#BioAP

[grogmead-blog]

Bumbling Birds

Introduction

In biology this week, our class investigated natural selection and its effects on a population of “birds.” We wanted to discover how a certain population may favor an individual with a certain mutation.

Natural selection, of course, was developed by a young man named Charles Darwin in the 19th century. While journeying across the oceans, this young naturalist made important observations about various differences in seemingly similar animals. One of the most important observations, however, were the finches he observed on the Galapagos Islands. Here he discovered that certain finches had beaks which were suitable to their food source. For example, those bird which cracked nuts to eat what was inside had beaks that were large and powerful enough to crack such nuts.

In order to do our own investigation, we created birds that were composed of one straw and two strips of paper wrapped into a circle and taped onto either end of the straw. We then flew these birds through the air by throwing them – those which flew the farthest were the ones most adapted to their environment. Our hypothesis was that the bird with the greatest ability to fly would have offspring that largely dominated the other types of birds; this would happen because the bird that has the best ability to fly would also have the best chances at reproduction.

Materials

Instructions

Tape

Die

meter stick

Paper

3 straws

Coin

Procedure

To begin, we made the ancestral bird. This bird was required to have a front wing that was 2 by 20 centimeters in length. It was also required that the front and back wings were taped 3 cm behind their respective ends of the straw.

Because it was the ancestral bird, it was the only bird in our population that reproduced; in other words, it had the best fitness in the ancestral population. However while creating the first generation, we also introduced some mutations…

This was done through the use of the die and the coin, which we used to decide what the mutation to a specific bird would be.

After creating our first generation with three birds – one similar to the parent, two mutated – we tested out their flying capabilities, making a note of which bird flew the farthest. In the end, the following generation would have one individual similar to the most successful bird, and two more mutated birds. In other words, the most successful bird would have the chance to have its genes passed down through time – if those genes were also the most successful among the offspring. Using this process, we created four different generations of birds.

Results and Analysis

As seen above, our first generation of offspring had three different individuals. One of these was similar to the parent generation, while two were mutated according to the system indicated in the procedure section. In this generation, the most successful bird was the second bird, whose front wing was 3 by 20 cm (as opposed to the normal 2 by 20 cm). As opposed to the other birds, this bird flew 6.25 ft on average, much farther than the others, who were in the 5 ft range. I thought that the reason this bird flew the farthest was because it was heavier in the front, which increased its capability to negate air resistance. Thus, it was closer to a javelin because it had more weight on the front.

Disclaimer: for the first generation, our lab group did far more trials than necessary, and took the average of all those trials – this made our average more reliable. However, we only wrote down two of these trials on the note. 

In our second generation of birds, a mutant once again flew the farthest. The bird that flew the farthest this time had a larger circumference than normal for the front wing, at 22 cm. Its average was far greater than the rest of the birds at 9.25 ft, whereas the others flew around 6 ft. As a result, bird 3 in this group was the most adapted to its environment, and the others birds all died. While this was confusing, I thought that it may be the same reason that the previous bird flew farther – because the front of the bird was heavier than the back.

In our third generation, the third bird once again flew the farthest. And once again, this bird was a mutant, having a back wing closer to the end of the straw (2 cm away instead of 3). As displayed in our results, this apparently made the bird fly around 8 ft, as compared to its brethren, which did not fly nearly as much. Naturally, we attributed this superior ability to fly to the positioning of the back wing. Because it was closer to its end, I thought that the reason it flies better is because the weight is distributed better.

Finally, the victor of our fourth generation was the bird which had a 3 cm wide back wing. This was similar to the mutation in our first generation, which had a 3 cm wide front wing. However, it seems that in this case the bird performed just as well, having an average of around 9 ft after two trials. I attributed this to the fact that the bird was heavier, and therefore less subject to air resistance. As a result, pushing this bird with the same amount of force and the correct trajectory may lead it to fly farther than the normal birds.

Conclusion

Overall, our results somewhat validated our hypothesis. The birds that were best adapted to their environment did have progeny in the next generation. However, in the next generation, the individual most similar to the parent from the previous generation did not seem to have any great advantage compared to the other birds in that generation. This could be seen as evidence to how natural selection continually functions and the ones which mutate the best are the ones who have the greatest likelihood to reproduce.

At the same time, I believe that this lab contained many unreliable results as well. Oftentimes, the birds would fly various distance due to how they were built. For instance, the birds oftentimes looped around in such a way that the distance they flew was negative or the same place at which they started flight. Other times, the birds  would loop around so much that the distance flown was far smaller than the distance that they could fly. This meant that while in one trial a bird would fly 9 ft, in the second trial it would loop around so much that it would end up at 1 ft. After some experimentation, however, we stuck another straw onto the front of the bird. This dramatically improved flight performance, as the birds did not loop around anymore.

This hinted me to one way that the lab may be improved. Instead of only using one straw while making the birds, the “researchers” should attach a secondary straw before they conduct any trial. If this is done to all the various birds, then our results would be purely due to the variations in wings caused by the mutations; in our method, it was far more dependent on the way the person threw the bird and what the air inside the room was like. In other words, the birds were so sensitive to extremely minor changes that the results were difficult to obtain. However, by attaching a secondary straw, the birds become far less subject to changes in the way that a person throws or any other factors; as a result, any differences in flight distance must be attributed to the differences in wings.

[grogmead-blog]

Conditioning C. Elegans

Introduction

For this investigation we will be utilizing the nematode, C. elegans.  C. elegans are small worms with very simple nervous system, but what makes them very fit for this investigation is their fast lifespan and reproduction cycles and most importantly their ability to learn.  In this lab we will be conditioning the worms to feed on NaCl concentrated areas of food.  From this the C. elegans will learn to seek out environments with the NaCl because they will associate it with food.  After we have accomplished this we will introduce the mutant strain daf-18 that produces nonfunctioning  DAF-18 protein that is needed for associative learning. From this we will see if the C. elegans can still learn to seek out the NaCl feeding environments or if the daf-18 strain will prevent this from being possible.  We will also be able to see if it is a genetic trait based on whether or not the C.elegans’ inability to learn can passed down to offspring. We expect for the mutant C. elegans to not be able to learn to move to the NaCl as a result of the daf-18 strain, while the wildtype should be able to learn and move towards the NaCl.  This experiment is significant because it shows how learning is an gene associated trait that can be passed on from generation to generation, which may seem like a complicated science topic but when analyzing it in more simpler terms it just shows that there is a scientific explanation for how we learn in our lives.  

