Investigation 10: Energy Dynamics

An Energy Pyramid

Introduction
One cannot talk about science without also talking about energy. Energy is the power source for everything that happens in nature, from waterfalls to a growing plant to a bear catching a salmon in a river. For almost all organisms, life is focused solely on energy gathering.

Energy is input into the system through producer level plants performing photosynthesis to capture the energy of the Sun and make it available for organisms on Earth to use. The total energy output in an ecosystem is called its gross productivity, and is affected by the production of energy by the plants. Then first level consumers then consume the plants, but are only able to maintain 10 percent of the energy that the plant captured from the Sun. Then second level consumers eat the first level consumers, once again only recovering 10 percent of the energy that was gained by the organism lower in the food chain. Finally, the apex predators or third level consumers eat the second level consumers and conserve 10 percent of that energy.

But the question remains, how are energy and biomass related? Biomass is created when plants capture the energy of the Sun and use it to build up their own bodies, and then this biomass travels down the food chain to the consumers. For this experiment, the lab group of Vikram, Vinay, Shreyan and Mark decided to find out what sort of relationship energy and biomass have. If there is an energy change will the biomass change as well? Or will biomass stay the same because of the law of conservation of mass? There are questions we decided to explore in our experiment.

For our experiment, we had to model ecosystem energy dynamics to then determine flow of energy through the system. To do this we wanted to figure out net primary productivity of a system of our own design, which is the amount of energy produced minus the amount of energy used by respiration. Because the group could not magically track energy usage through the system, we looked at the change in biomass of our system over a few days, and then use this data to extrapolate the change in energy in the system.

For our system we decided to use mealworms and wheat bran because they were two easily obtainable materials and we have used mealworms in the past to great success. The purpose of this experiment was to track the movement and flow of energy through an environment and through organisms in that environment. We hypothesized that the system of worms and bran would have the same mass at the beginning of the experiment as it will at the end of the experiment because of the law of conservation of mass, regardless of the flow of energy. We think that energy will flow through the system, but energy and biomass are two separate components of life that are not related in a closed system. It seems as if the two are related in nature because the environment is open, but hopefully we can prove that in a laboratory experiment mass and energy are unrelated.

Mealworms in Bran

Procedure
First, the lab group had to measure out and weigh our materials to establish a baseline for our experiment. We weighed out a small sample of wheat bran that will be used to feed the worms. Then we pulled 12 worms out of their containers and weighed them as best as we could by shaking the residual bran off them so we got an accurate measurement. We then weighed our container that we will place them in and poked a few holes in the container to provide air to the worms. Combining the worms and the bran in the container, we put the lid on tight and set our worms aside.

We were not sure how many days was proper to leave the worms aside in order to get an accurate read on the change in mass of the worms, but we decided on 5 days for this trial. 5 days is a good amount of time because it will allow the worms to eat the bran but also not eat too much or die of thirst from lack of water because there is no water in our container.

After 5 days we came back into the lab and weighed out the worms, the bran and the container as a whole system again, and our results are listed below.

Data/Observations

	Weight of Worms	Weight of Bran	Total Weight of Container
Day 1	7.89 grams	8.42 grams	41.9 grams
Day 5	7.31 grams	8.33 grams	41.4 grams
Percent Change	-7.35%	-1.07%	-1.19%

Our results were, at first glance, quite surprising and interesting. On the one hand, the total weight of the bran decreased as expected. It makes sense that the weight of the bran decreased because, obviously, the worms ate bran over the five day period, although we did expect the bran mass to decrease by more than just .09 grams, or 1%. However, on the other hand, we had expected the decrease in the weight of the bran to be countered by an increase in the weight of the worms. To our complete surprise, the weight of the worms actually decreased by .58 grams – or 7.35% – over the five day period. Thus, although the worms obviously consumed around .1 grams of bran, the total biomass of the worms decreased. 

Then we determined the amount of energy in the worms and the wheat bran so we could see the flow of energy. Our initial hypothesis was that the energy of the mealworms would increase as they consumed the energy from the bran and used it. To find the energy of each component, we used the conversion factor given to us by the lab manual, which was 6.5 kcal/gram for the mealworms and 4 kcal/gram for the dry mass of the wheat bran. We also had to calculate the dry mass of the mealworms, which was 36% of their total mass as stated by the lab manual. Here are our calculations:

	Weight of worms (grams)	Dry Weight of Worms (grams)	Weight of Bran (grams)	Energy of Worms (kcal)	Energy of Bran (kcal)
Day 1	7.89	2.8404	8.42	18.4626	33.68
Day 5	7.31	2.6316	8.33	17.1054	33.32
Net Change	-0.58g	-0.2088g	-0.09g	-1.3572 kcal	-0.36 kcal

We were quite confused at these results because, though we knew there would be a net energy loss due to energy being expended by the mealworms through simple respiration and the energy would be given off as heat, the data shows that energy lost by the mealworms was greater than the energy lost by the bran. So even though the worms consumer food, they still lost biomass and had a net decrease in total energy in the system.

