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Module 4: Silk, Venom & Defensive Chemistry

Insects and arachnids have evolved an extraordinary arsenal of biochemical weapons and materials. Spider silk rivals the best engineering fibers, bombardier beetles deploy boiling chemical sprays, fireflies produce light with near-perfect quantum efficiency, and monarch butterflies sequester cardiac poisons that deter vertebrate predators. This module derives the physics and chemistry underlying these remarkable adaptations.

1. Spider Silk: A Hierarchical Nanocomposite

Spiders produce up to seven types of silk, each optimized for a specific function: dragline (major ampullate) for the structural frame of the web and lifeline, capture spiral (flagelliform) for prey retention, minor ampullate for auxiliary frame, tubuliform for egg sacs, aciniform for wrapping prey, piriform for attachment discs, and aggregate for the sticky glue on capture threads.

Dragline Silk: Structure and Properties

Dragline silk (from the major ampullate gland) is the most studied. Its remarkable mechanical properties arise from a hierarchical nanocomposite architecture:

\(\beta\)-sheet nanocrystallites (\(\sim 2 \times 5 \times 7\,\text{nm}\)): formed from polyalanine (A\(_n\)) repeats, these cross-linked crystallites provide stiffness and strength. Young's modulus of the crystalline phase: \(E_c \sim 160\,\text{GPa}\).
Amorphous matrix: composed of glycine-rich motifs (GGX, GPGXX) that form 3\(_1\)-helices and \(\beta\)-spirals, providing extensibility and energy dissipation through conformational changes.

Two-Phase Stress-Strain Model

The mechanical response of dragline silk can be modeled as a parallel composite of crystalline and amorphous phases with volume fractions \(f_c \approx 0.3\) and \(f_a \approx 0.7\):

\[\sigma(\varepsilon) = f_c\,\sigma_c(\varepsilon) + f_a\,\sigma_a(\varepsilon)\]

The crystalline phase behaves elastically until yield at \(\varepsilon_y \approx 2\%\), then softens as \(\beta\)-sheet hydrogen bonds begin to fail:

\[\sigma_c(\varepsilon) = \begin{cases} E_c\,\varepsilon & \varepsilon < \varepsilon_y \\ E_c\,\varepsilon_y\,e^{-(\varepsilon - \varepsilon_y)/\varepsilon_0} & \varepsilon \geq \varepsilon_y \end{cases}\]

The amorphous phase exhibits entropic elasticity at small strains (uncoiling of flexible chains) followed by strain hardening as chains align and hydrogen bonds reform:

\[\sigma_a(\varepsilon) = G_0\left(\varepsilon + \beta\varepsilon^3 + \gamma\,\max(0,\,\varepsilon - \varepsilon_h)^{5/2}\right)\]

This yields the characteristic four-region stress-strain curve: (1) stiff initial response (crystal-dominated), (2) yield plateau (crystal failure), (3) strain hardening (amorphous chain alignment), and (4) final failure at \(\sim 30\%\) strain. Toughness (area under the curve) reaches \(\sim 160\,\text{MJ/m}^3\), exceeding Kevlar (\(\sim 50\,\text{MJ/m}^3\)) and far surpassing steel (\(\sim 6\,\text{MJ/m}^3\)).

Spidroin Proteins

The molecular building blocks are spidroins—large proteins (250–350 kDa) with highly repetitive core sequences. Major ampullate spidroins MaSp1 and MaSp2 contain characteristic motifs: GGX (3\(_1\)-helix, stiff spacer), GPGXX (\(\beta\)-spiral, elastic), and A\(_n\) (polyalanine, forms \(\beta\)-sheet crystals). MaSp2 is enriched in proline-containing GPGXX motifs, conferring greater extensibility.

Silk Stress-Strain: Two-Phase Nanocomposite

2. Bombardier Beetle: Chemical Cannon

The bombardier beetle (Brachinus, Stenaptinus) deploys one of nature's most dramatic chemical defenses: a boiling, noxious spray ejected with audible detonations at up to 500 pulses per second. The reaction chamber operates as a biological pulse-jet engine.

