Title: <\/strong> BIOS 1610 Final Lab Project: Lab 9<\/strong><\/h2>\n

Even seasoned instructors like Dr. Geiser sometimes come up empty when searching for background information. A few years ago when he started working on his first biology education paper, he did some background research. The topic was an active learning activity that he developed to help students understand early development. He did all kinds of literature searches to find what others had done before him. He searched for active learning, activities, biology active learning, biology activities, biology development activities, and on and on and on. He found interesting references but nothing that sounded like what he had done. As he continued to look at the articles he found, it really seemed like he was missing a lot of useful articles. Finally giving in, he went to see a librarian (He should have done this first!). The librarian was familiar with education literature and Dr. Geiser described his project. The librarian\u2019s response was \u201cthey call that role-playing\u201d and proceeded to find all of the information that Dr. Geiser was unable to find. Moral of the story? Start with the librarian. They are experts in what they do. They can teach you strategies to find what you need and help you think outside the box if you are unfamiliar with a topic. Formulate a Hypothesis A hypothesis is a statement that makes a prediction based upon some initial observations, questions you have asked about the observations and any background research that you have performed. For this statement to be a correct hypothesis, it must meet two conditions: it must be testable and falsifiable. The testable condition makes sense if you think about it. If you make a statement that cannot be tested in any way, then you are only providing an opinion (whether it is true or not). The falsifiable condition requires that the statement can be constructed in such a way, that after appropriate experimentation, it can be shown to be false. This falsifiable part of the hypothesis actually designs your experiment for you. By setting it up so it can be falsified, you have determined some of the parameters in the experimental design. Your goal is to test through experimentation the predictions or explanations in your hypothesis to see if they are correct. Why do we create a falsifiable hypothesis and not one that we want to be true? This is because many hypotheses can be created to describe and test an observation of interest. If we have ten different hypotheses that may explain the observation, the only way we can determine which is most likely correct, is through systematically getting rid of one at a time (falsifying one at a time). Thus we want a hypothesis that can be shown to be wrong if the experimental results do not support it. If we cannot do this, then we are stuck just like we are with an untestable hypothesis; it is only an opinion. To see how this works, let\u2019s take an example where you have a bag of balls. You know nothing about the color of the balls, how many there are, etc. You know there are balls in a bag. You reach into the bag and pull out a black ball. This is your Observation. Your hypothesis for this observation might be, \u201cAll balls in the bag are black.\u201d For your experiment, you could pull 10 balls from the bag. If they were all black, your hypothesis is not proven, nor is it disproven (falsified). Your hypothesis is still useful and could be used as you pull more balls from the bag. If you had pulled a different colored ball from the bag, then you would have proven the hypothesis false. When is the hypothesis considered true? Never. It is only considered to reflect the understanding as long as it is not false. Let\u2019s take another look at the bag of black balls. If you pulled out the first 10 and they were all black. The hypothesis is not false and still explains your observations. You pull out another 10; still black. Another 10 are examined; still black. Your hypothesis is still not false, but still not true. Why? Because you do not know how many balls are in the bag. For us to be able to say that this hypothesis was true, we would have to know exactly how many balls were in the bag. Once we had examined all balls we would be able to say it was true. In all scientific research, we have to leave open the possibility that we do not know all the details or possibilities in our experimental system. We only know what we have observed and what we can gather through experimentation. There can always be more to learn. This does not mean that as we do more and more experiments that we do not begin to conclude that our hypothesis is likely correct. We call this a theory. This occurs when the hypothesis has not been falsified through many, many forms of experimentation. Our conclusion is that this hypothesis is likely true. But what we do not know is whether the next experiment will finally falsify the hypothesis. This is true for the Theory of Evolution. All hypotheses have not been falsified and it all fits our understanding, but you never know about the next experiment. Design a Study We use the word study, and not experiment, because the root of what we are trying to accomplish with the scientific process is identification of data that is not known. In some disciplines the study does fit the definition of experiment. From Webster\u2019s dictionary, an experiment is something that is done as a test: something that you do to see how well or how badly it works. In other disciplines (think studying human behavior), we use a similar process but we do not experiment on people. It is not ethical and is illegal. Think of a study as a procedure used to observe and collect data. To properly design a study, you need to decide on a procedure, determine as many of the dependent and independent variables as possible, provide appropriate controls, decide which equipment you will need to use, and finally decide upon a way to collect and analyze your data. If you do all of these things before you set off on your study, you will acquire the correct data to address whether you can falsify your hypothesis. We\u2019ll take each of these parts of study design individually, but ideally they all need to work together