyes mol-dm-3 solution using a 100 ? mL volumetric flask. Firstly, I complexometric titration. processes.

Firstly, making had to calculate the mass of Therefore, in this experiment I solutions of EDTA and NaOH of solid NaOH needed to make 100 used EDTA as a complexometric known concentrations based on mL of 8.0 mol-dm-3 NaOH titrant and PR indicator to chemistry calculations. Secondly, solution.

Hence, approximate 32.00 g of sodium hydroxide pellets were added into a 100 mL volumetric flask, and then distilled water was carefully poured into the flask through a funnel till the 100 mL mark to make an 8.0 mol-dm-3 NaOH solution.

EDTA sol ut ion (0.025 mol - dm-3) Similarly, the EDTA solution was prepared from EDTA powder into a 0.025mol-dm-3 solution using a 500 mL volumetric flask. The same method was used to calculate the mass of powered EDTA needed to make 0.025 mol-dm-3 EDTA solution. For this concentration of EDTA, since the molar mass of EDTA was 292.2424 g-mol-, 3.65g of EDTA powder was added into a 500 mL volumetric flask, and then distilled water was carefully poured into the flask through a funnel till the 500 mL mark to make 500 mL of 0.025 mol-dm-3 EDTA solution.

Perf orming t he Tit rat ion Using a pH probe, it was determined that when 4.0 mL of 8.0 mol-dm-3 NaOH solution was added into the mixture of 10.0 mL of milk and 40.0 mL of distilled water, the solution would turn into a pH around 8.5.

Due to the fact that the PR indicator only works in a basic system, this allowed the PR

indicator to work properly.

Thus, I decided to use these quantities for each sample of milk. In titration, I carefully added EDTA solution drop wise using a burette to the milk sample. The PR indicator was used, changed from pink, when Ca-PR complex was present, to blue, when all of the calcium ions had been reacted with EDTA. Titration was then performed on every sample of milk (Appendix II). The preliminary results showed that it was very difficult to determine the blue endpoint since it appeared

Fig. 4 Purple color during titration

gradually but no instant color change.

Therefore, three preliminary trials were performed to observe the color change before the quantitative measurements were completed.

The milk ?divided? noticeably when heated, and further heating (over 50?C) even resulted in blue solution without adding any EDTA. To complete further research, milk was replaced with a known concentration of CaCl2

solution using the same method in order to determine if the indicator was working properly.

This was because I thought the accuracy of the PR indicator was affected by high temperatures.

Possibly the bonds in the Ca-PR complex or the protein, casein, that carries Ca2+ ions degenerated at high temperature.

However, the CaCl 2 solution turned white after adding NaOH, indicating that CaCl 2 had reacted with NaOH to produce NaCl and Ca(OH)2 precipitate. Thus, the PR indicator could not form Ca-PR complex with Ca2+ ions in the solution as Ca(OH)2 was a stable compound, so the solution didn?t turn pink. In other words, the solution stayed blue from the beginning to end. This did not provide results to determine the reason why high temperature resulted blue solution of milk without adding any EDTA. On that account, I finished the titration of milk using the aforementioned method at different temperatures.

Fig. 5 Blue color at endpoint.

Results Data Collection: The titration of whole milk was performed on a different day whereas the titrations of lactose-free milk and fat-free milk were completed on the same day. The followings are raw data collected during the complexometric titrations.

At 60?C, when PR indicator was added to the initial sample solutions, the solution appeared blue, so the titrations generated no results at this temperature.

Processed Data:

According to the uncertainties for the average values of EDTA solution added, the titration of whole milk appears to have the least precision. The calcium concentration in whole milk, lactose-free milk and fat-free milk can now be plotted into a column graph in order to investigate the relationships among them: The graph indicates whole milk has the highest calcium concentration at room temperature, and fat-free milk has the lowest calcium concentration. This is surprising because according to the background information, fat-free milk should contain slightly more calcium than whole milk and lactose-free milk. More than that, it is clear that the known uncertainties generated in this experiment did not affect the results because the differences among the values are genuinely large compared to the absolute uncertainty.

As a determination of the effect of temperature on the calcium content in milk, these following calcium concentrations were also calculated and compared. The same calculations were used to determine the calcium concentration in milk at different temperatures.

These tables show the calcium concentration and the amount EDTA solution used during the titrations of whole milk, lactose-free milk and fat-free milk at different temperatures. According to the uncertainties for the average values of EDTA added, the titration generally appears to be more precise at high temperatures. This could be due to the fact that a more definite color change could be observed at high temperatures. The calcium concentration in different types of milk at different temperatures can now be plotted into a line graph to see how change in temperature can affect the calcium content in milk and to determine the optimum temperature for milk: This graph indicates the optimum temperature for milk is 20?C where it has the highest calcium concentration. The calcium content decreases as the temperature increases, meaning that the higher the temperature, the less calcium can be absorbed by the body. However, this opposes the aforementioned statement ?the calcium content in milk should not change significantly at high temperature?.

In addition, the titration of whole milk was performed on a different day whereas titrations of lactose-free milk and fat-free milk were completed on the same day. This

could be the reason why the titration of whole milk resulted in a relatively large calcium concentration compared to other titrations. Therefore, random error and systematic error were significant and had affected the results in this experiment.

Discussion According to the results shown in graph 1, it is clear that whole milk contains the highest calcium concentration, and fat-free milk contains the lowest calcium concentration. In this experiment, the calcium concentration in whole milk, lactose-free milk and fat-free milk was found to be 1.307 mg mL-, 1.282 mg mL-, and 1.262 mg mL- respectively. Therefore, the results obtained from this experiment are not in agreement with the initial research, which states that the calcium content in fat-free milk should be slightly higher than the calcium content in whole milk and lactose-free milk due to the fact that the fact content in milk does not contain calcium. Although in this experiment, the fat content in milk was found to slightly affect its calcium content, choosing fat-free milk is wise. Fat-free milk is low in calories and fat, so drinking fat-free milk instead of whole milk can significantly reduce our daily calorie intake for healthy weight management. It is worthy to know that all types of milk have similar calcium content and can provide us a decent amount of calcium intake in our daily diet. According to the results shown in graph 2, it is clear that heating up the milk decreases its calcium content. For example, in this experiment the calcium concentration in whole milk at 20?C, 30?C, 40?C, 50?C and 60?C was found to be 1.313 mg mL-, 1.255 mg mL-, 1.075 mg mL-, 0.589 mg mL-, and 0 mg mL- respectively. The experiment revealed that the milk at room temperature had sufficient amounts of calcium and protein that can be absorbed by the body whereas milk at high temperatures showed denatured features of protein and possible less calcium content. This suggests that high temperature may cause protein denaturation and affect the calcium content in milk. Although the extension investigation using CaCl 2 provided inconclusive results, I believe that the weak intermolecular forces within the protein complex were disrupted at high temperature. Therefore, we should drink fresh milk to gain maximum nutritional value from milk. In addition, milk should not be overheated because heat treatment may denature important protein contents in milk. However, it should be noted that milk must be moderately processed to destroy microorganisms that exist in raw milk.

Evaluation Finding the reason why the solution turned blue without adding any EDTA at 60?C was challenging.