Conclusion

Overall, our group set out to find the connection between biomass and energy. Since energy is such an important part of life, we looked at the energy conservation through the use of mealworms and wheat. Our results showed a decrease in the energy of the worms especially because of the decrease in the weight of the worms after leaving the worms with the bran over the weekend. When we found out the mass of the worms actually decreased, we realized our experiment could have been subject to some errors. First, we hypothesized that maybe temperature had a factor with the loss of the weight. We also thought that maybe the worms could have endured a lot of stress, and this could have also contributed to the loss of weight.

But, looking back at the data, perhaps our experiment wasn't as fraught with error as we previously thought. Perhaps the reason why the worms lost mass was because of their energy consumption. The worms used more energy than they took in, so perhaps they had to dip into their storage of fats in their bodies to survive, and therefore lost some weight. 

This hypothesis, however, brings up another question. Why did the worms have to dip into their fat storage for energy with such an abundant food source? I think the lack of water in the container contributed to this process. Perhaps the worms used the water already in their bodies to perform cellular respiration, and in doing so used up their water reserves in their bodies. So therefore, their weight loss was actually just a loss of water weight. This was one hypothesis that we pursued as a possibility for why the worms lost weight and energy.

In conclusion, we did not receive the results we expected, but this means we made some sort of error somewhere, and repeating the experiment would help us pinpoint this error for more accurate data and results.

Investigation 11: Plant Transpiration

On April 15th, the lab group consisting of Vikram, Vinay, Shreyan and myself were tasked with observing plant stomata and recording our data to derive some sort of meaning from it. In plants, stomata are the small openings on the underside of a leaf that open and close to allow gas exchange to occur with the plant's surrounding environment. This is an extremely important role played by the stoma because it helps leaves take in water from the roots through a process called transpiration. Transpiration is the way a plant uses the cohesion of water molecules to pull water up from the roots as more water vapor is evaporated out of the leaves. The stomata also open and close depending on environmental factors, but we are not investigating the opening and closing of stomata in our experiment.

Transpiration in action

Now how will we count the number of stomata on the bottom of the leaf? We could manually count each one using a microscope, but that would take forever and the stomata are green so they blend into the leaf and are difficult to spot. The lab group decided that we would use clear nail polish and apply it to the bottom of the leaf, to get impressions of the stomata. Then we would use tape to remove the nail polish from the bottom of the leaf with the impressions still intact, and apply the tape to a microscope slide in order to view the stomata. Then, because we have a specific field of vision using a microscope, we would calculate how big the area under the microscope was and then extrapolate that data to make an estimate on how many stomata cover the leaf. Then we could use this data to make hypotheses about the relationship between plants, the number of stomata they had and their environment.

The group performed the procedure outlined above using three different types of leaves: ivy, grass and ginkgo. Using the nail polish, we painted a small section of the leaf with it and waited for it to dry. After it was dried, we carefully applied tape to a section of nail polish and delicately pulled it off of the plant so as not to disturb the polish. Then we looked at the slides that we had prepared under a microscope at different magnifications.

Here is the ivy leaf we observed at 400X magnification:

Here is the blade of grass we observed at 400X magnification:

Here is the ginkgo leaf we observed at 100X magnification:

Next, we had to calculate the field of view that the microscope gave us for each leaf at each different magnification so that we could determine the stomata to surface area ratio for each leaf. To do this, we started by putting a plastic ruler under the microscope at the lowest magnification, 40X, and saw how large the field of vision was measure on the ruler. We discovered that at 40X magnification, the microscope's vision had a diameter of 5 millimeters. Using this data, and our knowledge that the diameter of the field of vision was inversely proportional to the magnification of the microscope. Therefore, to find the diameter of the field at 100X magnification, we had to multiply the original 40X diameter by 40/100 or 0.4, and to find 400X magnification, we had to multiply the original by 0.1.

After finding the diameters of the microscope's field of vision, we wanted to find the surface area of the field, so we used our knowledge of the formula for the surface area of a circle, which is A = pi*r^2, where r is the radius. Also, we know that the diameter is twice as long as the radius, so the formula we will use will look more like A = pi*(d/2)^2, where d is the diameter of the field of vision.

Finally, using the number of stomata per leaf and the area of the field of vision, we determined the stomata to surface area ratio for each plant. This calculation is in stomata per millimeter squared.

