BIO 181 Chapter 1 - The Study of Life: Nature of Science and Biology

Nature of science

  • Science is a process to understand the universe that uses observation and reasoning with more stringent rules to collect evidence and justify explanations.
  • It proposes and tests explanations to gather evidence that lends support to conclusions; hypotheses are the core explanations for natural phenomena or observations.
  • Hypotheses can be used to make predictions for further testing and become validated through testing; a hypothesis must be demonstrably testable.
  • Science must keep an open door for revision; it cannot claim to know something as absolute truth.
  • Scientific conclusions are not absolute proofs in the mathematical sense; they are supported with varying degrees of confidence based on evidence.
  • The term proof in science is inappropriate for most contexts; we speak of evidence supporting ideas, with a probability or confidence level (e.g., x% confidence).
  • A p-value concept is introduced: a p-value less than 0.05 means there is only a 5% chance that the observed results occurred by random chance, indicating results look legitimate but are not a guarantee.
  • The term theory has a precise scientific meaning: a well-supported, extensively tested explanation with strong predictive power.
  • Distinction between theory and untested belief: a theory is not just a guess; it is a hypothesis that has withstood extensive testing and independent verification across many lines of evidence.
  • Examples of well-known theories (e.g., evolution, germ theory) arise from a vast collection of papers, data, and analyses and remain powerful models for predicting outcomes in experiments.
  • The scientific method is often depicted linearly (question, background, hypothesis, test, analyze, report) but in practice science proceeds circuitously with competing hypotheses, multiple experiments, and refinement along the way.

Hypotheses, predictions, and falsifiability

  • A hypothesis explains a natural phenomenon or a set of observations and leads to predictions that can be tested.
  • Falsifiability: a claim is scientifically valid only if it can be tested in a way that could potentially prove it wrong.
  • Explanation that cannot be tested is not science; it remains a belief.
  • If a claim cannot be challenged or disproved, it falls outside science.
  • The key criterion: testability that could falsify the hypothesis, even if the test does not actually falsify it.

The nature of proof and statistics in science

  • Science emphasizes evidence, not absolute proof.
  • Probability-based reasoning is central: conclusions are drawn with degrees of confidence.
  • Statistical tools (e.g., p-values) quantify how likely results are due to chance rather than a real effect.
  • Example interpretation: a significant result increases confidence but does not guarantee truth.

The scientific method and experimental design

  • The method is often taught linearly: question → background → hypothesis → test → analyze → report.
  • In practice, science is iterative: multiple hypotheses, simultaneous experiments, revisiting and refining ideas.

Variables and experimental design

  • Variables can be altered directly or indirectly; two main types:
    • Independent variable: the variable that is deliberately changed or manipulated.
    • Dependent variable: the variable that is measured and observed.
  • Example: Do more sunflower seeds in a chicken diet affect egg production?
    • Independent variable: amount of sunflower seeds in the diet.
    • Dependent variable: number of eggs produced.
  • A good experiment varies only one variable at a time to avoid confounding factors (other variables must be controlled).
  • Controlled variables: factors kept constant across all groups to ensure that any observed effect is due to the manipulation of the independent variable.
  • The relationship between variables is often assessed via correlation, which, combined with controlled experiments, supports or refutes conclusions.

Inductive and deductive reasoning

  • Inductive reasoning: using specific data to develop general conclusions (from observations to generalizations).
  • Deductive reasoning: using general conclusions to make specific predictions (from theory to hypothesis to observable outcomes).
  • Together, specific experimental data enable inductive generalizations, which can then be used deductively to predict new outcomes.

Controls and sample size

  • Positive control: a treatment that is known to produce a positive result (ensures the system can produce the expected effect).
  • Negative control: a treatment that is known to produce no effect (ensures that a positive result is due to the manipulation and not to confounding factors).
  • Sample size: the number of times a test is repeated or the number of subjects/samples; larger sample sizes increase confidence in the results.
  • A checklist for robust experiments: test only one variable at a time, use valid inductive/deductive reasoning, apply experimental controls, and ensure a large sample size.