to design the appropriate study. You should be able to identify each of these in every laboratory exercise we do. Controls are duplicated trials that do not change an independent variable and thus will not have any changed outcome. You can think of the control as the default. This is what we already know. This is how we know the system or trial is going to work before we tweak or change something. In some cases, we use several types of controls. A positive control is a control trial that gives a consistently known outcome. For this to be the case you need to have done the trial multiple times before and have an understanding of the results. A negative control is a trial that will not work. But, many things may not work. A negative control is one that the independent variable is removed and therefore it will not give the expected change in outcome of the dependent variable. For example, if we wanted to test if an aquarium of fish would eat the new food that we provided, we would also want to know if the fish would actually eat food. We might set up a trial with two different aquariums with fish evenly divided between the two. We would feed the control fish, the food they were used to eating. The other aquarium would receive the new food. We controlled in this case for whether the fish were actually hungry. This is not part of our experiment because we only want to know if the fish eat the new food. But if the fish receiving the normal food (the control), do not eat and the experimental fish do not eat, we cannot make any conclusions about it because neither set of fish ate food. Note: A control is distinct and different from a controlled variable. A control in the example above is a completely different fish tank that is receiving similar conditions. A controlled variable in this example would be the temperature of the water, the number of fish in each tank, the size of the tank, access to light, etc. Some studies require development of techniques or use of specialized equipment. In many cases the development of a technique might take years while the actual study to test your hypothesis might only take a day or two. In this course we have tried our best to make the techniques work the first time, but often this is not possible when doing it in a research laboratory. Techniques that do not work as expected are just part of the everyday life of a scientist. It is not unusual that a trial gives you the results you expected the first time you test it, but may not work again that way. Many times even careful scientists have published results that cannot be repeated by other labs. The whole goal of any study is to assess whether the change you made (independent variable) has an effect upon the data (dependent variable). This is done through data collection and analysis. This begins by taking extensive notes on how the study is set up, what the variables are, what the control trials are and what the question\/hypothesis being tested is. You should also note the date of the study and those involved (lab partners). In analyzing the data, scientists need to decide the best way to display the results and ultimately how these results compare with what is expected. Often statistical analysis is needed and the results need to be graphed so that we can share them with our colleagues (a picture speaks a thousand words). Generally, biological data is presented in a line or bar graph format. These formats provide a visual impact while representing patterns or differences in response clearly. Collect Data What does the word data mean to you? In this class, we are going to use the following definition to define data. Data is a thing known or assumed as fact and it serves as the basis of reasoning or calculation based on something you have done (some action). Data is information that describes the actions of a study. There are two types of data: quantitative and qualitative. Quantitative is easy. It is likely the type of data that comes to mind when you think about data. It is most easily represented by a number; the temperature, number of black balls in a bag, number of squirrels on the lawn, etc. Qualitative is descriptive; the sky is red, the squirrel\u2019s tail is bushy, people were happy, etc. Qualitative data can be just as powerful as numbers in describing things. There are just some things that cannot be represented by numbers but are just as important as the numbers. As we talked about last week, the whole goal of any study is to assess whether the change you made (independent variable) has an effect upon the data (dependent variable). This is done through data collection and data analysis (next week). Data collection begins by taking extensive notes on how the study is set up, what the variables are, what the control trials are, what question you are asking and the specific hypothesis being tested. You should also note the date of the study, those involved (lab partners) and anything else that seems important. Some of this information will be recorded as quantitative data and others as qualitative data. How do you know what is important? It is actually hard to say. Things that seem important some times are not. Other times, things that seem inconsequential are very important. It is best to record everything so that when you get your results, you have all the data available whether you realized you needed it or not. As you become more experienced in collecting data and with a particular procedure, you will begin to understand what needs to be collected. For now, note down everything and collect every piece of data possible pertaining to your procedure. Not all data will be used to determine your results, but deciding what data to use and not use is part of data analysis and will be discussed in more detail next week. Analyze Data It is a systematic process that uses logical thinking to describe or illustrate the data that you have collected so that you can evaluate the data and draw conclusions as to whether your hypothesis was supported. Wow! There is a lot in that sentence; let\u2019s break it down. It is a system of thought and process. There is no one method that works here. All are important and all have validity. It is about using everything you know about your study and topic