Reaction Chemistry

Two reactants are stored separately in a reservoir gland: hydrogen peroxide (H\(_2\)O\(_2\), ~25% w/v) and hydroquinones (C\(_6\)H\(_4\)(OH)\(_2\)). When threatened, the beetle forces these into a thick-walled reaction chamber containing catalase and peroxidase enzymes:

\[\text{H}_2\text{O}_2 + \text{C}_6\text{H}_4(\text{OH})_2 \xrightarrow{\text{catalase, peroxidase}} \text{C}_6\text{H}_4\text{O}_2 + 2\text{H}_2\text{O}\]

\[2\,\text{H}_2\text{O}_2 \xrightarrow{\text{catalase}} 2\,\text{H}_2\text{O} + \text{O}_2\]

Adiabatic Temperature Rise

The combined reaction enthalpy is \(\Delta H \approx -203\,\text{kJ/mol}\). For an adiabatic reaction (no heat loss to surroundings), the temperature rise is:

\[\Delta T = \frac{c \cdot |\Delta H|}{\rho \cdot C_p}\]

where \(c\) is the effective molar concentration of reactant, \(\rho\) is the solution density, and \(C_p\) is the specific heat capacity. At an effective concentration of ~1.5 M, this gives \(\Delta T \approx 73\,°\text{C}\), sufficient to bring the mixture from ambient (~25°C) to the observed ~100°C.

Pulsed Discharge Mechanism

Arndt et al. (2015) used synchrotron X-ray imaging to reveal that the discharge is inherently pulsed at ~500 Hz. A passive valve between the reservoir and reaction chamber opens and closes due to pressure oscillations: the exothermic reaction generates steam and O\(_2\), building pressure that expels the spray and closes the valve. As pressure drops, the valve reopens, admitting fresh reactants. This cycle repeats autonomously—a biological relaxation oscillator.

Bombardier Beetle Reaction Chamber

3. Firefly Bioluminescence

Firefly bioluminescence achieves a quantum yield of 0.41 —meaning 41% of chemical reaction events produce a photon. This is the highest known bioluminescence efficiency (compare: jellyfish GFP fluorescence ~0.79, but the underlying aequorin reaction has quantum yield ~0.16).

Reaction Mechanism

\[\text{Luciferin} + \text{ATP} + \text{O}_2 \xrightarrow{\text{luciferase}} \text{Oxyluciferin}^* + \text{AMP} + \text{PP}_i + \text{CO}_2\]

\[\text{Oxyluciferin}^* \to \text{Oxyluciferin} + h\nu\]

The excited-state oxyluciferin (\(^*\)) relaxes to its ground state by emitting a photon. The emission wavelength depends on the HOMO-LUMO energy gap of the oxyluciferin:

\[\lambda = \frac{hc}{\Delta E_{\text{HOMO-LUMO}}} = \frac{1240\,\text{eV}\cdot\text{nm}}{\Delta E}\]

For Photinus pyralis (peak at 562 nm), the HOMO-LUMO gap is \(\Delta E = 1240/562 = 2.21\,\text{eV}\). The emission color can be tuned by the luciferase active site environment: a more polar environment stabilizes the excited state (red shift), while a rigid hydrophobic pocket preserves the energy gap (green emission).

Flash Patterns as Species Codes

Firefly flash patterns are species-specific mate recognition signals. Photinus males fly while flashing characteristic temporal patterns; females respond from vegetation with species-specific delays. The femme fatale Photuris females mimic Photinus flash patterns to lure and eat males of other species—aggressive mimicry using the communication channel of their prey.

4. Monarch Butterfly Cardiac Glycosides

Monarch butterflies (Danaus plexippus) sequester cardiac glycosides (cardenolides) from their milkweed (Asclepias) host plants during larval feeding. These steroid compounds inhibit the Na\(^+\)/K\(^+\)-ATPase pump, a ubiquitous membrane enzyme essential for maintaining electrochemical gradients.