Here are our calculations:

Diameter at 40X: 5mm
Diameter at 100X: 5mm * 0.4 = 2mm
Diameter at 400X: 5mm * 0.1 = 0.5mm

Area at 100X: pi * (1mm)^2 = 3.14mm^2
Area at 400X: pi * (0.5mm)^2 = 0.785mm^2


And here is a graph of our findings:

Leaf	Number of stomata counted in FOV	Are of the FOV (mm^2)	Stomata to surface area ratio (stomata/mm^2)
Ivy	30	0.785	38.2
Grass	10	0.785	12.7
Ginkgo	26	3.14	8.3

We found that Ginkgo leaves have the lowest stomata to surface area ratio of the 3 plants that we tested with only 8.3 stomata per millimeter squared, and Ivy had the highest ratio, with a whopping 38.2 stomata per millimeter. Now the question that the lab group pondered was why there was such a large difference in the number of stomata that each plant had.

We hypothesized that the ginkgo has a low number of stomata because of its environment. Used to be planted in full sun and in warmer climates, the ginkgo would lose a lot of water due to transpiration if it had many stomata. Therefore it only has a few so that it can bring water up the trunk from the roots, but not too many as to create an inefficiency by losing a lot of water. We also believe that the ivy has so many stomata because it is a vine plant that grows upwards and in the shade. Ivies, when given the proper support, can grow up the side of a building and reach 30 meters above the ground. Therefore, to create enough of a pulling force on the water in the roots to get it to the leaves 30m upwards, there must be many stomata to transpire rapidly. Also, because ivies grow in the shade, they dont have to worry about losing a lot of water due to evaporation and are able to have so many stomata. Finally, the blade of grass we looked at had a somewhat low amount of stomata per millimeter squared, about 12.8 which is slightly higher than that of the ginkgo. Unlike the ivy, grass does not grow very tall so it doesn't have to have a lot of stomata to bring water to the leaf. Also, grass grows in the sun, so like the ginkgo, it is in danger of losing a lot of water to heat, so it can't have too many stomata otherwise it will die.

In conclusion, this lab has shown us that there is a strong correlation between the number of stomata that a plant has on its leaves and its environment. The three plants we tested had differing stomata counts due to heat and height, but other plants also have different stomata counts because of humidity or scarcity of water. A plant in an extremely humid climate would have many stomata so that it could transpire as much as possible even though the concentration gradient of water to air is radically different than plants in a desert climate. Also, plants like cacti must conserve water because it is so scarce in the desert, so it cannot have many stomata.

Investigation 3: BLASTing Keratin

Introduction
As a follow up to our group's work at determining the lineage of an ancient reptilian bird, which was completed last week, this week each member of the lab group was tasked with BLASTing a human gene coding for some sort of protein and then discovering the similarity of that gene to genes of other closely related organisms. For my experiment, I decided to BLAST human keratin, an important gene for human structures like hair and nails. My initial prediction for this gene was that it would be closely related to other keratin genes of mammals because other mammals, from dogs to rhinos, have keratin based structures like hair. I did not think that other organisms outside of the mammal family would have this gene because snakes or fish or worms do not have hair or nails as mammals do.

Procedure
After picking my gene of interest, I began by going onto the BLAST Entrez Gene website to search for the particular gene that I would use for this investigation. I entered "human keratin" into the search box at the top of the webpage and pressed enter. From there I was taken to a results page on which many different types of human keratin were displayed, shown below.

I was not sure which keratin gene to pick because I did not know that humans had that many types of keratin in their bodies. In the end I decided to choose Keratin 18 for two reasons. First and foremost, it was top of the search list so perhaps it is more keratin-y than other keratin genes. Secondly, and most importantly, 18 is my lucky number so I thought I would have a better chance of producing a good experiment with the number 18.

Clicking on the Keratin 18 link, I was taken to the homepage for keratin 18 with many different pieces of information. this is shown below. I needed to figure out the actual nucleotide sequence of keratin so that I could BLAST it, so I scrolled down to the section labeled "mRNA and Proteins" and clicked on the first link as the lab manual instructs.

From there I was taken to another page full of information, but I clicked the link labeled "FASTA" to find the actual nucleotide sequence. The link too me to the sequence, which is pictured below once again.

I then copied and pasted the sequence into the BLAST search page, labeled it "Human Keratin", and selected the option to check the gene against both similar and dissimilar genes from other organisms. I did this so that I could gather more results and have a more well-rounded report. From there I BLASTed the gene and waited for my results to come back.

Results
After BLASTing my gene, I was met with a long page of results from my search, and I have included a screenshot of the top few results.

Looking at these results, I see that my predictions were, for the most part, quite true. as expected, the Keratin 18 gene is extremely similar to other Homo sapien genes, but it is also strikingly similar to Keratin 18 genes of different organisms such as the Gorilla gorilla, the Western gorilla, or the Pan trogolodytes, the common Chimpanzee.

Other results that I received, such as similarity to the Papio anubis or the Macaca mullata, were also quite predictable because both of these organisms are primates, being the Olive Baboon and the Rhesus Macaque respectively. In fact, all of the results that I obtained that were displayed on the first age of results, or the first 100 results, were all similar primates. Therefore, one can obviously conclude that humans are descended from primates, but also that Keratin 18 is an extremely common form of Keratin found in many different primates.One can also conclude that the gene for Keratin 18 is extremely unique to primates because it is not found in other organisms such as dogs or rhinos, as I had previously hypothesized.