After researching and reconsidering this aspect again, I reached a conclusion. The fact that the NaOH reacted with CaCl 2 when I replaced milk with CaCl 2 reminded me that the calcium content in milk could have reacted with NaOH as well. According to the IB Chemistry textbook, protein loses its three dimensional shape at high temperature and thus denature. This means that casein, the main protein in milk, might have denatured at 60?C, leaving free Ca2+ ions in the solution that then reacted with NaOH to produced Ca(OH)2 . It was difficult to determine because the Ca(OH)2 precipitate produced had the same color as milk. This is the reason why the milk seemed ?divided? when heated up. Therefore, it is incorrect to conclude that the calcium content in milk decreases as temperature increases, as if the calcium may be in a different state that is undetectable by the indicator itself. Another source of error is due to the fact that Ca2+Casinate is a protein complex. From this, it can be deducted that there are more factors other than the pH could have affected the bonding of Ca2+ ions. My method was completely dependent on the complex ion Ca-PR. High temperature causes intermolecular forces within amino acid to degenerate, destabilizing the protein structure (Neuss 328). Hence, it is possible that at high temperatures calcium concentration remained the same, but the method wasn?t able to determine the calcium ions?existence. On the other hand, milk was processed in the milk factory and has already

gone through heat treatments; so further heating shouldn?t result in reduction of the nutritional properties of milk.

Random Error: Three preliminary trials were completed in order to observe the color change before the quantitative measurements were taken. This decreased some random errors in the experiment.

Nonetheless, since there was no definite color change, the determination of the blue endpoint could still be inaccurate after running the preliminary trials. More than that, viewing the meniscus from the bottom at the line of the burette was also a source of random error. However, since the percentage uncertainty is only 0.4865% , which is much smaller than the percentage error -30.71% , systematic error was significantly greater than the random error in this experiment.

Systematic Error: There were several systematic errors that could have occurred when doing this experiment. One systematic error was I always over titrated while doing the titrations because I was uncertain about the blue endpoint since it appeared gradually. Another source of error was the existence of magnesium in the milk, which would form Mg-PR with the PR indicator that also results in pink solution. This error made the solution require more EDTA in order to reach the blue endpoint, and deludes people into thinking there is a higher calcium concentration in milk than the amount of real calcium content. This systematic error was caused by not letting Mg(OH)2 form after adding NaOH solution into the milk sample, which led the magnesium content in milk interferes with the PR indicator.

Also, the presence of some other metal ions, such as iron, cobalt, nickel, zinc, or manganese in high concentrations may also cause error using this method (?Determination of calcium ion?). These metal ions are naturally found in water. Therefore, distilled water was used in the experiment to avoid contamination of the results. However, although the process of milk should be highly regulated in a well-known company such as Gloria, it is indeterminate whether distilled water was used in the process of milk. These errors resulted in a large negative percentage error, which was -30.71% , meaning that the experimental value was always bigger than the literature value ? systematic error.

Suggestion for improvements: There are parts of the experiment that I would improve to prevent the sources of error and thus to obtain more accurate and precise results. One possible way to improve this experiment would be to wait for five minutes with occasional swirling to let the milk and NaOH precipitate Mg(OH)2 after the preparation of milk sample. This method of the determination of calcium concentration in the presence of Mg2+ should rely on the fact that enough NaOH is present to ensure that all Mg2+ ions precipitate as Mg(OH)2 before the indicator is added. The PR indicator is still a suitable indicator when the method is changed because it works at high pH values. More than that, in order to reduce the random errors, the results should average over a large number of observations. In other words, five trials should be completed instead of three trials.

Possible Extension After my extended essay experience, I am left asking more questions. To ensure the results obtained were reliable, Ca2+Caseinate should be extracted from milk. Then, the sample should be placed at different temperatures in order to minimize the effects of others factors in milk. An isolation of Ca2+Caseinate from milk can help gaining more reliable results. This could be a clue why calcium concentration ?decreases? at higher temperatures. Another possible extension is to investigate on the reasons why the indicator changes color. At last, an investigation on how high temperature affects intermolecular forces in different compounds also sounds very interesting.

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Acknowledgements I am using this opportunity to express my gratitude to everyone who supported me throughout the course of this experiment. I am thankful for their aspiring guidance and constructive criticism. I express my warm thanks to my supervisor Ms. Leigh Petty for her support and guidance at FDR. I would also like to thank Mr. David Veinot for helping me to find resources and all the people who provided me with the facilities being required and conductive conditions for my journal.

Effect of CArbohydratesourcesongrowthof E.Coli The Ef f ect of changing carbohydrat e sources on t he growt h of DH52 Escherichia coli

By Dolores Sanchez- Carrion, Grade 12 Biology Abst ract This lab explores the effect different carbohydrate sources have on the growth of a strain of DH52 E.coli by answering the following question: What effects do different monosaccharides (glucose, fructose), disaccharides (sucrose, lactose), and polysaccharides (starch) at controlled concentrations of 0.08 mol- dm- 3, 0.04 mol- dm- 3 and 0.015 g/ mL, respectively, have on the growth of DH52 E.coli bacteria in solution, at a controlled temperature of 37 degrees Celsius over the course of 72 hours?

And, after the bacterium has been grown in broth with its particular carbohydrate source, what affect does switching the bacteria?s carbohydrate source have on its growth? Understanding the carbohydrates bacteria metabolize most effectively will allow scientists to determine how to maximize bacterial efficiency. This experiment will explore the effect of different carbohydrates on bacterial growth by placing strains of bacteria in nutrient broth solutions infused with different carbohydrates. The solutions were then placed in an incubator and their growth was recorded over the course of 72 hours using a colorimeter. Once the bacteria had grown, they were switched into new nutrient solutions infused with different carbohydrates, so that the effect switching a carbohydrate source would have on bacterial growth. The results suggest that fructose and glucose were the carbohydrates that caused the most initial bacterial growth. This data indicates that monosaccharides were the most successful source of energy for the E.coli strain used. When the carbohydrate sources were switched, the fructose- grown bacteria placed in lactose solution showed the greatest growth. This could be explained by lactose?s facility to metabolize bacteria in the absence of glucose. But, overall, all of the carbohydrate sources used indicated general growth of the E.coli bacteria. With this information I was able to conclude that all of the carbohydrate sources used in the experiment were easily metabolized by the E.coli and caused growth.

Int roduct ion can metabolize and use as a Bacteria are unicellular source of energy for growth. Most organisms with the capacity to commonly, it uses carbohydrates divide exponentially and adapt to as sources of energy different environmental and (Carbohydrate Metabolism in biological conditions, making Bacteria). A common source of them versatile in basic biological energy for bacteria is the processes (University of monosaccharide glucose (Allot California Museum of and Mindorff 44). Fructose, Paleontology). There are actually another monosaccharide, is ten times more Saf et y Precaut ions bacterial cells

genes involved in the metabolism of lactose (Lac Operon). Lactose enters the E.coli cell and binds to a repressor molecule on the bacteria. The repressor molecule, once bound to the lactose molecules, is released from the bacteria, and the operon, which codes for the particular proteins involved in the metabolism of lactose, is within the human Because a strain of bacteria was used to conduct the transcribed and translated. The body than human experiment, some safety precautions were taken to two proteins translated from cells (Bohach). ensure the bacteria was used and disposed of the operon, beta-galactosidase One of the main responsibly. The inoculating loop used to remove the and permease, are then used biological bacteria from the petri dish was sterilized using a facilitate the movement of processes Bunsen burner to ensure all equipment coming in lactose from the extracellular bacteria facilitate contact with the bacteria was clean. After bacterial environment inside the cell, is the metabolism solutions were removed from their respective conical and to break down lactose into of organic flasks and used to collect data, they were placed in two simple sugars, glucose and compounds. the autoclave and sterilized at high-pressure galactose (Lac Operon). The lac Bacteria undergo conditions. The bacteria were then disposed of operon provides evidence to the process of appropriately. show that particular bacteria metabolism in can express specific genes to which organic help them metabolize molecules, like broken down into energy similarly carbohydrates as a source of carbohydrates, are broken down to glucose. Both simple sugars energy. Bacteria require into into smaller, simpler undergo the process of controlled conditions in order to compounds that bacteria then phosphorylation to form a grow. Some variables like use to produce adenosine pyruvate, and the pyruvate is then temperature, pH, and amount of triphosphate, also known as ATP. used to produce molecules of ATP nutrient solution or agar in plate This ATP provides energy for the to provide the cell with energy. are controlled when growing bacteria to perform basic cellular Glucose and fructose are E.coli?s bacteria artificially to ensure functions necessary for survival preferred source of energy they all grow in the same (Jurtshuk N.A.). The particular because they can be metabolized conditions. Bacterial growth can bacteria being observed in this more efficiently, and with the use be observed and recorded in experiment is a colony of DH52 of fewer enzymes, than lactose many ways. Some direct Escherichia coli. The experiment and other disaccharides, because methods of recording include will focus on the effect exposing they are simpler sugars (Todar 1). using counting chambers or E.coli to different organic Genetic mechanisms can also be streaking the bacteria and compounds will have on its used to explain metabolism. The counting the number of growth. There are many different lac operon is a unit of DNA that organic compounds that E.coli encodes a functioning set of