Mechanism of Toxicity

Cardiac glycosides bind the extracellular face of the \(\alpha\)-subunit of Na\(^+\)/K\(^+\)-ATPase, stabilizing the E2-P phosphoenzyme intermediate and blocking the pump cycle. This raises intracellular Na\(^+\), which in turn increases intracellular Ca\(^{2+}\) via the Na\(^+\)/Ca\(^{2+}\) exchanger (NCX), leading to cardiac arrhythmia and emesis in vertebrate predators.

Dose-Response Relationship

The emetic response in avian predators follows a sigmoidal (Hill) dose-response curve:

\[E = E_{\max}\,\frac{C^n}{EC_{50}^n + C^n}\]

where \(C\) is the cardiac glycoside dose (\(\mu\text{g/g}\) body weight), \(EC_{50} \approx 5\,\mu\text{g/g}\) is the half-maximal effective concentration, and \(n \approx 2.5\) is the Hill coefficient reflecting cooperative binding. A typical monarch contains ~200 \(\mu\)g of cardenolides; for an 80 g blue jay, this gives an effective dose of \(200/80 = 2.5\,\mu\text{g/g}\), sufficient to induce strong nausea and vomiting.

Monarchs themselves are resistant due to amino acid substitutions in the \(\alpha\)-subunit (notably N122H), which reduces cardenolide binding affinity by ~100-fold. This target-site insensitivity has evolved convergently in at least four insect orders that feed on cardenolide-producing plants.

Simulation: Silk Mechanics & Two-Phase Model

Python

script.py178 lines

import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

np.random.seed(42)

fig, axes = plt.subplots(2, 2, figsize=(16, 14))
fig.patch.set_facecolor('#030712')
fig.suptitle("Spider Silk Mechanics & Two-Phase Model", fontsize=16, color='white', fontweight='bold', y=0.98)

# ── Panel 1: Stress-strain curves for silk types vs engineering materials ──
ax1 = axes[0, 0]
ax1.set_facecolor('#0a1a0a')

strain = np.linspace(0, 0.35, 500)

# Dragline silk (major ampullate): stiff initial, then yield, then strain-hardening
def dragline_stress(eps):
    # Two-phase model: crystalline (stiff) + amorphous (extensible)
    E_cryst = 10e9  # Pa (crystalline modulus)
    E_amorph = 0.5e9
    eps_yield = 0.02
    eps_hard = 0.15
    stress = np.where(eps < eps_yield,
        E_cryst * eps,
        E_cryst * eps_yield + E_amorph * (eps - eps_yield) +
        0.5e9 * np.maximum(0, eps - eps_hard)**1.5)
    return stress / 1e9  # GPa

# Capture spiral (flagelliform): very extensible
def capture_stress(eps):
    E_low = 0.003e9
    hardening = 0.05e9
    return (E_low * eps + hardening * eps**3) / 1e9

# Steel
def steel_stress(eps):
    E_steel = 200e9
    yield_steel = 0.002
    sigma_y = 0.4  # GPa
    return np.where(eps < yield_steel, E_steel * eps / 1e9, sigma_y * np.ones_like(eps))

# Kevlar
def kevlar_stress(eps):
    E_kev = 70e9
    max_strain = 0.035
    stress = E_kev * np.minimum(eps, max_strain) / 1e9
    stress[eps > max_strain] = 0  # brittle failure
    return stress

strain_ext = np.linspace(0, 2.7, 500)  # extended range for capture silk

ax1.plot(strain * 100, dragline_stress(strain), color='#4ade80', linewidth=2.5, label='Dragline silk (MA)')
ax1.plot(strain_ext * 100, capture_stress(strain_ext), color='#c084fc', linewidth=2.5, label='Capture silk (Flag)')
ax1.plot(strain * 100, steel_stress(strain), color='#ef4444', linewidth=2.5, label='High-tensile steel')
ax1.plot(strain * 100, kevlar_stress(strain), color='#facc15', linewidth=2.5, label='Kevlar')