Procedure

To start the experiment we had two agar plates subcultured with E. coli containing the C. elegans.  One of the agar plates was labeled wild type, while the other, which contained the daf-18 strain, was labeled mutant.  After this we observed each of the agar plates underneath a microscope to see C. elegans up close. 

Next we took a pipette and transferred 1 mL of wash buffer onto each of the agar plates with the E. coli on them.  We spread the wash buffer all across the surface then let it sit for 30 seconds then used a new pipette to remove worms from the wild type and and mutant agar plates and placed them into two respective, properly labeled microcentrifuge tubes. 

Following this, we let the C. elegans settle to the bottom of the tube over the next 2 minutes.  Next we used two pipettes labeled wash and waste to remove the supernatant liquid (waste pipette) from the top of the microcentrifuge tube and then add .5 more mL of the wash buffer using the wash pipette.  

Later, we proceeded to mix the two tubs and let them sit for 2 more minutes, and repeated these steps 2 additional times. Next we removed all but 50-100 microliters of the supernatant liquid. 

Next we use a micropipette to transfer 10 microliters of C. elegans onto two new respective agar plates labeled wild type and mutant that have NaCl feeding environments on one side and none on the other. 

Finally, we let the remaining supernatant liquid dry over the following 30 minutes than at the end viewed the plates under the microscope to observe the distribution of C. elegans across the two sides of both plates.

Data and Calculations

Using Class Data

Wild-type C. elegans

# C. elegans on NaCl side: 471

# C. elegans on Control side: 157

Total number of wild type C. elegans counted: 628

Mutant C. elegans

# C. elegans on NaCl side: 124

# C. elegans on Control side: 347

Total number of mutant C. elegans counted: 471

Calculation of Chemotaxis Index

1. Calculate the chemotaxis index as follows for the assays for both the wild-type and the mutant C. elegans:

Chemotaxis index = (# C. elegans on NaCl side – # C. elegans on control side)/                        

                                 (# C. elegans on NaCl side + # C. elegans on control side)

Chemotaxis index for wild-type C. elegans: (471-157)/(471+157)= .5

Chemotaxis index for mutant C. elegans:  (124-347)/(124+347)= -.47

Chi Squared Analysis

Wild Type C. Elegans

Total worms on NaCl side: 50

Total worms on Control side: 19

Total worms: 69

Df = 2-1 = 1

Null Hypothesis (data is consistent from the expected results): The worms will learn to associate NaCl with E. Coli as food.

Alternative Hypothesis (data is not consistent with expected results): the worms will not learn the association.

Using our data, we obtained the data below:

Because the chi square value, .441, is lower than the critical value of 3.84, so the null hypothesis cannot be rejected. Therefore, thanks to this analysis, we know that the data is consistent with the expected results. In other words, the worms learned through Pavlovian conditioning.

Now for the mutant worms:

Mutant worms on NaCl side: 25

Mutant worms on Control side: 23

Total mutant worms: 48

In this case, the chi value is larger than the critical value. Therefore, the worms in this case did not learn to associate the salt with their food and the null hypothesis was rejected.

*disclaimer: for this part of the experiment, we used Anay’s data (in regards to the number of worms) in order to determine our chi square value.

Focus questions

1. List three reasons why a researcher would want to use a model organism.

     1. First, worms have short development periods and short life cycles which allow us to create a culture very quickly

     2. Additionally, they allow for us to have many of them due to their small size which leads to more accurate results.

      3. Finally, they are readily available to order making them cheaper for experimentation than other organisms

2. List the stages of the C. elegans life cycle.

      C. elegans has 2 embryonic stages, 4 larval life cycles (L1-L4), and then adulthood. Each stages happens when the worm molts once, which allows them for a new cuticle marked with a  different protein structure than the previous cuticle.

3. What are two characteristics that make C. elegans a good model organism?

C. elegans reproduces relatively quickly, it feeds on e. Coli so it is easy to grow, and it is very small so that large cultures can be cultivated, stored, and examined very easily.

4. Where are C. elegans found in nature and what do they eat?

It is found in soil all around the world and it feeds on microbes like bacteria. We used e.coli in our experiment to feed them.

5. Are there any visible physiologic differences between the wild-type and mutant strains of C. elegans?

No because the phenotypes of the genes that vary do not have any physiological implications for the worms. The main ones that varied were in learning behaviors.

6. What is the purpose of subculturing C. elegans prior to the chemotaxis assay?

     This is part of the conditioning to make the worms associate the NaCl with food. Therefore when we put them on the NGM lite plate, they will move towards the salt, at least according to our hypothesis.

PART 2

1. In what way are the solutions placed at the NaCl and the Control spots on the assay agar plate similar or different?

The NaCl and Control spots are the exact same except for that the NaCl side contains salt. Both have the wash buffer.

2.  Is daf-18 an essential gene for C. elegans’ survival? Explain

It is possible for them to survive because the mutants have been able to survive and reproduce.

3. What is the benefit of having a functional daf-18 gene in C. elegans?

This allows them better searching or food “hunting” abilities. They are better able to associate things in their environment with food sources which leads them to go towards environments more conducive to their survival and reproduction.

4. Why are NGM Lite agar plates not used for the chemotaxis assay?

The NaCl in the NGM Lite plates would mess with the chemotaxis assay.

5. What would happen during the chemotaxis assay if C. elegans were starved rather than fed prior to the chemotaxis assay?

This would mess with the classical conditioning of the worms. NaCl would be associated with starvation, instead of a food source leading worms to go away from NaCl rather than towards it.

6. Design an experiment to test this hypothesis.

C. elegans are put on a salt containing plate with no food and then subcultured onto an NGM lite agar plate. The direction they move would indicate if our hypothesis was correct.

