CLYDE FREEMAN HERREID NANCY A. SCHILLER KY F. HERREID

CLYDE FREEMAN HERREIDNANCY A. SCHILLERKY F. HERREID

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Library of Congress Cataloging-in-Publication DataHerreid, Clyde Freeman, author. Science stories you can count on : 51 case studies with quantitative reasoning in biology / Clyde Freeman Herreid, Nancy

A. Schiller, Ky F. Herreid. pages cm ISBN 978-1-938946-05-9 — ISBN 978-1-938946-59-2 (electronic) 1. Biology--Study and teaching—United States--Case studies. 2. Qualitative reasoning. I. Schiller, Nancy A., 1957- author.

II. Herreid, Ky F., 1965- author. III. Title. QH319.A1H47 2014 570.71—dc23 2014009833


C O N T E N T SChapter 1 Introduction: The Numbers Game 1

Clyde Freeman Herreid

Chapter 2 A Case Full of Numbers: The Why and Wherefore 19 of Teaching With Cases

Clyde Freeman Herreid

Chapter 3 Math and Data Exploration 33 Dennis Liu

Chapter 4 Reinventing the Wheel: Quantifying Cases for Your Classes 41 Patricia A. Marsteller and Drew Kohlhorst

SECTION I: THE SCIENTIFIC METHODChapter 5 Cell Phone Use and Cancer 67

Wilma V. Colón Parrilla

Chapter 6 Is High-Fructose Corn Syrup Bad for the Apple Industry? 71 Jeffri C. Bohlscheid

Chapter 7 Feeling Detoxified: Expectations, Effects, and Explanations 87 Giselle A. McCallum and Annie Prud’homme Généreux

Chapter 8 Rabbit Island and the Search for Tuberculosis Treatment 95 Karen M. Aguirre

SECTION II: CHEMISTRY OF LIFEChapter 9 Sweet Indigestion: A Directed Case Study on Carbohydrates 107

Peggy Brickman

Chapter 10 Dust to Dust: The Carbon Cycle 111

Jennifer Y. Anderson, Diane R. Wang, and Ling Chen

Chapter 11 A Can of Bull: Do Energy Drinks Really Provide 117 a Source of Energy? Merle Heidemann and Gerald Urquhart

SECTION III: THE CELLChapter 12 The Mystery of the Seven Deaths: A Case Study 127

in Cellular Respiration

Michaela A. Gazdik

Chapter 13 Wrestling With Weight Loss: The Dangers of a Weight-Loss Drug 131 Susan M. DeSimone and Annie Prud’homme Généreux


C O N T E N T SChapter 14 Nanobacteria: Are They or Aren’t They Alive? 137

Merri Lynn Casem

SECTION IV: MICROBIOLOGYChapter 15 Fecal Coliforms in Antarctica 155

Stephen C. Nold

Chapter 16 Elvis Meltdown! Microbiology Concepts of Culture, 163 Growth, and Metabolism

Richard C. Stewart, Ann C. Smith, and Patricia A. Shields

Chapter 17 Resistance Is Futile ... or Is It? The Immunity System 173 and HIV Infection

Annie Prud’homme Généreux

Chapter 18 An Infectious Cure: Phage Therapy 187 Dustin J. Eno and Annie Prud’homme Généreux

SECTION V: GENETICSChapter 19 The “Blue People” of Kentucky 197

Celeste A. Leander and Robert J. Huskey

Chapter 20 To the Bitter End: The Genetics of PTC Sensitivity 201 R. Deborah Overath

Chapter 21 In Sickness and in Health: A Trip to the Genetic Counselor 207 Barry Chess

Chapter 22 The Case of Desiree’s Baby: The Genetics and Evolution 215

of Human Skin Color Patricia Schneider

Chapter 23 A Sickeningly Sweet Baby Boy: A Case Study on Autosomal 223 Recessive Inheritance

Jacqueline Washington and Anne Zayaitz

SECTION VI: MOLECULAR BIOLOGYChapter 24 “Not Exactly”: The Complexity of a Human Genetic Disease 231

William Morgan and Dean Fraga

Chapter 25 The Case of the Druid Dracula 239 Peggy Brickman


C O N T E N T SChapter 26 Which Little Piggy Went to Market? Bioinformatics and 245

Meat Science Debra A. Meuler

Chapter 27 Classic Experiments in Molecular Biology 251 Robin Pals-Rylaarsdam

SECTION VII: EVOLUTIONChapter 28 My Brother’s Keeper: A Case Study in Evolutionary Biology 263

and Animal Behavior Kari E. Benson

Chapter 29 As the Worm Turns: Speciation and the Maggot Fly 269 Martin Kelly

Chapter 30 Trouble in Paradise: A Case of Speciation 275 James A. Hewlett

Chapter 31 PKU Carriers: How Many Are in Your Hometown? 279 David J. Grisé

Chapter 32 Super Bug: Antibiotics and Evolution 285 Kristy J. Wilson

Chapter 33 Exaggerated Traits and Breeding Success in Widowbirds: 297 A Case of Sexual Selection and Evolution

J. Phil Gibson

SECTION VIII: PLANT FORM AND FUNCTIONChapter 34 Is Guaiacum sanctum Effective Against Arthritis? 309

An Ethnobotany Case

Eric Ribbens, Barbra Burdett, and Angela Green

Chapter 35 I’m Looking Over a White-Striped Clover: A Case 315 of Natural Selection

Susan Evarts, Alison Krufka, Luke Holbrook, and Chester Wilson

Chapter 36 The Ecology of Opuntia fragilis (Nuttall) Haworth 325 Eric Ribbens

Chapter 37 Tougher Plants: Beating Stress by Protecting Photosynthesis 333 in Genetically Modified Plants

Robin Pals-Rylaarsdam and Monica L. Tischler


C O N T E N T S

SECTION IX: ANIMAL FORM AND FUNCTIONChapter 38 The 2000-Meter Row: A Case in Homeostasis 343

Nathan Strong

Chapter 39 Girl Pulled Alive From Ruins, 15 Days After Earthquake 347 Susan B. Chaplin

Chapter 40 Hot and Bothered: A Case of Endocrine Disease 353 Karin A. Grimnes

Chapter 41 Keeping Up With the Joneses 361 Philip J. Stephens

Chapter 42 The Hunger Pains: Ghrelin, Weight Loss, and Maintenance 371 Lynn M. Diener

SECTION X: HEALTHChapter 43 Michael’s Story: A Case Study of Autism 379

Kristen N. Hausmann and Karen M. Aguirre

Chapter 44 Breast Cancer Risk: Using Real Medical Histories to 389 Rank Genetic and Environmental Influences

Michèle I. Shuster and Karen Peterson

Chapter 45 A Light Lunch? A Case in Calorie Counting 395 Brahmadeo Dewprashad and Geraldine S. Vaz

Chapter 46 Pharmacogenetics: Using Genetics to Treat Disease 401 Jeanne Ting Chowning

SECTION XI: ECOLOGY AND BEHAVIORChapter 47 The Dead Zone: Ecology and Oceanography in the Gulf of Mexico 413

Kathleen Archer and Lauren Sahl

Chapter 48 Threats to Biodiversity: A Case Study of Hawaiian Birds 427 Sarah K. Huber and Paula P. Lemons

Chapter 49 The Wolf, the Moose, and the Fir Tree: A Case Study of 437 Trophic Interactions

Gary M. Fortier

Chapter 50 Mathematics in Conservation: The Case of the Endangered 443 Florida Panther

Geffrey F. Stopper and Andrew G. Lazowski


C O N T E N T SChapter 51 Search for the Missing Sea Otters: An Ecological 457

Detective Story

Mary Allen and Mark L. Kuhlmann

SECTION XII: BIOSPHERE AND CONSERVATION

Chapter 52 Living Downstream: Atrazine and Coliform Bacteria 471 Effects on Water Quality Thomas A. Davis

Chapter 53 Do Corridors Have Value in Conservation? 477

Andrea Bixler

Chapter 54 The Wealth of Water: The Value of an Essential Resource 485 Melanie K. Rathburn and Karina J. Baum

Chapter 55 The Effects of Coyote Removal in Texas: A Case Study in 493 Conservation Biology

Margaret A. Carroll

LIST OF CONTRIBUTORS 499

APPENDIXESAppendix A Expectations for Medical School Students 505

Appendix B Biology Education to Prepare Research Scientists for 513 the 21st Century

National Research Council

Appendix C Resources for Quantifying Cases: Simulations, 527 Games, and Data Sets

INDEX 535



SCIENCE STORIES YOU CAN COUNT ON 1

There was a young man from Trinity,

Who solved the square root of infinity.

While counting the digits,

He was seized by the fidgets,

Dropped science, and took up divinity.

— Author Unknown

There was no need to do that. Drop science, that is. The young man from Trinity

could have buckled down and learned the necessary mathematics to have a

happy and fruitful career in science. He might even have discovered the physi-

cists’ version of the Holy Grail, the “Theory of Everything,” or the answer

to the biologist’s head-scratching question of how consciousness arose. Then again, not

everyone needs to be a scientist. In fact, C. P. Snow, British cultural raconteur and author

of The Two Cultures, argued it would be downright dangerous. We need folks in the arts

and humanities, and perhaps a philosopher or two is desirable. But even these individu-

als should be able to handle a bit of rudimentary mathematics, according to Snow, even

the second law of thermodynamics! And if we believe the most recent behavioral studies,

crows can count and so can pigeons, and mathematical calculations appear to be within the

purview of even the lowly squid. Surely, we can expect as much from our undergraduates. Our students should be able to at least reason quantitatively: to read and interpret data,

graphs, and statistics. They should be astute enough to demand to see the evidence when some politician claims that a new drug cures cancer, job numbers are up, our carbon foot-print is too big, the president’s budget is the highest ever, and the world is coming to an end on December 21. And once having been shown the data, our intelligent citizen should not cringe if graphs stare him in the face, but fearlessly look at the data, and challenge the purveyor of false doctrines and celebrate the “truth-sayer” when found. But if this is a worthy ideal, how do we achieve numerical nirvana?

Traditional courses do not appear to achieve this ideal goal, for various reasons we will discuss in this chapter. We need to revise our approach to the required courses. In addition, we need to introduce quantitative skills throughout the curriculum as an integral part of courses, especially those that purport to teach STEM students.

Introductory biology seems an ideal place to start. Most students enroll in this course to fulfill their general elective credits required for graduation. They see it as a user-friendly science, integrating information from the physical sciences and the life sciences. Moreover,

INTRODUCTIONThe Numbers GameClyde Freeman Herreid

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NATIONAL SCIENCE TEACHERS ASSOCIATION2

it is a gateway course for students on the way to health science careers. Teaching biology using real stories with quantitative reasoning skills enmeshed in the story line is a power-ful and logical way to teach the subject and to show its relevance to the lives of future citizens, regardless of whether they are science specialists or laypeople. Yet the fundamen-tal questions remain: What kind of education should a student have to deal with today’s world? How much of it should focus on quantitative skills, and what kind of quantitative skills should we be teaching? And how should we do it?

Why Numeracy MattersNumeracy means the ability to reason and to use numbers, and at its simplest level we are talking about arithmetic, adding, subtracting, multiplying, and dividing. This topic, why numeracy matters, was raised at the National Forum on Quantitative Literacy hosted by the National Academy of Sciences in Washington, D.C., on December 1–2, 2001. In the published proceedings (Madison and Steen 2003), Patricia Cohen gives us an historical perspective, reminding us that an informed and quantitatively literate society is essen-tial for democracy. She notes that Massachusetts statesman Josiah Quincy wrote in 1816 about the “growing” importance of what he called the “art” of “Political Arithmetick.” He “expounded on the connections … between statistical knowledge and ‘the duties of citizens and lawmakers in the fledgling American republic’” (Cohen 2003, p. 7). Cohen notes in her essay that “Arithmetick” connects to democratic government in three distinct ways:

First, the very political legitimacy of a representative democracy rests on repeated acts of counting: tallying people in periodic census enumerations to apportion the size and balance of legislative bodies, and tallying votes in varieties of elections to determine office-holding and public policies. Second, as Quincy suggested, a government whose goal is the general welfare of its citizens needs good aggregate information about those citizens on which to erect and assess public policy. It is no coincidence, then, that the word “statisticks” was coined in English in the 1790s…. And third, the citizens of democratic governments also need good information, to assess their leaders’ political decisions and judge them on election day. (2003, p. 7)

The educational demands for a U.S. citizen have grown enormously since 1816, and the issue of how mathematics impacts our democracy is even more important today.