to come up with a way to think about your data. It is also the reason that we can come back to data 20 years later and analyze it again and come up with new understandings. Data stays the same, but the way we analyze or think about the data may change. The goal is to use thought and a process to make sure you examine your data in its entirety and not allow your own personal biases to get in the way (a major problem for us all). It requires logical and critical thinking. It is a process that you build over time as you experience more and more data to analyze. Thus it is one of the hardest parts of the scientific process and no amount of studying will make you better. It is a life-long learning process. Many of you are already doing this in other aspects of your life. Now you need to apply it to the scientific process and your data. It requires evaluation of the data. The good news is that most data lend themselves to one or two types of evaluation. This makes it easier to learn but also easier for us to fool ourselves. If we forget to think about the data in other ways that are not so common, we run the risk of missing important understandings or potentially missing what the data is telling us altogether. This is when thinking \u201coutside the box\u201d is useful. We designed a study to give us the data. We are now evaluating the data based on how we designed the study. It is human nature to expect that the data we get will be an answer to what we asked in our hypothesis. But what if we missed something? Maybe there is some variable that we are not even aware of that was part of our study. Something that we did not control for. If so, our data is a product of this unknown variable as well. Any evaluation of the data requires us to look for these unintended consequences of our study. A way to visually present data and its evaluation is required. Visual does not necessarily mean pictures or graphs, but it can. Something that we can use our eyes to see or read so that others can follow how we evaluated the data is a must. Present Results Part of the Scientific Process is sharing of findings with others. This is the end of the current scientific process that you have been working on, but it can be the beginning of a new scientific process that comes from what you have learned. The reason that we share our findings is for two main reasons. We want others to follow our logic and make sure it is correct. Too many times when we follow our own logic we can deceive ourselves. The second is that we want to provide the opportunity for others to replicate our work and to extend it. It is not often that anything is understood by one study or experiment. It takes many years and many investigators to understand some aspect of biology. The form of the sharing can be through a poster, a research talk, a written paper or any other means of conveying the information. Before you present your results though, you need to take a step back and look for conclusions that have resulted from your 8 step process. Basically you are observing how well the whole process worked and addressing whether you did it correctly. This is relatively straightforward, but not always easy. You can start by looking at your analyzed data and asking the following questions. 1) Are my comparisons between my analyzed data consistent with my predictions? If you designed your study well, it is likely that you will be able to say \u201cYes\u201d to this question. Sometimes you realize that your predictions were wrong and in that you learn something about the procedure you designed and how you should adjust if for the next time. 2) Does the data and analysis support the hypothesis? Once again we learn something about our understanding of the study we designed and questions we have. In the end it is not important whether your hypothesis was supported or not. What is important is that you learn more about the study and questions that you have and that you used a logical process to determine whether you are thinking about the variables correctly. 3) Does the data and analysis add to my understanding of the question asked in step 2 of the scientific process? If it doesn\u2019t, then you did not design the study well. There may have been variables that you missed and next time, you will add them into the study. Good conclusions lead to the future and start the scientific process once again. You have a new observation, you now may have more or different questions, you might realize there is more background research to do, etc. Conclusions are never the end. They are actually the beginning of an iterative process that continues indefinitely. It may not continue with you, but you provide answers and a logical thought process that may serve as the beginning for someone else. \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026. Data: information that describes the actions of a study. Observation: the process of using our senses (sight, taste, touch, smell, and hearing) to understand the world around us. Experiment: an entire set of trials that tests one hypothesis. Independent Variable: the object in a trial that you change. Dependent Variable: the object that is measured in one trial. Question: A sentence worded or expressed so as to elicit information. Ask a Question: a good question will always contain a concept you have observed (a noun) with a clearly defined action (a verb) that can be examined as part of a trial. Ask a question in this form until you get better at it: \u201cIf I change __________, I wonder what effect it will have on __________?\u201d Control: a trial in which the Independent Variable does not change and the Dependent Variable is still measured. A positive control is a control trial that gives a consistently known outcome. A negative control is one that the independent variable is removed and therefore it will not give the expected change in outcome of the dependent variable. Hypothesis: a statement that makes a prediction based upon some initial observations, questions you have asked about the observations and any background research that you performed. It must be testable (in an experiment that you can do) and falsifiable (able to be shown not correct) for it to appropriate. It also must be in the following format: \u201cIf X, then Y.