individual colonies. For this particular experiment, bacterial growth will be measured using a colorimeter, a device used to

increase in metabolic activity is disaccharides, because evident (Todar 3). The second monosaccharides are simple phase, known as the exponential sugars that require less phase, consists of a growth period enzymatic activity to be broken in which the bacteria down into their simplest form.

Research Quest ion divide through the process Glucose only undergoes the of binary fission (Todar 3). process of glycolysis, in which What ef f ect s do dif f erent In the third phase, known glucose is converted into a monosaccharides (gl ucose, f ruct ose), as the stationary phase, the pyruvate, and the energy disaccharides (sucrose, l act ose), and bacteria have consumed released in the processed is pol ysaccharides (st arch) at cont rol l ed most of the nutrients and then used as ATP to allow the concent rat ions of 0.08 mol -dm -3 , 0.04 cell to grow. Complex mol -dm -3 and 0.015 g/ mL, respect ivel y, resources available to them to allow them to carbohydrates must first be have on t he growt h of DH52 E.coli grow. During this phase, broken down into simple sugars, bact eria in sol ut ion, at a cont rol l ed and then they must undergo the t emperat ure of 37 degrees Cel sius over secondary metabolites occasionally occur, and process of glycolysis. Because t he course of 72 hours? And, af t er t he there is an extra step involved bact erium has been grown in brot h wit h metabolize further in their metabolism, it s part icul ar carbohydrat e source, what substances after the majority of the bacteria disaccharides take longer to be af f ect does swit ching t he bact eria?s processed and broken own, carbohydrat e source have on it s growt h? have already grown (Bacterial Growth determine the particular Choice of Topic Curve).

The final concentration of a solute or I have always had a particular interest in phase, known as the substance in a given solution microbiology and the study of bacteria and death phase, is when using wavelengths of light. its functions, but what really peaked my the bacteria Although this method does not interest was understanding how bacteria population declines. provide an exact number of grow. I wanted to understand he mechanisms The experiment for bacteria or colonies of bacteria that caused bacterial growth, and the growing in a particular medium, it this research paper particular factors and resources necessary will be conducted provides sufficient information for bacteria to reach and maintain an over set period of to analyze which bacteria grow optimum physical state. I decided to look at time to establish a better than others by the the metabolism of carbohydrates in E.coli bacterial growth fogginess of the solution in specifically, because of the resources curve from the data which they are submerged. available to me within my school and the collected.

Scientists have established a Cayetano Heredia University, a local medical Hypothesis growth curve to help explain the institution in Lima. different phases in bacterial If the E.coli is given a growth. The first stage is the lag hence the E.coli being given monosaccharide (glucose, phase, which occurs immediately fructose) as a source of energy, disaccharides and after bacteria have been placed polysaccharides (lactose, then the E.coli will break down in a growth medium. Although no the organic compound into sucrose, soluble starch) will initial activity is apparent, grow slower than the E.coli smaller organic compounds, and bacteria grow slightly in size receiving monosaccharides use the energy to grow faster during this phase, and an (glucose, fructose). than it would if given

Met hods The experiment was conducted in two parts: First: growing the initial strain of bacteria in different carbohydrate sources Second: once the bacteria had grown, switching its sugar source to see the effect it had on its growth.

Part 1: Preparing Carbohydrat e Sol ut ions and Nut rient Brot h Different carbohydrate solutions were prepared in 100 mL conical flasks. Each of the flasks were labelled with the respective carbohydrate sources:glucose, fructose, sucrose, lactose and starch. Each of these carbohydrate sources were collected. 1.5 grams of glucose were weighed on a scale in a petri dish, and placed in a conical flask. The step was repeated 4 more times so that there were five of the same variation. The same procedure was repeated for the other four carbohydrate sources. A nutrient broth was then created using broth powder and 2000 mL of distilled water. The broth was placed in a refrigerator, then removed and placed in the autoclave to be sterilized. 99 mL of nutrient broth were then added to each of the conical flasks.

A stock solution of bacteria needed to be created, so that the same strain of bacteria could be added to each of the conical flasks. 100 mL of the stock nutrient broth solution were poured into a conical flask.

The petri dish containing the DH52 strain of E. coli was removed from the biological incubator and placed next to the Bunsen burner. The Bunsen burner was turned on and the inoculating loop was placed under the flame of the burner until it turned bright orange from the heat. The lid of the petri dish was opened, and the inoculating loop was placed vertically through the agar to cool the loop down. A single colony of bacteria was then scraped off the surface of the agar using the loop tip of the inoculating loop. The loop was then submerged into the 100 mL conical flask containing the broth and mixed until clumps of bacteria were no longer visible. The 100 mL conical flask containing the bacteria-infused broth was placed in the incubator at a controlled temperature of 37°C for 48 hours.

After the bacteria was given time to sterlize, 1 mL of the bacterial solution was collected using a micropipette and placed in each of the conical flasks so that each flask had the same amount of bacterial solution in it. The conical flasks were placed back in the incubator for 24 hours.

The growth of the bacteria was then measured indirectly using a calorimeter. The 5 bacterial solutions labeled ?glucose? were removed from the calorimeter after 24 hours of initial incubation.

The calorimeter sensor was plugged into the Vernier Probe and set at a wavelength of 565 nm. 2 milliliters (2000 micrometers) of solution were extracted from one of the flask of glucose-infused bacterial solution and into the cuvette. The cuvette was then placed in the calorimeter and the light absorbency value was collected. The same steps were repeated for the remaining glucose flasks and the 4 other carbohydrate sources. The same method of collecting data was repeated 48 Fig. 1 Use of funnel for and 72 hours after initial incubation as well. measurement of LB broth For the second part of the experiment. New carbohydrate-infused

broths were made following the same procedures explained before. But, no starch solution was made.

The only carbohydrates used for this component of the experiment were glucose, sucrose, lactose, and fructose. Once the new flasks were made, the following procedures were followed. 1 mL of Glucose-grown bacteria was extracted from each of the 5 glucose-grown bacteria solutions using the micropipette. Each of the extracted 1 mL of glucose-grown bacteria was placed in the new sucrose nutrient broth (prepared prior), creating a new bacterial solution composed of sucrose-broth and glucose-grown bacteria. The 5 flasks were labeled ?glucose in sucrose solution?. The 5 flasks were sealed using aluminum foil and placed in the incubator at a controlled temperature of 37 °C. The same steps were repeated with the lactose-grown bacteria, but this time, the lactose-grown bacteria were added to the prepared fructose nutrient broth and the flask was labeled ?lactose in fructose solution?.