Conclusion

Overall, data provided inconclusive results for our lab group. The mutant type C. elegans had clumped together and could not be observed moving to either side of the agar plate. Although the wild type C. elegans did have some success in movement with 50 worms on the NaCl side and 19 on the control side, a comparison for a difference between mutant and wild type learning cannot be made without statistical data of the mutant C. elegans.

Using the class combined data, however, a statistical analysis of another group could be conducted. The chi-square value of 0.441 is less than the critical value. As a result, we fail to reject the null hypothesis and believe that there is a difference in learning between mutant and wild type C. elegans.

The data from another lab group used to calculate the chi-square statistic demonstrates that the lab experiment has failed as the wild type C. elegans should have had a clear difference in association between food (E. coli) and NaCl. In future repetitions of this experiment, the use of C. elegans from one source could limit any confounding variables in difference in the C. elegan population. Furthermore, the timing and washing of the C. elegans could be more carefully monitored. As a microscopic species, picking up a sample from the population to place in the agar plates could be challenging, and as a result, a small sample of worms could be picked up instead of a larger sample which would aid in observations and statistical analysis. With a greater sample size, we may calculate a more accurate chi-square result that is more representative of the true difference in classical conditional learning between mutant and wild type C. elegans.

New diet coke – dubbed “Coke Plus” – with fiber inside, available only in Japan.

Article: http://nymag.com/thecut/2017/04/coca-cola-is-now-selling-coke-with-added-fiber.html

[grogmead-blog]

What are you? a Specimen Investigation

This week in Biology AP, our lab group visited the topic of genetics and its relationship to evolutionary relationships between species. In this experiment, our goal was to use genetic codes to determine the placement of a Chinese fossil in a cladogram. We conducted this experiment in order to obtain first-hand experience of how DNA data can contribute to scientists’ understanding of relationships between organisms. Our approach to this problem was to use BLAST, a software that has the ability to compare strands of DNA and observe the similarities between them. Our initial hypothesis was that the organism would fit at the point where aves, or birds, diverged from the common ancestor.

Methods and Materials

In order to conduct the lab, we first required a few tools:

The Internet

BLAST (Basic Local Alignment Search Tool)

Cladogram

Basic knowledge of evolutionary relationships

Our procedure for doing this lab began as follows:

Initially, we observed the fossil, making basic morphological inferences based on what we saw. Personally, I believed that the organism’s skeleton seemed similar to the skeleton of the archaeopteryx, commonly thought of as the very first bird. It had the same, nearly velociraptor like body, and its tail was similar to the tail of a dinosaur. However, the animal’s skull looked distinctly bird-like in appearance, and it had a large amount of cervical vertebrae, which is typical for a bird. Moreover, the velociraptor itself it seen as a fairly close relative to the bird, and I therefore believed that it was similar to the archaeopteryx.

As a result, I placed the specimen in the category of “birds.”

Following this part of the experiment, we downloaded all the various genes from the website provided (http://blogging4biology.edublogs.org/2010/08/28/college-board-lab-files/) and input each file into the BLAST tool. This resulted in a number of results, many of which had a surprising amount of similarity to the gene sequence we saw before us. We repeated this procedure for the next three genes, which left us with certain conclusions.

Results

After inputting the first gene into BLAST, we received a few resulted. By far the most probable result that BLAST returned was Gallus Gallus, which had a far higher score than the other specimens with a similar gene. As seen in the related picture, Gallus Gallus’ score of 10129 far outstripped the score values of the other organisms. The closest that another organism came was the transcript variant X1, which was simply an altered gene from the same organism. It is also important that Gallus Gallus is otherwise known as the Red Junglefowl, and is essentially a type of rooster. Therefore, based on the findings from the results of Gene 1, we assumed that the specimen was more likely related to the clade of Aves than it was to the crocodilians, a result which conformed to our initial hypothesis.

Therefore, I placed the specimen exactly at the point where the common ancestor was located.

The second group surprised me greatly. After we input the gene into BLAST, the resulting similar genes mainly included animals which are commonly not related to birds whatsoever. The highest score was achieved by Drosophila Melanogaster, or the common fruit fly. As seen above, the score for the fruit flies was around 4419, while the e value was 0.0, which is less than the 1E-4 suggested by the lab manual. This generally demonstrates that the fruit fly, when it comes to this gene, was very similar to the specimen we were observing, even though morphological characteristics seemed different. Moreover, this was quite different from our hypothesis, as the common fruit fly, an insect, diverged long before the crocodilians and the birds. The next few species on the list were also Drosophila. Based on this new information, the specimen would more likely be placed closer to the insects on the cladogram; I decided to place it midway between the insects and the diverging point for the crocodilians and birds.

Because this was slightly odd, I decided to place the specimen based on this information closer to the insects, yet not so far away as to completely separate it from the crocodilians and the aves. As seen below, I placed it around the diverging point for the crocodilians and aves, before the development of feathers.

The third test, however, once again conformed to our hypothesis; the resulting species of animal was a bird. Specifically, it was Taeniopygia Guttata, often called the Zebra Finch. This animal, as seen above, had a very high score of 2224, which was significantly higher than the next animal below. Considering that the Zebra Finch is positioned within the clade of the aves, this verifies that the specimen under inspection is likely related to the birds. The next animal down on the list was the American Crow, or Corvus Brachyrhynchos, with a score of 2138, nearly 100 points below the Finch. Because this is another bird, once again with an e value of 0, it makes sense that the specimen we were observing is related to the clade of aves.

Overall, the third test made the likelihood that the specimen was closely related to a bird greater, and I therefore placed it with the birds.

Finally the fourth test brought credence to the idea that this specimen is related to reptilians. This fourth test generated various reptiles, the most similar of which were two species of alligator, the Alligator Sinensis (Chinese Alligator) and the Alligator Mississippiensis (American Alligator), both of which had scores near or above the 1000 mark. However, the Chinese Alligator was definitely more similar, as it had a score more than double the score of the American alligator, at 1768. These both had an e value of 0, which indicates that there is less and a 0.01% chance of error. Therefore, these results bring the placement of the specimen far closer to the crocodilians.