ax1.set_xlabel('Strain (%)', color='white', fontsize=11)
ax1.set_ylabel('Stress (GPa)', color='white', fontsize=11)
ax1.set_title('Stress-Strain: Silk vs Engineering Materials\nDragline: 1.1 GPa strength, 30% extensibility', color='#86efac', fontsize=12)
ax1.set_xlim(0, 40)
ax1.set_ylim(0, 3.5)
ax1.tick_params(colors='white')
for sp in ax1.spines.values():
    sp.set_color('#4ade80')
ax1.grid(True, alpha=0.15, color='#4ade80')
ax1.legend(fontsize=9, facecolor='#0a1a0a', edgecolor='#4ade80', labelcolor='white')

# ── Panel 2: Two-phase model decomposition ──
ax2 = axes[0, 1]
ax2.set_facecolor('#0a1a0a')

strain_model = np.linspace(0, 0.30, 300)

# Crystalline phase (beta-sheets)
f_cryst = 0.3  # crystalline fraction
E_c = 160e9  # GPa for beta-sheet nanocrystals
eps_break_c = 0.015  # crystal yield strain

stress_cryst = np.where(strain_model < eps_break_c,
    f_cryst * E_c * strain_model,
    f_cryst * E_c * eps_break_c * np.exp(-(strain_model - eps_break_c) / 0.05))

# Amorphous phase (flexible chains)
f_amorph = 0.7
# Entropic elasticity at low strain, then chain alignment
N_kuhn = 20  # Kuhn segments
b_kuhn = 0.4e-9  # Kuhn length
kT = 4.11e-21
L_contour = N_kuhn * b_kuhn
r_frac = strain_model * 0.5  # fraction of contour length

# WLC model simplified
stress_amorph = f_amorph * 0.5e9 * (strain_model + 2 * strain_model**3 + 10 * np.maximum(0, strain_model - 0.15)**2.5)

stress_total = stress_cryst / 1e9 + stress_amorph / 1e9

ax2.plot(strain_model * 100, stress_cryst / 1e9, color='#ef4444', linewidth=2, linestyle='--', label='Crystalline (beta-sheet)')
ax2.plot(strain_model * 100, stress_amorph / 1e9, color='#60a5fa', linewidth=2, linestyle='--', label='Amorphous (flexible)')
ax2.plot(strain_model * 100, stress_total, color='#4ade80', linewidth=3, label='Total (two-phase)')
ax2.fill_between(strain_model * 100, 0, stress_cryst / 1e9, alpha=0.08, color='#ef4444')
ax2.fill_between(strain_model * 100, stress_cryst / 1e9, stress_total, alpha=0.08, color='#60a5fa')

ax2.set_xlabel('Strain (%)', color='white', fontsize=11)
ax2.set_ylabel('Stress (GPa)', color='white', fontsize=11)
ax2.set_title('Two-Phase Model of Dragline Silk\nCrystalline beta-sheets + amorphous matrix', color='#86efac', fontsize=12)
ax2.tick_params(colors='white')
for sp in ax2.spines.values():
    sp.set_color('#4ade80')
ax2.grid(True, alpha=0.15, color='#4ade80')
ax2.legend(fontsize=9, facecolor='#0a1a0a', edgecolor='#4ade80', labelcolor='white')

# Annotate regions
ax2.annotate('Yield point\n(crystals fail)', xy=(1.5, stress_cryst[int(0.015/0.30*300)] / 1e9),
    xytext=(8, 1.5), fontsize=9, color='#ef4444',
    arrowprops=dict(arrowstyle='->', color='#ef4444', lw=1.5))
ax2.annotate('Strain\nhardening', xy=(25, stress_total[int(0.25/0.30*300)]),
    xytext=(18, 2.0), fontsize=9, color='#4ade80',
    arrowprops=dict(arrowstyle='->', color='#4ade80', lw=1.5))