The same steps were repeated with the sucrose-grown bacteria, but this time, the bacteria were added to glucose nutrient broth and the flask was labeled ?sucrose in glucose solution?. Lastly, the same steps were repeated with fructose-grown bacteria, but this time, the bacteria were added to lactose nutrient broth and the flask was labeled ?fructose in lactose solution?. After the different bacterial solutions were created and placed in the incubator at a controlled temperature of 37 °C for 24 hours, the 20 bacterial solutions were removed from the incubator and their growth was measured using a colorimeter following the same procedures as used prior. Growth was measured after 48 and 72 hours as well.

Fig. 2 Use of colorimeter to measure bacterial growth

Resul t s Raw Dat a For First Part of Experiment

Raw Dat a For Second Part Of Experiment

Processed Dat a For First Part of Experiment

All lines of best fit in the graph above visually indicate that there is a positive correlation between the time since initial incubation and the average light absorbance. This means that all carbohydrate sources led to a general growth curve in the bacteria. As time passed, the bacteria grew. Each individual graph is included in the appendix so that the error bars and lines of best fit can be further observed with more clarity. The error bars will be explored in further detail later on.

Processed Dat a For Second Part of Experiment

Similar to the previous graph, a clear positive correlation can be identified between amount of time since incubation and the average light absorbance of each of the bacterial solutions. There was a clear growth curve established for all of the bacteria growing in different carbohydrate sources. The positive correlation between time and average light absorbance indicates that the E.coli grew as time progressed. The error bars at each of the points on the graph will be explored in greater detail later.

Discussion After conducting the experiment, I was able to conclude that fructose was the best source of energy for the E.coli. The data from part one of the experiment indicated that the monosaccharide fructose, was the carbohydrate source that caused the greatest growth in E.coli, because the values collected for light absorbance of the solution grown in fructose were highest, which indicated the most growth because it was the foggiest solution. This experimental observation can be further supported in previous studies, which also indicate that fructose is one of the ?preferential sugars utilized by E. coli? (Chu) because it is already found in its simplest form and does not need to be broken down into simpler sugars, hence metabolism and assimilation of the energy from the carbohydrate sources can occur immediately (Jurtshuk N.A.). If one looks at the three points on Graph 1 representing light absorbance of bacteria grown in fructose solution, it is clear that these values are the highest on the graph. An exponential curve of best fit was extracted to help develop a general trend for the graph. It is clear through these points that has time passed, the metabolism of fructose by the bacteria increased. This phase of growth in which the bacterial

"...f ruct ose is one of t he ?pref erent ial sugars ut il ized by E. col i? (Chu) because it is al ready f ound in it s simpl est f orm and does not need t o be broken down int o simpl er sugars..."

population increased exponentially is known as the Exponential Phase (Todar 3). It is clear that the exponential phase of growth for bacteria grown in fructose solution was higher than that of any other bacteria in the four other solutions. Fructose, after 72 hours, reached a peak light absorbance of 0.3974, which was 0.224 greater than the light absorbance recorded after 72 hours for the bacteria grown in starch solution (0.172). Starch is a complex carbohydrate composed of many smaller sugar molecules, further supporting the notion that more complex carbohydrates take more time to break down fully and be used by the bacteria. The graph also indicates that the solution with the second-highest light absorbance was the one containing glucose. Glucose was the other monosaccharide used in this part of the experiment, further supporting the idea that the bacteria exposed to monosaccharides would grow the fastest. Glucose, like fructose, can be metabolized more efficiently than other carbohydrate sources because it is a smaller molecule, and requires the use of fewer enzymes. That being said, the difference between light absorbance across carbohydrate sources was not large enough for the experimenter to assume that monosaccharides are the only successful carbohydrate source for E.coli growth. Although fructose and glucose showed the greatest bacterial growth, the other carbohydrate sources still showed significant growth

throughout the 72 hours during which the data was recorded.

For each of the carbohydrate sources, I was able to extrapolate a growth curve to represent the positive correlation between time and bacterial growth. This leads me to conclude that although some carbohydrate sources showed greater growth than others, all five of the carbohydrate sources provided the bacteria with sufficient energy to grow consistently over the course of 72 hours. Part two of experiment consisted of taking the bacteria that had already grown in different solutions containing carbohydrates, and switching their carbohydrate source to see if there was a change in growth. It was expected that the bacteria placed in fructose and glucose solutions would grow the fastest, similar to the first part of the experiment, because of the simpler nature of the sugars and their tendency to break down quicker. But, the bacteria that grew the most were the fructose-grown bacteria placed in lactose solution. Although the bacteria had initially grown in a fructose solution, it showed the greatest growth when placed in the lactose solution. This is partially because the bacteria had already grown significantly after being placed in the fructose solution initially. The lactose-grown bacteria placed in the fructose solution grew the

least, which did not support the hypothesis. Enzymatic mechanisms can be used to explain why the bacteria growing in lactose solution showed the greatest growth. The metabolism of lactose is usually repressed by glucose molecules, for lactose cannot be metabolized until all glucose molecules have been.

Glucose ?requires two less enzymes? than lactose to be metabolized, so it is broken down first and inhibits the enzymes involved in lactose metabolism.

(Todar). But, in the absence of glucose, lactose can be easily metabolized without repression of the enzymes necessary to break it down. In the experiment conducted, a fructose-grown bacterium was combined with lactose solution. Because glucose was not present, the lactose could be metabolized and exposed to the enzymes necessary to break it down. Although fructose is also a monosaccharide that is broken down quickly, it does not inhibit the enzymatic activity for lactose metabolism the same way glucose does. The fructose-grown bacteria in lactose solution reached a light absorbance of 0.3474 after 72 hours, while the lactose-grown bacteria placed in fructose solution reached a light absorbance of 0.1252 after 72 hours. This means that the bacteria grown in lactose solution absorbed 0.2222 more light than the bacteria grown in fructose, hence it experienced a greater growth, further supporting the notion that bacteria grows well in

"...in t he absence of gl ucose, l act ose can be easil y met abol ized wit hout repression of t he enzymes necessary t o break it down."

"Al t hough concl usions were drawn as t o what carbohydrat e source was t he most ef f ect ive in providing energy f or t he growt h of E.coli, al l of t he carbohydrat e sources showed t he capacit y f or growt h."

presence of lactose and in the absence of glucose. The solution with the second-highest bacterial growth was the sucrose-grown bacteria in glucose solution, with a light absorbance of 0.3454 after 72 hours.

This light absorbance value is only 0.002 lower than that of the bacteria grown in lactose solution. This implies that lactose and fructose both provide similar sources of energy for fructose grown-bacteria and sucrose-grown bacteria, respectively. But, the data indicates that overall, switching the carbohydrate source did not have a drastic affect on the bacterial growth. Even though the source was switched, all 4 different solutions experienced a growth curve similar to the ones in the previous graph, which indicates that all of the carbohydrate sources were easily metabolized by the bacteria and provided them with energy to grow. Overall the data collected is partially reliable. The experiment was carried out meticulously, very detailed procedures were followed, and data was collected accurately.

But, the data is not fully reliable. Although conclusions were drawn as to what carbohydrate source was the most effective in providing energy for the growth of E.coli, all of the carbohydrate sources showed the capacity for growth. A clear growth curve was established for all 5 sources, indicating that all of the organic compounds used could be considered plausible energy sources for E.coli. Due to time constraints, the data collected is only representative of a minimal period of time (72 hours). For future experiments, the same experiment can be conducted, except the bacteria can be left in the incubator to grow for a longer period of time (couple of months, year, etc.). Then, the carbohydrate source can be switched to see the effect it has on growth. The bacteria were not given sufficient time to grow to establish a fully developed growth curve representing all four phases of bacterial growth. This also decreased the reliability of the data significantly.