In their essay “The Democratization of Mathematics,” Carnevale and Desrcochers (2003) write that our current system of teaching mathematics across the curriculum is flawed and threatens to undermine our democracy (p. 21). Mathematics acts as a filter-ing device, playing a significant role in who gets into the best colleges and the best professions even if higher level mathematics is not required for the day-to-day work in those fields:

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The sequence of abstract high school mathematics courses that prepares students for advanced degrees in mathematics and science is still crucial to our advanced economy, but moving the entire school-age population through the academic hierarchy from arithmetic to calculus as a sorting strategy for producing elite mathematical talent required of a small share of college majors and fewer than 5 percent of the workforce does not match well with our more general needs for applied reasoning abilities and practical numeracy …. It means making mathematics more accessible and responsive to the needs of all students, citizens, and workers. The essential challenge in democratizing mathematics applies to the sciences and humanities as well. The challenge is to match curricula to cultural, political, and economic goals rather than continuing the dominance of discrete disciplinary silos. (pp. 28–29)

The obvious follow-up question is tackled by Arnold Packer (2003) in his essay “What Mathematics Should ‘Everyone’ Know and Be Able to Do?” Packer asks rhetorically, what is wrong with the present system and then answers: “The way middle school teachers teach fractions provides a clue. They teach their students to add fractions by: First finding the lowest common denominator. Then converting all fractions to that denominator. Then add-ing the numerators. Finally, reducing the answer, if possible” (p. 34). As Packer goes on to point out, “Nobody does that outside the schoolroom. Imagine a school cafeteria in which the selected items totaled three quarters and three dollars and four dimes. The schoolroom method would be to change all these in for nickels.”

What Kinds of Quantitative Skills?Quantitative literacy and the need for a general education that includes some quantitative reasoning are important. It seems clear that we need to revise the way we teach these skills, placing greater emphasis on practical applications rather than abstract principles. This is the position of the National Council of Teachers of Mathematics (NCTM), which urges teachers to include real-world problem solving into their lesson plans and the need for students to be able to communicate in the language of mathematics (NCTM 2012). But if some quantitative skills are needed for the general public, they are even more important for students entering the fields of science and engineering.

In this section, we take a look at how the K–12 and college communities are grappling with these issues. A number of organizations and committees whose business it is to set standards have been active in this area. In reviewing the educational standards and goals for K–12 and college science education, we find many similarities. Our focus in this book is on the quantitative skills that people training to be biologists need to master. However, we cannot neglect secondary and postsecondary students, for they are all likely to stream

Introduction



through the general biology course, either because it is required for their major or because they have chosen the course as a general education requirement.

After we summarize the requirements, we turn to the question: What can we do in these introductory biology courses to enhance the quantitative skills of the students? We advocate the use of real-world problems, or cases, that teach quantitative skills to students in intro-ductory biology courses via active learning strategies. We have been pioneers in the use of case study teaching for 20 years. In that time, many studies have supported our contention that teaching in context improves learning. Consistent with this view, this book offers many different examples of cases that have been tested in the classroom by both high school and college instructors. Before turning to those examples, let us see what various communities of scholars say about the foundational knowledge and skills students should acquire at differ-ent levels of their education. But it will be evident that the definition of numeracy depends upon the educational level considered and the career aspirations of the individual.

K–12 StudentsThe Committee on Conceptual Framework for the New K–12 Science Education Standards under the direction of the National Academies has recently published A Framework for K–12

Science Education: Practices, Crosscutting Concepts, and Core Ideas (NRC 2012). They state: “We consider eight practices to be essential elements of the K–12 science and engineering curriculum:

1. Asking questions (for science) and defining problems (for engineering).

2. Developing and using models.

3. Planning and carrying out investigations.

4. Analyzing and interpreting data.

5. Using mathematics, information and computer technology, and computational thinking.

6. Constructing explanations (for science) and designing solutions (for engineering).

7. Engaging in argument from evidence.

8. Obtaining, evaluating, and communicating information.” (p. 49).

But, of course, the real issue is: How will the school systems implement these elements? For an answer, let’s refer to the details of the framework. Here is the overarching vision:

By the end of the 12th grade, students should have gained sufficient knowledge of the practices, crosscutting concepts, and core ideas of science and engineering to engage in public discussions on science-related issues, to be critical consumers of scientific information related to their everyday lives, and to continue to learn about

CHAPTER 1



science throughout their lives. They should come to appreciate that science and the current scientific understanding of the world are the result of many hundreds of years of creative human endeavor. It is especially important to note that the above goals are for all students, not just those who pursue careers in science, engineering, or technology or those who continue on to higher education. (p. 9)

For our purposes, let’s look closer at what they have to say about practices 4 and 5:

Practice 4: Analyzing and Interpreting Data (pp. 61–63)Once collected, data must be presented in a form that can reveal any patterns and relationships and that allows results to be communicated to others. Because raw data as such have little meaning, a major practice of scientists is to organize and interpret the data through tabulating, graphing, or statistical analysis. Such analysis can bring out the meaning of the data—and their relevance—so that they may be used as evidence…

GoalsBy grade 12, students should be able to:

• Analyze data systematically, either to look for salient patterns or to test whether the data are consistent with an initial hypothesis.

• Recognize when data are in conflict with expectations and consider what revisions in the initial model are needed.

• Use spreadsheets, databases, tables, charts, graphs, statistics, mathematics, and information technology to collate, summarize, and display data and to explore relationships between variables, especially those representing input and output.

• Evaluate the strength of a conclusion that can be inferred from any data set, using appropriate grade-level mathematical and statistical techniques.

• Recognize patterns in data that suggest relationships worth investigating further. Distinguish between causal and correlational relationships.

• Collect data from physical models and analyze the performance of a design under a range of conditions.

Practice 5: Using Mathematics and Computational Thinking (pp. 65–66)Mathematics (including statistics) and computational tools are essential for data analysis, especially for large data sets. The abilities to view data from different

Introduction



perspectives and with different graphical representations, to test relationships between variables, and to explore the interplay of diverse external conditions all require mathematical skills that are enhanced and extended with computational skills.

GoalsBy grade 12, students should be able to:

• Recognize dimensional quantities and use appropriate units in scientific applications of mathematical formulas and graphs.

• Express relationships and quantities in appropriate mathematical or algorithmic forms for scientific modeling and investigations.

• Recognize that computer simulations are built on mathematical models that incorporate underlying assumptions about the phenomena or systems being studied.

• Use simple test cases of mathematical expressions, computer programs, or simulations—that is, compare their outcomes with what is known about the real world—to see if they “make sense.”

• Use grade-level-appropriate understanding of mathematics and statistics in analyzing data.

The material above has been extracted from a large document and the original version should be consulted in order to get the full flavor of the discussion. Further, the Framework does not specify a list of standards; rather, it sets forth an overarching vision for achieving quantitative literacy that makes sense at this time in history. Nor does the document give suggestions about how we can reach the stated goals; no specific courses are prescribed. Individual school systems and statewide recommendations are left to their own devices.

Keeping that in mind, let us turn to the standards initiative adopted by 45 states (www.

corestandards.org). The Common Core State Standards for Mathematics (National Governors Association Center for Best Practices 2010) describes the concepts that students should know in mathematics at each grade level. Although they do not specify how these objec-tives should be reached, they do provide four model pathways that are used by various school systems. State school districts are now grappling with how they wish to implement the standards. New York and other states have adopted the traditional approach, which consists of two algebra courses and a geometry course, with some data, probability, and statistics included in each course (see www.p12.nysed.gov/ciai/common_core_standards/pdf-

docs/ccssi_mathematics_appendix_a.pdf). Nonetheless, these courses with their familiar titles do not have to be traditional at all, as we will soon discover.

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College Non-Science MajorsMany individuals and organizations have grappled with the question of what quantita-tive skills college students will need to achieve quantitative literacy (NRC 2003; AAAS 2009). An outstanding collection of papers have documented how various schools have attempted to overcome our mathematical illiteracy (CBE – Life Sciences Education 2010). In these 17 articles, 7 essays, and 7 features, we find multiple ways to infuse undergraduate biology with math and computational science. The Association of American Colleges and Universities (AACU) has even developed a rubric for quantitative literacy (www.aacu.org/

value/rubrics/pdf/QuantitativeLiteracy.pdf). One cannot help but be impressed by the innova-tive solutions that faculty have come up with, but these are the exceptions.

Too commonly we find that when a school decides that they must overhaul their math requirements, they turn the problem over to their academic departments to mandate which courses they will accept for graduation. Not surprisingly, they often choose a selection of traditional math courses already in existence. The General Education Committees follow suit: They select pre-existing courses from the institution catalogue. Seldom do you find a faculty or an administration willing to start from scratch and work out which quantitative skills they think students need and then refurbish the curriculum.

Carleton College is an interesting exception. They have institutionalized a different approach to quantitative reasoning (QR), which they define as “the habit of mind to con-sider the power and limitations of quantitative evidence in the evaluation, construction, and communication of arguments in public, professional, and personal life” (Carleton College 2011a). They have attacked the problem head-on with their Quantitative Inquiry, Reasoning, and Knowledge (QuIRK) Initiative. Basically, the program has encouraged a wholesale, schoolwide overhaul of many courses to include quantitative skills in the course work. The school has a website that lists “Ten Foundational Quantitative Reasoning Questions,” written by psychology professor Neil Lutsky, which Carleton College wants their students to be able to ask when confronted with data (Carleton College 2011b):

I. What do the numbers show?• What do the numbers mean?

• Where are the numbers?

› Is there numerical evidence to support a claim?

› What were the exact figures?

› How can seeking and analyzing numbers illuminate important phenomena?

• How plausible is a possibility in light of back-of-the-envelope calculations?

Introduction



II. How representative is that? • What’s the central tendency?

› “For instance” is not proof; it is an example.

› Mean, Mode, and Median.

• Interrogating averages:

› Are there extreme scores?

› Are there meaningful subgroups?

› What’s the variability (standard deviation)?

• What are the odds of that? What’s the base rate?

III. Compared to what?• What’s the implicit or explicit frame of reference?

• What’s the unit of measurement?

• Per what?

• What’s the order of magnitude?

• Interrogating a graph:

› What’s the Y-axis? Is it zero-based?

› Does it K.I.S.S., or is it filled with ChartJunk?

IV. Is the outcome statistically significant? • Is the outcome unlikely to have come about by chance?

› “Chance is lumpy.”

› Criterion of sufficient rarity due to chance: p < .05

• What does statistical significance mean, and what doesn’t it mean?

V. What’s the effect size?• How can we take the measure of how substantial an outcome is?

• How large is the mean difference? How large is the association?

• Standardized mean difference (d): d = (μ1-μ2)/σ

VI. Are the results those of a single study or from literature?• What’s the source of the numbers: PFA, peer-reviewed, or what?

• Who is sponsoring the research?

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• How can we take the measure of what the literature shows?

• The importance of meta-analysis in the contemporary world of QR.

VII. What is the research design (correlational or experimental)?

• Design matters: Experimental vs. correlational design.

• How well does the design support a causal claim?

• Experimental Design:

› Randomized Controlled Trials (RCT): Research trials in which participants are randomly assigned to the conditions of the study.

› Double blind trials: RCTs in which neither the researcher nor the patient know the treatment condition.

• Correlational Design: Measuring existing variation and evaluating co-occurrences, possibly controlling for other variables.

› Interrogating associations (correlations):

о Are there extreme pairs of scores (outliers)?

