Biology is the scientific study of life. Studying biology is an opportunity to ask exciting questions about the world that surrounds us. It is an opportunity to dig into some of humanity's deepest questions about our own origins, the history of our planet, and our connections to other living beings (big and small/extant or extinct). It is also an opportunity to dive into a world of practical problem solving and to think hard about possible solutions for improving health care, maintaining sustainable food supplies, and producing renewable energy technologies.
The study of biology is also relevant to understanding issues and addressing problems we encounter every day. For instance, you can better understand how what you eat and the amount you exercise influence your health when you understand the biochemical reactions that describe how the food (matter) is transformed, how it and your body store energy, and how this energy can be transferred from the food to your muscles. Making the decision of whether or not to buy products labeled with terms like "antimicrobial" or "probiotic" can be easier if you understand what the microbes, which live in, on, and around us, do. Understanding the biochemical principles that describe the changes that happen to eggs as they cook can also help us to the understand how similar physical processes may be central to the cellular stress response and some diseases. Your eye color can be better appreciated with an understanding of the genetic and biochemical mechanisms that link genetic information to physical traits.
The study of biology is also important for helping us understand things that may be literally out of this world. For instance, understanding the requirements for life can help us look for life in places like Mars or deep in the earth’s crust. When we get an understanding of how to properly “rewire” cellular decision making networks we may finally be able to regenerate functional limbs or organs from someone’s own tissue, or reprogram diseased tissues back to health. There are many exciting opportunities. The key point is that learning a few basic principles well can help you to understand and think more deeply about a wide array of topics. Keep this notion in mind as you proceed through the course.
Biology: An interdisciplinary science
Questions in biology span size scales in excess of ten orders of magnitude, from the atomic make-up and chemical behavior of individual molecules to planetary-scale systems of interacting ecologies. Whatever the scale of interest, to develop a deep and functional understanding of biology we must first develop a keen appreciation for biological concepts. This involves integrating important ideas and tools from across the spectrum of scientific inquiry, including chemistry, physics, and mathematics. Biology is truly an interdisciplinary science.
The potential application of knowledge is broad
The study of biology leads to a vast number of applications that range from treating (human or other animal) patients in the health sciences, to creating improved agricultural varieties and practices, to the development of new building materials, to writing new energy policy, to helping craft solutions to global climate change, to creating new works of art - the list goes on and on. The study of biology can therefore lead to or influence many careers. For the curious, biology also has plenty of major mysteries left to explore.
As you go through your coursework, remember to keep an open mind and appreciate all of the exciting questions and topics that biology has to offer. Even though course topics may not always seem related at first, they likely are. Doing so may lead you to discover and appreciate the connections between topics covered in class and your current interests. You'll also find that understanding how seemingly different topics are interrelated can give you a deeper appreciation for the things you enjoy and maybe even spark a new passion.
BIS2A - From molecules to cells
In BIS2A, our focus is on the cell, one of the most fundamental units of life. Cells can be as simple as those of the disease-causing bacterium Mycoplasma genitalium whose genome encodes just 525 genes (only 382 of which are essential for life) or as complex as a cell belonging to the plant Oryza sativa (rice) whose genome likely encodes ~40,000 genes. However, in spite of this diversity, all cells share some fundamental properties. In BIS2A, we explore basic problems that must be dealt with by all cells. We study the building blocks of cells, some of their key biochemical properties, how biological information is encoded in genetic material, how it is expressed and how all this comes together to make a living system. We will also discuss some of the ways in which living systems exchange matter, energy and information with their environment (including other living things). We focus primarily on core principles that are common to all life on Earth, and we try to put these ideas into a variety of contexts throughout the quarter.
The Scientific Method
An example of oversimplification that confounds many students of biology (particularly early in their studies) is the use of language that hides the experimental process used to build knowledge. For the sake of expediency we often tell stories about biological systems as if we are presenting unquestionable facts. However, while we often write and speak about topics in biology with a conviction that gives the appearance of "factual" knowledge, reality is often more nuanced and filled with significant uncertainties. The "factual" presentation of material (usually lacking discussion of evidence or our degree of confidence in the evidence) plays to our natural tendency to feel good about "knowing" things but it tends to create a false sense of security in the state of knowledge and does little to encourage the use of imagination or the development of critical thinking.
