IB Biology SL

R. Martin
Marko Aranda-Landa
15 February 2024

Yeast effectivity correlated with cellular age.

INTRODUCTION
Is there a correlation between mixtures of yeast(Saccharomyces cerevisiae) and the relative age, between 0-8
days, of each solution in the efficiency of a yeast's anaerobic respiration(fermentation) as measured by the
volume of CO2 produced (in cm3) after a period of time?

The main biological process tested is fermentation in cells. Fermentation is an anaerobic form of respiration in
which a cell digests sugars consumed from the environment without the presence of oxygen and produces
ATP(Adenosine triphosphate), Carbon dioxide, and ethanol (Maicas). The other form is Aerobic respiration in
which an organism uses oxygen to break down sugars and produce water, Carbon Dioxide, and ATP. ATP is
a readily available fuel source derived from glucose to power essential cellular processes (Koutsokali).
Anaerobic respiration is found only in anaerobes and facultative anaerobes (J et al.). Jing Wang, a leader in a
study of anaerobes, classified anaerobes as organisms that use fermentation to break down resources while
facultative anaerobes, like yeast, can use both aerobic and aerobic respiration depending on the availability of
oxygen. In environments with low levels of oxygen.This process is still heavily studied and relevant as
organisms like parasitic worms, bacteria, And even muscle tissue under high stress can go into fermentation to
produce ATP (J et al.). Fermentation has been studied heavily with the creation of Microfluidic devices, small
platforms that hold precise measurements of fluids. Biologists have been able to research how fermentation is
used on a cellular level by experimenting with different environments in these precise environments to make
hypotheses on the functions of organelles common in all cells (Botstein and Finks).

Yeast, Saccharomyces cerevisiae, is a unicellular eukaryotic organism (Botstein and Finks). Categorized as a
fungus, yeast is the most commercially bought organism, its unique ability to do both aerobic and anaerobic
respiration on almost command, allows for precise experimentation of either process with a wide range of
unique and alterable variables and instant results. These variables include sugar concentration, temperature,
glucose source, glucose, and pressure (Botstein and Finks). Since these are easily alterable variables, any
experimentation on yeast needs a lot of attention to make sure each variable is isolated and the correlation is
relevant

Coming from a family of both bakers of savory bread and confectioners bread, yeast and its careful curation
have been skills passed down in my family, along with methods to bring out its efficiency. One method several
websites and listings offered was thoroughly aging yeast batches. Advertised as incredibly powerful, I asked
my parents whether time changed the fermentation rate of bread in the respiration of yeast cells. Though they
didn’t explain what specifically in yeast changes over time, they believed it functions like yeast in wine and that
there was a better efficiency from better-aged yeast. I didn’t believe that time would affect a yeast's metabolism
so to test my hypothesis and that of my parents I'm running an experiment to see its effect on the processes of
fermentation.

Because the age of yeast cells is relatively short. aging won't have a significant impact on a yeast's rate of
fermentation and the release of Co2. I think that age can affect the respiration of singular cells in other
organisms with longer lifespans, but since yeasts live short lives, there is no significant change in the rate of
fermentation. Other studies have found around the same results, found that the only difference found is
between different species of yeast, though traditions claim that yeast for bread if kept for longer times, does
yield airier bread, and therefore more efficient yeast.

VARIABLES
The manipulated variable for my experiment would be the amount of time a batch of yeast is left active and
aging. In yeast cells, age can determine the phases of yeast and their rate of metabolism (Koutsokali). Yeast
cells reproduce every 1-3 hours and around 20-30 times in a life cycle (Koutsokali). This is important since
yeast cells reproduce through budding, a process in which a mother cell forms a daughter cell on its surface
and shares nutrients and cellular components until it divides (Koutsokali). When a cell reproduces, cellular
error and environmental factors lead to damaged or faulty proteins, tissue, and organelles building up in the
mother cell (Koutsokali). These errors from age can impact the organelles involved in the metabolism and
respiration of yeast cells. We can measure the age by keeping track of the time a yeast solution has been left
active. This variable has been studied before similarly by Leonard Guarente, who tracked aging in yeast by
isolating cells and focusing on yeast’s metabolic and reproduction rates. He related his research with age by
comparing differently stressed cells and their impact on cellular activity in organelles.