о Are there meaningful subgroups?

о Is the range of scores in a variable restricted?

о Is the relationship non-linear?

VIII. How was the variable operationalized? • What meaning and degree of precision does the measurement procedure justify?

• What elements and procedures result in the assignment of a score to a variable?

• What exactly was asked?

• What’s the scale of measurement?

• How might we know if the measurement procedure is a good one?

› Reliability = Repeated applications of the procedure result in consistent scores.

› Validity = Evidence supports the use to which the measure is being put.

• Is the measure being manipulated or “gamed”? The iatrogenic effects of measurement.

IX. Who’s in the measurement sample?• What domain is being evaluated? Who’s in? Who’s not?

Introduction



• Is the sample from that domain representative, meaningful, and/or sufficient?

• Is the sample random?

• Are two or more samples that are being compared equivalent?

X. Controlling for what?• What other variables might be influencing the findings?

• Were these assessed or otherwise controlled for in the research design?

• What don’t we know, and how can we acknowledge uncertainties?

This seems to me to be an eminently reasonable approach to the question of what we wish every citizen to know. Unfortunately, our typical college courses don’t come close to achieving these goals. Interestingly, nearly all of these topics are covered in a typical statistics course, yet most schools do not list statistics as a graduation requirement though many have it as an option. So if we really care about teaching our students to be “quantita-tive reasoners,” how do we accomplish that?

A vote for statistics in the required curriculum comes from Marie Davidian and Thomas Louis in their editorial, “Why Statistics,” which appeared in the April 6, 2012, issue of Science. They remind us that “Statistics is the science of learning from data, and of measur-ing, controlling, and communicating uncertainty; and it thereby provides the navigation essential for controlling the course of scientific and societal advances.” They point out that statistics informs policy development in governmental budgets as well as medical discoveries and science advancement in general. They applaud the new U.S. Common Core K–12 Mathematics Standards, which introduces statistics as a key part of pre-college education, encompassing skills in describing data, developing statistical models, making inferences, and evaluating the consequences of decisions.

Ecologists Carol Brewer and Louis Gross (2003) foreshadowed these arguments, say-ing science students and the public should have an education that allows them to deal with uncertainty and variability so that they are better able to grapple with topics such as climate change. Ecologists require a background in probability, including the concepts of random variables, stochastic processes, and Bayesian statistics. But the general public needs an education as well, one that will allow them to appreciate important assumptions and limitations that are part of model building and the reasoning involved in predictive forecasting. Brewer and Gross argue that “regular exposure to probabilistic ideas (e.g., weather forecasts, lotteries) does not provide much of a basis for public appreciation of uncertainty in ecological forecasts …. Beyond formal training in schools and universities, education of the general public can be aided by targeted articles in the press, especially when they can be related to local or regional projects (e.g., restoration projects, land-use reviews) for which ecological projections inform decision-making. This implies that scien-

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tists involved in the development of these projections have an obligation to disseminate information at a level that is clear to a general audience” (p. 1413).

But they offer no panacea for how to accomplish this; they simply urge faculty to develop new course materials and attend workshops where the emphasis is on better com-munication of probabilistic models for future students. One solution to the problem has been touted by a group of instructors who teach mathematics using case studies that they have developed using material from newspapers (Madison et al. 2009).

Pre-Professional Health StudentsAn inordinate number of students in most introductory biology classes start college dream-ing of a career in the medical, dental, pharmacy, nursing, physical therapy, or related health professions. In my general biology course, they make up about 80% of the population. Less than half of them survive the first two years’ requirements.

In the March 30, 2012, issue of Science, S. James Gates and Chad Mirkin, members of the President’s Council of Advisors on Science and Technology, noted that in the United States over 60% of the students who enter college intending to major in a STEM field fail to graduate with a STEM degree. (Read that again: Over 60% of the students who enter college

intending to major in a STEM field fail to graduate with a STEM degree!) They report that students leave STEM during the first two years for three major reasons: uninspiring intro-ductory courses, difficulty with the required math, and an academic culture in STEM fields that is unwelcoming. The problem is especially acute for women and minorities. (These comments resonate strongly with Sheila Tobias’s 1990 and 1992 books on the topic 20 years ago.) Gates and Mirkin recommend that the “federal government catalyze widespread adoption of active learning approaches using case studies, problem-based learning, peer instruction, and computer simulations.” They further emphasize hands-on research and laboratory experiences that begin early in the college career.

How do our current standard curricula stack up to the general criticisms made by the President’s Council of Advisors? First, there is the problem that 86% of the natural sci-ences faculty say that lecturing is their primary method of instruction (National Research Council 2003). Then there are the math requirements. Let’s take a look at what they are just for pre-professional health students. The curricula across the United States show little variation among schools and disciplines; they have not seriously changed over the last several decades.

From the American Association of Colleges of Pharmacy course requirements (AACP) for 2012–2013 (AACP 2012) we learn that 94% of the institutions require calculus, 60% require statistics, and 9% require computer science applications. Is this the curriculum that we think is ideal for training today’s pharmacists—or, for that matter, for physicians,

Introduction



dentists, or for any health professionals? They have similar requirements for mathematics. Virtually everyone thinks they have to have calculus to get into professional health schools.

Appendix A gives a list of “Expectations for Medical Students” developed by the AAMC-HHMI Scientific Foundations for Future Physicians. But when we look at the desired competencies, there seems to be little need for the standard calculus course. Instead, we see statistics and data interpretation are eminently valued. Yet, courses in these subjects are not apparently on the list of required courses developed by the Association of American Medical Colleges. That seems odd.

What is it about calculus that makes it so desirable, when virtually none of the health practitioners will ever integrate or differentiate anything in their life? As a counterpoint: I have been assured by a dean of pharmacy that students in their PharmD program take pharmacokinetics where the kinetics of drug action does often involve differential equa-tions. There are undoubtedly occasions in medical school where similar examples occur. But do students really need two semesters of calculus while statistics is left to languish?

Still, calculus may have unsuspected potential benefits. Philip Sadler and Robert Tai (2007) wondered if courses in high school affected students’ grades in introductory college courses. They studied 8,474 undergraduate students enrolled in one of the three introduc-tory science courses at 63 colleges and universities. Not surprisingly, students who had taken physics, chemistry, or biology in high school performed better in those respective subjects than those that did not. But they did not do better in other science subjects; that is, there was no cross-subject benefit for someone who took chemistry and thus improved their performance in say biology or physics. But here is the kicker: The students who had

taken high school calculus did better in the science subjects than those who did not. This was true even if calculus was not part of the course curriculum in many of these courses. Was there something about the students who took calculus that brought about higher performance, or was it the course material itself that promoted better performance, or were there other causes at work? Cause/effect questions notwithstanding, the evidence is clear. Whatever brought this effect about, whether it was that taking calculus actually improved science performance or whether the effect was caused by another variable, we should take this result seriously and examine what is driving this correlation.

Professional BiologistsIn a 2004 essay Joel E. Cohen emphasized the importance of mathematics to biology, mak-ing the point in his title: “Mathematics is Biology’s Next Microscope, Only Better; Biology is Mathematics’ Next Physics, Only Better.” His points were these: Just as the microscope opened up new vistas for biology, mathematics has the potential to do even more for biology. Cohen reminds us that Mendel’s discoveries of the general principles of genetics leaned heavily upon mathematics. William Harvey’s calculations of blood flow were cru-

CHAPTER 1



cial to his understanding of human circulation. And dozens of other biological principles are undergirded by quantitative reasoning, including the Hardy-Weinberg Equilibrium of evolution, forensic analysis of DNA and the probability of parentage or criminal activity. Conversely, biology will promote new mathematical discoveries, just as Isaac Newton and Gottfried Leibniz were stimulated by physical problems such as planetary orbits and optical calculations and developed calculus. Hastings et al. (2000) in their NSF report Quantitative Biology for the 21st Century develops this thread in more detail, as does the National Academies Press publication, BIO 2010: Transforming Undergraduate Education for

Future Research Biologists (NRC 2003).What kind of quantitative skills do we want our biology majors to have if they intend to

go to graduate school and become research scientists? The standard requirements at differ-ent schools include calculus, with statistics running a poor second. Unless a student has a strong penchant for mathematics or computer competency or is a quantitative masochist, that’s it for our requirements. Most students take no more than the minimum.

BIO 2010 (extracts are reprinted in Appendix B) makes these points: (1) “In contrast to biological research, undergraduate biology education has changed relatively little during the past two decades. The ways in which most future research biologists are educated are geared to the biology of the past, rather than to the biology of the present or future” (p. 1). (2) “Much of today’s biomedical research is at the interface between biology and the physical, mathematical, or information sciences. Most colleges and universities already require their biology majors to enroll in courses in mathematics and physical science. However, faculty often do not integrate these subjects into the biology courses they teach.” (3) “Most biology majors take no more than one year of calculus, although some also take an additional semester of statistics …. While calculus remains an important topic for future biologists, the committee does not believe biology students should study calculus to the exclusion of other types of mathematics. Newly designed courses in mathematics that cover some calculus as well as the other types of math mentioned above would be suitable for biology majors and would also prove useful to students enrolled in many other undergraduate majors.” (4) “One way to start is to add modules into existing biology courses. Throughout this report, modules are mentioned as a way to modify courses without completely revamping the syllabus.” As an example of this, see the lab MathBench modules that have been developed by the University of Maryland (Thompson et al. 2010). Also note the BioQUEST site with modules on bioinformatics, quantitative biology, molecular biology, and the math behind biology (http://bioquest.org). Changes like these will require a major commitment of faculty and administrators, with faculty development being a central part of such a revolution.

What do I make of this? Most of our students in introductory science courses are not going to be scientists—it is not even a close call. They are just fulfilling a general education requirement, unless it is a course that is catering to biology majors or pre-professional

Introduction



CHAPTER 1

health students. Since the overwhelming number of our students will never take another biology class, our goal in the introductory course should be to show students how science is really practiced and convey to them the excitement of the subject. Quantitative material should be introduced, but only in the context of the subject matter. We should leave the heavy-duty mathematics for the higher-level courses, but even then we should change what we are doing. Taking a page from BIO 2010, we should revamp or add modules to our courses by weaving quantitative skills into the material in the context of real-world problems. Moreover, since all schools insist that calculus is the sine qua non ingredient for any student who is considering anything remotely scientific, then surely the current course content should be changed. Too many calculus courses seem to emphasize memorizing a set of equations without any apparent connection to the real world; rather, they should be like the integrated two-semester course taught at the University of Tennessee that replaces the traditional calculus course (see Appendix B). This is “A new mathematics sequence that exposes students to statistics, probability, discrete math, linear algebra, calculus, and modeling without requiring that a full semester be spent on each topic.” Other examples are presented in the series of articles in the special quantitative issue of the journal CBE—

Life Sciences Education (2010). Especially note Marsteller et al.’s (2010) list of schools that are using biological examples to teach mathematical concepts and the inventory of colleges and universities with mathematical biology education modules.

Case Studies and Quantitative LiteracyNow we know several things. Experts believe that mathematics is important, not only for a well-rounded education but because they believe that quantitative literacy skills are essential to making intelligent decisions as citizens. Do students need more than the fundamentals taught in K–12? Some higher education institutions apparently do not think so, for they do not have any math requirements. Other schools, such as my own university, require only a semester of “mathematics” as part of their general education requirements. They include statistics as one of the alternatives, but there are more than 25 other possible options that fulfill the requirement! Obviously, the school does not have a clear vision of what constitutes quantitative literacy and has taken the easy way out of the potential controversy.

Nonetheless, there is a general consensus among the experts: If we are going to go with the standard courses that are on the books, then statistics is the one course that all students should have—scientists and laypersons alike. This point is made indirectly by the AAMC-HHMI report (Appendix A), which lists the course expectations for entering medi-cal students. The same point is made explicitly by the CRAFTY Curriculum Foundations Project (Johnson, Peterson, and Yoshiwara 2002) recommending the courses for students graduating from two-year colleges in technical training. However, if we are going to develop a novel approach, then a practical one-semester course can do the job, such as



Introduction

the one used by the University of Arkansas where quantitative skills are taught via case studies from media sources (Madison et al. 2009; Dingman and Madison 2010; Madison and Dingman 2010).