A better way to describe our knowledge about the natural world would be to explicitly qualify that the knowledge presented represents our current best understanding that has not yet been refuted by experiment. Unfortunately, repeated qualification becomes rather cumbersome. The important thing to remember is that while we may not say so explicitly, all of the knowledge we discuss in class represents only the best of our current understanding. Some ideas have withstood repeated and varied experimentation while other topics have yet to be tested as thoroughly. So if we're not as certain about things as we'd like to believe sometimes, how do we know what to put confidence in and what to be skeptical of. The complete answer is non-trivial but it begins with developing an understanding of the process we use in science to build new knowledge. The scientific method is the process by which new knowledge is developed. While the process can be described with long lists of "steps" (often seen in textbooks), its core elements can be described more succinctly.
Succinct Description of Scientific Method (adapted from Feynman)
- Make an observation about the world.
- Propose a possible explanation for the observation.
- Test the explanation by experiment.
- If the explanation disagrees with experiment, the explanation is wrong.
At its core, that's it! In science there may be multiple simultaneously proposed explanations or ideas that are tested by experiment. The ideas that fail experimentation are left behind. The ideas that survive experimentation move forward and are often retested by alternative experiments until they too either fail or continue to be retained.
Making an observation and asking a question
The ability to make useful observations and/or ask meaningful questions requires curiosity, creativity and imagination – this cannot be over-stated. Indeed, historically, it is first and foremost the application of these skills, perhaps more than technical ability, which has led to big advances in science. Many people think that making meaningful observations and asking useful questions is the easiest part of the scientific method. This is not the case. Why? Perceiving what other people have not perceived before, and coming up with possible explanations, takes work, imagination, creativity, and thoughtful reflection! In addition, our senses of observation are often biased by life-experience, prior knowledge, or even our own biology. These underlying biases influence how we see the world, how we interpret what we see, and what we are ultimately curious about. This means that when we look at the world, we can miss a lot of things that are actually right under our noses. Douglas Adams, who is best known for his book The Hitchhiker’s Guide to the Galaxy, once expanded on this point by writing:
“The most misleading assumptions are the ones you don't even know you're making.”
Scientists, therefore, need to be aware of any underlying biases and any assumptions that may influence how they internalize and interpret observations. This includes approaching the variety of places we get our knowledge (i.e. textbooks, instructors, the Internet) with a healthy dose of skepticism. We need to learn to examine the evidence underling the “facts” we supposedly know and make critical judgments about how much we trust that knowledge. More generally, taking the time to make careful observations and to uncover any assumptions and biases that could influence how they are interpreted is, therefore, time well spent. This skill, like all others, needs to be developed and takes practice and we’ll try to start you on this in BIS2A.
For fun, and to test your observation skills, Google “observation tests”. Many of the search results will take you to interesting psychological tests and/or videos that illustrate how difficult accurate observation can be.
Generating a testable hypothesis
The "possible explanation" referred to in step 3 above has a formal name; it is called a hypothesis. A hypothesis is not a random guess. A hypothesis is an educated (based on prior knowledge or a new viewpoint) explanation for an event or observation. It is typically most useful if the hypothesis can be tested. This requires that the tools to make informative measurements on the system exist and that the experimenter has sufficient control over the system in question to make the necessary observations.
Most of the time, behaviors of the system that the experimenter wants to test can be influenced by many factors. We call the behaviors and factors, dependent and independent variables, respectively. The dependent variable is the behavior that needs explaining while the independent variables are all of the other things that might change and influence the behavior of the dependent variable. For example, an experimenter that has developed a new drug to reduce blood pressure would want to test whether her new drug actually influences blood pressure (i.e., "Does blood pressure depend on the dose of this drug?" or "Is there a negative correlation between the dose of this drug and blood pressure"). In this example, the system is the human body, the dependent variable might be blood pressure, and one independent variable (among the many other independent variables that might affect blood pressure- diet, stress, physical activity, age...) would be the dose of drug. The null hypothesis might be that the new drug has no effect on blood pressure, and the alternative hypothesis is that the drug affects blood pressure. In a well-designed experiment the investigator must either have excellent control over the number of independent variables (for example, this drug efficacy experiment would be easier to do with a set of genetically identical mice raised identically and tested in identical environments- but are mice are mice a good model for the vascular physiology and biochemistry of humans?), or the investigator their best to randomly distribute the test drug among a very large population of humans that reflect all possible aspects of the "independent variables" that the investigator cannot control.
