As you may know, I have been teaching BIO101 (and also the BIO102 Lab) to non-traditional students in an adult education program for about twelve years now. Every now and then I muse about it publicly on the blog (see this, this, this, this, this, this and this for a few short posts about various aspects of it – from the use of videos, to the use of a classroom blog, to the importance of Open Access so students can read primary literature). The quality of students in this program has steadily risen over the years, but I am still highly constrained with time: I have eight 4-hour meetings with the students over eight weeks. In this period I have to teach them all of biology they need for their non-science majors, plus leave enough time for each student to give a presentation (on the science of their favourite plant and animal) and for two exams. Thus I have to strip the lectures to the bare bones, and hope that those bare bones are what non-science majors really need to know: concepts rather than factoids, relationship with the rest of their lives rather than relationship with the other sciences. Thus I follow my lectures with videos and classroom discussions, and their homework consists of finding cool biology videos or articles and posting the links on the classroom blog for all to see. A couple of times I used malaria as a thread that connected all the topics – from cell biology to ecology to physiology to evolution. I think that worked well but it is hard to do. They also write a final paper on some aspect of physiology.
Another new development is that the administration has realized that most of the faculty have been with the school for many years. We are experienced, and apparently we know what we are doing. Thus they recently gave us much more freedom to design our own syllabus instead of following a pre-defined one, as long as the ultimate goals of the class remain the same. I am not exactly sure when am I teaching the BIO101 lectures again (late Fall, Spring?) but I want to start rethinking my class early. I am also worried that, since I am not actively doing research in the lab and thus not following the literature as closely, that some of the things I teach are now out-dated. Not that anyone can possibly keep up with all the advances in all the areas of Biology which is so huge, but at least big updates that affect teaching of introductory courses are stuff I need to know.
I need to catch up and upgrade my lecture notes. And what better way than crowdsource! So, over the new few weeks, I will re-post my old lecture notes (note that they are just intros – discussions and videos etc. follow them in the classroom) and will ask you to fact-check me. If I got something wrong or something is out of date, let me know (but don’t push just your own preferred hypothesis if a question is not yet settled – give me the entire controversy explanation instead). If something is glaringly missing, let me know. If something can be said in a nicer language – edit my sentences. If you are aware of cool images, articles, blog-posts, videos, podcasts, visualizations, animations, games, etc. that can be used to explain these basic concepts, let me know. And at the end, once we do this with all the lectures, let’s discuss the overall syllabus – is there a better way to organize all this material for such a fast-paced class.
Today, we start with the very beginning – the introductory lecture on Biology and the Scientific Method. Follow me under the fold:
This is the summary of the first part of the first lecture in Introduction to Life Science (this is a science requirement for non-science majors at an accelerated adult education program at a community college). The summary is more than just a series of bulleted points to be memorized, but it also does not contain all the examples, details, anecdotes, jokes, veering off on tangents, answering students’ questions etc. nor does it contain all the graphics, including those drawn on the whiteboard over the course of the lecture.
BIO101 – Bora Zivkovic – Lecture 1, Part 1
Introduction to Biology and the Scientific Method
A. Biology and Life
Biology is the science that studies life. What is life?
Unlike non-living matter, living things exhibit the following properties:
Order: a hierarchical organization (a ‘nested hierarchy’, like Russian dolls). This means that organisms are composed of organs that work together in a systematic manner, the organs are composed of tissues, tissues of cells, cells of organelles, organelles of molecules and molecules of atoms, with the entire organization built in a way that maximizes the internal order, survival and reproduction of the organism.
Crystals exhibit order, but it is not hierarchical, and does not give the crystal a maximal chance of survival and reproduction. In living organisms, the properties of higher levels of organization cannot, unlike in crystals, be explained by the elements at the lower level of organization. Interactions between lower-level elements result in emergent properties at higher levels. For instance, from the order of nucleotides in the DNA we cannot infer how the whole organism looks like or behaves because the sequence does not specify the rules of interactions between the genes, gene-products (proteins), cells during development, and organisms inside their environments.
Sensitivity: response to stimuli in the environment. Even the simplest organisms, like bacteria, are capable of sensing changes in the environment and responding to such changes – they may swim away from or towards areas with higher concentrations of nutrients, salt, oxygen, or levels of illumination. Such responses (e.g., swimming) are active. A seed or a spore, seemingly “dead”, will actively respond to good growing conditions by germinating. A piece of dead matter may expand or even melt at high temperature, but that response is passive – due purely to the laws of physics.