The value being observed would be the rate of carbon dioxide being released from a mixture. In anaerobic
respiration, carbon dioxide and water are products of turning glucose into ATP(adenosine triphosphate)
(Pedersen et al.). I can measure this carbon released by measuring the change, in volume, of gas in the
environment and with this information, assume the rate of respiration. This is an especially important variable
in cellular research where the efficiency of an organism can be a clear sign of cell health and efficiency
(Bouska et al.). A microbial researcher, Dr. B. M. (Basil Martin) Wright, invented a precise machine to calculate
this. He designed a respirometer, that studies the air by testing the composition of gas on reacting substances,
his procedure factored in the rate at which cells took in oxygen instead of just output carbon allowing further
research into the metabolisms of different organisms and functions of cellular organelles.

For my data to stay consistent, I needed to make sure the preparation and feeding of my yeast only contributed
to the aging and not any other significant change. To make sure of this, I used 8g(grams) of yeast, the size of a
regular yeast packet as it’s a manageable size to keep alive with sugar. To keep them active, I used a daily
feeding of 4g of a readily metabolizable cane sugar (D'amore et al.), this amount ensured the yeast had
enough to keep up critical cellular activity and aging but not enough for significant cell growth or division. I got
this number from, “Reproductive Potential of Yeast Cells Depends on Overall Action of Interconnected
Changes in Central Carbon Metabolism, Cellular Biosynthetic Capacity, and Proteostasis, they found that
yeast consumes around 1-2% of their body weight in sugar an hour for basic aerobic respiration, I multiplied
this by the initial weight of yeast and calculated 0.08-0.16 grams of sugar an hour or 1.92-3.84g a day, which I
rounded to 4g to account for favorable conditions in each culture. I also had to keep a consistency in the
medium of yeast. To maintain a sugar concentration of at least 4%, which is the percent that yields the most
yeast cellular activity and metabolism (Maslanka and Zadrag), I added 100g of Aquafina branded water to all 5
cultures, this brand also had a Ph level of around 6, which is favorable to yeast (Pampulha and Loureiro-Dias).
The climate and conditions had to stay consistent too, as heat can impact the respiration of yeast (Pampulha
and Loureiro-Dias). I placed my experiments in my garage which had an approximate climate of around 15.5
degrees Celsius, prime yeast temperatures for aerobic respiration. Another important part to keep consistent in
my procedure was the measuring process of each culture's output. To make sure each has the same
temperature, I used a mug heater to keep each solution at 80-90 degrees warm, the prime temperature for
yeast fermentation(anaerobic respiration) (Yilmaztekin et al.). Anaerobic respiration also has a different specific
sugar concentration for best efficiency. A study from Oklahoma State University, “The Effects of Different
Concentrations of Sucrose on the Growth of Yeast”, found this concentration to be around 4%. Using this data
I settled on using 10g of sugar in my measurements for fermentation.

METHODOLOGY
To start and feed each culture, I used 5 16 oz PET plastic disposable cups, a 113g bottle of Fleischmann's
active dry yeast, and 130g of pure cane granulated sugar. I also needed a spot with warm temperatures to
keep the cultures, I used my garage as it had a consistent and stable climate. For measuring the respiration I
used a plastic pet measuring cylinder of 100ml, 500ml of purified Aquafina branded water, 35 cm of 0.5 wide
aquarium tubing, and one INTLLAB 316 Stainless Steel Small 2-Port Vent Assembly. To keep the results
consistent between measurements, I also used a cup warmer from Sealon and an Escali Primo Digital Food
Scale, a 750 ml glass lab beaker, the stopwatch app by Apple, and lastly 8 days worth of time.

There are some important risks to acknowledge in my experimentation. There was a risk of burns in my
measuring process from the cup warmer’s surface, the containers don’t get dangerously hot but it was still
important to not touch or bring combustibles near, as well as leave them unattended. There’s also a relevant
ethical concern on the experimentation of yeast. Though they do possess all of the features of living
organisms, since they are unicellular and have the characteristics of fungi, testing performed on yeast would
be classified as microbial research and not a form of animal testing (Koonin and Starokadomskyy).