# ── Panel 3: Toughness comparison (area under curve) ──
ax3 = axes[1, 0]
ax3.set_facecolor('#0a1a0a')

materials = ['Dragline\nsilk', 'Capture\nsilk', 'Kevlar', 'Steel', 'Rubber', 'Tendon']
toughness = [160, 150, 50, 6, 100, 7.5]  # MJ/m^3
strength = [1.1, 0.5, 3.6, 0.4, 0.05, 0.15]  # GPa
extensibility = [30, 270, 3.5, 0.8, 850, 10]  # %

colors_mat = ['#4ade80', '#c084fc', '#facc15', '#ef4444', '#60a5fa', '#fb923c']

bars = ax3.bar(materials, toughness, color=colors_mat, alpha=0.7, edgecolor=colors_mat, linewidth=1.5)

for bar, t, s in zip(bars, toughness, strength):
    ax3.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 3,
             f'{t} MJ/m^3\n({s} GPa)', ha='center', color='white', fontsize=9)

ax3.set_ylabel('Toughness (MJ/m^3)', color='white', fontsize=11)
ax3.set_title('Material Toughness Comparison\nSilk: unmatched combination of strength + extensibility', color='#86efac', fontsize=12)
ax3.tick_params(colors='white')
for sp in ax3.spines.values():
    sp.set_color('#4ade80')
ax3.grid(True, alpha=0.15, color='#4ade80', axis='y')

# ── Panel 4: Spidroin protein motif composition ──
ax4 = axes[1, 1]
ax4.set_facecolor('#0a1a0a')

motifs = ['GGX\n(31-helix)', 'GPGXX\n(beta-spiral)', 'An\n(beta-sheet)', 'GGX+GPGXX\n(spacer)', 'Other']
masp1 = [37, 15, 25, 13, 10]
masp2 = [20, 35, 20, 15, 10]

x_pos = np.arange(len(motifs))
width = 0.35

bars1 = ax4.bar(x_pos - width/2, masp1, width, color='#4ade80', alpha=0.7, label='MaSp1', edgecolor='#4ade80')
bars2 = ax4.bar(x_pos + width/2, masp2, width, color='#c084fc', alpha=0.7, label='MaSp2', edgecolor='#c084fc')

ax4.set_xticks(x_pos)
ax4.set_xticklabels(motifs, fontsize=9)
ax4.set_ylabel('Composition (%)', color='white', fontsize=11)
ax4.set_title('Spidroin Protein Motifs\nMaSp1 vs MaSp2 in dragline silk', color='#86efac', fontsize=12)
ax4.tick_params(colors='white')
for sp in ax4.spines.values():
    sp.set_color('#4ade80')
ax4.grid(True, alpha=0.15, color='#4ade80', axis='y')
ax4.legend(fontsize=10, facecolor='#0a1a0a', edgecolor='#4ade80', labelcolor='white')

plt.tight_layout()
plt.savefig('output.png', dpi=150, bbox_inches='tight', facecolor='#030712')
print("=== Spider Silk Mechanics ===")
print(f"Dragline silk toughness: {toughness[0]} MJ/m^3")
print(f"Kevlar toughness: {toughness[2]} MJ/m^3")
print(f"Dragline silk tensile strength: {strength[0]} GPa")
print(f"Steel tensile strength: {strength[3]} GPa")

Click Run to execute the Python code

Code will be executed with Python 3 on the server

Simulation: Bombardier Beetle, Bioluminescence & Toxicology

Python

script.py183 lines

import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

np.random.seed(42)

fig, axes = plt.subplots(2, 2, figsize=(16, 14))
fig.patch.set_facecolor('#030712')
fig.suptitle("Bombardier Beetle, Bioluminescence & Defensive Chemistry", fontsize=16, color='white', fontweight='bold', y=0.98)