References Allott, Andrew, and David Mindoff. IB Diploma Programme Biology Course Companion. Oxford: UP, n.d. Print.

"Bacterial Growth Curve." Value @ Amirita. NME ICT initiative of MHRD, n.d. Web. 11 Aug. 2014. <http:/ / amrita.vlab.co.in/ ?sub=3&brch=73&sim=1105&cnt=1>.

"Carbohydrate Metabolism in Bacteria." N.d. Microsoft Word Document file.

"Colorimeter." Vernier Software & Technology. N.p., n.d.

Web. 13 Sept. 2014. <http:/ / www2.vernier.com/ booklets/ col-bta.pdf>.

"How to Use a Micropipettor." Davidson. N.p., n.d. Web. 13 Sept. 2014. <http:/ / www.bio.davidson.edu/ molecular/ Protocols/ pipette.html>.

"Humans Carry More Bacterial Cells than Human Ones."

Scientific American. Scientific American, 30 Nov. 2007.

Web. 22 Oct. 2014.

"Introduction to the Bacteria." University of California Museum of Paleontology. Microbe World, n.d. Web. 11 Aug. 2014. <http:/ / www.ucmp.berkeley.edu/ bacteria/ bacteria.html>.

Jurtshuk, Peter, Jr. "Chapter 4: Bacterial Metabolism."

Medical Microbiology. 4th Edition. N.p.: n.p., n.d. National Center for Biotechnology Information. Web. 11 Aug. 2014. <http:/ / www.ncbi.nlm.nih.gov/ books/ NBK7919/ >.

Lac Operon. Youtube. N.p., n.d. Web. 11 Aug. 2014. <https:/ / www.youtube.com/ watch?v=oBwtxdI1zvk>.

"Streaking and Innoculating Plates." Center for Polymer Studies. N.p., n.d. Web. 13 Sept. 2014. <http:/ / polymer.bu.edu/ ogaf/ html/ chp51exp2.htm>.

Todar, Kenneth, Dr. "Bacterial Adaptation to the Nutritional and Physical Environment." Regulation and Control of Metabolism in Bacteria. Todar's Online Textbook of Bacteriology. N.p.: n.p., n.d. 1-5. Todar's Online Textbook of Bacteriology. Web. 11 Aug. 2014. <http:/ / textbookofbacteriology.net/ regulation.html>.

Acknowledgements I?d like to thank Mr. Gilles Buck, head of the Science Department at Colegio FDR. Without his support and guidance this investigation would not have been possible. I would also like to thank Colegio FDR alumnus Fabiola Valdivia of the Cayetano Heredia Medical University of Lima for providing me with the resources necessary (such as the strain of bacteria used in the experiment) to write a thorough, investigative paper that led to true scientific conclusions.

Relationshipbetweenthequantityof activedryyeast inbreaddoughandtherateof Fermentation The relationship between the quantity of active dry yeast in bread dough and the rate of fermentation

By Gisella Silva, Grade 12 Biology

Abstract The aim of this experiment was to investigate how a change in the quantity of active dry yeast placed in bread dough would affect the rate of Carbon Dioxide (CO2 ) produced by the fermentation reaction of yeast in bread making when the rising time was 80 minutes, the baking time was 15 minutes, the temperature of the oven was 478 degrees Kelvin, the size of the bread rolls was 1/ 3 of a cup in volume, the activation time for the yeast with warm water was 10 minutes, and the quantity of all ingredients (except the yeast) were controlled. The hypothesis predicted that if the quantity of active dry yeast placed in bread dough increased, then the rate of CO2 production would increase accordingly until the substrate concentration became a limiting factor because there would be a greater number of yeast enzymes breaking down carbohydrates (starch) into carbon dioxide (CO2 ) and Ethanol (C2 H5 OH) as a by-product of anaerobic fermentation. The results indicate that the hypothesis was partially supported.

Increasing the amount of yeast did result in increases in the rate of CO2 production because there were more yeast enzymes breaking down starch (carbohydrates) into CO2 and ethanol through anaerobic fermentation.

Introduction Although most people consume bread at least once a day, most people also remain unaware of the chemistry behind bread making. In fact, did you know that yeast fermentation, a type of anaerobic cellular respiration, is largely responsible for the leavening and taste of bread?

Cellular respiration ?is the controlled release of energy from organic compounds in cells to form ATP (adenosine triphosphate)?

(Bio Ninja).

Anaerobic cellular respiration refers to the creation of ATP when there is no oxygen available. Fermentation is a form of anaerobic respiration that occurs in yeast and bacteria where a carbohydrate (such as sugar) is converted into an acid, alcohol, and/ or gas. In bread making, yeast fermentation converts starch into Carbon Dioxide (CO2 ) and ethanol (C2 H5 OH) (Cheeseburger Chemistry). Bread is made up of four basic ingredients: flour, yeast, water, and salt. In bread making, yeast? a single celled organism? is first activated when combined with warm water (Cheeseburger Chemistry).

When flour and salt are added to the mixture of water and yeast, a protein matrix called gluten is formed from the contact of water with proteins in the flour (Cheeseburger Chemistry). While kneeded, this

"the yeast begins to consume the pulverized starches that make up the flour and converts them into CO2 and ethanol.

This is known as fermentation."

matrix forms an elastic dough that holds the bread together and gives baked bread its structure (Cheeseburger Chemistry). Then, the yeast begins to consume the pulverized starches that make up the flour and converts them into CO2 and ethanol (alcohol) (Cheeseburger Chemistry). This is known as fermentation. The CO2 produced is what makes the bread ?inflate? when left to rise before being baked (Cheeseburger Chemistry). Once placed in the oven, this ?inflation? continues because the air bubbles produced by the CO2 expand in the presence of heat and the yeast becomes more active, therefore the starch is consumed more quickly and CO2 is produced at a faster rate (Cheeseburger Chemistry). Nonetheless, yeast microbes can only survive high temperatures for a certain amount of time because they eventually reach a point where the heat causes them to become denatured (Cheeseburger Chemistry). Once the yeast enzymes are denatured, the bread does not continue to rise because starch is no longer being converted to CO2 and ethanol (Cheeseburger Chemistry).

Moreover, although only small quantities of salt are used in the bread making process, salt plays an important role in fermentation because it acts as an inhibitor to yeast.

The cell wall of bread-making yeast is semi-permeable; when a significant amount of salt is nearby, a yeast cell will release water ("How Would Salt Affect").

Because this water is necessary

for its cellular activities, releasing it will slow down the reproduction and fermentation activities of the yeast ("How Would Salt Affect"). Thus, salt is beneficial to the bread making process, but only to a certain extent. When small quantities are used, the salt slows down the rising process to allow the gluten to strengthen, develop, and efficiently hold the released CO2 ("How Would Salt Affect").

If too much salt is used, however, the process of fermentation occurs so slowly that there is a reduction in the rate of CO2 produced, and thus, a reduction in the volume of the bread ("How Would Salt Affect").

This experiment tested the relationship between the quantity of active dry yeast placed in the dough of the bread with the rate of CO2 produced when variables such as the time the yeast was left to activate and react with the warm water, the time it was left to rise, the time it was left to bake, the temperature of the oven, the brand of ingredients used, and other variables mentioned later on throughout the investigation were controlled. Based on the conducted research, what the experiment is trying to prove is that at the beginning, increasing the amount of yeast will increase the rate of CO2 production because there would be a greater quantity of yeast enzymes breaking down starch into CO2 and ethanol ("Enzymes"). Nonetheless, there

will reach a point where regardless of an increase in the quantity of yeast, the rate of CO2 production will not be any quicker because the number of yeast enzymes will be greater than the amount of substrate that needs to be broken down ("Enzymes").