Perhaps I did not see any matches for organisms other than primates simply because there were so many matches to Keratin 18 that they could not be displayed in the 100 results that I was given. If BLAST gave more results, I bet that I could find a similar gene in a non-primate, but perhaps this gene would be more dissimilar to Keratin 18 in humans than it is similar.

Thoughts
I think that it would be beneficial to an analysis of the BLAST tool that we have used for the past two weeks, as well as to the concept of evolution as a whole, to answer a question posed by Mr. Wong in his assignment. The question is: "Does the use of DNA in the study of evolutionary relationships mean that other characteristics are unimportant in such studies?"

I do not think that DNA should be the be-all end-all when it comes to evolutionary relationships. I think that many different factors must be taken into account, such as morphology and environmental factors. I think that, although DNA can provide substantial support for relationships between many organisms, it is not foolproof. For example, there could be an organism on an island in the middle of the Pacific that evolves a certain gene that is beneficial to survival. On another island in the middle of the Atlantic, another organism develops an extremely similar gene because it is faced with the same environmental conditions as the organism in the Pacific. Though these genes might be extremely closely related, even up to 99%, it is possible for two genes in two separate organisms that are completely unrelated to develop similarly. Though the odds of this occurring are so infinitesimally slim, it is still possible.

Investigation 3: Comparing DNA Sequences to Understand Evolutionary Relationships with BLAST

Introduction
Recently, a team of scientists have discovered a fossil specimen in China after extensive digging. The scientists have sent the fossil to Mr. Wong's Period 7 AP Biology Class to try and place the species on a cladogram to graph its evolutionary relationships. Luckily for our team, small samples of DNA were salavged from the long dead species, unfortunately only 4 small sequences could be saved. The first approach to figuring out where the fossil lies on the tree of life undertaken by Christos, my fellow researcher, and myself was to use morphology to make a hypothesis of where the fossil lies in the evolutionary tree of life. then we will use gene sequence analysis to compare the genes of the organism that were salvaged in the Basic Local Alignment Search Tool, or BLAST, with genes of other, modern organisms. BLAST contains the DNA sequences of many different organisms so we will be able to pinpoint the location that the species lies on the cladogram.

Procedure:
The first step in our procedure is to create a hypothesis of where the species would lie on the evolutionary cladogram based solely on morphology. Looking at the morphology of the fossil, Christos and I agreed that the organism was bony and therefore also has a backbone. The organism also has eyes on each side of its head, so it does not have binocular vision like humans do. Also, the head shape of the organism is reminiscent of a lizard, so perhaps the organism is closely related to reptiles. The long tail that tapers to a point, further supporting evidence that the creature is related to reptiles. The specimen has long legs and short stubby arms, so we can assume that the creature was bipedal, unlike other reptiles. Along with the dark patches along the top of the organism, which may be some sort of feather or proto-feather, the body shape and long legs shows that perhaps the creature is also related to birds. Unfortunately, the researchers cannot see the internal organs of the fossil because soft tissue does not fossilize, so we cannot make any more guesses about the structures of the organism. The organism is picture below.

I believe that the organism belongs in its position on the cladogram below because it does not have fur, yet it is also closely related to reptiles because its appearance is similar to that of reptiles. Also, the creature is obviously a vertebrate, so it must go after the vertebrate branch of the cladogram.

The next step in our procedure is to use the BLAST database and compare the similarity of the genes that we salvaged from the specimen's fossil. To do this, we downloaded the files from Canvas onto a laptop, and then using the "Saved Strategies" link on the homepage of BLAST, which is located at http://blast.ncbi.nlm.nih.gov/Blast.cgi, we uploaded the downloaded files onto the website. Then, after waiting for a few seconds for the website to recognize the file and input all of the information it needed to do a BLAST search, we pressed the "View" button on the webpage. This took us to the official BLAST search page, but the search parameters were all filled out by the "Saved Strategy" that we downloaded. Pressing the "BLAST" button at the bottom of the page activated the search engine and then we waited as the file we uploaded was compared against hundreds of thousands, if not millions, of genes sequenced by different Genome projects

Below are pictures of the 4 different gene sequences' top five matches in the BLAST database. These are genes in other organisms that are similar or even exactly the same as the genes found in the fossil. The more similar the gene is to the gene of another organism, the more closely related the two organisms are.

BLAST of Gene 1

BLAST of Gene 2

BLAST of Gene 3

BLAST of Gene 4

For the first gene tested, the top result for similarity with the new species is the species Gallus gallus, which is a red junglefowl, or a tropical chicken. This agrees with my previous assumption that the new organism is reptilian because chickens and all aves are descended from reptiles. The similarity in genes is 99%.