Thanks to this test, I placed the specimen closer to the crocodilians, almost exactly at the point where crocodilians and aves diverged.

Conclusion

The results of the experiment mostly supported our hypothesis; for the most part, the results demonstrated that the specimen had both birdlike and crocodilian characteristics. However, the placement for the specimen should be slightly after the diverging point towards the birds. Despite its similarities to the Drosophila, the specimen was still much more consistent in its similarities to the crocodilians and the aves. Upon even closer examination, however, it becomes clear that the specimen belonged more in the realm of the aves, due to the two genes which displayed a very close overall similarity with different kinds of birds. In particular, we also believe that this specimen likely did not come much later after the diverging of aves from the crocodilians, because it still has certain starkly reptilian characteristics, and because of its enormous score when the first gene was compared to Gallus Gallus, the redfowl.

If I were to make any changes to this lab, I would like to see more genes tested and run through the BLAST software. This would allow for a more precise and accurate placement of the specimen, and would also give us a more in depth view into the similarities between animals that oftentimes seem completely different.

[grogmead-blog]

Modeling Hardy Weinberg Equilibrium

Last week, our class embarked on a journey to model whole populations in Hardy-Weinberg equilibrium, as well as observing how natural selection affects populations. We eventually found that allelic frequencies are heavily altered due to allelic frequencies. This essentially means that disadvantages related to an organism’s genes drastically change the likelihood of that particular genotype surviving and reproducing as part of the population.

Using microsoft excel, we wanted to demonstrate how populations appear when they are in Hardy-Weinberg equilibrium and how this can subsequently be affected by the tools of evolution, such as natural selection. The importance of investigating such a problem lies in the fact that it helps us see how rapidly and effectively evolution and natural selection can alter a population, and what effect the size of the population can have upon such a change. My hypothesis for our first experiment was that the allelic frequencies would stay very similar over the course of time and past each generation. For my second experiment, I hypothesized that while testing natural selection, the total numbers of each allele would change in favor of the dominant allele, which in this case was “A.”

The beginning of such investigations started out with Charles Darwin, who planted the seeds of evolution in his famous book, The Origin of Species. Throughout his research, Darwin postulated various tools that lead to evolution, or a change in the genotypic frequencies of a population. One method for evolution which remains extremely famous to this day is natural selection, the process by which allelic frequencies change to favor the most organisms, who are identified by their increased ability to survive and reproduce. In the vast majority of cases, the dominant allele will be the most adapted to the environment, and thus a population will change over time to favor the naturally advantageous allele.

However, a theoretically possible population stuck in what is now called Hardy-Weinberg Equilibrium would experience little change in allelic frequencies over the course of time. In other words, it would starkly contrast with a population experiencing natural selection, which would reduce the allelic frequencies of the less advantageous allele. While realistically impossible to attain, we decided to model such a situation using the proportionally sound equation, p^2 + 2pq + q^2 = 1, which essentially states that the proportion of each genotype must be in a constant proportion of p^2:2pq:q^2.

Methods and Materials

In order to do this lab, we required:

Youtube

Microsoft Excel

Learned knowledge of how to use Excel

We began our lab by creating a model of a population in Hardy-Weinberg Equilibrium in excel, setting the values of “p” and “q” to represent the allelic frequencies “A” and “B”, the two alleles that we used.

We then continued by creating tables which represented the various genotypes that were created with our formula for determining whether a gene was an A allele or a B allele.

Subsequently, we summed the total number of each genotype, counted the total number of each allele within the genotypes, and determined whether or not the frequency matched up with what we were expecting – consistency with the original frequencies.

After modeling Hardy Weinberg Equilibrium, I also tested out the effects of natural selection by giving those individuals homozygous recessive (BB) a 0% survival rate, and measured the change in genotypic demographics over the course of each generation.

Results

The links below lead to the files themselves, but they must be downloaded in order to actually use them in Excel.

https://drive.google.com/file/d/0B1a4jZq32sTEQjlOWEppcEw3NlE/view?usp=sharing

https://drive.google.com/file/d/0B1a4jZq32sTEcmJTNFZNMWg5YVU/view?usp=sharing

As shown in the excel attachments, the Hardy Weinberg experiment went extremely well, and both tests actually conformed to what we expected.

In the first test to demonstrate Hardy Weinberg Equilibrium, the frequencies stayed fairly the same throughout each generation. As shown below, the greatest variation was from 0.7 to around 0.72 with the frequency of p.

In our second test, the natural selection consistently demonstrated an upwards trend in the frequency of p, the dominant allele. Conversely, the frequency of q decreased about the same amount during each run. This happened every during every single one of my tests, and the typical amount of change in frequency over the course of five generations was around 0.1. These results mean that natural selection would ultimately decrease the frequency of q until it is nearly nothing.

Conclusions

As is clear in my excel sheets, both my hypotheses were upheld by the results. In Hardy Weinberg Equilibrium, the allelic frequencies remain fairly constant because the population is in equilibrium. During the course of natural selection, on the other hand, the allelic frequencies change significantly to favor organisms with the dominant allele, A. In fact, while testing my natural selection experiment, I found that oftentimes by the fifth generation organisms with the B allele, whether it was heterozygous or homozygous, were not present at all. This was likely because the population size was very small. However, in a larger population size, the results are similar. As time progresses, the allelic frequencies shift in favor of the dominant allele, and if given enough time, the recessive allele will be wiped out entirely.

Over one generation in Hardy Weinberg Equilibrium, the organisms did not experience much change. However, in some experiments the change would be fairly significant on the very first generation, then return to equilibrium during the other generations; thus, if one only looked at the first generation, there is a chance that they could assume that there is no equilibrium when there actually is. When looking at multiple generations, however, the equilibrium is far easier to see, as the line graphs take the shape of an nearly horizontal line as the number of generations increases.

One generation of the Natural selection experiment, however, almost always reflects the eventual results of multiple generations. Therefore, whether it is one generation or multiple, the upward trend for the dominant allele remains, and the declining trend for the recessive allele similarly remains.