In this class, most of what we discuss (but not all) will be hypotheses that have SUBSTANTIAL experimental backing. You may have heard that a hypothesis that has withstood the test of many experimental observations is dignified with the term "theory". Some theories and the hypotheses are so well tested that they are no longer referred to as theories at all, but are accepted as incontrovertable facts. For example, at one time the hypothesis that DNA carried the information required for heredity was debatable- other hypotheses were equally plausible. But no one refers to DNA (sometimes RNA) = biological heredity as a "theory" anymore. It is, frankly, accepted as incontrovertible "fact", or "dominant paradigm". The DNA <-> RNA -> protein universal mechanism for information expression is actually termed the "Central Dogma".
Overthrowing a dominant paradigm is certainly one of the most useful things a scientist can do, but a scientist that is so inclined would need to choose their battles wisely.
Note: For more on dependent and independent variables
In BIS2A, and beyond, we prefer to avoid using language like “the experiment proved her hypothesis” when referring to a case like the blood pressure example above. Rather we would say, if there is significant negative correlation between blood pressure and drug dose, “the experiment is consistent with her hypothesis". For convenience (one of the language shortcuts we discussed earlier). It would be more correct to state, “the experiment falsified her null hypothesis and is consistent with her alternative hypothesis.”
Note: Possible discussion
What does the statement about falsifying hypotheses mean in your own words? Why is falsification critical to the scientific method? In the drug vs. blood pressure example discussed above, what is the null hypothesis?
In an ideal case, an experiment will include what are known as control groups. Control groups are experimental conditions in which the values of the independent variables (there may be more than one) are maintained as close to those in the experimental group with the exception of the independent variable being tested. In the blood pressure example, an ideal scenario would be to have one identical group of people taking the drug and another group of people identical to those in the experimental group taking a pill containing something known to not influence blood pressure. In this oversimplified example, all independent variables are identical in the control and experimental groups with the exception of the presence or absence of the new drug. Under these circumstances, if the value of the dependent variable (blood pressure) of the experimental group differs from that of the control group, one can reasonably conclude that the difference must be due to the difference in independent variable (the presence/absence of the drug). This is, of course, the ideal. In real life we also need to determine whether the difference between the control and test groups is significant, or due to other uncontrolled difference between our two groups. Statistics will be required to determine whether and differences are significant. You won’t need to understand the nuances of these statistical issues in BIS2A, but if you're going to become a scientist, or doctor, or even a good citizen, some knowledge of statistics will be required- make sure you take a course while you're here!
Accuracy in Measurement, Uncertainty, and Replication
Finally, we mention the intuitive notion that the tools used to make the measurements in an experiment must be reasonably accurate. How accurate? They must be accurate enough to make measurements with sufficient certainty to draw conclusions about whether changes in independent variables actually influence the value of a dependent variable. If we take, yet again, the blood pressure example above. In that experiment we made the important assumption that the experimenter had tools that allowed her to make accurate measurements of the changes in blood pressure associated with the effects of the drug. For instance if the changes associated with the drug ranged between 0 and 3 mmHg and her meter capably measured changes in blood pressure with a certainty of +/- 5 mmHg she could not have made the necessary measurements to test her hypothesis or would have missed seeing the effect of the drug. For the sake of example, we assume that she had a better instrument and that she could be confident that any changes she measured were indeed differences due to the drug treatment and that they were not due to measurement error, sample-to-sample variability, or other sources of variation that lower the confidence of the conclusions that are drawn from the experiment.
The topic of measurement error leads us to mention that there are numerous other possible sources of uncertainty in experimental data that you as students will ultimately need to learn about. These sources of error have a lot to do with determining how certain we are that experiments have disproven a hypothesis, how much we should trust the interpretation of the experimental results and by extension our current state of knowledge. Even at this stage, you will recognize some experimental strategies used to deal with these sources of uncertainty (i.e. making measurements on multiple samples; creating replicate experiments). You will learn more about this in your statistics courses later on.
For now, you should, however, be aware that experiments carry a certain degree of confidence in the results and that the degree of confidence in the results can be influenced by many factors. Developing healthy skepticism involves, among other things, learning to assess the quality of an experiment and the interpretation of the findings and learning to ask questions about things like this. A good scientist has a good sense of how well-supported (by experimental evidence) their current, working models for various processes are.
Note: Possible discussion
After moving to California to attend UC Davis, you have fallen in love with fresh tomatoes. You decide that the tomatoes in the stores just don’t taste right and resolve to grow your own.
You plant tomato plants all over your back yard; every free space now has a freshly planted tomato seedling of the same variety. You have planted tomatoes in the ground in full sunlight and next to your house in full shade.