Growth, Development and Reproduction: having a life-cycle. Crystals may grow, but the growth does not change the basic organization of the crystal. On the other hand, growth of an organism is accompanied by reorganization, cell division and cell differentiation. Each organism, at least during some parts of its life cycle, undergoes growth, developmental changes, and production of offspring. The results of reproduction – the offspring – are similar to the parent(s) due to the code inherited via a molecule, either DNA or RNA.
Regulation: All organisms have evolved well-orchestrated biochemical, physiological and behavioral mechanisms that regulate all the organism’s functions, which include finding and ingesting nutrients, processing nutrients and supplying all cells with the end-products of such processing, sequestering and eliminating the by-products of nutrient use. Likewise, every organism has evolved elaborate mechanisms for absorbing, storing, converting, using and dissipating energy – this last criterion may be the most important criterion for testing if something is alive or not, e.g., if one discovers a potentially living form on another planet.
Homeostasis: maintaining relatively constant internal conditions. We will cover this in much detail when we start the unit on human anatomy and physiology.
We will study the details of all five of the above criteria in this course. During the first three lectures, we will look at general properties of living organisms at all levels, from molecules, organelles and cells, through tissues, organs, systems and organisms, to populations, species, communities and ecosystems. During the remainder of the course we will take a look at specific cases: bacteria, protista, fungi, plants and animals, as well as details of the functioning of the human body.
A. Scientific Method and Process
Deductive reasoning applies general principles to predict specific results. Inductive reasoning uses specific observations to construct general principles. Here is a brief description of the steps in the hypothetico-deductive method:
Scientists make observations of processes and events found in nature. Scientists often use technology and carefully designed protocols to overcome their sensory limitations, and to minimize observer bias.
The observations lead to questions: what is this, how does it work, why does it work the way it does? This may necessitate further observations to be made.
The questions are then asked in a form that suggests a possible explanation (hypothesis) for the observations. Scientists try to come up with all possible explanations and pit them against each others as alternative hypotheses.
Using the available knowledge and understanding of the related phenomena, the scientist makes a best guess at which of the alternative hypotheses is most likely to be correct.
Experiments are designed in such a way that one or more hypotheses are tested. This means that the experiment is geared specifically towards rejecting one’s favored hypothesis: it is directly testing if that hypothesis is wrong. If the results are positive, the favored hypothesis is not rejected, but the alternative hypotheses may be rejected. If the results are negative, the favored hypothesis is rejected and one or more of the alternative hypotheses are accepted and further directly tested.
Often, two experiments are conducted at the same time. In one experiment, all the variables are kept constant except one, while the other experiment is called the control experiment, and in that experiment, that variable is left unaltered. The results of the two experiments are compared to each other using statistical methods to determine if the tested variable (the one not kept constant) indeed has an effect on the outcome.
After performing a series of experiments, a paper is written that provides some background information, describes the experimental methods and results, provides the statistical analysis, and draws conclusions from the results. The paper is then submitted for peer review and published in a scientific journal. We will take a look at some real scientific papers later on in the course, so you can see the structure and form of it and be able to find and read such primary literature.
Once all but one alternative hypothesis has been rejected over a series of experiments, the one remaining hypothesis is further tested. The hypothesis, if correct, can be used to make predictions which can be directly tested in subsequent experiments. Predictions provide a way to test the validity of a hypothesis.
As more and more studies are done and the hypothesis gets stronger and stronger (as all possible alternatives get rejected), it grows in its predictive power and it may also grow in its ability to explain a broader range of phenomena. Once a hypothesis reaches the stage at which it is supported with large amounts of evidence after repeated testing, it becomes a theory.
A theory is a body of interconnected concepts most strongly supported by scientific reasoning and experimental evidence. It is a scientific term that is used to denote the scientific concepts that have stood the test of time and are best supported by experimental evidence.
This sense of the word “theory” – the scientific ideas with the greatest certainty that they are correct – is in contrast to the colloquial use of the term, which means almost opposite – lack of certainty (as in “it’s my theory that Secretariat was the greatest American athlete of all times”, or “it’s just a theory – nothing you should trust on its face”). Purveyors of pseudoscience (for financial, religious or political reasons) like to utilize the difference between the two senses of the word, dishonestly implying that a scientific theory they don’t like is uncertain when just the opposite is true.
The strongest theories are those that are supported by a wide variety of kinds of evidence. Theory of evolution is one of the best supported theories of all science not only because it is backed up by mountains of evidence (and no evidence against it), but also because the evidence comes from many different areas of science: paleontology (fossils), biogeography, ecology, mathematical modeling, population and quantitative genetics, comparative genomics, medicine, agricultural breeding, study of animal behavior, comparative anatomy, comparative physiology and comparative embryology.