Therefore, unless there is an increase in substrate concentration the reaction cannot become any more efficient.

Method First, a mug was filled with 240 ml. of water and heated in the microwave for a minute. 180 ml of the 240 ml. of heated water was dumped in a large bowl; the remaining water was thrown out.

2.5 grams of active dry yeast (independent variable) was then weighed and added to the bowl with warm water. 1 tablespoon of sugar was also measured and added to the bowl. The warm water, yeast, and sugar were stirred with a spoon until well mixed. Then, the yeast mixture was let to stand for 10 minutes.

After 10 minutes, 15 ml. of oil, ½ teaspoon of salt, and 2 cups of flour were measured and added to the yeast mixture in the large bowl. The ingredients were stirred with a spoon until the dough began to pull away from the sides of the bowl. The surface of a table was floured with four tablespoons of flour, and the dough was placed on top of it. The dough was kneaded for eight minutes. 15 ml. of oil was then measured and placed into another large bowl.

Question What is the effect of changing the quantity of active dry yeast by increments of 2.5 grams (2.5 g., 5 g., 7.5 g., 10 g., 12.5 g.) on the rate of Carbon Dioxide (CO2 ) produced by the fermentation reaction of yeast in bread making, measured by the volume of the yielded bread using the water displacement method when the rising time was 80 minutes, the baking time was 15 minutes, the temperature of the oven was 478 degrees Kelvin, the size of the bread rolls was 1/ 3 of a cup in volume, the activation time for the yeast with warm water was 10 minutes, and the quantity of all ingredients (except the yeast) were controlled?

Using a paper towel, the oil was spread throughout the bowl until lightly oiled. The dough was then placed in the lightly oiled bowl, covered with a damp cloth, and left to rise for 40 minutes. After 40 minutes, the dough? which had risen by now? was ?deflated? by using hands to apply pressure on the dough. Then, the surface of a table was floured with 3 tablespoons of flour, and the deflated dough was placed on it.

Afterwards, part of the dough was grabbed and fit to a 1/ 3 measuring cup. This was done 5 times in order to have 5 pieces of dough that were equal in volume.

Each of the 5 pieces was formed into a round ball. The baking sheet was slightly greased with butter, and the 5 pieces of dough were placed on the baking sheet, 3 inches apart from one another.

The balls where then covered with the damp cloth used in Step 7, and the dough balls were left to rise for another 40 minutes. After 40 minutes, the oven was preheated to 478 degrees Kelvin (400 degrees Fahrenheit) and the dough balls were placed in the oven. The oven timer was set for 15 minutes, and the dough was left to bake. Once the 15 minutes had gone by, the oven was turned off, and the bread was left to cool for 15 minutes. After 15 minutes, the volume of all 5 of the breads was measured using a water displacement method. To do this, all five breads were placed into separate plastic bags and closed with a knot (no air was inside).

Using tape, all of the ?extra? portions of the 5 bags were taped

down to avoid them from having an impact on the displacement of the water. Then, a bowl, which was deep enough to fully submerge the bread, was filled with water all the way to the top and carefully placed inside a larger bowl, as shown in the image below. If water spilled into the larger bowl, the process was repeated. Once the bowl was completely filled with water? and inside the larger bowl? one of the 5 bags was grabbed and fully submerged into the bowl with water, causing part of the water of the bowl to be displaced into the larger bowl. The water that was displaced onto the larger bowl was carefully poured into a 500 ml beaker, and the quantity was recorded. The amount of water displaced (dependent factor) represents the volume of the bread because 1 ml = 1cm3 . (Note: Each of the trials represents one of the five breads from the batch of bread) This process was repeated for all 4 remaining bagged bread rolls. All of the processes mentioned above where then repeated 5 times. For each repeated trial, however, the concentration of active dry yeast used was changed by increments of 2.5 grams. Thus, since 2.5 grams of yeast was used in the first trial, 5 teaspoons of yeast was used in the second trial, so on and so forth. After the final trial was conducted, the workspace was cleaned up, the oven was turned off, and the bread was stored to enjoy!

Results The graph below shows that

there is a correlation between the quantity of yeast (2.5 g, 5 g, 7.5 g, 10 g, 12.5 g.) placed in bread dough and the rate of CO2 produced because for the most part, the red curve, which figures the average rate of CO2 produced for all five tested quantities of yeast is upward sloping. This indicates that as the quantity of yeast increases, the rate of reaction increases as well. Nonetheless, this relationship is not fully supported due to the downward sloping section of the curve, which shows a decrease in the rate of reaction from 2.33 to 1.81 following an increase in the quantity of yeast to from 7.5 grams to 10 grams. This anomaly, however, is most likely due to an error in the collection of data, which is further explained in the conclusion and evaluation of the lab.

Moreover, as can be seen above, the graph

also indicates that the collected data is ?somewhat? reliable because the error bars shown when the quantity of yeast was 10 and 12.5 grams is relatively small, meaning that there is minor variation in the data collected for all five trials of both quantities. On the other hand, the error bars for quantities of yeast 2.5, 5, and 7.5 grams are a lot larger, indicating that there is a larger variation in the data collected for all five trials of the three different quantities. To a certain extent, the data contradicts itself because the point where there is an anomaly is the same point where the error bars are relatively small.

Nonetheless, this is because the same bread dough was used for all five trials within a single batch of dough. Therefore, an error made in the procedure of one batch (in this case the batch when the quantity of yeast was 10) had an effect on all five trials. As can also be noticed, the R2 value is equal to 0.5. Since an R2 value of 1 means the relationship is perfectly linear, it is fair to conclude that although it is evident the relationship is not strong, there is ?somewhat? of a linear relationship because the R2 value is exactly half of 1.

Discussion The hypothesis was partially supported by the data collected from the experiment. When the quantity of yeast was 2.5 grams the average rate of production of C0 2 was 1.52 ml/ min. When the quantity of yeast was 5 grams, the average rate of production of C0 2 increased to 1.94 ml/ min. When the quantity of yeast was 7.5 grams, the average rate of production of C0 2 further increased to 2.33 ml/ min. And finally, when the quantity of yeast was highest, at 12.5 teaspoons, the average rate of production of CO2 was also at it?s highest, at 2.47 cm^3/ min. This relationship between the quantity of yeast in the bread dough and the increase in rate of production is figured by the upward sloping sections of the graph. For the most part, as the x-values increase by intervals of 2.5 g, the y values increase as well. This indicates that as mentioned in the hypothesis, increasing the amount of yeast increases the rate of CO2 production because there are a greater quantity of yeast enzymes breaking down starch (carbohydrates) into CO2 and ethanol through anaerobic fermentation (Cheeseburger Chemistry). The quantity by which the y-values (rate of production of CO2 ) increase, however, for every increase in x values (quantity of yeast), does not follow a constant pattern. Moreover, the reason the hypothesis is not fully supported is because there is a clear and sudden drop in the rate of production from 2.33 (when the quantity of yeast was 1.5) to 1.81 (when the quantity of yeast was 5); this drop is figured by the downward sloping part of the curve and most likely indicates that there was an error in the collection of data that led to inaccurate results. One of the mistakes made during the process of making the bread was accidentally adding an additional ½ teaspoon of salt to the dough in the batch where 10 g of yeast was used.