As for most calculus courses, they are not germane to practically anyone. It is typically a plug and chug experience; i.e., they show you an equation; you memorize it, do a few problems, and then go on to the next equation. There are few opportunities for students to really use the skills on real problems they might encounter in the real world—and of course, few ever will. The course needs a major overhaul, perhaps merging other practical mathematics into a new applied course, such as taught at the University of Tennessee (see Appendix B, p. 513).

So here is the bottom line of this book. All students need some mathematics. They receive the fundamentals in their K–12 education. Once they are in higher education, the kind and extent of their quantitative instruction depends upon their career plans. This training should either be statistics or, even better, a specially designed course that deals with mathematics in an applied manner. This would be the end of most students’ math education unless they are headed for specific fields like engineering or advanced fields in biology with a mathematical bent, such as ecology or computational biology. Here is the important point: It is especially important that all students, regardless of their major, leave school knowing what questions to ask when they see data rolled out, like the students graduating from Carleton College.

How can this best be achieved? One way to approach this is to use active learning such as case study teaching. And this can be done in all general science courses, introducing cases whenever possible that have quantitative problems embedded within them. Fortunately, several key resources exist, including websites where case studies can be downloaded along with teaching notes, such as the web-based case collection of the National Center for Case Study Teaching in Science (NCCSTS) at the University at Buffalo (sciencecases.lib.

buffalo.edu). Currently, the NCCSTS website has about 500 case studies across all science disciplines. Other case collections include the Problem-Based Learning Clearinghouse at the University of Delaware (https://pblc.nss.udel.edu/Pbl), Emory University’s CASES Online (www.cse.emory.edu/cases), and the interactive molecular biology laboratory simulations in the Case It! collection (www.caseitproject.org). Many of the cases on these sites have quanti-tative material, such as tables, graphs, numerical data, and equations that are essential to the case story.

This book includes cases selected from the National Center for Case Study Teaching in Science website (http://sciencecaase.lib.buffalo.edu) that develop students’ quantitative skills and apply them to solve real-world problems. In this book we present each case along with a list of learning objectives for it. Detailed teaching notes for the cases can be found on our website along with answer keys, which instructors can register to access. The book



is designed for college and high school AP biology teachers. We expect that teachers who plan to use the cases will download the case PDF from the website and distribute it to students in class rather than direct students to the website itself, where the teaching notes are displayed.*

The advantage of these cases is that they teach science in context, not as a set of abstract principles and a jumble of terms without rhyme or reason, but as part of a story so that stu-dents can see the relevance of the material. Moreover, in using cases to teach quantitative skills and show their applications to real-world situations, we are inculcating in our students the practice of questioning data, not just as a classroom exercise, but as a tool for engaging and understanding the world around them. K–12 teachers will find the cases in-line with the recommendations of the Next Generation of Science Standards (www.nextgenscience.org/

next-generation-science-standards), which lists major biological topics as necessary parts of any curriculum: structure and function, matter and energy in organisms and ecosystems, interdependent relations in ecosystems, inheritance and variation of traits, natural selection and evolution, and understanding the nature of science. The cases included in this book also address the overarching framework presented in Vision and Change in Undergraduate

Biology Education: A Call to Action (http://visionandchange.org/files/2011/03/Revised-Vision-

and-Change-Final-Report.pdf), a document prepared by the National Science Foundation and the American Association for the Advancement of Science, which calls for enhanced quantitative and computational expertise in core competencies of biologists, “the ability to use quantitative reasoning” and “the ability to use modeling and simulation,” to gain a deeper understanding of the dynamics and complexity of biological systems.

Our book is divided into sections. These mirror the topical sections one finds in many introductory biology textbooks. We have organized cases in the following areas: Scientific Method, Chemistry of Life, the Cell, Microbiology, Genetics, Molecular Biology, Evolution, Plant Form and Function, Animal Form and Function, Health, Ecology and Behavior, and Biosphere and Conservation. High school, community college, and undergraduate college teachers can use these cases to illustrate many of the basic principles of biology, but more importantly, how science is really conducted. The cases—most of which are based on real events and problems—should engage students and put quantitative skills to use, hope-fully to illustrate just how necessary these competencies are to understanding our world, which is awash with numbers and people who are willing to exploit them for their own particular agendas.

*Teaching notes and answer keys for the case studies can be found at www.nccsts.org/nsta_quant.html. When you click on the links on that page, you will be asked to log in. For the username, enter “NSTA.” For the password, use the second word in the first line of text that appears on page 191 of this book.

ReferencesAmerican Association for the Advancement of Science (AAAS). 2009. Vision and change in

undergraduate biology education: A call to action. Washington, DC: AAAS.

CHAPTER 1



American Association of Colleges of Pharmacy (AACP). 2012. Summary of pre-professional course requirements by pharmacy degree institution. www.aacp.org/career/grants/Documents/2012-13_PharmacyPre-RequisiteInformation.pdf

Brewer, C. A., and L. J. Gross. 2003. Training ecologists to think with uncertainty in mind. Ecology 84: 1412–1414.

Carleton College. 2011a. What is quantitative reasoning? serc.carleton.edu/quirk/forstudents/index.html

Carelton College. 2011b. 10 foundational quantitative reasoning questions. wiki.carleton.edu/display/itskb/Things+to+Think+About+When+Writing+About+Data

Carnevale, A. P., and D. M. Desrochers. 2003. The democratization of mathematics. In Quantitative literacy: Why numeracy matters for schools and colleges, ed. B. L. Madison and L. Arthur Steen, pp. 21–31. Princeton, NJ: National Council on Education and the Disciplines.

CBE—Life Sciences Education. 2010. Special issue in quantitative biology. 9 (3).

Cohen, J. E. 2004. Mathematics is biology’s next microscope, only better; Biology is mathematics’ next physics, only better. PLoS Biology 2 (12): e439. doi:10.1371/journal.pbio.0020439

Cohen, P. C. 2003. Democracy and the numerate citizen: quantitative literacy in historical perspective.” In Quantitative literacy: Why numeracy matters for schools and colleges, ed. B. L. Madison and L. Arthur Steen, pp. 7–20. Princeton, NJ: National Council on Education and the Disciplines.

Davidian, M., and T. A. Louis. 2012. Why statistics. Science 336 (6077): 12.

Dingman, S., and B. Madison, 2010. Quantitative reasoning in the contemporary world, 1: The course and its challenges. Numeracy 3 (2): 1–16.

Gates, S. J., Jr., and C. Mirkin. 2012. Engage to excel. Science 335 (6076): 1545.

Hastings, A., et al. 2000. Quantitative biology for the 21st century. www.maa.org/mtc/quant-bio-report.pdf

Johnson, E., J. C. Peterson, and K. Yoshiwara. 2002. Technical mathematics: Biotechnology and environmental technology. Mathematical Association of America: CRAFTY curriculum foundations project. Focus 22 (8): 157–180.

Madison, B. L., and L. A. Steen, eds. 2003. Quantitative literacy: Why numeracy matters for schools and colleges. Princeton, NJ: National Council on Education and the Disciplines.

Madison, B. L., S. Boersma, C. L. Diefenderfer, and S. W. Dingman. 2009. Case studies for quantitative reasoning: A casebook of media articles, 2nd ed. New York: Pearson.

Madison, B. L., and S. W. Dingman. 2010. Quantitative reasoning in the contemporary world, 2: Focus questions for the numeracy community. Numeracy 3 (2): 1–16.

Marsteller, P., L. de Pillis, A. Findley, K. Joplin, J. Pelesko, K. Nelson, K. Thompson, D. Usher, and J. Watkins. 2010. Toward integration: From quantitative biology to mathbio-biomath? CBE—Life Sciences Education 9: 165–171.

National Governors Association Center for Best Practices, Council of Chief State School Officers, 2010. Common core state standards for mathematics. Washington, DC: National Governors Association Center for Best Practices, Council of Chief State School Officers.

National Research Council. 2012. A framework for K–12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: National Academies Press.

National Research Council, Committee on Undergraduate Biology Education to Prepare Research Scientists for the 21st Century. 2003. BIO2010, transforming undergraduate education for future research biologists. Washington, DC: National Academies Press.

Introduction



Packer, A. 2003. What mathematics should “everyone” know and be able to do? In Quantitative literacy: Why numeracy matters for schools and colleges, ed. B. L. Madison and L. Arthur Steen, pp. 33–42. Princeton, NJ: National Council on Education and the Disciplines.

Sadler, P. M., and R. H. Tai. 2007. The two high-school pillars supporting college science. Science 317 (5837): 457–458.

Thompson, K., K. Nelson, G. Marbach-Ad, M. Keller, and W. Fagan. 2010. Online interactive teaching modules enhance quantitative proficiency of introductory biology students. CBE—Life Sciences Education 9: 277–283.

Tobias, S. 1990. They’re not dumb, they’re different: Stalking the second tier. Tucson, AZ: Research Corporation.

Tobias, S. 1992. Revitalizing undergraduate science: Why some things work and most don’t. Tucson, AZ: Research Corporation.

CHAPTER 1



CHAPTER 11

A CAN OF BULLDo Energy Drinks Really Provide a Source of Energy?Merle Heidemann and Gerald Urquhart

AbstractThis case teaches students about large biomolecules, nutrition, and product analysis. Students conduct a biochemical analysis of several popular energy drinks on the market and determine whether these products nutritionally match their marketing claims.

Learning Objectives• Describe and categorize chemically

the components of various popular “energy drinks.”

• Determine the physiological role of these components in the human body.

• Explain scientifically how the marketing claims for these drinks are supported (or not).

• Determine under what conditions each of the “energy drinks” might be useful to the consumer.

• Write an analysis of energy drinks for a popular magazine.

Quantitative Reasoning Skills/Concepts• Articulate complete and correct claims based on data.

• Use appropriate reasoning to support the validity of data-based claims.

The Case Study

Case ScenarioAfter spending several years working the Sport’s Desk of the Lansing State Journal, Rhonda had landed the job of her dreams as a writer for Runners’ World magazine. The job was fantas-tic! Since high school, where she had excelled in cross country, Rhonda had been a consistent runner, participating in local races and those assigned to her for her job. For her last assign-ment, she had run and reported on the Leadwood, South Dakota, marathon—it was a blast!



CHAPTER 11

As if reading her mind, her boss Charley walked in just then with a can of XS Citrus Blast® in one hand and a list of several other energy drinks in the other.

“We’ve been getting a lot of inquiries about the different energy drinks on the market, including XS Citrus Blast. Do you know anything about them?” Charley asked.

“I know that people use them for various reasons,” replied Rhonda. “It seems they’re primarily used by athletes to provide some ‘fuel’ as they practice and compete. Other people use them more casually as a way to become ‘energized.’ That’s about all I know.”

“That seems to be about all any of us knows,” Charley said. “For your next assignment,” Charley continued, “I want you to find out what each of the ingredients in these drinks is and what it does for a runner or for a non-athlete. You need to be very accurate in your analysis—determine what each component really does for the body, not what the market-ers want you to believe it does. Then look at the marketing claims of some of these drinks and see if the scientific facts match up to them. Many of our readers are using these drinks with some general notion that they’re helpful, but they’re basing their use of them on no scientific information. I’ve got the marketing claims, a list of ingredients and nutrition facts provided on the cans for consumers, and a short list of questions that should get you started. When you research these, be sure to document all your sources of information, keeping in mind that all resources are not equal. Here’s the information.”

With that, Charley left the office. Rhonda looked over the list. “Guess I’ll have to brush up on my biochemistry. No problem. I’m interested in knowing if my running would be improved by drinking this stuff.” Rhonda recalled that a food’s calorie content was the simplest reflection of its energy content. Looking at Charley’s list she saw that the different energy drinks contained the numbers of calories in Table 11.1.