# ── Panel 1: Bombardier beetle temperature pulse ──
ax1 = axes[0, 0]
ax1.set_facecolor('#0a1a0a')

# Pulsed discharge: 500 Hz pulses
t = np.linspace(0, 20e-3, 2000)  # 20 ms window
pulse_freq = 500  # Hz
pulse_period = 1 / pulse_freq  # 2 ms

# Each pulse: rapid heating to ~100C, then cooling
T_ambient = 25  # C
T_peak = 100  # C
tau_heat = 0.1e-3  # heating time constant
tau_cool = 0.8e-3  # cooling time constant

temperature = np.zeros_like(t)
for i, ti in enumerate(t):
    t_in_cycle = ti % pulse_period
    if t_in_cycle < 0.3e-3:  # heating phase
        temperature[i] = T_ambient + (T_peak - T_ambient) * (1 - np.exp(-t_in_cycle / tau_heat))
    else:  # cooling phase
        T_at_peak = T_ambient + (T_peak - T_ambient) * (1 - np.exp(-0.3e-3 / tau_heat))
        temperature[i] = T_ambient + (T_at_peak - T_ambient) * np.exp(-(t_in_cycle - 0.3e-3) / tau_cool)

ax1.plot(t * 1000, temperature, color='#ef4444', linewidth=1.5)
ax1.fill_between(t * 1000, T_ambient, temperature, alpha=0.15, color='#ef4444')
ax1.axhline(y=100, color='#facc15', linewidth=1, linestyle='--', alpha=0.7)
ax1.text(15, 102, '100 C (boiling)', color='#facc15', fontsize=10)
ax1.set_xlabel('Time (ms)', color='white', fontsize=11)
ax1.set_ylabel('Temperature (C)', color='white', fontsize=11)
ax1.set_title('Bombardier Beetle Pulsed Discharge\n500 Hz pulses, each reaching ~100 C', color='#86efac', fontsize=12)
ax1.tick_params(colors='white')
for sp in ax1.spines.values():
    sp.set_color('#4ade80')
ax1.grid(True, alpha=0.15, color='#4ade80')

# Inset: reaction enthalpy
ax1_inset = ax1.inset_axes([0.55, 0.5, 0.4, 0.4])
ax1_inset.set_facecolor('#0a1a0a')
reactants = ['H2O2 +\nHydroquinone', 'Catalase +\nPeroxidase', 'Quinone +\nH2O + O2']
energies = [0, -50, -203]  # kJ/mol relative
ax1_inset.plot([0, 1, 2], energies, 'o-', color='#4ade80', linewidth=2, markersize=8)
ax1_inset.fill_between([0, 1, 2], energies, alpha=0.1, color='#4ade80')
ax1_inset.set_xticks([0, 1, 2])
ax1_inset.set_xticklabels(['Reactants', 'TS', 'Products'], fontsize=8, color='white')
ax1_inset.set_ylabel('Energy (kJ/mol)', fontsize=8, color='white')
ax1_inset.set_title('Reaction Energy', fontsize=9, color='#86efac')
ax1_inset.tick_params(colors='white', labelsize=7)
for sp in ax1_inset.spines.values():
    sp.set_color('#4ade80')

# ── Panel 2: Adiabatic temperature rise calculation ──
ax2 = axes[0, 1]
ax2.set_facecolor('#0a1a0a')

# H2O2 concentration vs temperature rise
# H2O2 + hydroquinone -> quinone + H2O + O2
# Delta_H ~ -203 kJ/mol total
# Adiabatic: Delta_T = Delta_H / (m * Cp)
c_h2o2 = np.linspace(0.1, 5, 200)  # mol/L
delta_H = 203e3  # J/mol
Cp_water = 4186  # J/(kg*K)
rho_sol = 1000  # kg/m^3