Preparation:
1. To start aging yeast cultures, I found a space with static temperatures and a climate of around 15.5
degrees Celsius. I used my apartment's garage as it's slightly warm and isolated from any other
variables.
2. Then, I placed 5 of my disposable cups with 8 grams of yeast each.
3. I also added 4 grams of sugar to only 4 of the cultures, leaving one out as a control.
4. To provide a medium for the yeast I added 100g of Aquafina branded water to all 5 cultures, including
the control
5. Since yeast only lives around 8 days, I made sure to feed each culture daily with the necessary 4
grams at constant intervals of 24 hours to make sure there isn't too much sugar at any one time.
Testing
1. To measure the efficiency of fermentation at constant intervals, I made sure to test a control at 0 age
and then another every 2 days to have a total of 5 levels of a manipulated variable, ranging from 0-8
days in age, the total lifespan of yeast cells.
2. The system I used to measure the carbon dioxide output of each culture, I found from Pub med Central,
an article “A Simple, Low-cost, and Robust System to Measure the Volume of Hydrogen Evolved by
Chemical Reactions with Aqueous Solutions” listed a process involved taking a vial of reactants and
plugging the entrance with tubing to a waterlogged measuring cylinder where the gas would
accumulate and push water down and show the volume of gas emissions (Brack et al.). Instead of
reactants, I added my cultures with the vent lid to the inside of my water-logged measuring cylinder
3. As soon as the lid was secured I started a stopwatch and waited for the measuring cylinder to reach
100 ml, this way I could take the resulting data and find the fermentation rate of each level.
DATA AND ANALYSIS

First, the data needed preliminary calculations to determine the rate of gas from each culture of yeast before it
could be processed. For each trial, I divided the gas exchanged taken from each culture by the amount of time
each trial took. This gave me the rate of respiration for each culture which I can use to find the skew. In data,
skewness can be used to determine asymmetry in distributions of points and find where most points and
central tendency can be found. A Negative skew shows a tendency to the left of a graph while positives show a
tendency to the right. This measure can help identify where central tendency can be found. By taking the rates
of respiration from each level of manipulated variable(time), all 5 cultures with 0 days, all 5 with 2 days, etc…,
and making a normal distribution graph, I was able to get the measure of skew. The values indicate a weak
skew to either side at all levels, allowing the mean to be used as the best measure of central tendency,

calculations can be performed on this data to further determine if the results can be attributed to
chance, or represent a population or trend. These are known as Inferential statistics, where a high
significance value indicates an unlikely chance that results occurred due to chance. Low significance values
suggest a higher chance the results occurred because of chance. ANOVA, or the analysis of variance, would
be an appropriate method to find inferential statistics with the data I took because of the relevant mean and
type of data points. The ANOVA test takes the differences between each mean from every level of the
correlative variable Since I ran trials with 5 levels this would be an appropriate method. If the significance level
of my data is low, then I reject the null
hypothesis, meaning that my data supports
that differently aged yeast groups have no
difference in respiration rates. If the
significance level is high, we reject the null
hypothesis, meaning that our data supports
the alternate hypothesis that the differently
aged yeast groups have a significant
difference in respiration rates.

Pearson sample size (n) Significance value conclusion

Correlation (R) Calculated p-value
(p)

0.8851. 25 0 .00001 0.05 Significant

Above is the results of the NOVA statistical test which determine if the results were statistically significant.
Since my calculated Anova significance value was < .05 and the calculated p-value was < .00001. Because
the calculated P-value was higher than the critical value at a 0.05 significance level, I conclude that the
results are significant. Therefore, I accept the alternate hypothesis.