TABLE 11.1.

Calorie Content for a Sample of Energy Drinks.

Energy Drink Calories

XS Citrus Blast® 8

Red Bull® 110

Sobe Adrenaline Rush® 140

Impulse® 110

For comparison: Coca Cola® (12 oz.) 140



A Can of Bull

Marketing ClaimsNext, Rhonda perused the marketing claims for each drink, shown in Table 11.2.

TABLE 11.2.

Marketing Claims for a Sample of Energy Drinks.

Energy Drink Marketing Claims

Red Bull • The Red Bull energy drink is a functional product developed especially for periods of increased mental and physical exertion.

• It can be drunk in virtually any situation: at sport, work, study, driving and socializing.

• Improves performance, especially during times of increased stress or strain.

• Improves concentration and reaction speed.

• Stimulates the metabolism.

XS Citrus Blast • There is less than 1/2 calorie of sugar in XS Citrus Blast. This qualifies for the government-approved statement “No Sugar.” The 8 calories in XS Citrus Blast are from amino acids and are protein calories that aid your body’s natural metabolic process.

• Most 8-ounce energy drinks in the market today have over 100 calories and from 27 to 30 grams of sugar, which is a simple carbohydrate. Most 12-ounce non-diet soft drinks have 170 calories from 40 grams of sugar. Most 5.5-ounce juice drinks have 80 calories from 20 grams of sugar.

• Calories from sugar and carbohydrates may increase fat deposits. Simple carbohydrates are also called high glycemic (high sugar) foods. High glycemic foods cause your body to pump insulin to digest the sugar, which sends a message to your body to store calories as fat. Low glycemic foods do not pump insulin to the same degree and aid in your body’s natural metabolism of fat, using your body’s fat resources as fuel. Many experts fear that the epidemic incidence of diabetes in North America today may be significantly contributed to by high-glycemic diets. The 8 calories in XS Citrus Blast are from amino acids and are protein calories that aid your body’s natural metabolic process.

• XS Citrus Blast uses a proprietary blend of Sucralose, Acesulfame Potassium (Ace K), and fruit essences to give the drinks their great flavor without sugar or empty calories. In fact, the 8 calories in the drink come from the 2 grams of amino acids, which are protein calories.



CHAPTER 11

Energy Drink Marketing Claims

Sobe Adrenaline Rush

• This maximum energy supplement delivers an energy boost with a natural passion fruit flavor. It’s lightly carbonated with a clean smooth feel.

• This maximum energy supplement is fortified with a unique blend of natural energizing elements, including d-ribose, l-carnitine and taurine. It’s pure, concentrated energy in an 8.3-fluid-ounce can.

Impulse • Elevate Your Performance

• Impulse Energy Drink contains special supplements to immediately enhance mental and physical efficiency and give you the energy boost you deserve… replenishing your strength.

• Impulse gets its energy from a simple source: nutrients, minerals, and vitamins that occur naturally in the body and foods we eat. Enjoy: the wake-up power of caffeine, the alertness-inducing properties of taurine, the lift you get from vitamins B6 and B12. Combined with Impulse’s other ingredients, these are known to increase mental focus and physical well-being, enhance performance, and accelerate metabolism.

Charley’s List of QuestionsRhonda realized that before she could start analyzing the energy drinks, she needed to know the answer to the following question: “When we say that something gives us ‘energy,’ what does that mean? What is a biological definition of energy?”

After satisfying herself that she had a good definition, she turned to the first set of questions on Charley’s list:

1. What is the nature (sugar, amino acid, vitamin, etc.) of each ingredient listed on the cans?

2. What is the physiological role of each in the human body?

3. Which ingredients provide energy?

4. Which ingredients contribute to body repair, i.e., which help build or rebuild muscle tissue?

Ingredients and Nutrition FactsRhonda was determined to wade through the confusing labeling of the drinks. For example, XS Citrus Blast boasted that it had no calories but still provided “energy.” That made absolutely no sense based on what Rhonda knew about biological energy! The first thing she needed to do was sort out the various ingredients on the labels—a task that consumers rarely undertake. Her findings are summarized in Table 11.3. As in most labels, ingredients are listed in order of mass in drinks, from highest to lowest.

Table 11.2 (continued)



A Can of Bull

TABLE 11.3.

Energy Drink Ingredients and Nutrition Facts

Energy Drink Ingredients and Nutrition Facts

XS Citrus Blast • Ingredients: carbonated water, l-taurine, l-glutamine, citric acid, adaptogen blend (eleutherococcus senticosus, panax ginseng, panax quinquefolium, echinacea purpurea, schisandra, astragalus, and reishi), natural flavors, acesulfame potassium, caffeine, sodium benzoate, potassium sorbate, sucralose, niacin, pantothenic acid, pyridoxine HCL, yellow 5, cyanocobalamin

• Nutrition Facts: serving size: 8.4 fl oz; servings per container: 1; calories: 8; fat: 0 g; sodium: 24 mg; potassium: 25 mg; total carbs: 0 g; sugars: 0 g; protein: 2 g; vitamin B3: 100%; vitamin B6: 300%; vitamin B5: 100%; vitamin B12: 4900%

Red Bull • Ingredients: carbonated water, sucrose, glucose, sodium citrate, taurine, glucuronolactone, caffeine, inositol, niacin, D-pantothenol, pyridoxine HCL, vitamin B12, artificial flavors, colors

• Nutrition Facts: serving size: 8.3 fl oz; servings per container: 1; amount per serving: calories: 110; total fat: 0 g; sodium: 200 mg; protein: 0 g; total carbohydrates: 28 g; sugars: 27 g

Sobe Adrenaline Rush • Ingredients: filtered water, high fructose corn syrup, citric acid, taurine, d-ribose, l-carnitine, natural flavor, inositol, sodium citrate, ascorbic acid, caffeine, monopotassium phosphate, salt, gum arabic, ester gum, siberian ginseng root extract, pyridoxine hydrochloride, guarana seed extract, caramel color, beta-carotene, folic acid, cyanocobalamin

• Nutrition Facts: serving size: 8.3 fl oz; servings per container: 1; amount per serving: calories: 140; total fat: 0 g; sodium: 60 mg; protein: 1 g; total carbohydrates: 36 g; sugars: 34 g; taurine: 1000 mg; d-ribose: 500 mg; l-carnitine: 250 mg; inositol: 100 mg; siberian ginseng: 50 mg; guarana: 50 mg

Impulse • Ingredients: carbonated water, sucrose, taurine, glucuronolactone, caffeine, inositol, niacinimide, pyridoxine HCL, vitamin C (citric acid), vitamin B12, artificial flavors, colors

• Nutrition Facts: serving size: 8.3 fl oz; servings per container: 1; calories: 110; fat: 0 g; sodium: 200 mg; total carbs: 28 g; sugars: 27 g; protein: 1 g; niacin: 100%; vitamin B6: 250%; vitamin B12: 80%; pantothenic acid: 50%: vitamin C: 100%

Coca Cola (for later comparison)

• Ingredients: carbonated water, high fructose corn syrup and/or sucrose, phosphoric acid, natural flavors, caffeine

• Nutrition Facts: serving size: 12 fl oz; servings per container: 1; calories: 140; fat: 0 g; total carbs: 38 g; sugars: 38 g; protein: 0 g



CHAPTER 11

Your TaskResearch each ingredient found in these energy drinks. This information can be found in biochemistry and nutrition textbooks. Web sources may provide valuable information, but be critical in their use. Many will make unsubstantiated claims. One that can get you started for basic information is www.chemindustry.com. Basic information can also be garnered from www.usda.gov/wps/portal/usda/usdahome?navid=FOOD_NUTRITION&navtype=SU.Determine the chemical structure, the type of chemical each is, and the physiological role played by each compound. You should have sufficient information to answer Charley’s list of questions as well as the additional questions listed below.

Post-Research AnalysisUsing the information that your group gathered, fill out Table 11.4, placing each of the ingredients for your drink under the proper heading, and answer the questions that follow. Cite any websites that you used in your analysis.

TABLE 11.4.

Results of Your Research

Sources of Energy Amino Acids Stimulants and VitaminsOther

(please categorize)



A Can of Bull

Questions

1. When we say that something gives us “energy,” what does that mean? What is a biological definition of energy?

2. What is the physiological role of each of the molecules in your table?

a. Which ingredients provide energy? How do they do that?

b. Which ingredients contribute to body repair, i.e., which help build or rebuild muscle tissue?

3. In what ways might the one(s) that does (do) not have a metabolic energy source (caffeine) provide the perception of increased energy after consumption?

4. How are the ingredients in these drinks helpful to someone expending a lot of energy, e.g., a runner?

5. Does your analysis substantiate the claim that this is an “energy drink”? If so, what molecules are the sources of energy?

6. Could your drink serve different purposes for different consumers? Explain.

7. What is the normal physiological response to increased intake of sugars? to increased intake of caffeine?

8. Is there such a thing as a “sugar high”? Explain your answer.

9. Evaluate, in terms of basic physiology and biochemistry, the statement: A lack of sleep causes a lack of energy.

10. Are the product claims legitimate? Why?

11. Should you simply buy a can of Coke rather than one of these energy drinks? Why/why not?

AssessmentIndividually, or as a group, write an evaluation of the marketing claims for your drink. You may write the evaluation in the form of an article for readers of Runner’s World. Be sure to include answers to the questions above.

Web VersionDetailed teaching notes (including a table with biochemical information for ingredients commonly found in energy drinks), the case PDF, and an answer key are available on the NCCSTS website at sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=203&id=203.



INDEX

Page numbers printed in boldface type refer to figures or tables.

AA Can of Bull, 117–123

abstract of, 117case study and questions for, 117–123,

118–121assessment, 123calorie content of energy drinks, 118case scenario, 117–118ingredients and nutrition facts, 120, 121marketing claims, 119–120post-research analysis, 122, 122

learning objectives for, 117quantitative reasoning skills/concepts for, 117web version of, 123

A Framework for K–12 Science Education: Practices, Crosscutting Concepts, and Core Ideas, 4–6

A Light Lunch?, 395–400abstract of, 395case study and questions for, 395–400, 399learning objectives for, 395quantitative reasoning skills/concepts for,

395web version of, 400

A Sickeningly Sweet Baby Boy, 223–228abstract of, 223case study and questions for, 224–227

failure to thrive, 224pedigree analysis, 224–225, 225, 226treatment options, 227

learning objectives for, 223quantitative reasoning skills/concepts for,


Abbott, Allison, 149Acoustic neuroma and cell phone use, 67–69Active learning strategies, 4, 11, 15, 20, 23,

28, 29continuum of, 43, 43–44

Acute lymphocytic leukemia (ALL), 401–409Adaptive immune system, 175, 175–176Alarm calling in Belding’s ground squirrels,

263–268Aleutian Island sea otter population, 457–467

Algebra, 6, 14, 19, 44, 212, 513–514, 517, 518, 523, 524, 525, 532

American Association for the Advancement of Science (AAAS), 16, 44

American Association of Colleges of Pharmacy (AACP), 11

An Infectious Cure, 187–194abstract of, 187case study and questions for, 188–194

big fleas have lesser fleas on their backs to bite them, 189, 189–192, 190

suspicious treatment for cholera, 188–189use of antibiotics, 192–193, 193use of phage therapy, 193–194



Animal form and functionGirl Pulled Alive From Ruins, 15 Days After

Earthquake, 347–351Hot and Bothered, 353–359The Hunger Pains, 371–376Keeping Up With the Joneses, 361–369The 2000-Meter Row, 343–346

Annan, Kofi A., 488Anorexia nervosa, 361–369Antarctica, fecal coliforms in, 155–161Antibiotics