One thing I could have changed was altering the survival rate of organisms with the B allele and make them somewhat able to survive. This would likely cause a large change, as it would take far longer for the recessive condition to be nearly wiped out. In fact, it would be nearly impossible to completely destroy the recessive allele. Therefore, if I made a variable less extreme, it would likely have taken longer (i.e. taken more generations) to achieve the results that I eventually got.

Interestingly, this coincides nicely with the result of an increase in population size. As was said earlier, if the population size was increased dramatically, the allelic frequencies would take far longer to change as much as they did with the smaller populations. Similarly, if the variable for natural selection was not as extreme, it would also take a considerably longer amount of time to reduce the occurrence of the recessive gene.

[grogmead-blog]

A Plot Twist with Mitosis

A few weeks ago, Jack and I began our most recent lab: discovering whether or not certain substances could affect the rate of mitosis in the roots of plant cells. We found, at the end of our investigation that lectin does increase the number of cells in mitosis; however, these results were not correct, as is discussed later on in this report. Ultimately, this signifies that our results were altered somehow because we presumed the results of the lab. In order to do this lab, we obtained plant cells on a slide, and put it under the scope of a light microscope.

Introduction

The key problem to be investigated during this lab was the effect of lectin on plant cells. We conducted this experiment in order to understand more about the relationship that mitosis might hold with surrounding factors. In addition, we approached this problem by observing and counting plant cells under (what we thought) was the influence of lectin. In order to measure the accuracy of our results, we also used Chi Square analysis, which is brought up later in the report. Our hypothesis for this investigation was that lectin does indeed promote mitosis within the cells and that we would observe an increase in the amount of cells in mitosis.

But first, What is Mitosis?

Mitosis is a process which allows the cell of an organism to divide; this process, however, occurs mainly in eukaryotes, while many prokaryotes have a separate process called (binary fission)[could hyperlink that].

First, in order to learn about mitosis, one must have a basic understanding of the cell cycle, or how a cell progresses through its life. The cell cycle is split into four parts (collectively called interphase and mitosis): the G1 (Growth 1) phase, the Synthesis (S) phase, the G2 (Growth 2) phase, and the Mitotic (M) phase. The cell, after it is “born” from division, grows in the G1 phase, creating organelles such as mitochondria or chloroplasts. It then moves on to Synthesis, where the cell replicates its DNA in anticipation of division. During the final stage of Interphase, G2, the cell creates more organelles, growing even larger in size, and simultaneously prepares for mitosis.

A cell in interphase:

Finally, after the cell manages to pass through all these stages, it moves on to mitosis, whose steps are detailed below.

First, the cell enters prophase:

During this phase, the chromatin fibers (DNA and octamer histone proteins) condense into chromosomes.

The nucleoli disappear.

The duplicated chromosomes appear as sister chromatids, tightly connected along their length by cohesins.

The mitotic spindle, which functions later on, begins to form by polymerizing using tubulin proteins originating from the microtubules of the cell which have partially depolymerized. In addition, the centrosomes move towards opposite poles of the cell.

The cell then continues into prometaphase:

The nuclear envelope disappears

Certain microtubules, dubbed kinetochore microtubules, attached their ends to the kinetochores of the chromosomes (located at the centromere) and start to pull the chromosome to their respective pole.

Other microtubules, nonkinetochore microtubules, interact with one another and slowly push apart, lengthening the cell.

Following prometaphase, the cell enters metaphase:

The longest stage of mitosis

The kinetochore microtubules have attached to all the kinetochores, and are in the process of tugging them towards their respective poles. Eventually, they reach a stalemate, and the chromosomes are balanced midway between the two poles of the cell.

Metaphase is quickly followed by anaphase:

The sister chromatids are split apart by an enzyme, which cleaves the chromosomes apart by destroying the cohesins attaching them together.

The sister chromatids are dragged by the microtubule attached to their kinetochore to the pole that they are facing.

The cleavage furrow begins to form

Meanwhile, the nonkinetochore microtubules continue to lengthen the cell.

Finally, the cell completes telophase, which occurs simultaneously with cytokinesis.

As part of telophase:

the nuclear envelopes develop for each of the daughter cells, and the chromatids are now considered their own chromosomes.

The mitotic spindle depolymerizes

The nucleoli reappear

Chromosomes become less condensed.

As part of cytokinesis:

The nonkinetochore microtubules lengthen out the cell

In order to split the cell into two daughter cells, two key necessities are required: actin microfilaments and myosin molecules. During this process, the actin microfilaments create a ring around the cleavage furrow of the cell, and after subsequent interaction with myosin molecules, proceed to contract to the extent where they eventually pinch the cell into two daughter cells.

As a result of mitosis, one cell with 2 sets of 23 chromosomes eventually splits itself apart into two cells, each with an identical genome and each with 2 sets of 23 chromosomes.

Methods and Materials

In order to complete this lab, we needed:

Light Microscope

Plant cells (onion root tips) covered in lectin

Plant cells (onion root tips) without lectin

Procedure:

First, we obtained the slides of “lectin” covered root tips and placed them under the light microscope

*plot twist: the slides never were covered in lectin, we had just assumed so while doing the lab – so at this point, we thought that they had been lectin-treated.*

Second, we took a picture through the lens of our microscope and counted the number of total cells, the number of cells in mitosis, and the number of cells not in mitosis.

After recording this data, we used chi-square analysis to determine the correctness, or accuracy of our data.

Results and Analysis

As seen above in the class data, the cells in interphase contained a large number of cells which were still in interphase, and a relatively small number of cells which had begun mitosis. These numbers matched closely with the expected values. However, when looking at the values for the treated cells, the class managed to outdo the expectations. In fact, we had far fewer cells in interphase than we expected, and significantly more cells counted in mitosis. These results are important because they prove that our experiment was flawed in some way. In fact, there was never any lectin on the onion root tip at all! Since this was the case, our head count for the number of cells in either mitosis or in interphase should have been similar to the results for the control group.

When analyzing these results, one interesting takeaway is that a lower percent of deviation from the expected, which among large numbers such as 3000 is very small, the lower the number for (o-e)^2/e. On the other hand, even if they may have an even smaller difference from the expected answer, the smaller values of cells in mitosis have extremely small values input. In addition, o-e for all of these are essentially the same number because it is the difference between the expected and the observed, and therefore the value between their differences will be the same.