\u201d Background Research: The process by which you identify information that gives you the truest understanding of what is already known about your topic. Designing A Study: A properly designed study will be a systematic set of changes to the independent variable(s) that will be followed by observing the resulting change in a dependent variable. The study will need to include the following: a procedure, dependent and independent variables, appropriate controls, any necessary equipment and materials, and a way to collect and analyze data. Analyzing Data: A systematic process that uses logical thinking to describe or illustrate collected data in order to evaluate the data and draw conclusions as to whether a hypothesis was supported. Trial: A procedure designed to collect one set of data from one independent variable change. BIOS 1610 Final Lab Project: Lab 9 This assignment is worth 30 points. Your goal is to think about using yeast to complete a cellular respiration experiment similar, but not identical, to the lab simulation that you already did. Your goal is to use the simulation as a starting point and create a new, unique (to you) study. Think about what you have around the house that will allow you to do an actual experiment. Work through the first six steps of the scientific process and do your best to communicate your ideas for your experiment. You will be creating two different hypothesis and study designs. Do your best to make them different. After your TA reads your ideas, they will communicate with you as to which one you should use. You only need to complete one of the experiments that you will set up in this assignment. When you have submitted your work, your TA will review and grade your work. Feedback will be provided so that you can address any holes or concerns before you begin the experiment. Scientific Process, Step 1: Observation I have observed the following about yeast and cellular respiration \u2026 (2 points) List one observation here 1 List second observation here 2 Scientific Process, Step 2: Ask a Question Because of my observations, I wonder \u2026 (2 points) Ask one question here 1 Ask second question here 2 BIOS 1610 Lab 9 Graphic Organizer, 2022 Scientific Process, Step 3: Find Background Research Because of my questions, I want to find out more about the following concepts and\/or relationships: (6 points) 1 2 3 List one List one List one These are the references I have identified and formatted in APA style: 1 2 3 2 Scientific Process, Step 4: Formulate Hypothesis Write two different hypothesis statements that follow from your observations. (5 points) Write one hypothesis statement for each observation listed above in Step 1: Observation. 1 2 Write what you think you will observe if you carry out a study. Write what you might observe if your hypothesis is false. BIOS 1610 Lab 9 Graphic Organizer, 2022 Scientific Process, Step 5: Design a Study Describe your procedure in enough detail so that we can understand what you intend to do. Fill in as many different pieces in the rest of the table. If you do the work now, it will be easier and you can receive feedback to help you create a fully functioning experiment. (10 points) Hypothesis 1 Hypothesis 2 Procedure Materials and Equipment Data Collection Method Variables and Controls Potential Pitfalls 4 BIOS 1610 Lab 9 Graphic Organizer, 2022 Scientific Process, Step 6: Collect Data You will need to collect data. Think about the data you expect to get and create a table to organize that data. It is possible that you will need more than one table to collect data. If so, you can copy this table or make your own. Include it below. Make sure headings and titles are provided. (5 points) Table 1 5 Assignment 14 \u2013 Cell Signaling 1. Draw a generic G-protein receptor cascade including substrates, products, enzymes and anything else that you think is important for me to appreciate your understanding of this signal transduction pathway. 1 2. Draw a generic tyrosine kinase receptor cascade including substrates, products, enzymes and anything else that you think is important for me to appreciate your understanding of this signal transduction pathway. 2 3. The following enzyme signaling amplification cascade has just been identified. Assuming that each signaling step amplifies the signal by 7, except for the B to C step which amplifies by 6 times and the E to F step which amplifies by 16. How many active G enzymes would there be at the end if 4 ligands initially bound to 4 receptors of A? A-B = 8 enzymes B-C = 8 x 11 = 88 C enzymes C-D = 88 x 6 = 528 D enzymes D-E = 528 x 11 = 5,808 E enzymes E-F = 5,808 x 35 = 203,280 F enzymes F-G = 203,280 x 11 = 2,236,080 G enzymes Number of G enzymes = (8 x 11 x 6 x 11 x 35 x 11) x 4 = 8,944,320 G enzymes (Morris, et al., 2019) 3 4. Compare and Contrast Enzymes and Receptors. See the assignment help module to understand how to fill this out correctly. All boxes and arrows are editable. Feel free to move them around and adjust, but the relationships to the two given words need to stay the same. (Morris, et al., 2019) Catalyzes reaction Both stimulate cellular activity Converts the extracellular signal to intracellular Binds with substrate Both are proteins Stimulates a response beyond membrane Stimulates chemical change in substrate Enzyme Both have binding sites 4 Receptor Binds with ligand References Morris, M. J., Hartl, D., Knoll, A., Lue, R., Michael, M., Berry, A., . . . Holbrook, N. (2019). Biology: How Life Works (3RD ed.). New York: W.H Freeman. 5<\/p>\n","protected":false},"excerpt":{"rendered":"

BIOS 1610 Final Lab Project: Lab 9 Sample Answer for BIOS 1610 Final Lab Project: Lab 9 Included After Question This assignment is worth 30 points. By now, you should have identified a research topic for you to explore in more detail. While this may seem different to you than a research experiment, the best […]<\/p>\n","protected":false},"author":11,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-30727","post","type-post","status-publish","format-standard","hentry","category-nursing-papers"],"blocksy_meta":{"styles_descriptor":{"styles":{"desktop":"","tablet":"","mobile":""},"google_fonts":[],"version":6}},"yoast_head":"\n