An Infectious Cure, 187–194Rabbit Island and the Search for

Tuberculosis Treatment, 95–104Super Bug, 285–296

Apple and hawthorn maggot flies, 269–273Arithmetic, 2, 3Armstrong, Norris, 54Arthritis, lignum vitae for, 309–313As the Worm Turns, 269–273

abstract of, 269case study of, 269–273

evolutionary outcomes in apple maggot flies, 273

facts about hawthorn and apple maggot flies, 270, 270



Index

facts about hawthorn and apple plants, 271, 271, 272

fruit characteristics, 272–273introduction, 269–270



Assessment(s), 47–48authentic, 47–48international, 33just-in-time quizzes, 47know/need-to-know charts, 47, 527learning issue reports, 47scoring rubrics for, 48of student misconceptions, 44

Association of American Colleges and Universities (AACU), 7

Association of American Medical Colleges (AAMC), 12

AAMC-HHMI Scientific Foundations for Future Physicians, 12, 14, 505–511

AthletesA Can of Bull, 117–123Keeping Up With the Joneses, 361–369The 2000-Meter Row, 343–346

Atrazine in water samples, 471–476Autism, 379–387Autoimmune disease, 353–359Autosomal dominant traits, 210Autosomal recessive traits, 211

A Sickeningly Sweet Baby Boy, 223–228Avery, Oswald, 251, 254, 254–255, 255

BB cells, 175, 175–176Bacteria

An Infectious Cure, 187–194Fecal Coliforms in Antarctica, 155–161Living Downstream, 471–476Nanobacteria, 137–151Rabbit Island and the Search for

Tuberculosis Treatment, 95–104Super Bug, 285–296

Bacteriophage therapy, 187–194BEDROCK Project, 531Beehive loss, high-fructose corn syrup and,

71–85Belding’s ground squirrels, alarm calling in,

263–268Bennett, John S., 53BIO 2010: Transforming Undergraduate

Education for Future Research Biologists, 13, 14

Biodiversity threats, 427–434Bioinformatics and meat science, 245–250Biology Education to Prepare Research

Scientists for the 21st Century, 513–526

Biomineralization, 138, 142BioQUEST simulation modules, 13, 47,

528–531problem spaces, 60, 61

Biosphere and conservationDo Corridors Have Value in Conservation?,

477–483The Effects of Coyote Removal in Texas,

493–497Living Downstream, 471–476The Wealth of Water, 485–492

BirdsExaggerated Traits and Breeding Success

in Widowbirds, 297–305Threats to Biodiversity, 427–434

Blackboard course management system, 29Blended courses, 29Blind Spot case, 59Bloom’s taxonomy, 45, 46, 48, 50, 50, 54The “Blue People” of Kentucky, 197–200

abstract of, 197case study and questions for, 197–200

“blue people,” 197–198a different shade of blue, 199, 199–200,

200pieces of the family puzzle, 198–199



Box plots, 33–34, 36BozemanScience, 29Breast Cancer Risk, 389–393

abstract of, 389case study and questions for, 390, 391–393learning objectives for, 389quantitative reasoning skills/concepts for,

389–390web version of, 393

Brewer, Carol, 10–11Bringing Nature Home: A Case for Native

Gardening, 38Byrd, Jeffrey J., 53



Index

CCalculus, 3, 15, 19, 44, 513–514, 517

in integrated course at University of Tennessee, 14, 15, 523, 524–526

for pre-professional health students, 11–12for professional biologists, 13–14

Calorie counting, 395–400Cameron, Bruce, 19Campbell, Malcolm, 35Camtasia software, 29Cancer

Breast Cancer Risk, 389–393Cell Phone Use and Cancer, 67–69Pharmacogenetics, 401–409

Capstone cases, 23Carbohydrates, 107–110Carbon cycle, 111–115Carnevale, A. P., 2–3Case Difficulty Cube, 21, 21–22Case It! software, 15, 47, 61–62

bioinformatics extensions, 62influenza cases, 53–54

The Case of Desiree’s Baby, 215–222abstract of, 215case study and questions for, 215–222

“Desiree’s Baby” by Kate Chopin, 215–219

evolution of skin color, 222Mendelian approach, 219–220skin color is polygenetic trait, 220–222,

221learning objectives for, 215quantitative reasoning skills/concepts for,


The Case of the Druid Dracula, 239–244abstract of, 239case study and questions for, 240–244

DNA structure and PCR, 240–242more analysis, 242–244, 243, 244report, 242, 242



Case studies, 19–30advantages of using, 16alignment with Next Generation Science

Standards, 16and the cone of learning, 26–28, 27, 28difficulty of, 21, 21–22, 41–42effectiveness of case-based teaching, 19–20

flipped classroom and, 28–30giving credit to authors of, 62–63purpose of, 22–23quantifying, 41–63 (See also Quantifying

cases)quantitative literacy and, 14–16resources for, 15–16, 20, 45–47Science Case Network for use of, 62to teach numeracy, 4, 11, 19teaching method for, 23–26

clicker method, 23–24discussion method, 24individual case method, 25–26information flow related to, 25lecture method, 23small-group methods, 24–25

types of, 20–26capstone cases, 23trigger cases, 22

CASES Online, 15, 47, 53CBE—Life Sciences Education, 14CCR5 gene, 182, 183, 184CD4+ cells, 61, 175, 176, 178, 182

mutated gene for, 183, 183CD8+ cells, 175, 175–176, 178Cell

The Mystery of the Seven Deaths, 127–130Nanobacteria, 137–151Wrestling With Weight Loss, 131–136

Cell Phone Use and Cancer, 67–69abstract of, 67case study and questions for, 67–69

hang up, 67–68journal groups, 68–69scientific article, 69


Cellular immune system, 175, 175–176Cellular respiration, 127–130Centers for Disease Control and Prevention

(CDC), 49, 49, 50, 51, 532CFTR gene, 233–236Chemistry of life

A Can of Bull, 117–123Dust to Dust, 111–115Sweet Indigestion, 107–110

Childhood obesity, 48–51, 49Cholera, bacteriophage therapy for, 188–194Chopin, Kate, 215Ciftcioglu, Neva, 139, 140, 141, 142, 143–144,

146, 147, 149



Index

Cisar, J. O., 143–144, 148–149, 150Class characteristics, 43–44Classic Experiments in Molecular Biology,

251–259abstract of, 251case study and questions on DNA

replication, 256–259nitrogen, 257, 257results, 258, 258second generation, 259, 259take-home assignment, 259three possible methods, 256, 256

case study and questions on transforming principle, 252–255

enzymes as tools, 254, 254molecule of inheritance, 252, 253more results, 255, 255results, 253, 253–254take-home assignment, 255



Classroom discussions, 24, 25, 28, 29Clicker case method, 23–24, 25, 28Cochabamba, Bolivia, water revolt, 485–492Cohen, Joel E., 12Cohen, Patricia, 2Coliform bacteria

Fecal Coliforms in Antarctica, 155–161Living Downstream, 471–476

Colony collapse disorder (CCD), 72, 83, 84Common Core State Standards for

Mathematics, 6, 10Conant, James, 23Cone of Experience, 26–27, 27Cone of Learning, 26–28, 27, 28Conservation

Do Corridors Have Value in Conservation?, 477–483

The Effects of Coyote Removal in Texas, 493–497

Living Downstream, 471–476Mathematics in Conservation, 443–456The Wealth of Water, 485–492

Cooperative learning, 29Core concepts, 42Coyote removal in Texas, 493–497CRAFTY Curriculum Foundations Project, 14Critical thinking, 21, 38, 48Curriculum outcomes, 45CXCR4 gene, 182, 184, 184

CyanideI’m Looking Over a White-Striped Clover,

315–323The Mystery of the Seven Deaths, 127–130

Cystic fibrosis (CF)“Not Exactly,” 231–237In Sickness and In Health, 209–213

Cystic Fibrosis Mutation Database, 235Cytotoxic T cells (CD8+ cells), 175, 175–176,

178

DDale, Edgar, 26–27Darwin, Charles, 23Data analysis, 1, 4, 5

data sets for, 38–39, 532–533of “Inactive Brains” case, 48–51resources for, 34–39, 47

Davidian, Marie, 10The Dead Zone, 413–426


conclusion, 425–426how do Gulf waters change with the

seasons?, 416–418, 417, 418how do organisms affect dissolved

oxygen concentration?, 418–420, 419, 420

the problem, 414–415what affects the dissolved oxygen

content of water?, 415, 415–416, 416

where does nitrogen come from?, 423, 423–424, 425

why does phytoplankton population increase?, 420–421, 421

why is the Dead Zone a seasonal phenomenon?, 422


Dehydration and starvation, physiological responses to, 347–351

Desrochers, D. M., 2–3Dialogue papers, 25–26Diet/nutrition

A Can of Bull, 117–123The Hunger Pains, 371–376Keeping Up With the Joneses, 361–369A Light Lunch?, 395–400PKU Carriers, 279–284Sweet Indigestion, 107–110



Index

Wrestling With Weight Loss, 131–136Differential equations, 12, 517, 523, 526, 532Differentiated instruction, 45Dill, Fabian, 342,4-Dinitrophenol (DNP) for weight loss,

131–136Discussion method, 24, 25, 28, 29Dissolved oxygen content of Gulf of Mexico

waters, 413–426DNA as genetic material, 252–255DNA forensic analysis, 13

The Case of the Druid Dracula, 239–244DNA microarrays, 35–36, 36DNA replication, 256–259Do Corridors Have Value in Conservation?,

477–483abstract of, 477case study and questions for, 477–482

experimenting with corridors, 479–480, 480

introduction and review of relevant biology, 477–478

methods and results of study, 480–482, 482

nature preserves and corridors, 478–479learning objectives for, 477quantitative reasoning skills/concepts for,


Donovan, Sam, 59Drug-resistant infections

Rabbit Island and the Search for Tuberculosis Treatment, 95–104

Super Bug, 285–296Dust to Dust, 111–115

abstract of, 111case study and questions for, 111–115learning objectives for, 111quantitative reasoning skills/concepts for,


EEcology and behavior

The Dead Zone, 413–426Mathematics in Conservation, 443–456Search for the Missing Sea Otters, 457–467Threats to Biodiversity, 427–434The Wolf, the Moose, and the Fir Tree,

437–442The Ecology of Opuntia fragilis (Nuttall)

Haworth, 325–332


your first-year plans, 328–329your first-year results, 329–331, 330your new career, 325–328, 327your second year, 331–332



Eddinger, T., 245Educreations app, 29The Effects of Coyote Removal in Texas,


experimental design, 494introduction, 493–494mesopredators, 494–496, 496rodent diversity, 496–497, 497rodent population size, 496, 496



Elliott, Samantha L., 53Elvis Meltdown, 163–172


all shook up, 170, 170–171a little less conversation, 172return to sender, 164–168, 167suspicious minds, 168–170, 169



Endangered Florida panther, 443–456Endocrine disease, 353–359Energy drinks, 117–123Environmental stresses on agricultural plants,

333–340Epidemiology, 69Escherichia coli

Classic Experiments in Molecular Biology, 251

Elvis Meltdown, 170Fecal Coliforms in Antarctica, 157–158, 158Super Bug, 288–291

Estes, James, 458, 461–462, 464EteRNA computer game, 532Eukaryotic cells, 138



Index

EvolutionExaggerated Traits and Breeding Success

in Widowbirds, 297–305My Brother’s Keeper, 263–268PKU Carriers, 279–284Super Bug, 285–296Trouble in Paradise, 275–278As the Worm Turns, 269–273

Exaggerated Traits and Breeding Success in Widowbirds, 297–305


handicap hypothesis, 299–303, 301–303introduction, 298–299longer tails in a short-tailed species,

303–305, 304sexual selection, 298–299study species, 298truth in advertising, 299



Excel Simulations and Tools for Exploratory Experiential Mathematics (ESTEEM), 531–532

Explain Everything app, 29Exploratory Data Analysis, 34Exploratory data analysis (EDA) approach,

34–35, 36, 38Exploring Data, 38Extraterrestrial microbes, 163–172

FFactor VIII deficiency, 208–212Fecal Coliforms in Antarctica, 155–161


counting environmental bacteria, 157–159, 158

data prediction, 159, 159–160decision time, 160, 160–161McMurdo Station, 155–156, 157