In addition, to calculate the accuracy of our results, we utilized chi square analysis, the results of which are detailed below.

For the cells that we used in class – the observed values:

For the numbers that we expected out of our lab:

x^2 ��= 163.4

x^2 = 163.74

The problem with these values, however, is that they do not even fit on the chart for chi squared values; these two numbers are simply too large to fit on tone. It was easy to see that we had done something wrong and that the numbers that we obtained were far too large. If we had one degree of freedom, in fact, the x^2 would still only be 6.63, meaning that the probability of a chi-square value larger than that would be less than 0.01. However, our experiment was way off that number and therefore made no sense.

Conclusions

Ultimately, our experiment proved nothing about the effect of lectin on plant cells and our hypothesis, therefore, was never even tested. In fact, it functioned more as a psychological experiment, which proved that being told of a condition imposed on the cells gave us a tendency to actively search for any indication that the cells were in mitosis. Essentially, the goal of this experiment was to see whether telling us of some effect would change our results, and it did.

Therefore, I propose that what went wrong in this experiment was entirely human error. Before coming into the lab room, we already knew that lectin would encourage mitosis within the cells, and when we thereafter looked at our cells, those with the “treated” cells were far too trigger-happy and ready to call mitosis on a cell that maybe looked very slightly different from the others. The end result of this action is that our results were skewed in favor of our original hypothesis.

Another problem that may have arose was that we simply had never identified mitotic cells under a microscope before, and were not sure what to look for while deciding whether a cell was mitotic or not. For example, there was a large amount of confusion as to whether or not a cell with two nucleoli were in prophase of mitosis, and many people decided to say that they were because they held the treated cells.

Another interesting psychological experiment to carry out would be to see whether or not the same results would be yielded if we knew nothing of the substance that the cells should have been treated with. In our lab book, it was heavily implied that lectin’s effect on plant cells should be mitosis-promoting. However, if we knew nothing as to whether lectin would increase or decrease the number of mitotic cells in the slide, our results may have been more accurate.

[grogmead-blog]

An Investigation of Light and the Rate of Photosynthesis

Last week in Biology AP, Bao, Jack, Tharun and I conducted an experiment to measure the rate of photosynthesis under different sources of light. In essence, we wanted to figure out whether photosynthesis occurs faster under natural light or under artificial light. Our findings were contrary to what we expected; the artificial light actually caused the rate of photosynthesis to increase. These findings seem to indicate that the light to which plants are normally exposed to is not as conducive to photosynthesis from an artificial light source. However, as seen in our conclusions, the errors we made during the lab may have fatally affected the results of our experiment. Nevertheless, we concluded from this experiment that different light sources can either increase or decrease the rate of photosynthesis in plants.

Introduction

For this experiment, our goal was to discover whether or not natural light is the supreme source of energy for plants during photosynthesis and to also discover whether a readily available CO2 source would increase a plant’s rate of photosynthesis. In order to do this, we measured the time it took for small disks of leaf (which we cut out of a leaf) to float to the surface of a certain solution. We placed each solution, with the leaf disks inside, under natural light and under artificial light. Our hypothesis, based off of evolution, was that plants would photosynthesize fastest under a source of natural light – mostly because terrestrial plants have flourished under the sun for hundreds of millions of years.

But what is Photosynthesis?

Photosynthesis is the process by which a plant cell creates glucose. It does this by taking in energy from light and using that to power the work done by various systems in the chloroplast. Below is the detailed formula for photosynthesis.

Photosynthesis begins with the light reactions, which take place within Photosystem II, a membrane protein complex embedded inside the membrane of a thylakoid. Inside this small structure, light energy is absorbed by chlorophyll molecules and passed to one another, until it finally passes on to P680, a specialized chlorophyll molecule. This special chlorophyll then gets excited and releases an electron to a primary electron acceptor, which sends said electron to the electron transport chain, a bundle of proteins which together allow the cell to make ATP.

During this process, the P680 loses an electron, becoming P680+. However, a water molecule is soon split, and the two electrons that result are attracted to the ionized P680+(it is suspected to be the strongest oxidizing agent in the world!). Meanwhile, the two hydrogen ions and half a mole of O2 are released into the thylakoid lumen (the inner space of a thylakoid).

Following this activity, the electron moves from the primary electron acceptor to the electron transport chain. Specifically, the electron is transported to plastoquinone, then to a cytochrome complex consisting of various proteins embedded within the thylakoid membrane, and finally to a plastocyanin molecule. The electron, however, is only able to pass from one member of the electron protein complex to the other because each subsequent protein in the chain is more electronegative than its predecessor, and therefore attracts the electron away from the previous protein.

While it does this, however, the transfer of an electron from one protein in the chain to another also generates energy, which the proteins then use to pump hydrogen ions into the lumen of the thylakoid. These hydrogen ions are then transferred to an ATP synthase, which uses them to combine ADP and Pi.

But moving back to our original focus, the electron transport chain finishes its duty by passing its electron finally to a special chlorophyll molecule, P700 in Photosystem I. This electron is subsequently released to another primary electron acceptor, which delivers it to a second electron transport chain, composed of ferrodoxin and NADP+ Reductase, an enzyme which, when it obtains 2 electrons, 1 NADP+ , and 1 hydrogen ion (H+), makes NADPH.

Following the light reactions, photosynthesis then continues in the stroma of the chloroplast through the Calvin Cycle, a series of reactions which eventually create glyceraldehyde-3-phosphate. In order to make glyceraldehyde 3 phosphate, however, the cycle must occur 3 times.

The first phase of the cycle (carbon fixation) begins when a carbon atom is bonded to ribulose bisphosphate by an enzyme called rubisco. This then turns into two molecules of a 3-carbon compound, and the cycle enters phase 2 (reduction). In this second phase, the cycle makes use of 6 ATP molecules and 6 NADPH molecules, turning them into ADP and NADP+ and Pi respectively. It is during the final step of this phase that glyceraldehyde 3 phosphate is formed and released into the stroma.