The way disparate data from quite different areas of science, when put together, all strengthen a single theory, is called consilience. Recently, this word has been misused in popular literature (including a book of the same name) and press to mean quite the opposite – taking the methodology or findings from one discipline and applying it to a variety of other disciplines, e.g., taking the logic of evolution by natural selection and applying it to chemistry, pharmacology, psychology or computer science. That is a worthy endeavor, but it is not a correct meaning of the term ‘consilience’.
Sometimes you will see (as opposed to the image in your textbook) scientific method schematically depicted like this:
There are two reasons why the Biology textbook does not show a graph like this: a) it is not applicable to biology, and b) it is wrong.
It is wrong because it places “law” above the theory. Actually, the opposite is true – many laws (in physics, for instance) are elements of a greater theory and are parts of the evidence that the theory is correct. Laws are usually mathematical depictions of regular behavior of some aspect of nature. In other words, laws describe nature but do not explain it. Theories explain nature and are thus on the top of the hierarchy of scientific knowledge.
The model above is inapplicable to biology (it was probably drawn by a physicist) because there are no laws in biology. There are rules (like Bergmann-Allen Rule in ecology or Cope’s Rule in evolutionary biology), there are generalizations (e.g., Scaling), there are mathematical models (e.g., in population genetics) and there are Principles (e.g., the Principle of Natural Selection), but there are no laws. Biology deals with processes at much higher levels than does physics, where emergent properties of complex systems introduce a dose of unpredictability. All potential “laws” in biology have many exceptions, or have to be limited to a very small subset of processes, or to a small subset of organisms – they are not exception-less as laws of physics are.
Hypothetico-deductive method described above, while arguably the most powerful part of the scientific method, is not the only one. There is a continuum of scientific “methods” as depicted here (from Brandon 1996):
Collecting the information about all the species of birds and salamanders in the mountains of North Carolina is not a test of hypothesis and is not manipulative (and is not experimental) – yet it is certainly science (place a dot in the bottom right corner of the graph) – it provides important information about the natural world. If patterns emerge from such a survey and prompt new ideas about species distribution, this can then be tested in a more experimental fashion.
Human Genome Project is highly manipulative (and expensive!), yet it is not hypothesis-testing (place a dot in the bottom left corner). Nobody predicted that we would find anything but the four nucleotides known to make up DNA. We had no predictions as what the sequence will be and what would it all mean. Once the work was done, we could use the HGP as a tool for testing new hypotheses, e.g., how many genes do we have, how they are related to the genes of chimps, how diverse are particular gene sequences in human population as a whole, etc.
Paleontology is somewhere in the middle. It is somewhat manipulative (it takes hard work and a lot of people to do it) and it is somewhat hypothesis-testing (place a dot smack in the middle of the graph). Paleontologists do not dig randomly – they dig in particular places on the planet in particular layers of the sediment, looking for fossils of particular kinds of organisms. For instance, a group recently did an excavation in a particular bed of Late Devonian layer, looking specifically for a fossil of an early tetrapod, i.e., a transitional organism between fully aquatic and fully terrestrial mode of life. They discovered exactly that – a fossil named Tiktaalik whose fins were better suited for walking on land than that of fishes (like mudskippers, catfish and lungsfish), yet not completely evolved for land use as in amphibians.
Sometimes nature provides an experiment that tests a hypothesis (a dot in the top right corner). For instance, a biogeographical model of island succession was tested when the volcano Krakatoa erupted and eliminated all life from the island. The scientists went there and observed which organisms flew in from the mainland, in which order, and how the ecosystem passed through several stages until it reached its mature stage, thus confirming (and somewhat modifying) their hypotheses.
No matter how strongly a theory is supported by empirical evidence, it is always theoretically conceivable that one day, some data will come in that will force the scientists to modify or even eliminate the theory. Even if the scientists are 99.999999999999999999999999999999999% certain that the theory is true, it is philosophically incorrect to say that it is 100% true and to call it the Truth with the capital T. That is why scientists, when interviewed in the media, often sound uncertain and wishy-washy, while some quack or pseudoscientist pronounces his absolute certainty. Audience not educated in the scientific method is likely to swallow the pseudoscience bait, hook and sinker because we, as humans, crave certainty. It takes some scientific training to be able to fully embrace and even love uncertainty. That is why it is difficult for scientific knowledge to counteract financially, religiously and politically motivated assaults on it. However, nature does not care about what we like and wish for: the apples will continue to fall down, the continents will continue to move around the globe (causing earthquakes and volcanic eruptions) and the organisms will continue to evolve whether we like it or not, whether we believe in it or not.
Brandon, RN, Does biology have laws? The experimental evidence. PSA 1996, vol. 2, 444–457.
Figure 2 from:
Brandon, RN, Does biology have laws? The experimental evidence. PSA 1996, vol. 2, 444–457.