FedStats website, 532–533Feedback mechanisms, 26Feeling Detoxified, 87–94


detoxifying ionic foot bath experience, 88

effect of expectations, 93–94feeling better after treatment, 91–93, 92proposed chemistry of foot baths, 89,

89–91, 91learning objectives for, 87quantitative reasoning skills/concepts for, 87web version of, 94

Female athlete’s triad, 361–369Finnegan, William, 487–488Flipped classroom, 28–30Florida panther conservation, 443–456Foldit computer game, 532Food label analysis, 109, 109–110Foot baths, ionic, 87–94Fox, Arthur, 202–203Fung, Kaiser, 34

GGailke, Mabel, 311–312Gapminder, 37, 38

to quantify HIV/AIDS cases, 55–58, 56–57Gates, S. James, 11GenBank, 248, 521, 522, 523Gene Expression Omnibus, 51Genetics

To the Bitter End, 201–206The “Blue People” of Kentucky, 197–200Breast Cancer Risk, 389–393The Case of Desiree’s Baby, 215–222Classic Experiments in Molecular Biology,

251–259Mathematics in Conservation, 443–456“Not Exactly,” 231–237Pharmacogenetics, 401–409PKU Carriers, 279–284A Sickeningly Sweet Baby Boy, 223–228In Sickness and In Health, 207–214

Genome Consortium for Active Teaching (GCAT), 35

Geometry, 6, 33, 520, 524Germ theory of infection, 95, 97, 99Girl Pulled Alive From Ruins, 15 Days After

Earthquake, 347–351abstract of, 347case study and questions for, 348–351

an answer?, 351calculating Darlene’s water balance,

348–350, 349, 350facts of the case, 348finding other explanations for Darlene’s

survival, 350–351learning objectives for, 347



Index

quantitative reasoning skills/concepts for, 347

web version of, 351Gottardos, Raphael, 36Graphs, 1, 33, 41Graves disease, 353–359Greenpeace, 156Grhelin, 371–376Griffith, Fred, 251, 252, 253Gross, Louis, 10–11Guaiacum sanctum for arthritis, 309–313Gulf of Mexico ecology and oceanography,

413–426

HHaddad, Nick, 479–482Haitian earthquake survival, 347–351Hardy-Weinberg equilibrium, 13, 207, 279,

282, 283, 443Harvey, William, 12Hastings, A., 13Hawaiian bird populations and introduced

species, 427–434Hawthorn and apple maggot flies, 269–273Head Start, 38Health

An Infectious Cure, 187–194Breast Cancer Risk, 389–393Cell Phone Use and Cancer, 67–69Fecal Coliforms in Antarctica, 155–161Hot and Bothered, 353–359Is Guaiacum sanctum Effective Against

Arthritis?, 309–313Keeping Up With the Joneses, 361–369A Light Lunch?, 395–400Michael’s Story, 379–387The Mystery of the Seven Deaths, 127–130Nanobacteria, 137–151“Not Exactly,” 231–237Pharmacogenetics, 401–409PKU Carriers, 279–284Rabbit Island and the Search for

Tuberculosis Treatment, 95–104Resistance Is Futile...Or Is It?, 173–186A Sickeningly Sweet Baby Boy, 223–228Super Bug, 285–296

Helper T cells (CD4+ cells), 61, 175, 176, 178, 182

mutated gene for, 183, 183Hemophilia A, 208–212Herreid, Clyde, 41Heyer, Laurie, 35

HIV infectiondata sets for, 58, 62problem space for, 60–61Resistance Is Futile...Or Is It?, 173–186using Gapminder to quantify cases on,

55–58, 56–57Homeostasis

Girl Pulled Alive From Ruins, 15 Days After Earthquake, 347–351

Hot and Bothered, 353–359The 2000-Meter Row, 343–346

HormonesBreast Cancer Risk, 389–393Hot and Bothered, 353–359The Hunger Pains, 371–376

Hot and Bothered, 353–359abstract of, 353case study and questions for, 354–359

additional testing, 357–358final outcome, 359follow-up visit, 356, 356treatment options, 358, 358–359well-baby exam, 354–356



Human skin color genetics and evolution, 215–222

Humoral immune system, 175, 175The Hunger Pains, 371–376


dessert for breakfast, 374–375easier weight loss?, 375–376sleep is important, 373–374you look fantastic!, 372–373



Hybrid courses, 29

II’m Looking Over a White-Striped Clover,


checking your understanding, 323“I’m looking over...”, 316–318, 317investigating clover distribution, 321–

322.322



Index

unlucky clover, 318–321, 319, 320what did you learn?, 322–323



Immune system and HIV infection, 173–186In Sickness and In Health, 207–214


autosomal dominant traits, 210autosomal recessive traits, 211pedigree construction, 208–210population genetics, 212–213sex-linked inheritance, 211–212unsettled issues, 213–214

learning objectives for, 207web version of, 214

“Inactive Brains” case, 48–51data sets for, 49, 49–51learning objectives for, 48–49potential quantitative skills-rich activities

for, 50Inbreeding of Florida panther, 451–456, 454,

455Individual case method, 25–26, 28Influenza cases, 52–54

learning objectives for, 52resources for, 52–53tools and data sets for quantifying, 54–55

International assessments, 33Introduced species and Hawaiian bird

populations, 427–434Ionic foot baths, 87–94iPad apps, 29Is Guaiacum sanctum Effective Against

Arthritis?, 309–313abstract of, 309case study and questions for, 309–313

anecdotal evidence, 309–311human study, 312–313, 313rat study, 311, 311–312



Is High-Fructose Corn Syrup Bad for the Apple Industry?, 71–85


analysis of high-fructose corn syrup samples, 75–77, 76

caged bee studies, 79–82, 80, 81demise of the hives, 72–74formation of hydroxymethylfurfaral in high-

fructose corn syrup, 77–79, 78industry response, 82–84

definitions of terms for, 74–75homework assignment for, 84learning objectives for, 71quantitative reasoning skills/concepts for,


Isle Royal National Park ecosystem, 437–442

JJust-in-time quizzes, 47Just-in-Time Teaching (JiTT), 29

KKajander, Olavi, 139, 140, 141, 142, 143–144,

146, 147Keeping Up With the Joneses, 361–369


the accident, 361–363blood results, 367, 367–368conclusion, 368–369the next morning, 363–364Suzie sees the doctor, 364–367, 366



Kettlewell, Bernard, 319–320Khan Academy, 29Kidney stones, 138, 143, 143, 144Know/need-to-know chart, 47, 527Koch, Robert, 97

LLearner characteristics, 44–45Learning

active learning strategies, 4, 11, 15, 20, 23, 28, 29, 43, 43–44

case studies and the Cone of Learning, 26–28, 27, 28

feedback mechanisms and, 26Learning issue reports, 47Learning objectives, 45, 48. See also specific

casesLecture method, 23, 25, 26, 28Leibniz, Gottfried, 13Liatis, Andrea, 54



Index

Lignum vitae for arthritis, 309–313Lindsey, Elizabeth, 54Living Downstream, 471–476


the problem, 472–474, 472–474resource/background information,

475–476the roles, 475



Lockhart, Amanda, 54Lord, Thomas, 27Louis, Thomas, 10Louizi, Gerda, 54

MMacLeod, Colin, 251, 254, 254–255, 255Maggot fly speciation, 269–273MAGIC Tool software for data analysis, 35Malnutrition, 361–369Maple syrup urine disease (MSUD), 223–228The Math Forum, 39MathBench modules, 13Mathematica software, 37Mathematics in Conservation, 443–456


back to the Florida panther, 448–449basic genetics, 444–448, 445–447endangered species, 444genetic variation and genetic drift,

449–451inbreeding, 451–456, 454, 455some extra panther probability problems,

456learning objectives for, 443quantitative reasoning skills/concepts for,


Mathematics instruction/competencycase studies for, 4, 11, 19for college non-science majors, 7–11importance of, 2–3, 33–39for K–12 students, 5–6for professional biologists, 12–14, 513–526resources for, 33–34, 34, 35

The MathWorks, 37MATLAB software, 37McCarty, Maclyn, 251, 254, 254–255, 255

McLaren, B. E., 437McMurdo Station, Antarctica, 155–161Meat science and bioinformatics, 245–250Medical students, expectations for, 12, 14,

505–511Medicinal value of lignum vitae, 309–313Meer, D., 245Mendelian genetics, 12, 201, 203–204, 204,

219–220Meselson, Matthew, 251, 256–259Methemoglobinemia, 197–200Michael’s Story, 379–387


choosing a treatment plan, 386–387diagnosing autism and parent education,

384–386meet the Greens, 380–382what causes autism spectrum

disorders?, 383–384, 384learning objectives for, 379quantitative reasoning skills/concepts for,


Michaelsen, Larry, 29Microarrays MediaBook, 35, 36Microbial culture, growth, and metabolism,

163–172Microbiology

An Infectious Cure, 187–194Elvis Meltdown, 163–172Fecal Coliforms in Antarctica, 155–161Resistance Is Futile...Or Is It?, 173–186

Mirkin, Chad, 11Misconceptions of students, 44Mitochondria

The Mystery of the Seven Deaths, 127–130Wrestling With Weight Loss, 131–136

Molecular biology, 61–62The Case of the Druid Dracula, 239–244Classic Experiments in Molecular Biology,

251–259“Not Exactly,” 231–237Which Little Piggy Went to Market?, 245–

250Moodle course management system, 29My Brother’s Keeper, 263–268


alternative hypotheses and predictions, 264–265, 265

applied kin selection, 268



Index

economics of kin selection, 267–268experimental results, 256–266, 265hypothesis development, 264kin recognition mechanism, 266other squirrels, 266–267Sherman’s conclusions, 266



Mycobacterium tuberculosis (MTb), 97, 99, 102, 104

Myotonic dystrophy (MD), 208–211, 213The Mystery of the Seven Deaths, 127–130


autopsy report, 128role of cyanide, 129–130subcellular metabolite analysis, 129, 129symptoms, 127–128



NNADH diaphorase, 199, 199Nanobacteria, 137–151


conclusion: debate, 149corroborating evidence, 143–148,

145–148evidence that nanobacteria are alive,

139–142, 140, 141final chapter, 148–149, 150more evidence of life, 142–143, 143what it means to be alive, 138–139, 139



National Academy of Sciences, 2National Center for Case Study Teaching in

Science (NCCSTS), 15, 20, 45, 52–53National Center for Educational Statistics

(NCES), 33Dare to Compare, 33, 34Kids’ Zone, 33, 34

National Council of Teachers of Mathematics (NCTM), 3

National Forum on Quantitative Literacy, 2

National Institutes of Health (NIH), 532National Oceanic and Atmospheric

Administration (NOAA), 532National Science Foundation (NSF), 13, 16,

61, 156, 531National Survey of Children’s Health, 50Native bird populations and introduced

species, 427–434Natural selection, 16, 23, 61, 528

An Infectious Cure, 187–194Exaggerated Traits and Breeding Success

in Widowbirds, 297–305I’m Looking Over a White-Striped Clover,

315–323Mathematics in Conservation, 443–456My Brother’s Keeper, 263–267Super Bug, 285–296Trouble in Paradise, 275–278

Nature Genetics, 51Newton, Isaac, 13Next Generation Science Standards, 16Noller, C. R., 202“Not Exactly,” 231–237


CFTR mutations, 233–234final decision, 236further analysis, 235–236meeting, 232–233Punnett squares, 233test, 234–235



Numeracy, 1–3definition of, 2importance of, 2–3teaching with case studies, 4, 11, 19–30

OOceanography and ecology in the Gulf of

Mexico, 413–426Online Mendelian Inheritance of Man (OMIM)

database, 231, 233Opuntia fragilis population ecology, 325–332Out-of-class student preparation, 28–30

