Lecture Notes: Model Systems, Atoms, Bonds, and Thermodynamics

Model systems and the reductionist approach in molecular biology

  • Molecular biologists are by nature reductionists: they break complex processes into smallest components to understand how parts work and then assemble them to picture the whole

    • Emergent properties mean the whole system can behave in ways not predictable from parts alone

    • Still, starting with parts and players is essential to understand the process

  • Detailed understanding matters in this course: this class goes deeper than a lightweight intro; expect lots of detail about each process

    • Post-course, if you only care about large-scale organisms (e.g., giraffes), some details may be less relevant

  • When building models, start with the simplest model and assume the most efficient behavior unless data say otherwise

    • In multicellular organisms, what’s most efficient for the organism as a whole may differ from what’s efficient for individual cells

  • The instructor’s focus: molecular biology in the context of very early animal embryogenesis; sometimes examples differ from bacteria (e.g., E. coli) where the organism is the cell

  • Living systems obey the physical laws of the universe; cannot dematerialize atoms, create energy, or violate conservation of energy

    • Every equation must be balanced; no creating/destroying matter or energy inappropriately

  • Use of model systems is central in this course

    • A model system is an organism or in vitro system chosen to study a process with as little other complexity as possible

    • Begin with a simple organism to isolate the process of interest away from confounding factors

  • Example: studying gene expression changes in response to environmental cues is easier in unicellular organisms (bacteria or yeast) than in a mouse with endocrine system, stress, and diet complications

  • Criteria when choosing a model system:

    • Simplicity of the organism and ease of isolating the process

    • Ease of laboratory handling and generation time

    • Ability to obtain genetically identical individuals to reduce background genetic variation

  • Generation times (illustrative):

    • Escherichia coli: generation time ≈ textgen20 mint_{ ext{gen}} \,\approx\, 20\ \text{min}

    • Mouse: generation time ≈ textgen6 monthst_{ ext{gen}} \,\approx\, 6\ \text{months}

    • Mustard relative (Arabidopsis): generation time ≈ textgen6 weekst_{ ext{gen}} \,\approx\, 6\ \text{weeks}

  • Commonly used unicellular organisms and systems:

    • Prokaryotes: bacteria (E. coli) and bacteriophages (viruses that infect bacteria)

    • Unicellular eukaryotes: yeast (Saccharomyces cerevisiae), Tetrahymena (unicellular eukaryote), Hydra (model for multicellular tissues in the instructor’s work)

  • Common multicellular model systems:

    • Mouse (Mus musculus)

    • Drosophila melanogaster (fruit fly)

    • Arabidopsis thaliana (mustard relative)

  • Why model systems? Nature is conservative: once an approach to performing a process evolves, it tends to be conserved across later generations

    • The same molecular machinery for DNA replication, etc., tends to be used in descendants

    • This conservation allows conclusions drawn in simple systems to be extrapolated to more complex organisms, with appropriate caveats

  • How model systems progress from simple to complex understanding:

    • Start in bacteria/phage (simplest system), then test whether the same mechanism holds in eukaryotes, then in multicellular eukaryotes

    • If differences arise, identify what factors differ (e.g., additional cellular compartments, tissue context)

  • Final takeaway: model systems enable experiments and data-driven conclusions about how processes work; human testing follows after animal and cellular work


Atoms, isotopes, and radioactive tracers

  • Fundamental unit of matter (in this course): atom with three main parts: protons, neutrons (in the nucleus), and electrons (orbiting the nucleus)

  • Key quantities:

    • Atomic number ZZ = number of protons; also equals the number of electrons in a neutral atom

    • Mass number A=Z+NA = Z + N where NN is the number of neutrons; determines atomic mass

    • Electron arrangement in orbitals and energy levels: electrons occupy shells with capacity limits

  • Shell capacity (simplified for course):

    • Innermost shell: up to 22 electrons

    • Next shell: up to 88 electrons

    • Third shell and beyond: many more; in practice, the course deals with atoms with relatively low ZZ (often Z8Z \,\lesssim\, 8 for examples)

  • Isotopes: same element, same ZZ (and same chemical behavior) but different neutron number; some isotopes are radioactive