For the second gene tested, the most similar result from the BLAST was a gene in Drosophilia melanogaster, which is the common fruit fly. This seems to go against my earlier hypothesis that the organism is a vertebrate and related to reptiles because an insect like the fruit fly has no internal bones and predates the evolution of reptiles by many millions of years. Perhaps the organism is more closely related to insects than Christos and I previously thought, but perhaps this is just a fluke and the organism only shares a single gene with the fruit fly. The other two genes will tell whether or not the organism is more reptilian or not. The similarity between the genes is only 92%.

For the third gene tested, the top result was a gene for Taeniopygia guttata ubiquitin-conjugating enzyme E2Q family member 1, which is quite the mouthful. This gene codes for a certain enzyme that is found in the zebra finch, which is another bird. This further supports the earlier hypothesis, as well as my thought that the species has some sort of proto-feather structures on its body. Furthermore, the relationship with the zebra finch distances the new organism from the insect genes it seems to have. The similarity between the genes is 95%.

The fourth and final gene tested was most similar to a gene found in Alligator sinensis mitochondria. Therefore it is a gene that is found in the mitochondrial DNA of the Chinese alligator. This is the final; piece of evidence linking this fossilized creature to reptiles and birds. Also, because it is closely related to the Chinese alligator with a gene similarity of 100% and the specimen was found in China, it must be some close ancestor of the Chinese alligator.

Conclusion
After looking at the BLAST results and discussing the outcomes, Christos and I came to a consensus on where this organism should lie on a cladogram of species, specifically the one we were given. We believe that the organism is a type of reptile that branched out to be the common ancestor for certain species of both birds and alligators. Because of the immense similarity to the Chinese alligator that we discovered, we believe that the organism is more closely related to alligators and crocodiles than birds, even though two of the genes discovered were bird genes with high reates of similarity. Keeping all of this in mind, we placed the organism on the cladogram below.

Our initial hypothesis taking into account simply morphology was somewhat accurate because we had placed the organism on the correct evolutionary branch, but we were not sure of how far down the branch the organism would be. Using both the morphological hypothesis we formed along with the accuracy of the BLAST database searches, we were able to determine to a finer degree where the organism should be placed on the above cladogram.

Inv 2: Hardy-Weinberg

Abstract
Through the creation of our own Hardy-Weinberg Equilibrium equation, the lab group tried to better understand the process by which evolution occurs and use a program such as Microsoft Excel to model passing on of alleles for a certain gene in an extremely simple way. After establishing our usable model and testing it a few times, then more complex evolutionary factors were added, such as a heterozygous advantage, to better understand the process of evolution in the world. I made two hypotheses for my own personal Hardy-Weinberg model, that the Hardy-Weinberg model will work best with large populations because the larger the population the less wild variation can occur between two successive generations. The other hypothesis that I made was that in the case of a heterozygous advantage, the alleles of a gene will approach even percentages in the population (i.e. 50-50) because the heterozygous individuals have 50% of each allele and the heterozygous will survive to pass on their genes.

Procedure
To create the Hardy-Weinberg model, I used Microsoft Excel, a spreadsheet program that can also perform basic mathematical operations, which would be extremely useful for modeling evolution. I began by inputting the initial frequency of p (the dominant allele) and q (the recessive allele). It does not really matter what these two values are, because the operations will work regardless of the value. The p and q values both had to be between 0 and 1, and must add to equal 1. So the q value is 1-p and vice versa. Then I began creating my zygotes by randomly assigning an A (for dominant allele) or B (for recessive) by using the random number and the if functions in the program.

The random number function produces a random number between 0 and 1 in the cell it is input into. The if function checks the value of a predetermined cell and decides to show one of two outcomes. The if function can become more complicated by embedding more if functions in the original if function, allowing for a variety of outputs. In the first allele cell, I typed out =IF(RAND()<=D$2, "A","B"). This may seem like computer gibberish, but in fact this is the combination of the if and random functions. It says "if the random number generated is less than or equal to D2 (which is the allele frequency of A in this generation), then the out put is an A. If the random number is greater than D2, then the output is a B. Through this, I was able to craft a system by which I could randomly generate zygotes, and then add them up using a simple sum function, which is simply adding together the values of different cells on the spreadsheet. Here is a sample of the spreadsheet:

With simple copy and paste functions, I was able to extend the spreadsheet as far as I liked, allowing me to have an almost infant population with which to test hypotheses.

For however many individuals I had in my population, I had to add together all of the different genotypes that I produced (AA, AB, or BB) and then figure out the allele frequency for A or B in that generation so that I could use this frequency in the ensuing generation and then create more generations all linked by "common ancestors". To find the total A gametes, I added 2 times the number of AA individuals (because they have 2 A alleles) and one times the number of AB individuals (because they only have 1 A allele) and this gave me the total number of alleles in the population. I then divided the total A gametes by the total number of gametes in the population, or 2 times the population size because every individual has two gametes. Then I got a number somewhere between 0 and 1 (sound familiar?) and plugged it in for the next generation. A picture of how this looks is below.