This graph of each group's mean and standard

deviation is the best for visualizing my data, here
each group of differently aged cultures is
represented with an accurate mean of the times
each took to produce 100ml of gas. It also shows
an important progression in both mean and
standard deviation, which represent the data’s
variability proficiently. This means that as each
culture aged older, their respective times to produce the 100ml of gas also went up. Further, the standard
deviation bars show that those times also became more varied and further from the mean. The standard
deviations also started to overlap after days 6 and 8, meaning that those cultures could have had the possibility
of taking the same amount of time to produce the gas as those before them. These overlaps though, don't
mean that there isn't a difference between the means. Since each overlapping group has a bigger deviation
error bar that extends further than the ones before it, each mean must be unique to each group.
I also added a trendline to my data as it followed a continually increasing line. As the X-axis increased,
the Y-axis increased too. Additionally, since the slope of the graph seems to increase the perfectly steady ratio,
a linear line can effectively show the trend of the data.
CONCLUSION AND EVALUATION

Is there a correlation between cultures of Saccharomyces cerevisiae and the relative age of each solution in
the efficiency of a yeast's anaerobic respiration?

The data I collected showed that as each culture aged, there was a slight difference in the efficiency the yeast
cultures took to respire. As I tested my cultures, over time each group yielded a slower time to produce the 20
ml of Co2 being released. This means that as time continued, the ability of the yeast cells in each culture to
process the sugar in their environment with anaerobic respiration was negatively affected. Though there were
few interlapping values in my data, most points and spread showed that, there was a general trend that would
support the alternative hypothesis, that there is a correlation between cultures of Saccharomyces cerevisiae
and the relative age of each solution in the efficiency of a yeast's anaerobic respiration.

I expected the times in each yeast to ferment to remain the same regardless of the age of each cell.
One plausible explanation for this is cellular aging. Since I attempted to keep each yeast culture alive with the
same amount of cells, it's plausible that in this stage of stagnation in which the cells couldn't grow more than
they could respire, without being able to fall back on cellular hibernation or dormancy they could in storage,
inner functions or organelles inside cells could have begun to break down due to inactivity. This is similar to the
results from the study, “Yeast as a Tool to Identify Anti-aging Compounds” by the University of Graz which
found that as yeast cells age, their functions also degrade over time( Zimmermann et al.). This is important
because while yeast aging does damage the cell, most is from replication, suggesting that common functions
inside yeast and other cells degrade over time too.

It’s also important to comment on the variability of the data since some of the cultures crossed times with other
groups, this could signal that as the yeast aged, there could have been new cells forming from the sugar
introduced, but went into dormancy from overpopulation. (Maire et al.) This could explain why some of the
cultures were so varied, as different populations could have impacted the rate. I also had some limitations on
storing the yeast, while I kept them covered in a stable environment, they still needed an open source of
oxygen, so it’s also possible that as the yeast aged, other airborne organisms could’ve seeped into the culture
and affected the results. Additionally. Because of compounding variability at every level, the means of each
level and overall data could have been skewed as these points might have occurred from chance more than
following the general trend.

I tried my best to account for the average amount of sugar the base amount of yeast would take to keep from
going into dormancy but also keep from significant cellular replication. I would improve the consistency of the
feeding times, to make sure they were aging at the same rate with the same resources better, accounting for
times after feedings when there was more sugar in the solution compared to the concentration after some time.
Additionally, data on populations of each culture could have improved the data. Having an observable or
testable amount of organisms can allow future experiments to only focus on one variable, age. Holding many
more trials to find a more focused mean on data with less variability could also help. The next step I would take
in my question is to scale the number of cultures up to have more data points, I would decrease the size of
each culture to make sure the area inside the bottles could have evenly distributed the sugar and account for
the setting sugar of each bottle and any replicating yeast cells.
 Works Cited

Barnnett, Tala, et al. “The Effects of Different Concentrations of Sucrose on the Growth of Yeast |

Barnett | Journal of Introductory Biology Investigations.” Undergraduate Science Journals,

https://undergradsciencejournals.okstate.edu/index.php/jibi/article/view/2500. Accessed 16 February

2024.

Botstein, David, and Gerald R. Finks. “Yeast: An Experimental Organism for 21st Century Biology.”

NCBI, 2011, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3213361/. Accessed 16 February 2024.