Practical example (chickens and sunflower seeds)

  • Independent variable: amount of seeds in the diet (varying levels).
  • Dependent variable: number of eggs laid.
  • Observational factors to control: timing of feedings, timing of egg collection, duration on diet, and other environmental variables.
  • Result: a positive correlation between seeds and eggs laid when other variables are controlled.

The nature of biology and the eight characteristics of life

  • Biology is the study of living organisms.
  • To be considered living, an entity must meet eight criteria:
    1. Sensitivity or response to stimuli: organisms respond to environmental signals (e.g., jerking away from a hot surface; sunflower heliotropism).
    • Note: the lecture uses terms like phototrophy (sunflowers tracking the sun) and chemotrophy (responding to chemical cues).
    1. Reproduction: organisms reproduce to produce offspring of their kind.
    2. Adaptation: organisms evolve adaptations that improve survival in their environment (e.g., microbes evolving antibiotic resistance; camouflage).
    3. Growth and development: organisms grow and develop traits encoded in their genes; this is not analogous to simple combustion or growth of a fire.
    4. Regulation: organisms regulate their internal processes (e.g., enzyme production, timing of gene expression).
    5. Homeostasis: maintenance of a stable internal state (e.g., body temperature, metabolic balance).
    • Example: polar bear insulation for temperature regulation; humans metabolically tolerating certain changes.
    1. Energy processing: organisms acquire, convert, and use energy from chemical or light sources.
    • Plants convert light into chemical energy via photosynthesis; animals metabolize food.
    1. Order: living things are organized from molecules to cells to tissues to organs to organ systems to bodies.
    • Hierarchical organization: macromolecules → organelles → cell → tissues → organs → organ systems.

Macromolecules and the levels of biological organization

  • Macromolecules are built from four major building blocks:
    • Carbohydrates
    • Lipids
    • Proteins
    • Nucleic acids
  • Subcellular structures (organelles) perform specific functions within a cell.
  • Levels of organization (bottom-up):
    • Macromolecules → Organelles → Cell → Tissues → Organs → Organ systems → Organism

Real-world relevance and advances in biology

  • 2017 FDA milestones and biotechnological applications:
    • First gene therapy approved by the FDA for a form of retinal disease.
    • FDA approval of the first gene therapy for leukemia.
    • Arctic apples: removal of the browning gene, enabling sliced apples to resist browning.
  • Biotechnological capabilities expanding:
    • Lab production of silk fibers (bioengineered silk).
    • Cloning of pets.
    • Gene drives: engineered genes to reduce offspring viability in mosquitoes; in testing phases.
    • Genetic testing on remains to reveal past population groups and migration patterns.
    • Analysis of cellular chassis and minimal cells: designing a metabolic chassis for cells.
  • Minimal cell concept: a synthetic microbe with fewer than 500 genes, in contrast to humans (~$25{,}000$ genes) and E. coli (~$4{,}500$ genes).
  • Understanding essential components required for a functioning cell remains a major research focus.

Summary and connections

  • Science seeks the best current explanations through testable hypotheses and evidence, always subject to revision with new data.
  • Biology explains life through a consistent framework of rules and hierarchies from molecules to ecosystems, with practical applications spanning medicine, agriculture, and biotechnology.
  • The course emphasizes critical thinking about experimental design, interpretation of results, and the real-world implications of biological advances.

Key equations and numerical references (LaTeX)

  • Significance threshold (p-value): p ext{-value} < 0.05
  • Gene counts (examples):
    • Human genome: approximately 25{,}000 genes
    • E. coli genome: approximately 4{,}500 genes
    • Synthetic minimal cell chassis: fewer than 5 imes 10^{2} genes
  • Confidence and probability language in science: results are described in terms of confidence levels or probabilities, not absolute proofs.