By this method I obtained values for simple populations so that I could track allele frequencies and their changes due to randomness. There were no external variables such as natural selection or incomplete dominance accounted for in this simple spreadsheet.

After completing the simple stuff, I wanted a challenge so I moved on to something more difficult. I also modeled a heterozygous advantage situation in which the heterozygous individuals had a better survival rate than homozygous individuals for whatever reason. An example of this in real life is the gene for sickle cell anemia. If you are homozygous for this gene, you get either sickle cell anemia or susceptibility to malaria. If you are heterozygous, however, you do not have sickle cell anemia and you are also immune to malaria.

For this experiment, I would take the totals numbers of individuals of each of the three genotypes and then I would multiply them by a percentage that represented the survival rate. In my simple experiment I tested a survival rate of 75% for AA and BB and a survival rate of 100% for AB. Then the allele frequencies were calculated in much the same way, the only difference being that when I divided the total number of alleles by the population size, I divided by the surviving population size. This would make sure my numbers did not get messed up.

Results

First experiment:Changing population sizes
For the first experiment modeling different sizes of populations, I found that the Harvey Weinberg Equilibrium works much better with larger populations rather than small ones. This proved my hypothesis correct about larger populations being more stable. But how did the model show me this?

Below are the graphs for the 5 generations of an organism with only 10 individuals in the population. Also the original allele frequencies that I began with were 50% for both A and B.

As you can see, there is quite wild fluctuation in the number of individuals with each genotype. In fact, in the first population there are 0 of the AA genotype, but by generation 5, there are 3. These wild shifts in genotypic ratios showed me that a small sample size was not good. I thought then perhaps a medium sample size would be best, so I created a spreadsheet with 100 individuals per generation. I also started with the same allele frequencies at the beginning, 50% for both A and B. The graphs are given below.

Ass seen in these graphs, the wild fluctuations that were seen in the population size of 10 are gone. Though there are still some fluctuations, seen in the jump of BB individuals between generations 2 and 3, but they are much more mild than that of population of 10. I saw that increasing the population size led to less wild fluctuations, so I thought that I could increase the population size even further. I then increased the size of the population to 1000, started with equal amounts of allele A and allele B in the population, and created graphs for those five generations as well. These graphs are shown below.

As seen in the graphs above, the ratios of genotypes are much more concrete with a very large population. There is some slight fluctuation, but nothing major at all. Therefore, I concluded that the genotypic ratios proposed by Hardy-Weinberg work best in a large population because there is less of a chance for dramatic changes that are extremely possible in small populations. After this I moved onto my next experiment, that of the heterozygous advantage.

Second experiment: Heterozygous advantage
For this experiment, I created 3 sets of generations. The variable that I changed in this experiment was the ratio of A and B alleles in the starting population. I held my population size steady at 1000 and I gave the homozygous individuals a 75% chance of survival and the heterozygous individuals a survival rate of 100%. My first trial was done with allele frequencies of 50-50 to begin with. I thought this would be a good experiment because it could show that even though fluctuations may occur in the first few generations, there will be a trend of the frequencies approaching 50-50 again. Here is a graph of the allele frequencies over the 5 generations that I tested.

As I predicted, there was some initial fluctuation and the A allele, represented by the blue line, was less common than the B allele, over successive generations, I can see that the two lines are getting closer together. This means that the two frequencies are approaching more equal ratios than they began as. To test my hypothesis further, I changed the starting allele frequencies to 70% A and 30% B alleles. Here is a graph of those results.

The graph shows that the A allele began the experiment as having much more of a share of the gene pool's allele frequency, and the B as having less. Yet over every single generation, the A allele lost some of that share and the B allele gained some of that share. Therefore, the two alleles are once again approaching equality in representation in the gene pool. Finally, to really test my hypothesis, I started an experiment with the A allele being 90% of the entire gene pool, and the B only having a mere allele frequency of 10%. Here is a graph of that trial.

Once again, there is an obvious trend that shows the two allele frequencies are getting closer together. The A allele frequency, which started out so high, consistently decreased while the B frequency consistently increased. These trials proved my hypothesis was correct because eventually, all three trials will approach 50% allele frequency for both A and B. Because this is not an exact science and incorporates an element of randomness, the allele frequencies won't hit exactly 50-50 and stay there, but they will continues to fluctuate. However, the two frequencies will be quite close to one another in value and close to a 50-50 split.

Conclusion
I think that the entire experiment was a success. I successfully modeled the Hardy-Weinberg hypothesis and also proved both of my hypotheses correct. Larger populations will produce the best results for the Hardy-Weinberg equilibrium, and if there is a heterozygous advantage, no matter what the int ital allele frequencies are, the allele frequencies will approach 50-50. I think that the only part of the equation that I wish I had done was tested different evolutionary methods such as natural selection or a bottleneck effect. These would have been fun and interesting to do, but unfortunately other commitments have forced me to confine myself to only testing a single evolutionary method.