# Temperature rise per liter
delta_T = (c_h2o2 * delta_H) / (rho_sol * Cp_water)

ax2.plot(c_h2o2, delta_T, color='#4ade80', linewidth=2.5)
ax2.fill_between(c_h2o2, delta_T, alpha=0.1, color='#4ade80')
ax2.axhline(y=75, color='#ef4444', linewidth=1.5, linestyle='--', alpha=0.7)
ax2.text(0.5, 78, 'Delta T to reach 100 C from 25 C', color='#ef4444', fontsize=10)

# Beetle uses ~25% H2O2 => ~7.3 M (but mixed, effective ~1.5 M)
beetle_conc = 1.55
beetle_dT = (beetle_conc * delta_H) / (rho_sol * Cp_water)
ax2.plot(beetle_conc, beetle_dT, 'o', color='#facc15', markersize=10, zorder=5)
ax2.text(beetle_conc + 0.2, beetle_dT + 3, f'Beetle: ~{beetle_conc:.1f} M effective\nDelta T = {beetle_dT:.0f} C', color='#facc15', fontsize=9)

ax2.set_xlabel('Effective H2O2 Concentration (mol/L)', color='white', fontsize=11)
ax2.set_ylabel('Adiabatic Temperature Rise (C)', color='white', fontsize=11)
ax2.set_title('Adiabatic Heating: H2O2 Decomposition\nDelta T = c * Delta_H / (rho * Cp)', color='#86efac', fontsize=12)
ax2.tick_params(colors='white')
for sp in ax2.spines.values():
    sp.set_color('#4ade80')
ax2.grid(True, alpha=0.15, color='#4ade80')

# ── Panel 3: Firefly bioluminescence spectrum ──
ax3 = axes[1, 0]
ax3.set_facecolor('#0a1a0a')

# Bioluminescence emission spectra for different firefly species
wavelength = np.linspace(450, 700, 300)  # nm

def emission_spectrum(lam, peak, width):
    return np.exp(-((lam - peak)**2) / (2 * width**2))

# Different Photinus species have slightly different peak wavelengths
species_data = [
    ('Photinus pyralis', 562, 25, '#4ade80'),
    ('Photinus scintillans', 570, 28, '#86efac'),
    ('Photuris versicolor (green)', 538, 22, '#22c55e'),
    ('Photuris versicolor (orange)', 583, 30, '#facc15'),
    ('Luciola cruciata', 556, 24, '#a3e635'),
]

for name, peak, width, col in species_data:
    spectrum = emission_spectrum(wavelength, peak, width)
    ax3.plot(wavelength, spectrum, color=col, linewidth=2, label=f'{name} ({peak} nm)')
    ax3.fill_between(wavelength, spectrum, alpha=0.05, color=col)

# HOMO-LUMO gap annotation
E_green = 1240 / 538  # eV
E_yellow = 1240 / 562
ax3.text(620, 0.85, f'HOMO-LUMO gap:\n538 nm = {E_green:.2f} eV\n562 nm = {E_yellow:.2f} eV', color='white', fontsize=9,
         bbox=dict(boxstyle='round', facecolor='#0a1a0a', edgecolor='#4ade80', alpha=0.8))

ax3.set_xlabel('Wavelength (nm)', color='white', fontsize=11)
ax3.set_ylabel('Relative Intensity', color='white', fontsize=11)
ax3.set_title('Firefly Bioluminescence Spectra\nQuantum yield = 0.41 (highest known)', color='#86efac', fontsize=12)
ax3.tick_params(colors='white')
for sp in ax3.spines.values():
    sp.set_color('#4ade80')
ax3.grid(True, alpha=0.15, color='#4ade80')
ax3.legend(fontsize=8, facecolor='#0a1a0a', edgecolor='#4ade80', labelcolor='white', loc='upper left')

