Foundations of Biology: Lecture Notes (Lectures 1–4)

Course Logistics and PLTL

PLTL (Peer-Led Team Learning) is REQUIRED and accounts for 20% of your grade.
- Grade components: Attendance, Preparation, Participation.
- Structure: 1¼ hour weekly workshop; Sections meet Wednesday–Saturday; Sections are limited to 12 students each.
- Enrollment: Sign up via Canvas in the “Welcome” module; Enrollment site opens at 10 am the day after information is posted.
Course materials and platforms
- Foundational texts: OpenStax and Campbell Biology, Chapter references vary by week.
- Syllabus and schedule: Available on Canvas; plan for exams conflicts and course deadlines.
- iClicker: Required for in-class active participation; register iClicker correctly; after class register your clicker online (Canvas announcements forthcoming).
Syllabus highlights and week-by-week schedule (selected items)
- Week 1 (Intro): Introduction to Foundations of Biology; OpenStax Campbell references; syllabus overview.
- Week 2: Foundations of Biology (Ch. 1) coverage continues; topic alignment with lecture slides.
- Week 3 to Week 4: Elements of life and molecular diversity (Chs. 2, 3).
- Week 8: A tour of the cell; Week 9: Membranes (Chs. 5, 5.1–5.5).
- Week 10: Membranes (continued); Week 11: Cell signaling (Ch. 9; 5.6).
- Exam 1: Based on lectures 1–8, focusing on science and cells.
Classroom etiquette and devices
- Minimize distractions: Turn off non-biological devices; laptops allowed for biology work only.
- After class: use office hours for quick questions; email for fast Qs (avoid Canvas messaging for quick questions).
- Poster/reading references and recommended videos (e.g., CytoSkeleton by David Goodsell).
Office hours and contact
- Instructor: Dr. Cozza; Office hours in OE 216: Mon 4–5, Weds 4–6, Fri 4–5; informal drop-ins after class; email: jcozza@fiu.edu.

Study Strategies and Classroom Norms

General success tips (from syllabus and handouts)
- Skim chapters before lectures; review pictures and diagrams.
- Attend every class and take high-quality notes.
- Minimize distractions; use technology for biology-related tasks only.
- Engage with peers; help classmates; review concepts regularly.
- Utilize office hours and seek help ASAP if struggling.
- Practice by doing the easy stuff first; read announcements and follow directions.
PLTL-specific prep
- Prepare for weekly PLTL sessions; bring questions and draft solutions.
- Watch Dr. Chew videos on study strategies as part of preparation.
In-class tips for clicker questions
- In-class practice questions earn full credit; use clicker to engage with material.
- Join and participate actively in PLTL and clicker tasks to maximize the 20% PLTL grade.

Foundational Concepts: What is Life? Attributes and Organization

Foundations of biology outline (recurring themes):
- How to succeed in the course
- What is life?
- Levels of organization
- Scientific method
- Climate change (contextual science)
Attributes of life (OpenStax and Campbell alignment)
- Sensitivity or response to stimuli
- Regulation
- Reproduction
- Growth and development
- Homeostasis
- Energy processing
- New properties emerge at successive levels of biological organization
- Organisms interact with other organisms and the physical environment
- Expression and transmission of genetic information
- Evolution
Levels of biological organization (examples and scales shown in slides)
- Atoms → Molecule → Macromolecule → Organelle → Cell
- Scale hints: $0.5\ \mu m\quad$ , $0.2\ \mu m\quad$ (scale references from Raven, Biology 9th ed.)
- Cell to organism progression: Cell → Tissue → Organ → Organ system → Organism; typical size reference: $100\ \mu m\quad$
- Population → Species → Community → Ecosystem → Biosphere
Foundational schemas and readings
- OpenStax vs Campbell mappings for attributes of life; ensure you can match each attribute to its definition or example
- Metapatterns (Volk & Bloom 2007) as frameworks for understanding complex systems in biology and education

Scientific Method, Hypotheses, Predictions, and Experimental Design

Popperian scientific method framework (as presented)
- Observations → Question → Hypothesis → Prediction → Experiments → Hypotheses supported or rejected
- Visual flow: Observations → Question → Hypothesis → Predictions → Experiments → Support/Refute → Repeat
What is a hypothesis vs a prediction?
- Hypothesis: Tentative explanation that can be tested; often a general statement about relationships
- Prediction: Specific, testable outcome derived from the hypothesis
Example from lecture material (class attendance study)
- Hypothesis: Class attendance improves learning and critical thinking via peer interactions
- Prediction: Students who attend class will score higher on exams than those who miss class
- Control: All students use the textbook as a reference; attendance as independent variable; outcome: exam scores
- Reported finding: For each missed class, students averaged a 2% decrease in exam scores
Hypothesis vs Prediction practice (interactive questions overview)
- Practice questions illustrate identifying which statements are hypotheses vs predictions
- Emphasis on how to interpret results and how to phrase testable predictions
Statistical reasoning in biology
- P-value concept: Significance threshold often set at $P \le 0.05$ for rejecting the null hypothesis
- Correlation vs causation: Correlation does not imply causation without manipulation/experimental design
Other aspects of the scientific method discussed in slides
- Observations, questions, hypotheses, predictions, experiments, and whether hypotheses are supported or rejected
- The role of replication, peer review, and rigorous evidence in science