ALIEN LIFE FOUND

During our mission on the C-223 Bellarmine class starship, the Biology AP crew crash landed on an unknown planet in the far reaches of our solar system. Seeing our misfortune as an opportunity to explore and maybe take some samples, the crew did a quick search of the perimeter. During our exploration, multiple alien life forms were discovered, and these samples were bagged and brought back to the shit for further examination. Below is the video log of our ensuing dissection of the alien life form.

https://www.youtube.com/watch?v=zBaIaSfcYG4&feature=youtu.be

Why should this creature be considered life? Well, as mentioned in the log, we discovered that the organism had some digestive structures, as well as a beak to take in food, so it must digest and eat its own food. We also found some structures that looked like reproductive organs, so the organism must reproduce. Also, the creature has tentacles, used either for moving, grasping, or both, so the organism interacts with its environment under its own volition. Even though we did not see the creature move, we can infer this regardless. The creature has multiple layers of membranes separating different organs and protecting it from the outside world, so therefore there is a level of higher organization that goes along with this organism. I am not 100 percent sure of the qualities that are necessary to determine whether or not something is "alive", but i believe that this alien organism can be considered a life form.

This life form is an extremely important discovery for mankind because it shows us that we are not alone in the universe, and that there may be other similar organisms to us on planets similar to Earth. Though at first glance the alien we found does not look like a human, there is similarity. Both have bilateral symmetry, both have similarly shaped eyes, and both seem to be dependent on water. The creature was filled with many different types of nontoxic fluid, and even seemed to be covered in different fluids, so we can hypothesize that this creature consumes water to keep itself moist.

We are bringing these alien life forms back to Earth for further study, but I am happy to declare this mission a success and I hope for the C-223's safe and speedy return to Earth and our families.

Restriction Enzymes and Electrophoresis

Introduction
When talking about restriction enzymes, it is necessary to first talk about viruses. Viruses are small packets of protein and DNA that are not actually classified as lifeforms for a few reasons. Most prominent of which, viruses cannot reproduce. In order to keep the species alive and pass on genetic material to offspring, viruses infect other cells, either prokaryotic or eukaryotic, and insert their DNA into the DNA of that cell. Then the cell is forced to produce large amounts of the viral proteins and create many copies of the original virus inside of the cell until its usefulness runs out and the cell is killed by the viral DNA, allowing the new viruses produced to spread and infect others.

As a defense mechanism against viral attacks, many bacteria and eukaryotes have developed what are called restriction enzymes, or proteins that cut DNA. Therefore, when a virus inserts its DNA into a new host, that host's restriction enzymes would cut the DNA into fragments, not allowing the cell to become infected. The restriction enzymes only work at sites on the viral DNA called palindromes, or places where the base pairs are the same in one direction as they are in the opposite direction on the corresponding strand.

Restriction enzymes have become an important part of gene splicing by making it possible for scientists to connect two sets of genes of different organisms or replace harmful genes that a person has with healthy genes. Also, through a process called gel electrophoresis, in which DNA fragments are run through gel by a current, similarities or heredity can be found between people, being an important tool for paternity testing among other things.

During the week of February 8th, the lab group of Shreyan,  Mark, Vinay and Vikram returned to the lab to perform DNA cutting using restriction enzymes and also gel electrophoresis. Through this process, the group should be able to determine the size of the DNA fragments cut by the enzymes. The group was tasked with using lambda virus DNA, which is about 50,000 base pairs long.

Procedure
On the first day of the week, we gathered our materials together and then set to work. We had 3 microtubes of different restriction enzymes which were called; PstI, EcoRI, and HindIII. We also had a microtube full of uncut lambda DNA strands. For our four experiment microtubes, one we filled with Lambda DNA and a restriction buffer, and the other were filled with Lambda DNA, restriction buffer, and one of the enzymes listed above. The tubes were labeled P, E, H, and L, and below is a table of their contents.

Tube	Lambda DNA	Restriction Buffer	PstI	EcoRI	HindIII
P	4 µl	5 µl	1 µl	0 µl	0 µl
E	4 µl	5 µl	0 µl	1 µl	0 µl
H	4 µl	5 µl	0 µl	0 µl	1 µl
L	4 µl	6 µl	0 µl	0 µl	0 µl

After this we spun the tubes in a centrifuge to mix the contents completely. Having mixed the contents of each tube, the group left the tubes overnight so that the restriction enzymes could do their work.

On the second day, I was unfortunately absent due to illness, but Vinay, Shreyan and Vikram continued the experiment without me. The second day was the gel electrophroesis day. The group obtained marker DNA from our teacher, and the function of the marker DNA is to be completely split up by the enzymes we were using in order for us to estimate the length of the strands of DNA used in our experiment. The marker DNA, as well as the other DNA and restriction enzyme samples that we had, were loaded into agarose gel, pictured below. The agarose gel that was used was actually clear, but the picture below is of the gel after it has been stained so that we could see the distance traveled by the DNA fragments.