However, the cycle is not finished, as the chloroplast needs to regenerate the ribulose bisphosphate used during the first step. In order to do this, the chloroplast goes through many complex reactions which collectively use 3 ATP molecules, and the cycle begins again.

After the glyceraldehyde 3 phosphate is formed and released, it moves through certain metabolic pathways that synthesize glucose and other carbohydrates.

How did we do it?... Our Procedure

To start our experiment, we first cut out 20 leaf disks and prepared two solutions, one composed of 300 mL sodium bicarbonate and around 1 mL soap and another composed of plain water and 1 mL of soap.

We then put 5 mL of each solution into a separate syringe, along with 10 leaf disks each. Our goal in this portion of the lab was to create a vacuum inside the syringe so that the gases within the mesophyll tissue of the leaf disk would escape and the sodium bicarbonate would enter the leaf. If we achieved this, the leaf disks would sink to the bottom of the solution in the syringe.

After creating a vacuum in the syringes, we dumped the material in the syringes into their respective cups, and placed both cups in the sunlight to see whether or not photosynthesis would occur. If photosynthesis did occur, then some of the plant leaves would float to the surface thanks to the oxygen being produced.

Having finished this experiment, we decided to alter it a little bit and change the light source. In order to do this, we obtained a lamp, cut 20 new leaf disks, and remade the two solutions. Finally, we then sucked out the gases from the mesophyll layer of the leaf disks by creating a vacuum once again and poured them into the two cups.

Results and Analysis

Our data showed that the artificial light was much better at powering photosynthesis than natural sunlight.

As seen below, the amount of time it took leaf disks in natural sunlight to release oxygen and float to the surface of the solution was greater than the amount of time for artificial light, and the leaves in the solution with CO2 floated to the surface far quicker than the leaves that did not have CO2.

*Important: The axes on the graphs below are mislabeled. The x-axis should be labeled Minutes and the y-axis should be labeled Floating Disks 

In the graphs above, the first image details the results for the solutions in natural light. Clearly, the leaves without sodium bicarbonate surrounding them were a fair amount slower at creating oxygen than the leaves inside the sodium bicarbonate solution (35 minutes for all 10 disks to float vs 40 minutes for all disks to float). The second image of artificial light also displays this difference in the rate of photosynthesis, with the solution with sodium bicarbonate getting all the leaf disks up within 14 minutes, while the leaves immersed in the control solution with water were floating in 16 minutes. Therefore, we had proven through this experiment that leaves immersed in a solution of sodium bicarbonate, or any other viable source of carbon dioxide for the plant, would allow for a rate of photosynthesis greater than a solution without CO2.

However, the most important part of these results is the fact that in artificial light, the rate of photosynthesis seemed to be more than twice as fast as the rate of photosynthesis in natural light, whether the leaf disks were in sodium bicarbonate or in water. At the same time, we knew that something had gone wrong with our experiment. Natural sunlight is, after all, the home of these plants in nature. If they had evolved under the light of the sun, it would only make sense (through an evolutionary perspective) for the morning glory leaves to perform best in natural sunlight. But our results showed that oxygen production by the plants was best in artificial sunlight, meaning that there had to be some reason for those leaves to have produced more oxygen than the leaves in the natural light.

In the following section, I will discuss some probable mistakes we made while conducting this lab.

Conclusions

Overall, when looking at our experiment, our results did not prove our hypothesis correct. Rather, the results did the exact opposite. While they did show that leaves in the sodium bicarbonate solution were far more efficient at photosynthesis, it also showed that artificial light made plants more efficient than when they were in natural sunlight. In order for this to happen, the leaves in the solution under artificial sunlight must have accumulated enough oxygen to make them buoyant in water sooner than the leaves in natural light.

However, after doing some research, it seems that despite our results, natural light is in fact better for plants than artificial light because it emits a greater variety of wavelengths than artificial light. This results in the plants being able to absorb a greater amount of light than they could under artificial light, increasing the rate of photosynthesis.

As a result, the most likely conclusion is that our results were most likely altered due to human error. One possible error that we made was in placing the leaves behind a tinted window in the classroom for the natural sunlight portion of our experiment. Due to the tinting on the window, the transmitted light likely decreased in intensity and possibly decreased the range of wavelengths which were transmitted. If this occurred, the amount of light harvested in the light reactions from the filtered sunlight would be far less conducive compared to the light harvested under a direct source of light, like the lamp that we used for the artificial light source.

Another mistake that we may have made likely occurred in creating the vacuum in the syringe. During this stage, we were short on time and tried the best we could to get all the leaves to sink, but may have unknowingly messed up the vacuum or made an incomplete vacuum. As a result of this mistake, the leaf disks would still maintain some gases inside of themselves, which would give them a head start and expedite the process of floating to the surface. If we made this error, it would likely have been while we were making a vacuum for the leaves under the artificial light.

Finally, one thing that would have been interesting to test in this experiment would be the various wavelengths of light that the plants could absorb. In such an experiment, we would shine various colors of visible light to see how different the rate of photosynthesis really is and create our own chart to see which wavelength is absorbed best. 

I also think that experimenting with light intensity would be interesting in the context of trying to find an ideal intensity that would produce the greatest rate of photosynthesis. One way to do this would be to obtain dark but translucent objects that could decrease the intensity of sunlight, as well as magnifiers to increase the intensity of the sunlight. Along with finding an ideal intensity of light, we could also discover at what point the light simply destroys the chlorophyll or damages it so that it does not function anymore.

Overall, I think this experiment was not only valuable in determining what light source would be best for a plant, but also learning how to deal with error in our experiments.

[grogmead-blog]

Modeling Diffusion and Osmosis

Last week, Bao, Jack, Tharun, and I pulled out our lab coats and goggles in order to see for ourselves the various processes that occur in the cell thanks to concentration gradients and the surface area-to-volume ratio. Rather than simply looking at textbook diagrams of diffusion, we wanted to observe the processes occur through right in front of our eyes, for which we used gelatinous cubes, dialysis tubes, and an actual leaf from an Elodea plant. After conducting each of our three procedures, we found that the greater the surface area to volume ratio, the faster the reaction. We also discovered, through our second procedure, that the greater the concentration of solute in the dialysis tube, the faster the surrounding water will diffuse into each dialysis bag. In our third procedure, we discovered that plasmolysis occurs when the surrounding solution is hypertonic.