Although such a small mistake wouldn?t make much of a difference in most baking processes, additional salt can have a significant impact on the fermentation process of yeast, because it acts as an inhibitor to the enzyme. The yeast used for bread making, has a semi permeable cell wall, therefore, when there is a large amount of salt present, water is released from the yeast cell ("How Would Salt Affect"). This water, however, is essential for cellular activities, and thus, a loss of it leads to a decrease in the rate of fermentation of the yeast, which leads to a decrease in the quantity of C0 2 produced, and therefore, a decrease in the volume of the bread ("How Would Salt Affect"). Moreover, the hypothesis is not fully supported, because despite the increase in the quantity of yeast when the substrate concentration (the quantity of all the other ingredients used to make the dough) was controlled throughout each trial, the rate of CO2 production never reached ?plateau?. This most likely indicates that the intervals by which the quantities of yeast were increased were not large enough to reach a point where there were more yeast enzymes than the amount of substrate concentration that had to be broken down. Thus, an increase in the quantity of yeast continued to show an increase in the rate of CO2 produced. If this

experiment were to be conducted again, the intervals by which the independent variable was changed should be larger in order to1) witness a clearer and larger change in the rate of CO2 produced 2) prove that an increase in the quantity of yeast will increase the rate of CO2 production until the substrate concentration becomes a limiting factor. In terms of accuracy, the collected data was somewhat accurate because the error bars shown in the graph for quantities of yeast 10 and 12.5 grams were relatively small, whereas the error bars for quantities of yeast 2.5, 5, and 7.5 grams were a lot larger.

What this means for quantities of yeast 10 and 12.5 grams is that there is a tight distribution of points because at both of these quantities of yeast, the data values for all five trials range very little above and below the average. For instance, when the quantity of yeast was 12.5 g, the rate of CO2 production was 2.32 in the first trial, 2.53 in the second trial, 2.42 in the third trial, 2.63 in the fourth trial, and 2.47 in the fifth trial. The average of all those points is 2.47, and thus can be represented by a relatively small error bar because all of the values are close to the average. On the other hand, what this means for quantities of yeast 2.5, 5, and 7.5 grams, is that there is not a tight distribution of points because at all three of these quantities of yeast, the data values for all five trials ranged relatively high above the average. For example, when the quantity of yeast was 7.5, the rate of CO2 production was 1.95 in the first trial, 2.11 in the second trial, 2.63 in the third trial, 2.42 in the fourth trial, and 2.53 in the fifth trial.

The average of all those points is 2.33, and thus can be represented by a larger error bar than those when the quantity of yeast was 2 and 2.5 teaspoons of yeast because the values are further away from the average, and thus, the results are not as accurate. As mentioned previously, however, the data contradicts itself to a certain extent because the downward sloping area of the graph (most likely representing the error in the collection of data) is the same point where the error bars are relatively small. This is because for all five batches of dough, the same bread dough was used for all five trials within each batch. Therefore, an error made in the procedure when the quantity of yeast was 10 grams most likely had an effect on all five trials, affecting all five data points but having a tight distribution within those five points, leading to small error bars. By looking at the graph, one can also notice that there is an R2 value equal to 0.5. An R2 of 1 means that the relationship is perfectly linear. Thus, it is fair to conclude the collected data conforms somewhat to a linear relationship because the R2 value is exactly half of 1, which means that there is a 50% correlation between the X (quantity of yeast) and Y (CO2 rate of reaction) values.

References Bio Ninja. N.p., n.d. Web. 3 Oct. 2014. <http:/ / www.ib.bioninja.com.au/ higher-level/ topic-8-cellrespiration/ >.

Cheeseburger Chemistry. NBC Learn. NBC Universal Media, n.d. Web. 3 Oct. 2014. <http:/ / www.nbclearn.com/ portal/ site/ learn/ chemistrynow/ cheeseburger-chemistry>.

"Enzymes." Chemistry for Biologists. The Royal Society Of Chemistry, n.d. Web. 3 Oct. 2014. <http:/ / www.rsc.org/ Education/ Teachers/ Resources/ cfb/ enzymes.htm>.

"How Would Salt Affect Yeast?" ehow. Demand Media, n.d.

Web. 3 Oct. 2014. <http:/ / www.ehow.com/ info_8626031_would-salt-affectyeast.html>.

Acknowledgements The author would like to thank Mr. Gilles Buck for putting this journal together, as well as her Biology teacher Mr. Samuel Bourke for his constant excitement and support to provide feedback on the work.

effect of caffeineonyeast fermentation The effect of an increase in Caffeine Concentration on rate of respiration of 10 mL of yeast stock solution (100 g/ L of yeast and 25 g/ L of sucrose)0.01)

By Caterina Niccolini, Grade 12 Biology

Abstract This lab demonstrates how 10 mL of caffeine solutions varying in concentrations mixed with 10 mL of yeast stock solution would affect the rate of respiration of yeast measured through an average change in pressure after a span of 4 minutes. My initial thought about this experiment was that the rate of respiration of yeast would increase due to the available energy derived from caffeine, that would as an outcome affect the rate at which its cells respire aerobically. Given that caffeine increases the energy available in an organism, I thought that it would also affect the rate at which yeast?s metabolism functions, and therefore, an increase at which its cells respire. My fundamental belief was that as an organism receives higher concentrations of caffeine, the rate at which it consumes the available sucrose solution would also increase. As an outcome, I believed that the rate of respiration in yeast cells would notoriously increase throughout a span of 4 minutes. After concluding my experiment, I came to the realization that higher caffeine levels decreased the average changes in pressure measured.

Int roduct ion Cell respiration is a process by which organisms produce ATP through two processes: anaerobic and aerobic respiration. Glucose molecules are broken down inside the cell?s mitochondria and are synthesized through a series of chemical reactions in order to yield energy. This scientific paper had the purpose of proving how the process of cell respiration in an organism occurs with the effect of a natural purine analogue, such as caffeine. Caffeine affects us because it ?(? ) raises blood glucose levels by influencing the regulation of cellular respiration (breakdown of glucose to energy), glycogen metabolism and fatty acid metabolism ("Exercise Medicine.")?. Even though yeast is an organism that doesn?t have these metabolic pathways, I was still interested to see that if by any means the introduction of this purine would increase the available energy in cells, and therefore increase the rate at which their metabolism works. Therefore, to further investigate this, I chose to use caffeine as an independent variable next to yeast and sucrose stock solutions, (both controlled variables). I used 10 mL of caffeine solutions varying in concentrations of (0.0, 0.2, 0.4, 0.6, 0.8, and 1.0 g/ L) mixed with 10 mL of yeast stock solution (100 g/ L of yeast and 25 g/ L of sucrose) to see how the rate of respiration of yeast, measured through an average change in pressure (kPa ± 0.01) after a span of 4 minutes, changed.

I believed that an increase in the concentration of caffeine in each solution was going to increase the rate of respiration in cells as well, given that higher concentrations of caffeine could mean a larger availability of energy for cells to use in respiration.

Research Quest ion How do 10 mL of caf f eine sol ut ions varying in concent rat ions of (0.0, 0.2, 0.4, 0.6, 0.8, and 1.0 g/ L) mixed wit h 10 mL of yeast st ock sol ut ion (100 g/ L of yeast and 25 g/ L of sucrose) af f ect t he rat e of respirat ion of yeast , measured t hrough average change in pressure (kPa ± 0.01) af t er a span of 4 minut es at an act ivat ion t emperat ure of 40°?

Met hods Creat ing st ock sol ut ions of caf f eine

The caffeine stock solutions were created by measuring 100 mL of water into 5 separate beakers with a graduated cylinder of 100 mL. 1 caffeine pill (containing 100 mg of caffeine) was then weighed: 0.27 grams. This initial weight of an 100 mg pill served as an indicator for the rest of caffeine amounts required. Therefore, an 80 mg caffeine pill weighed 0.22 grams, a 60 mg caffeine pill weighed 0.16 grams, a 40 mg caffeine pill weighed 0.11 grams, and a 20 mg caffeine pill weighed 0. 05 grams.