When we are ready to begin the electrophoresis, a current will be run through the gel, and because it is a negatively charged acid, the DNA will flow towards the positive electrode. However, the gel acts as a viscous buffer to the DNA, so not all of the DNA will flow to the positive electrode quickly. Instead, smaller fragments of DNA pass through the gel easier and will travel further towards the positive electrode than larger fragments of DNA.

The agarose gel, with the DNA fragments loaded up inside of it, was then put into the electrode apparatus, pictured below, and a current was run through the gel for about 20 minutes.

After the gel was removed from the electrophoresis apparatus, the gel was dunked in fast acting blue dye overnight so that the DNA bands were visible. The process by which the gel was stained is shown below.

On the third day, results were gathered and the lab group measured the lengths that the DNA traveled down the gel from the DNA wells that all of the fragments started in. This is shown below.

Data/Observations
Unfortunately, because I was absent for all but the first day of the lab, I was not able to gather data with my lab group. But, after explaining what the data meant, and also a discussion with my teacher Mr. Wong, I was able to understand the data set that resulted from the experiment.

Using the data gathered in this experiment, the group was tasked with estimating the length of the DNA strands cut by each individual restriction enzyme. To do this we had to compare the bands that we saw in our agarose gel to lengths traveled by fragments that were in the marker group. These strands were known to have a certain base pair lengths, so we could estimate the lengths of our DNA fragments through comparison and a little bit of educated guesswork. Unfortunately, our groups's marker DNA lane was not recorded correctly or simply did not perform as it was meant to, so the DNA lengths were borrowed from our classmates.

Here is a table of the distances traveled by each lane, including the marker DNA lengths obtained from our peers:

	M (Marker DNA)		L (Uncut DNA)		P (PstI)		E (EcoRI)		H (HindIII)
Bands	Distance in mm	Actual base pairs	Distance in mm	Estimated Base Pairs	Distance in mm	Estimated Base Pairs	Distance in mm	Estimated Base Pairs	Distance in mm	Estimated Base Pairs
1	14	23,130	10.2	50,000	15.2	20,000	15.2	23,000	15.2	21,000
2	16	9,416			19.1	6,000	19.1	6,000	17.8	7,000
3	18	6,557			20.3	4,000
4	22	4,361
5	27	2,332
6	Not visible	2,027

Using simple guesswork and estimation, the group assigned values of base pair lengths to the DNA fragments that were cut using restriction enzymes. But is there a better way to estimate fragment lengths? A second way that the group estimated the lengths was using what is called a semilog graph. Using the info from the marker DNA group, we created a graph relating distance traveled to base pair length of the DNA strands by plotting the points of the marker strand and creating a line of best fit. This graph is shown below. Then, by using the graph, the group was able to estimate lengths of the DNA fragments somewhat easier.

Using this graph, we estimated the base pair length of each DNA fragment, shown below.

	M (Marker DNA)		L (Uncut DNA)		P (PstI)		E (EcoRI)		H (HindIII)
Bands	Distance in mm	Actual base pairs	Distance in mm	Estimated Base Pairs	Distance in mm	Estimated Base Pairs	Distance in mm	Estimated Base Pairs	Distance in mm	Estimated Base Pairs
1	15.2	23,130	10.2	55,000	15.2	18,000	15.2	18,000	15.2	18,000
2	17.8	9,416			19.1	8,000	19.1	8,000	17.8	10,000
3	19	6,557			20.3	6,000
4	22	4,361
5	27	2,332
6	Not visible	2,027

As you can see, some of the values are extremely different in the new table of estimations.

Conclusion
I believe that the experiment can be considered a success. Though our marker DNA did not turn out as planned, we were still able to create our semilog graph and also get some good estimations of the length of our other DNA fragments. I am not completely sure whether or not any of our results are correct and that is the actual length of each fragment, but I am willing to bet we got somewhat close to the right answer. I know that our estimations for the uncut lambda DNA, about 50,000 base pairs is correct, and I think the cuts made by PstI and EcoRI were valid too. I am not sure whether or not the cuts made by HindIII, or at least the data we recorded from it, were valid. I think I remember reading somewhere that the lambda DNA is cut in many places by HindIII, but it was only cut into two fragments in our experiment.

I believe the only error made during this experiment was in collecting data from the agarose gel. I know that the data we gathered regarding the marker DNA was wildly incorrect, so much so that had to use another group's data to get the correct graph. I am also skeptical about the results gained from the HindIII cuts, as mentioned above. I think that when looking at the agarose gel, it is necessary to shine light from the bottom up through the gell so the dyed lines of DNA are extremely clear and can be recorded easily. I also think that when graphing the semilog graph, it would have been easier to graph on paper rather than the iPad so that we may use a rule and get better, more accurate results.