PPacker, Arnold, 3PaperShow software, 29Pasteur, Louis, 97



Index

Paxton, William, 55, 173, 174, 178–179Pediatric Nutrition Surveillance System

(PedNSS), 49Pedigree construction/analysis

The “Blue People” of Kentucky, 197–200“Not Exactly,” 231–237PKU Carriers, 279–284A Sickeningly Sweet Baby Boy, 223–228In Sickness and In Health, 207–214

Peterson, R. O., 437Petrick, Carmen A., 54Phage therapy, 187–194Pharmacogenetics, 401–409


acute lymphocytic leukemia, 402–403enzyme activity, 403–406, 404–406putting it all together, 408SNPs and TPMT, 408–409, 409TPMT enzyme activity levels, 407, 407



Phenylketonuria, 279–284Phenylthiocarbamide (PTC) taste sensitivity,

201–206PhET simulations, 527–528Photosynthesis in genetically modified plants,

333–340Physiological responses

Girl Pulled Alive From Ruins, 15 Days After Earthquake, 347–351

Keeping Up With the Joneses, 361–369The 2000-Meter Row, 343–346

PKU Carriers, 279–284abstract of, 279case study and questions for, 279–284

carriers, 283–284Hardy-Weinberg equilibrium, 282PKU, 279–282



Plant form and functionThe Ecology of Opuntia fragilis (Nuttall)

Haworth, 325–332I’m Looking Over a White-Striped Clover,

315–323Is Guaiacum sanctum Effective Against

Arthritis?, 309–313

Tougher Plants, 333–340Plasmodium Problem Space, 59–60Polygenetic traits, 220Polymerase chain reaction (PCR), 47, 61

The Case of the Druid Dracula, 239, 240–241, 242

Nanobacteria, 144, 145, 148, 148Which Little Piggy Went to Market?, 245,

247–249, 248–250Population ecology of prickly pear cactus,

325–332Population genetics, 477, 525, 530

Mathematics of Conservation, 443–456In Sickness and In Health, 207–214

Pork industry and bioinformatics, 245–250Pre-professional health students, 11–14Predator-prey dynamics


The Wolf, the Moose, and the Fir Tree, 437–442

President’s Council of Advisors on Science and Technology, 11

Prickly pear cactus population ecology, 325–332

Privatization of water supply, 485–492Problem-based learning (PBL), 11, 20, 24, 62

potential quantitative skills-building activities based on Bloom’s taxonomy for, 46

Problem-Based Learning Clearinghouse, 15, 45Problem spaces, 58–61

definition of, 58on HIV, 60–61on Plasmodium, 59–60resources for, 59, 60role and development of, 58, 58–59uses of, 59

Proceedings of the National Academy of Sciences, USA, 139, 140, 141

Process-oriented guided inquiry learning (POGIL), 29

Professor characteristics, 43–44Program for International Student Assessment

(PISA), 33Prokaryotic cells, 138Prud’homme Généreux, Annie, 54, 55Public health, 505, 506. See also Health

“Inactive Brains” case, 48, 51The Mystery of the Seven Deaths, 127–130Rabbit Island and the Search for

Tuberculosis Treatment, 95–104



Index

Super Bug, 285–296Punnett squares, 201, 207, 215, 220, 221,

223, 231, 232, 233, 279, 390

QQuantifying cases, 41–63

“Inactive Brains” case, 48–51on influenza, 52–54, 52–55molecular biology simulations, 61–62to navigate bioinfomatics problem spaces,

58, 58–61, 60resources for, 527–533steps for, 42, 43–48

step 1: identify class and professor characteristics, 43, 43–44

step 2: identify learner characteristics, 44–45

step 3: identify learning objectives and curriculum outcomes, 45, 46

step 4: choose case to modify, 45–47step 5: find or develop data sets,

simulations, websites, scientific papers, or games, 47

step 6: develop performance assessments and scoring rubrics, 47–48

using Gapminder for HIV/AIDS cases, 55–58, 56–57

Quantitative Biology for the 21st Century, 13Quantitative Environmental Learning Project,

39Quantitative literacy, 2, 3, 6, 7, 14–16, 517,

523Quantitative skills/competency, 1, 3–4, 41–42

case studies and, 4, 11, 14–16, 19 (See also specific cases)

for college non-science majors, 7–11for K–12 students, 4–6for pre-professional health students, 11–12for professional biologists, 12–14, 513–526teaching of, 3–4

Quincy, Josiah, 2

RR Project for Statistical Computing, 36, 36Rabbit Island and the Search for Tuberculosis

Treatment, 95–104abstract of, 95case study and questions for, 96–104

Rabbit Island experiment, 96–101, 99tuberculosis in social context, 101–104,

102, 103



Racism, 215–222Research design, 9, 10 325, 506Resistance Is Futile...Or Is It?, 173–186


HIV and the immune system, 173–178, 174–176

Paxton’s hypothesis about HIV-protected individuals, 178

Paxton’s results, 180–181, 181predictions from Paxton’s two

hypotheses, 178–180, 180“super T helper cell” mechanism, 182–

184, 183, 184why some people are protected against

HIV, 185, 185–186learning objectives for, 173quantitative reasoning skills/concepts for,


Rodent speciation, 275–278Rosling, Hans, 37, 38Rothfeder, Jeffrey, 488–489

SSadler, Philip, 12Science, 10, 11, 202, 263, 437Science and engineering practices, 4–6Science Case Network, 62Scientific literacy, 42Scientific method

Cell Phone Use and Cancer, 67–69Feeling Detoxified, 87–94Is High-Fructose Corn Syrup Bad for the

Apple Industry?, 71–85Rabbit Island and the Search for

Tuberculosis Treatment, 95–104Scoring rubrics, 48Search for the Missing Sea Otters, 457–467


the problem, 458–459, 459sea otters, 459–461what predator could be causing the

large decrease in otter numbers?, 461–462, 463, 464

who cares if otter numbers are decreasing?, 464–465, 465–467



Index

why are killer whales eating sea otters now?, 464



Sex-linked inheritance, 211–212Sexual selection and evolution of widowbirds,

297–305Shodar, 33–34, 35Short tandem repeats (STRs), 239, 243,

243–244, 244ShowMe software, 29Skin color genetics and evolution, 215–222Small-group methods, 24–25, 25, 28Snow, C. P., 1Snyder, L. H., 203Space exploration and extraterrestrial

microbes, 163–172Speciation

Trouble in Paradise, 275–278As the Worm Turns, 269–273

Squirrel alarm calling, 263–268Stahl, Frank W., 251, 256–259Staphylococcus aureus, 192–193Statistics, 1, 2

for college non-science majors, 8, 10, 14definition of, 10FedStats website for, 532–533for K–12 students, 5–6for pre-professional health students, 11–12for professional biologists, 13–14training for all students, 14, 15, 30

STEM education, 1, 11, 20Streptococcus pneumoniae, 251, 252Stresses on agricultural plants, 333–340Strom, C. M., 234Students

interests of, 44misconceptions of, 44out-of-class preparation by, 28–30

Super Bug, 285–296abstract of, 285case study and questions for, 285–296

checking your understanding, 296data for individual roles in the case,

293–295, 293–296mechanism of evolution, 288–291,

289–291rise of the super bug, 285–288, 287significance of evolution today, 292,

292–293



Sweet Indigestion, 107–110abstract of, 107case study and questions for, 107–110

of cows and carbs, 107–109label analysis, 109, 109–110



TT cells, 175, 175–182, 180, 181Tai, Robert, 12Tallamy, Doug, 38–39TAS2R gene, 204Taste sensitivity for phenylthiocarbamide,

201–206Thiopurine methyltransferase (TPMT), 401–

409Thompson, William, 19Threats to Biodiversity, 427–434


background reading, 428–429in-class exercise, 429–431, 430–433take-home assignment, 434



Thyroid disease, 353–359To the Bitter End, 201–206


discovery, 202–203Mendelian genetics, 203–204, 204molecular genetics, 204–206, 205



Tobias, Shiela, 11Tonoany, Beth, 309–313Tougher Plants, 333–340


glycine betaine, 334, 334, 335



Index

heat tolerance, 336, 337photosynthesis, 335, 335–336photosystem II, 338, 338salt, 339, 339–340stress, 333–334


Transforming principle, 252Trends in International Mathematics and

Science Study (TIMSS), 33, 34Trigger cases, 22Trigonometry, 524, 526Tropic level relationships


The Wolf, the Moose, and the Fir Tree, 437–442

Trouble in Paradise, 275–278abstract of, 275case study and questions for, 275–278

assignment, 277–278background, 275–276, 276



Trudeau, E. L., 95–101, 104Tuberculosis, 95–104Tukey, John, 33, 34The 2000-Meter Row, 343–346

abstract of, 343case study and questions for, 343–346learning objectives for, 343quantitative reasoning skills/concepts for,


Tylenol capsules laced with cyanide, 127–130

UUniversity of Tennessee quantitative education

for biologists, 14, 15, 523, 524–526U.S. Department of Human Health and Human

Services, 532U.S. Environmental Protection Agency, 532U.S. Geological Survey (USGS), 458, 532Using mathematics and computational

thinking, 4, 5–6

VVision and Change in Undergraduate Biology

Education: A Call to Action, 16, 42

WWater balance, 347–351Water quality

An Infectious Cure, 187–194Fecal Coliforms in Antarctica, 155–161Living Downstream, 471–476

The Wealth of Water, 485–492abstract of, 485case study and questions for, 485–492

the before and after, 489, 490spring break, 485–489water and you, 491–492, 492



Weight lossThe Hunger Pains, 371–376Wrestling With Weight Loss, 131–136

Welty, Bill, 19–20Which Little Piggy Went to Market?, 245–250


assignment, 250boar taint, 246–247pigs is pigs?, 246success?, 248–249, 248–250Sue’s solution, 247–248test wanted, 247



White-striped clover, 315–323Widowbirds, sexual selection and evolution of,

297–305The Wolf, the Moose, and the Fir Tree, 437–442


introduction, 438–439ring width indices, 441–442, 442trophic system data, 439–441, 440



Wolfram, Stephen, 37Wolfram Research, 37WolframAlpha project, 37World Health Organization (WHO), 51, 532Wrestling With Weight Loss, 131–136

abstract of, 131



Index

case study and questions for, 132–136mitochondrial function, 132–134, 133too weighty one, 132what athlete should do, 135, 135–136what this has to do with DNP, 134, 135


XX chromosome, 211, 240, 241, 252

YY chromosome, 240, 241, 247–248, 252


51 CASE STUDIES WITH QUANTITATIVE REASONING IN BIOLOGY

College

PB353XISBN 978-1-938946-05-9

“Using real stories with quantitative reasoning skills enmeshed in

the story line is a powerful and logical way to teach biology and

show its relevance to the lives of future citizens, regardless of

whether they are science specialists or laypeople.”

—from the introduction to Science Stories You Can Count On

This book can make you a marvel of classroom multitasking. First, it helps you achieve

a serious goal: to blend 12 areas of general biology with quantitative reasoning in

ways that will make your students better at evaluating product claims and news

reports. Second, its 51 case studies are a great way to get students engaged in science.

Who wouldn’t be glad to skip the lecture and instead delve into investigating cases

with titles like these:

• “A Can of Bull. Do Energy Drinks Really Provide a Source of Energy?”

• “ELVIS Meltdown! Microbiology Concepts of Culture, Growth,

and Metabolism”

• “The Case of the Druid Dracula”

• “As the Worm Turns: Speciation and the Maggot Fly”

• “The Dead Zone: Ecology and Oceanography in the Gulf of Mexico”

Long-time pioneers in the use of educational case studies, the authors have written

two other popular NSTA Press books: Start With a Story (2007) and Science Stories:

Using Case Studies to Teach Critical Thinking (2012). Science Stories You Can

Count On is easy to use with both biology majors and nonscience students. The

cases are clearly written and provide detailed teaching notes and answer keys on

a coordinating website. You can count on this book to help you promote scientific

and data literacy in ways to prepare students to reason quantitatively and, as the

authors write, “to be astute enough to demand to see the evidence.”

SCIENCE STORIES YOU CAN COUNT ON