Observation: After the first year of harvest, you make the observation that the plants growing in full shade almost always seem shorter than those in the full sun. You think that you have a reasonable explanation (hypothesis) for this observation.
Based on the information above, you create the following hypothesis to explain the differences in height you noticed in your tomatoes:
Hypothesis: The height that my tomato plants reach is positively correlated to the amount of sunlight they are exposed to (e.g. the more sun the plant gets the taller it will be).
This hypothesis is testable and falsifiable. So, the next summer you decide to test your hypothesis.
This hypothesis also allows you to make a prediction. In this case you might predict that IF you were to shade a set of tomatoes in the sunny part of the yard, THEN those plants would be shorter than their full-sun neighbors.
You design an experiment to test your hypothesis by buying the same variety of tomato that you planted the previous year and plant your whole yard again. This year, however, you decide to do two different things:
- You create a shade structure that you place over a small subset of plants in the sunny part of your yard.
- You build a contraption with mirrors that redirects some sunlight onto a small subset of plants that are in the shady part of the yard.
- Question 1: We used a shortcut above. Can you create statements for both the null and alternative hypothesis? Work with your classmates to do this.
- Question 2: Why do you create a shade structure? What is this testing? Based on your hypothesis what do you predict will happen to the plants under the shade structure?
- Question 3: Why do you create the mirror contraption? Why do you potentially need this contraption if you already have the shade structure?
- New Data: At the end of the summer you measure the height of your tomato plants and you find once again that the plants in the sunny part of the yard are indeed taller than those in the shady part of the yard. However, you notice that there is no difference in height between the plants under your shade structure and those right next to the structure in full sun. In addition, you notice that the plants in the shady part of the yard are all about the same height, including those that had extra light shined on them via your mirror contraption.
- Question 4: What does this experiment lead you to conclude? What would you try to do next?
- Question 5: Imagine an alternative scenario in which you discovered, as before, that the plants in the sunny part of the yard were all the same height (even those under your shade structure) but that the plants in the shady part of the yard that got “extra” light from your mirror contraption grew taller than their immediate neighbors. What would this say about your alternate hypothesis? Null hypothesis? What would you do next?
- Question 6: What assumptions are you making about the ability to make measurements in this experiment? What influence might these assumptions have on your interpretation of the results?
In this class you will occasionally be asked to create a hypothesis, to interpret data, and to design experiments with proper controls. All of these skills take practice to master, we can start to practice them in BIS2A. Again, while we don’t expect you to be masters after reading this text, we will assume that you have read this text during the first week and that the associated concepts are not completely new to you. You can always return to this text as a resource to refresh yourself.
While the preceding treatment of the experimental method is very basic - you will undoubtedly add numerous layer of sophistication to these basic ideas as you continue in your studies – it should serve as a sufficient introduction to the topic for BIS2A. The most important point to remember from this section is that the knowledge represented in this course, while sometimes inadvertently represented as irrefutable fact, is really just the most current hypothesis about how certain things happen in biology that has yet to be falsified via experiment.
The Design Challenge
Your BIS2A instructors have devised something that we call “The Design Challenge” to help us approach the topics we cover in the course from a problem solving and/or design perspective. This pedagogical tool is nothing more than
(a) a frame of mind or way of approaching the material and
(b) a set of sequential steps that help structure thinking about course topics in a problem-solving context.
How is it intended to work? Briefly, when we encounter a topic in class, “The Design Challenge” encourages us to think about it in the following problem-solving centric way:
- Identify the problem(s) - this may include identifying "big" problems and also decomposing them into "smaller" nested sub-problems
- Determine criteria for successful solutions
- Identify and/or imagine possible solutions
- Evaluate the proposed solutions against the criteria for success
- Choose a solution
By using the structure of the design challenge, topics that are typically presented as lists of facts and stories are transformed into puzzles or problems that need solving. For instance the discussion about the topic of cell division is motivated by a problem. The problem statement can be: "The cell needs to divide into two identical cells". Some of the criteria for success can include needing to have a near identical copy of DNA in each daughter cell, distributing organelles between the daughter cells so that each remains viable etc. These would be considered sub-problems to the larger “the cell needs to divide” problem. One can then go on to explore what the challenges are and try to use their existing knowledge and imagination to propose some solutions for each of those problems. Different solutions can be evaluated and then compared to what Nature seems to have done (at least in the cases that are well studied).