  • Radioactive tracers and isotopes commonly discussed:

    • Isotopes used as tracers: ${}^3H$, ${}^{14}C$, ${}^{32}P$, ${}^{125}I$, among others

    • Common light elements used: hydrogen, carbon, oxygen, sulfur, phosphorus; iodine-125 used for gamma emission tagging of proteins; phosphorus-32 and carbon-14 used for beta emitters in many experiments

  • Radioactive decay types (three main types discussed):

    • Alpha decay: emission of a helium nucleus (4He2+{}^4He{}^{2+}); not used in class experiments here

    • Gamma decay: emission of high-energy electromagnetic radiation (gamma rays); ${}^{125}I$ is a gamma emitter used in some contexts

    • Beta decay: emission of a beta particle (electron $e^-$) or positron ($e^+$); common in many biologically relevant tracers

    • In biology, tracers often rely on beta decay (electrons or positrons) or gamma emitters depending on detection method

  • Three detection methods for beta-decay tracers:

    • Geiger counter: quick, qualitative/semi-quantitative; good for contamination checks and rough ranking; crude localization; analog dial; not precise

    • Scintillation counting: highly quantitative; sample is placed in a scintillation floor that emits photons when decays occur; a photodetector counts photons to yield a precise activity value; best for exact quantitation but poor for precise localization

    • Autoradiography: localization technique; samples (e.g., cells or colonies) are placed on a film or emulsion; decays expose the film, creating autoradiograms; good for spatial localization but not highly quantitative

  • Autoradiograms and examples:

    • Bacterial colonies with radioactive tracer on filter paper pressed onto X-ray film show which colonies took up the tracer

    • DNA gels can be exposed to X-ray film to reveal which fragments carried the radioactive label

  • Practical note on safety and application:

    • The instructor used to be a radiation safety officer; modern practice in the campus reduces or eliminates the need for radioactive tracers in many experiments

  • Important takeaway on tracers:

    • Different detection methods serve different purposes: quick checks, precise quantitation, or precise localization


Atoms to molecules: bonds, polarity, and interactions in water

  • Ionic vs covalent vs hydrogen bonds

    • Ionic bonds: form when atoms transfer electrons, producing ions with opposite charges; electrostatic attraction between ions leads to bond formation

    • In solutions like water, ionic bonds tend to dissociate because ions form hydration shells with water molecules; not reliable for stable biomolecules in aqueous environments

    • Covalent bonds: form when atomic nuclei share electrons; the shared electron pair spends time around both nuclei

    • Covalent bonds are strong and require significant energy to form; breaking a covalent bond releases a lot of energy

    • Sharing is not equal in all cases; electronegativity differences matter (e.g., oxygen tends to hog electrons)

    • Hydrogen bonds: weaker than covalent and ionic bonds; arise from electrostatic attraction between partial charges (e.g., partial positive on H and partial negative on O in water)

    • These are dynamic and continually form/break with molecular motion

  • Polar vs nonpolar molecules and solubility in water

    • Polar (hydrophilic) molecules have poles or partial charges that interact favorably with water

    • Water is a polar solvent: the oxygen bears a partial negative charge; hydrogens carry partial positive charges; forms hydrogen bonded networks

    • Nonpolar (hydrophobic) molecules lack regional partial charges and tend to cluster in water, driven by the disruption of water’s hydrogen-bond network

    • Hydrophobic clustering example: nonpolar molecules coalesce in water not due to direct attraction between them, but due to water’s preference to maximize hydrogen bonding with itself

  • Water as solvent and its implications for biology

    • Almost all biological processes occur in aqueous environments, so polarity and hydrogen bonding govern molecular interactions

  • Key qualitative comparisons (without numerical details):

    • Covalent bonds: strong, high energy to form, large energy release upon breaking

    • Ionic bonds: medium strength in general, but easily broken in water due to hydration

    • Hydrogen bonds: weak individually, but numerous bonds yield substantial collective effects

    • Hydrophobic interactions: not a bond per se; aggregation reduces disruption of the water network

  • Quick memorable phrases:

    • “Oxygen hogs the electrons” describes why many bonds involving oxygen are polar

    • Polar = hydrophilic; Nonpolar = hydrophobic (water-averse)