Bouska, Marko, et al. “Organelle aging: lessons from model organisms - PMC.” NCBI, 25 April 2019,

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6553499/. Accessed 16 February 2024.

Brack, Paul, et al. “A Simple, Low-cost, and Robust System to Measure the Volume of Hydrogen

Evolved by Chemical Reactions with Aqueous Solutions.” NCBI, 17 August 2016,

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5091921/. Accessed 16 February 2024.

D'amore, Tony, et al. “,.” , - Sugar utilization by yeast during fermentation, 19 May 2023,

https://academic.oup.com/jimb/article/4/4/315/5987389. Accessed 16 February 2024.

Galao, Rui P. “Saccharomyces cerevisiae: a versatile eukaryotic system in virology.” NCBI, 2007,

http://ncbi.nlm.nih.gov/pmc/articles/PMC2148055/. Accessed 16 February 2024.

Ianniello, Rocco G., et al. “Investigation of Factors Affecting Aerobic and Respiratory Growth in the

Oxygen-Tolerant Strain Lactobacillus casei N87.” NCBI, 3 November 2016,

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5094797/. Accessed 16 February 2024.

J, Wang, et al. “The Isolation of Anaerobic and Facultative Anaerobic Sulfate-Reducing Bacteria (SRB)

and a Comparison of Related Enzymes in Their Sulfate Reduction Pathways.” NCBI, 6 August 2023,

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10458228/. Accessed 16 February 2024.

Koonin, Eugene V., and Petro Starokadomskyy. “Are viruses alive? The replicator paradigm sheds

decisive light on an old but misguided question.” Science Direct, 19 May 2023,

https://www.sciencedirect.com/science/article/pii/S1369848616300103. Accessed 16 February 2024.

Koutsokali, Maria. “,.” , - YouTube, 19 May 2023,

https://pubs.acs.org/doi/10.1021/acs.jchemed.9b00994. Accessed 16 February 2024.

Maicas, Sergi. “The Role of Yeasts in Fermentation Processes.” NCBI, 28 July 2020,

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7466055/. Accessed 16 February 2024.

Maire, Théo, et al. “Dormancy-to-death transition in yeast spores occurs due to gradual loss of gene-

expressing ability.” PubMed, 2020, https://pubmed.ncbi.nlm.nih.gov/33206464/. Accessed 16 February

2024.

Maslanka, Roman, and Renata Zadrag. “Reproductive Potential of Yeast Cells Depends on Overall

Action of Interconnected Changes in Central Carbon Metabolism, Cellular Biosynthetic Capacity, and

Proteostasis.” NCBI, 3 October 2020, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7582853/.

Accessed 16 February 2024.

Pampulha, M. E., and M. C. Loureiro-Dias. “,Combined effect of acetic acid, pH and ethanol on

intracellular pH of fermenting yeast.” SpringerLink, 19 May 2023,

https://link.springer.com/article/10.1007/bf00270792. Accessed 16 February 2024.

Pedersen, Martin B., et al. “,.” , - YouTube, 19 May 2023,

https://www.annualreviews.org/doi/abs/10.1146/annurev-food-022811-101255. Accessed 16 February

2024.

Powell, Chris D., et al. “The impact of brewing yeast cell age on fermentation performance, attenuation

and flocculation.” PubMed, 2003, https://pubmed.ncbi.nlm.nih.gov/12702447/. Accessed 16 February

2024.

Yilmaztekin, Murat, et al. “Effects of Fermentation Temperature and Aeration on Production of Natural

Isoamyl Acetate by Williopsis saturnus var. saturnus.” NCBI, 2013,

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3703881/. Accessed 16 February 2024.

Zimmermann, Andreas, et al. “Yeast as a tool to identify anti-aging compounds.” NCBI, 4 March 2018,

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6001894/. Accessed 16 February 2024.

Appendix
Results from my experimentations.

Age of yeast
Time for gas to reach 100ml
0
6.38 5.25 5.74 7.22 6.36
2
9.19 8.34 7.94 9.04 7.45
4
9.51 9.16 11.2 14.6 10.53
6
12.8 11.14 14.72 16.5 10.14
8
17.2 14.51 17.91 16.7 11.71