This exercise requires us to use imagination and critical thinking. It also encourages the student and instructor to think critically about WHY the particular topic is important to study. The design challenge approach to teaching biology attempts to MAKE the student and instructor focus on the important core questions that drove the development of the knowledge in the first place! It also encourages students to dream up new ideas and to interact with the material in a manner that is question/problem-centered rather than “fact”-centered. The question/problem-centered approach is different from what most people are used to, but it is ultimately more useful for developing skills, mental frameworks and knowledge that will transfer to other problems that they will encounter during their studies and beyond.
The guiding problem in BIS2A is to understand “How to Build a Cell”. This rather complex problem will be broken down into several smaller sub-problems that include:
- acquiring the building blocks to construct cellular parts from the environment
- acquiring the energy to build cellular parts from the environment
- transforming the building blocks of the cell between different forms
- transferring energy between different storage forms
- creating a new cell from an old cell
- problems we identify in class
As we explore these sub-problems, we will at times explore some of the different ways in which biology has addressed each issue. As we get into details, let us however make sure to stay focused on and not forget the importance of always staying linked to the questions/problems that motivated us to talk about the specifics in the first place.
Scientific Method versus the Design Challenge
At this point you might be thinking: "What is the difference between the scientific method and the design challenge protocol and why do I need both?" It's not an uncommon question so let's see if we can clarify this now.
The design challenge and the scientific method are both processes that share similar qualities. The critical distinguishing feature, however, is the purpose behind each of the processes. The scientific method is a process used for eliminating possible answers to questions. A typical scenario where one might use the scientific method would involve someone making an observation, proposing multiple explanations, designing an experiment that might help eliminate one or more of the explanation and reflecting on the result. By contrast the design process is used for creating solutions to problems. A typical scenario for the design challenge would start with a problem that needs solving, defining criteria for a successful resolution, devising multiple possible solutions that would meet the success criteria and either selecting a solution or reflecting on changes that might be made to the designs to meet success criteria. A key operational difference is that the design challenge requires that criteria for success be defined while the scientific method does not.
While both are similar the differences are still real and we need to practice both processes. We'll assert that we use both of these processes in "real life" all of the time. A physician, for instance, will use both of these processes interactively as she forms hypotheses that try to determine what might be causing her patient's ailments. She will turn around and use the design process to build a course of treatment that meets certain success criteria. A scientist may be deep into hypothesis generation but he will eventually need to use a design process for building an experiment that will, within certain definable success criteria, help him answer a question.
Both of these processes, while similar, are important to use in different situations and we want to begin getting better at both.
Common misconceptions and a course-specific note
Finally, we draw your attention to a critical point and common misconception among beginning students in biology. This misconception can arise when, for the sake of discussion, we decide to anthropomorphize Nature by giving it an intellect. For example, we may try to build an example for evolution by natural selection by proposing that a surplus of a particular food exists in an environment and there is an organism close by that is starving. It would be correct to reason that if the organism could eat that food that this might give it a selective advantage over other organisms that cannot. If later we find an example of organisms that have the capability to eat that surplus food it might be tempting to say that Nature evolved a solution to the problem, for that organism, of not being able to eat the surplus food. The process of evolution by natural selection happens randomly and without direction. That is, Nature does NOT identify “problems” that are limiting fitness. Nature does NOT identify features that would make an organism more successful and then start creating solutions that meet this need. The generation of variation is not guided. Variation happens totally at random and natural selection filters what works best. The observation that an organism exists that can eat the surplus food is not a reflection of Nature actively solving a problem but rather a reflection that whatever processes that led to phenotypic variation in an ancestral population created – among many other (perhaps useless or deleterious) variants – a phenotype that increased fitness (because the ancestral organisms were able to eat the surplus food).
This point of the preceding paragraph is particularly important to understand in the context of BIS2A because of the way we utilizing the Design Challenge to understand biology. While the Design Challenge is intended to help focus our attention on functions under selection and their relationship to determining fitness, it can be easy – if we aren’t attentive – to lapse into language that would suggest that Nature purposefully designs solutions to solve specific problems. Always remember that we are looking retrospectively at what Nature has selected and that we are attempting to understand why a specific phenotype may have been selected from among many other possibilities. In doing so, we will be inferring or hypothesizing to the best of our ability (which is sometimes wrong) a sensible reason for why a phenotype might have provided a selective advantage. We are NOT saying that the phenotype evolved TO provide a specific selective advantage. The distinction between these two ideas may be subtle, but it is critical!
This course does not focus on evolution (BIS 2B does), but we will inevitably touch on evolution in order to reach and understanding of the significance of the relationship between phenotype and genotype.