Climate Change: Evidence, Hypotheses, Data, and Societal Context

Core hypotheses for climate change hypotheses (overview)
- Solar radiation changes, greenhouse gas concentrations (CO2, CH4, etc.), volcanic activity, natural variation, ozone changes, orbital changes
Evidence and data sources presented in slides
- Atmospheric CO2 records: Mauna Loa CO2 (Keeling curve)
- Ice core records: Vostok ice core (CO2 vs temperature over 420,000 years)
- Temperature records: Global land-ocean temperature index; NASA GISS data; five-year and annual means
- Sea level rise projections: Model-based predictions (1990–2100) across scenarios
- Mass balance of ice sheets: Greenland and Antarctica mass variation data from NASA Grace satellites
- Visualizations and readings from NYT climate coverage and NASA climate pages
Noteworthy data points and visuals from slides
- CO2 concentrations around the 380–400 ppm range in late 20th/early 21st century figures (Mauna Loa) and long-term ice core data showing CO2 and temperature correlation
- Keeling curve as a central representation of rising atmospheric CO2
- Sea level rise projections under different emission scenarios
- Mass loss in major ice sheets and glacier melt trends
Scientific consensus and causality chain
- A chain of causality linking human activities (increasing atmospheric CO2) to rising temperatures, ice melt, and sea-level rise, supported by a broad scientific consensus exceeding 97% in summarized sources
- References to sources such as Scientific American and NASA climate communications
Climate change education and public policy context
- The role of the IPCC 2018 Greater-than-1.5°C target, emissions reductions goals (45% by 2030, net-zero by 2050)
- Discussion of costs of inaction vs. action (economic and societal implications)
- Conceptual frameworks for solutions and political action (Green New Deal, Drawdown strategies)
The “Drawdown” framework and practical solutions
- Onshore wind, solar photovoltaics, reduced food waste, plant-rich diets, tropical forest restoration, peatland protection, improved stoves, distributed solar, refrigerant management, silvopasture, and other interventions
- Emphasis on implementing multiple solutions across individual, local, and societal scales
Public communication and misinformation themes
- Mandela effect and memory reliability in science communication context (as an example of cognitive biases in understanding scientific information)
- Dangers of cherry-picking data and the importance of robust, multi-source evidence

Practice Questions, Examples, and Exam Prep Notes

Practice on experimental design and interpretation
- Identify the parts of the scientific method in given scenarios (observations, question, hypothesis, prediction, experiment, results)
- Distinguish between correlation vs causation; understand how to test causal relationships with controlled experiments
Hypothesis and prediction classification exercises
- Example: In a plant study, hypotheses about sex expression related to size/environment; predicted outcomes tested with data (positive correlation between leaf area and sex expression)
Interpreting P-values and significance
- Understand that P ≤ 0.05 indicates statistical significance in many biological studies, but does not prove causation by itself
Climate data interpretation practice
- Read graphs showing CO2 vs temperature, Mauna Loa CO2 trends, and ice core records; interpret whether data support the hypothesis that human activities drive climate change
Conceptual questions from the slides
- Distinguish between hypotheses, predictions, and assumptions in experimental designs
- Understand Occam’s razor as a criterion for selecting simpler explanations with fewer assumptions

Additional Resources and Reading Suggestions (as referenced in slides)

Primary climate data sources
- NASA GISS: Global Temperature Index and Mauna Loa CO2 records
- Vostok Ice Core CO2 and temperature data
- Mauna Loa CO2 (Keeling Curve) long-term measurements
Climate change literacy and media sources
- The New York Times climate coverage and climate science explainers
- Australian Academy of Science: The Science of Climate Change (Questions & Answers)
Drawdown and policy discussions
- Project Drawdown solutions list and implementation ideas
- Green New Deal overview and policy discussions
Metapatterns and complex systems resources
- Volk & Bloom (2007) on metapatterns in nature and culture
- Metapatterns overview resources and student projects
Exam preparation notes
- Synthesize attributes of life with definitions and examples
- Be able to match OpenStax vs Campbell attributes of life to their definitions/examples
- Practice Popperian method, hypothesis vs prediction, and correlation vs causation questions

Quick Reference: Key Formulas and Notation

Statistical significance threshold used in class: $P \le 0.05$
Size scales shown in figures: $0.5 \mu m$ and $0.2 \mu m$ (scale references)
Common dimensional notations:
- $\mu m$ for micrometers in cellular-scale diagrams
Conceptual definitions to recall
- Hypothesis: tentative explanation testable by experimentation
- Prediction: testable outcome derived from a hypothesis
- Correlation: association between two variables; does not imply causation without manipulation
- Occam’s razor: prefer the simplest explanation with the fewest assumptions

Notes on Next Steps

Review lecture slides 1–3 for foundational setup, and lectures 4–6 for Elements of life and molecular diversity with overview of metabolism
Skim chapters 1–2 (and start of chapter 3) in your chosen textbook to align OpenStax Campbell mappings with in-class content
Enroll in PLTL and obtain an i-clicker; ensure registration is complete before the next session
Watch Dr. Chew’s study-strategy videos and complete the recommended practice questions before the next class
Prepare for Exam 1 by focusing on:
- Definitions and differences between hypotheses and predictions
- How to design and interpret a controlled experiment
- Core attributes of life and levels of biological organization
- Evidence and reasoning behind climate-change science and its data sources