# ── Panel 4: Monarch cardiac glycoside dose-response ──
ax4 = axes[1, 1]
ax4.set_facecolor('#0a1a0a')

# Dose-response curve for avian emesis
# Hill equation: E = Emax * C^n / (EC50^n + C^n)
dose = np.logspace(-2, 2, 200)  # ug/g body weight
EC50 = 5.0  # ug/g
n_hill = 2.5  # Hill coefficient (cooperative)
Emax = 100  # % emesis

response = Emax * dose**n_hill / (EC50**n_hill + dose**n_hill)

ax4.semilogx(dose, response, color='#4ade80', linewidth=2.5)
ax4.fill_between(dose, response, alpha=0.1, color='#4ade80')
ax4.axhline(y=50, color='#94a3b8', linewidth=1, linestyle='--', alpha=0.5)
ax4.axvline(x=EC50, color='#94a3b8', linewidth=1, linestyle='--', alpha=0.5)
ax4.text(EC50 * 1.3, 45, f'EC50 = {EC50} ug/g', color='#facc15', fontsize=10)

# Typical monarch body burden
monarch_dose = 200  # ug total per butterfly
predator_mass = 80  # g blue jay
effective_dose = monarch_dose / predator_mass
ax4.axvline(x=effective_dose, color='#ef4444', linewidth=1.5, linestyle=':', alpha=0.8)
ax4.text(effective_dose * 1.2, 20, f'Monarch dose\n{effective_dose:.1f} ug/g', color='#ef4444', fontsize=9)

ax4.set_xlabel('Cardiac Glycoside Dose (ug/g body weight)', color='white', fontsize=11)
ax4.set_ylabel('Emetic Response (%)', color='white', fontsize=11)
ax4.set_title('Dose-Response: Cardiac Glycosides in Avian Predators\nHill equation, n = 2.5 (cooperative binding)', color='#86efac', fontsize=12)
ax4.tick_params(colors='white')
for sp in ax4.spines.values():
    sp.set_color('#4ade80')
ax4.grid(True, alpha=0.15, color='#4ade80')

# Na/K-ATPase mechanism inset
ax4.text(0.015, 85, 'Mechanism: Cardiac glycosides\ninhibit Na+/K+-ATPase\n-> intracellular Na+ rises\n-> Ca2+ overload via NCX\n-> emesis + cardiac arrest',
         color='#94a3b8', fontsize=8, bbox=dict(boxstyle='round', facecolor='#0a1a0a', edgecolor='#4ade80', alpha=0.8))

plt.tight_layout()
plt.savefig('output.png', dpi=150, bbox_inches='tight', facecolor='#030712')
print("=== Defensive Chemistry Simulations ===")
print(f"Bombardier beetle adiabatic DeltaT at {beetle_conc:.1f} M: {beetle_dT:.0f} C")
print(f"Photinus pyralis emission peak: 562 nm ({1240/562:.2f} eV)")
print(f"Quantum yield of firefly bioluminescence: 0.41")
print(f"Monarch dose to blue jay: {effective_dose:.1f} ug/g (EC50 = {EC50} ug/g)")

Click Run to execute the Python code

Code will be executed with Python 3 on the server

References

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7. Shimomura, O. (2006). Bioluminescence: Chemical Principles and Methods. World Scientific.

8. Branchini, B. R., Murtiashaw, M. H., Magyar, R. A., & Anderson, S. M. (2000). The role of lysine 529 in firefly luciferase. Biochemistry, 39(18), 5433–5440.

9. Lloyd, J. E. (1975). Aggressive mimicry in Photuris fireflies: signal repertoires by femmes fatales. Science, 187(4175), 452–453.

10. Brower, L. P. (1969). Ecological chemistry. Scientific American, 220(2), 22–29.

11. Dobler, S., Dalla, S., Wagschal, V., & Agrawal, A. A. (2012). Community-wide convergent evolution in insect adaptation to toxic cardenolides by substitutions in the Na,K-ATPase. Proceedings of the National Academy of Sciences, 109(32), 13040–13045.

12. Agrawal, A. A., Petschenka, G., Bingham, R. A., Weber, M. G., & Rasmann, S. (2012). Toxic cardenolides: chemical ecology and coevolution of specialized plant–herbivore interactions. New Phytologist, 194(1), 28–45.

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