The problem we were investigating in this lab was how diffusion and osmosis work within the context of the cell and, specifically, the cellular membrane. Our reasons for doing such an experiment was to gain a better, more tangible view of how the cell works by seeing it occur. By weighing, observing through the lense of a microscope, and through the use of chemical indicators such as phenolphthalein, we were able to gain a better understanding as to how the cell does part of its job within an organism’s body.

But what is Diffusion?

Diffusion, in the context of cell biology, is a process during which solutes from some extracellular solution travel across the cell membrane down its concentration gradient; this essentially means that solutes from a highly concentrated, or hypertonic solution, will move into the less concentrated, or hypotonic, solution.

In order to approach a way to study the phenomena as it would occur within the typical cell, we generally created various situations during which two objects or solutions differing in tonicity were composed together, and we then observed the changes within the subject, whether that was a gelatinous cube, a dialysis bag, or plant cells.

Our hypotheses were that:

A greater ratio of surface area to volume would yield a faster reaction rate - thus, the smaller cubes would become transparent faster.

The bags with a greater concentration of solute would gain more mass due to water diffusing into them - therefore the bags with 50% NaCl would obtain a greater mass.

The Elodea leaf would plasmolyze when we apply salt water to it, and would exhibit a more turgid structure when we apply distilled water to the surface.

In order to conduct this experiment, we were provided with a very large amount of materials, which can be found below, labeled by procedure:

Procedure 1

Water

2.5 mL 1% phenolphthalein

1 M NaOH

1 M HCl

Rulers

Beakers and Test tubes

We began this procedure by procuring the gelatinous cubes of NaOH provided by our instructor. After cutting and measuring these cubes into three different sizes (211 inches, 111 inches, and 0.511 inches), we created three solutions, each containing 30mL of water and 10mL of HCl. Next we input each of our cubes of differing surface area to volume ratio into each 40mL solution. Following this, we waited for however long it took for the solution to become fully transparent.

Procedure 2

Distilled Water

1M Sucrose

1M NaCl

Dialysis Tubes

Electric Balance

Cups

In this procedure, we utilized 5 different dialysis bags, each with differing concentrations of sucrose and sodium chloride within them. The first and second bag contained 50% NaCl, and 50% sucrose. the third and fourth bags contained 75% sucrose and 25% NaCl. Our final, fifth bag contained plain distilled water, and functioned as a control. We then placed each dialysis bag into a cup with a hypotonic solution of distilled water. After 3 hours (we put the solutions in the cups in the morning and measured mass during lunch), we measured the mass again.

Procedure 3

Light Microscope

Elodea leaf

distilled water

salt water

pipettes

In this procedure, we began by obtaining an Elodea leaf and placing it on a slide in order to examine the cells under a light microscope. After observing the cells in their natural state, we proceeded to drop salt water and distilled water onto the leaf by using pipettes, in order to test how the plant cells would react to both hypotonic and hypertonic solutions.

Results

Procedure 1

In our first experiment, we found that the greater the volume of the cube relative to its surface area. As is shown below, the speed that the diffusion takes becomes greater depending on the Surface Area to Volume ratio of each cube.

As shown above, the gelatin cubes with the largest surface area to volume ratio were able to neutralize the HCl with their own NaOH much faster than the larger cubes. In fact, while the ratio only changes by 3 units, the time taken varies from the smallest cube, at 6 min, to the largest cube, at 26 minutes. In addition, we also took a time lapse video of the neutralization reaction, which is displayed at youtube through the video below.

Procedure 2

From our second procedure, we found that the tubes which had a greater concentration of NaCl would gain more mass than would the dialysis tubes with less NaCl and more sucrose. This was because NaCl, in a solution of water, dissociates into two ions, Na+ and Cl-; therefore, if the solution has a greater concentration of sodium chloride, it has a greater concentration of solutes, making it hypertonic and gain more mass in water. As displayed in the graph below, our two dialysis bags with 50% NaCl experienced the largest gain in mass due to water moving across the semi-permeable membrane and into the bag itself, while our two bags with 25% NaCl experienced a slightly smaller increase in mass. Also, comparing the percent increases in each type of solution also displays that the 50% NaCl is hypertonic enough to its surroundings to absorb a fair amount more than its 25% counterparts.

One of our dialysis tubes prior to being placed inside distilled water.

Procedure 3

In our third procedure, we found that the Elodea leaf, when placed in a hypertonic solution, would plasmolyze, and when placed into a hypotonic solution, would increase in turgidity. As depicted in the video below, the plasmolysis of the cell occurs thanks to the presence of a hypertonic solution outside the cell. This causes the cell walls to seem to break down, and the inner chloroplasts to start clumping together in blurry masses. Over the course of three minutes, essentially, we observed the cell “dry up” due to the presence of salt water. In hypotonic solution, the opposite happened, where the cell walls became more solidified and well defined; this means that the cells become more turgid, increasing the pressure within the cell and thus increasing the water potential.

Video of plasmolysis:

Conclusions

When looking at the conclusions of our lab, we found a few overall facts very important to each part. First, we saw the importance of surface area to volume ratio, and how that may affect the speed and rate of a reaction. This allowed us to understand more why cells may need a greater ratio in order to satisfy the demands of the human body. Secondly, we discovered how the concentration and molarity of solutes could affect the diffusion and osmosis across a semi-permeable membrane. This, in the context of the cell, is especially important when a cell has more or less of a certain solute than its surroundings, in which case it would either release solute to the outside (or release water to the outside environment), or open the transport proteins that stud the membrane to more solutes in order to become isotonic with the environment. Finally, we directly observed how tonicity could affect a plant cell through the example of Elodea, which we saw in both a hypertonic and hypotonic solution; while observing the hypotonic solutions and the degree to which turgidity increased within the plant cell, it was easier to see why the tougher parts of a tree, like the bark, have a more hypotonic solution surrounding them.