Each specific amount of caffeine pill was then crushed into powder using a pill crusher on a petri dish, and was then mixed into one beaker of 100 mL of water using magnet mixers and a heater.

Creat ing st ock sol ut ions of yeast 20 grams of yeast and 5 grams of sucrose were weighed in small plastic dishes, and 200 mL of water was measured into a graduated cylinder. The yeast and sucrose were poured into the water and mixed together.

Perf orming t he experiment 10 mL of the first stock solution was measured into a 10 mL graduated cylinder with a micropipette into an Erlenmeyer flask of 50 mL. Then 10 mL of yeast stock solution were measured into a 10 mL graduated cylinder with a micropipette. Both solutions were poured into a flask and mixed, and the pressure sensor?s plastic tube stopper was placed on top of the flask. The initial pressure was written down, and 4 minutes were timed. After 4 minutes passed, the final pressure in % was written down.

These procedures are repeated 5 times per each of the caffeine solutions.

Resul t s Raw dat a: Table 4 includes the raw data I collected in order to calculate the averages in change in pressure.

Data was collected by calculating the average change in pressure (kPaÂą0.1) of 10 mL of each of the 6 solutions varying in caffeine concentrations, (0.0, 0.2, 0.4, 0.6, 0.8, 1.0 g/ L), mixed with 10 mL of yeast and sucrose solution in total: yeast 100 g/ L and sucrose 25 g/ L. The different caffeine concentration solutions were tested 5 times each with the same yeast and sucrose solution for a span of 4 minutes through a Vernier pressure sensor that tells change in pressure in (kPaÂą 0.01).

Graphs: The error bars indicate one

standard deviation Qualitative Data As I performed this experiment, I began to notice how every time I added more caffeine to the yeast and sucrose solution, the average change in percentage in pressure decreased, opposite to what I thought would eventually happen. As I set up my lab, I made sure to repeat the exact same process for each trial so that the results could remain as accurate as possible.

I noticed how the bubbles in the yeast solution grew as more time passed by and the solution remained untouched, and how caffeine pills couldn?t be completely crushed. There were always remaining particles that sifted to the bottom of the beaker.

Discussion Caffeine doesn?t affect organisms at a cellular level, it affects bodies and tissues, muscles. After concluding my experiment, I came to the realization that as caffeine concentration in the solutions used increased, the average change in pressure measured decreased. Opposite to what I believed, ?Caffeine is a natural purine analogue that elicits pleiotropic effects leading ultimately to cell's death (Kuranda).?

Before beginning my experiment I assumed that as yeast was a eukaryotic organism, caffeine would affect it in the same way it affects humans or animals, by increasing the rate of its metabolism. Caffeine affects us because it ?(? ) raises blood glucose levels by influencing the regulation of cellular respiration (breakdown of glucose to energy), glycogen metabolism and fatty acid metabolism ("Exercise Medicine.")?, clearly indicating that an organism such as yeast is incapable of carrying out these biological reactions because it doesn?t have a bloodstream. In other words, caffeine doesn?t affect an organism at a cellular level directly. It influences the functions of a body overall through the help of bloodstream, body fats and tissues. Anyway, I thought that caffeine could potentially increase the rate of respiration in yeast because it increases the rate of phosphorylation occurring in its cells, and because it is exclusively a eukaryotic organism. I only took into consideration that caffeine is similar to a natural purine, and that it causes pleiotropic effects in genes, meaning that genes influence other genes to repeat a certain action via unrelated effects. I remained indifferent to the fact that it causes eventual death in yeast, because I thought that every organism eventually dies, and that it was an action more or less unrelated to caffeine and its effects on yeast. My logic was that an increase in caffeine would increase the energy available for reactions in eukaryotic organisms to occur, so it would increase the rate of aerobic cell respiration in yeast as well. My data disproved my hypothesis and that the information I thought was unrelated to caffeine and yeast, (about caffeine causing an eventual death in this organism), was in fact very related to my experiment. As can be seen in my data above, the trend in my graph is a negative correlation. There is a decrease in average change of pressure as an increase in caffeine concentration occurs. The highest average change in pressure (% Âą0.1) that can be seen in my

data is when the concentration of the caffeine solution is 0.0 g/ L, at around 1.5% . The concentration then rises to 0.2 g/ L and the average change in pressure decreases by approximately a point. The concentration continues to increase as it reaches 0.4 g/ L and the average change in pressure decrease to 1% . The concentration is then 0.2 g/ L and the average change in pressure is around 0.7% . There is one anomaly in my data though, because when the concentration is at 0.8 g/ L, there is a slight rise in average change in pressure % , as it goes up approximately one point. Finally, when the concentration is at 1 g/ L, the average change in pressure % is at its lowest, at around 0.4% . Through the results of my data a negative correlation can be clearly seen as there is gradual decrease in average change in pressure due to an increase in caffeine concentration. The largest error bars on my graph can be seen when caffeine concentrations in g/ L are at 0 and 0.4. The inaccuracy of the data I collected for these series of experiments could prove the unreliability of the data I collected, due to the potential sources of errors that could have contributed to the inaccuracy of my data. The error bars for concentrations 0.6, 0.8 and 1 g/ L are relatively small in comparison to the other ones, meaning that this could have influenced my data from being completely linear, and from having an even stronger negative correlation. As the concentration of caffeine that resulted with the largest error bar is that of 0 g/ L, this can be used as evidence to prove how there are other multiple factors that can potentially influence the results of cell respiration in yeast when there is no influence from a remarkable substance such as caffeine. As an outcome, an increase in caffeine concentrations in my data resulted in smaller error bars, meaning that the influence caffeine has on yeast cell respiration is very strong. When there was a higher caffeine concentration, the results of my data were closer to each other, furthermore proving that caffeine did in fact have a strong effect on yeast cell respiration, except in an opposite way of what I first believed. Overall, I do trust the results of my data. Restating the fact that ?Caffeine is a natural purine analogue that elicits pleiotropic effects leading ultimately to cell's death (Kuranda)?, it is evident that the yeast cells exposed to caffeine throughout my lab showed a decrease in respiration; exposing a negative correlation in my data. This statement is supported throughout my experiment, because evidently enough, when an organism?s respiration rate decreases, it begins to die as a result. My data is reliable in the sense that it suggests how caffeine contributes to the killing of yeast cells.

References "Exercise Medicine." Why Caffeine Boosts Performance.

N.p., n.d. Web. 23 Sept. 2014.

Kuranda, K., V. Leberre, S. Sokol, G. Palamarcyzk, and J. Franรงois.

"Result Filters." National Center for Biotechnology Information.

U.S. National Library of Medicine, Sept. 2006. Web. 23 Sept. 2014.

Standard Deviation Formula. Sd. N.p., n.d. Web. 4 Oct. 2014. <http:/ / mhs93525.files.wordpress.com/ 2013/ 04/ sd.gif>

Acknowledgements I?d like to thank Mr. Buck, Subject Area Leader for Science at Colegio FDR as well as my IB Biology teacher for helping me carry out this experiment.

Without his experience and extensive knowledge on the subject, it would not have been possible for me to undertake the process of such a challenging lab.

"Opposite to what I believed, ?Caffeine is a natural purine analogue that elicits pleiotropic effects leading ultimately to cell's death (Kuranda).?"

contact ColegioFDRscience gbuck@amersol.edu.pe scienceatfdr.weebly.com twitter.com/ GillesBuck1