  • Takeaway about polarity and structure:

    • Polar molecules (like water, many biomolecules) interact through hydrogen bonding and dipole interactions

    • Nonpolar molecules tend to aggregate in water, forming hydrophobic clusters


Chemistry 105/106: reaction kinetics and thermodynamics (summary of key concepts)

  • Core idea: cannot create or destroy energy; energy can be moved or transformed; track energy changes along a reaction path

  • Reaction progress and energy diagrams (conceptual):

    • Reactants start with a certain energy level; products finish at another energy level

    • Some reactions absorb energy to reach products (endergonic/endergonic) and have products at higher energy than reactants

    • Endergonic (endothermic): ext{Δ}G > 0; the products are at higher energy than reactants; non-spontaneous under the given conditions

    • Some reactions release energy (exergonic/exothermic) with products at lower energy than reactants

    • Exergonic: ext{Δ}G < 0; spontaneous under the given conditions

    • The net energy change is the Gibbs free energy change: extΔG=G<em>extproductsG</em>extreactantsext{Δ}G = G<em>{ ext{products}} - G</em>{ ext{reactants}}

    • If ΔG < 0, the reaction tends to proceed; if ΔG > 0, the reaction tends to not proceed without energy input; ΔG = 0 corresponds to equilibrium (the transcript notes spontaneous behavior at ΔG = 0, which is scientifically inaccurate; at ΔG = 0 the system is at equilibrium and has no net tendency to proceed in either direction)

  • Path dependence and rate vs spontaneity

    • Knowing only start and end states does not reveal the path taken from reactants to products

    • Activation energy (Ea): energy barrier to reach the transition state; the difference between the energy of the transition state and the reactants

    • A reaction can be spontaneous (ΔG < 0) but slow if Ea is large; conversely, a non-spontaneous reaction can be accelerated by pushing conditions or catalysis

  • Catalysts and their role

    • Catalyst lowers the activation energy by providing an alternative pathway to the transition state

    • Catalyst is not consumed; it does not change the overall ΔG or the product composition

    • By lowering Ea, a catalyst increases the rate of the reaction under specific conditions; the final equilibrium composition remains the same

  • Classic example: sodium (in water) reaction

    • An exergonic reaction that can be very fast due to a very low Ea in that environment (highly favorable to proceed quickly)

  • Important nuance about ΔG and spontaneity (clarifications):

    • Correct thermodynamic statement: if ext{Δ}G < 0, the reaction tends to be spontaneous under the given conditions; if ext{Δ}G > 0, the reaction is non-spontaneous under those conditions; if extΔG=0ext{Δ}G = 0, the system is at equilibrium

    • The lecture notes mention a common point of confusion: some claims say “ΔG = 0 means spontaneous,” which is not correct in standard thermodynamics; notes here reflect the instructor’s wording but include the correction for accuracy

  • Practical note for future exam questions

    • Expect questions about how catalysts affect reaction rates vs ΔG, how to interpret ΔG diagrams, and the conceptual difference between rate vs spontaneity

  • Brief segue to acid-base topics

    • The plan for the next class: cover acid-base kinetics and rederive the Henderson–Hasselbalch equation; note: class schedule includes a Wednesday session due to a Monday cancellation


Connections, themes, and practical implications

  • Modeling and experiments rely on simplifying assumptions (reductionism) but must acknowledge emergent properties and limits of simple models

  • Model systems enable controlled experiments to reveal mechanisms that often transfer to more complex organisms, with careful validation

  • Understanding atomic-level interactions (bonds, polarity, solvation) is essential to predict macroscopic properties like solubility, reaction rates, and protein folding

  • Radiotracers and detection methods illustrate different levels of information gathering: qualitative presence/absence, quantitative activity, and spatial localization

  • Thermodynamics and kinetics together determine whether a reaction occurs and how fast it proceeds under physiological conditions; catalysts are central to metabolism because they tune reaction rates without altering the final outcomes


Reminders for the course roadmap

  • Expect future discussions on acid-base kinetics and Henderson–Hasselbalch equation derivation

  • The material covered here ties into concepts from chem 105 and 106 and will underpin upcoming laboratory and problem-solving contexts