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Biochemistry/Interactive Tools/Metabolism Energy Calculator

Metabolism Energy Calculator

Interactive Tool | Calculate ATP yield from glucose oxidation, fatty acid$\;\beta$-oxidation, and compare energy densities of macronutrients. Modify the Python code below to explore different substrates and metabolic conditions.

Introduction: Tracking Cellular Energy

Every living cell depends on the continuous extraction of free energy from fuel molecules. The universal energy currency is adenosine triphosphate (ATP), whose hydrolysis drives virtually all endergonic cellular processes. Understanding how much ATP is produced from different substrates is fundamental to bioenergetics, exercise physiology, nutrition, and clinical medicine.

The complete oxidation of glucose involves four major stages, each contributing reducing equivalents (NADH and FADH$_2$) or substrate-level phosphorylation:

1. Glycolysis

Glucose $\rightarrow$ 2 pyruvate. Produces 2 ATP (net) and 2 NADH in the cytoplasm. Anaerobic; no oxygen required.

2. Pyruvate Dehydrogenase

2 Pyruvate $\rightarrow$ 2 acetyl-CoA. Produces 2 NADH. Links glycolysis to the TCA cycle in the mitochondrial matrix.

3. TCA Cycle (Krebs Cycle)

2 Acetyl-CoA $\rightarrow$ 4 CO$_2$. Produces 6 NADH, 2 FADH$_2$, and 2 GTP per glucose.

4. Oxidative Phosphorylation

NADH and FADH$_2$ feed electrons into the electron transport chain, driving ATP synthase. ~2.5 ATP per NADH, ~1.5 ATP per FADH$_2$.

Fatty acids undergo $\beta$-oxidation to produce acetyl-CoA, which then enters the TCA cycle. Because fatty acids are more reduced than carbohydrates, they yield substantially more ATP per molecule and per gram, explaining why fats are the body's primary long-term energy store.

Key Equations

Overall Glucose Oxidation

$$\text{C}_6\text{H}_{12}\text{O}_6 + 6\text{O}_2 \rightarrow 6\text{CO}_2 + 6\text{H}_2\text{O} \quad \Delta G^{\circ'} = -2840 \text{ kJ/mol}$$

ATP Yield from Electron Carriers

$$\text{NADH} + \text{H}^+ + \tfrac{1}{2}\text{O}_2 \rightarrow \text{NAD}^+ + \text{H}_2\text{O} \quad \Rightarrow \sim 2.5 \text{ ATP}$$

$$\text{FADH}_2 + \tfrac{1}{2}\text{O}_2 \rightarrow \text{FAD} + \text{H}_2\text{O} \quad \Rightarrow \sim 1.5 \text{ ATP}$$

Palmitate Oxidation (C16:0)

$$\text{C}_{16}\text{H}_{32}\text{O}_2 + 23\text{O}_2 \rightarrow 16\text{CO}_2 + 16\text{H}_2\text{O} \quad \sim 106 \text{ ATP}$$

Energy Density by Macronutrient

Macronutrient	Energy (kcal/g)	Energy (kJ/g)	Notes
Carbohydrates	~4	~17	Rapid energy source; glycogen stores limited
Fats	~9	~37	Most energy-dense; primary long-term store
Proteins	~4	~17	Not a primary fuel; nitrogen must be excreted
Ethanol	~7	~29	Metabolized by alcohol dehydrogenase pathway

Why Are Fats More Energy-Dense?

Fatty acids are highly reduced molecules (many C-H bonds), so they yield more electrons (as NADH and FADH$_2$) upon oxidation. Additionally, fats are stored in anhydrous form, while glycogen binds ~2 g of water per gram of glycogen, further reducing the effective energy density of carbohydrate stores in vivo. This is why adipose tissue stores approximately 6 times more energy per gram than hydrated glycogen.

Python: Metabolic Energy Calculator

The interactive code below performs a complete ATP accounting for glucose oxidation across all four stages, calculates ATP yield from fatty acid $\beta$-oxidation for chain lengths C12 through C22, and generates three comparative plots. Modify chain lengths, P/O ratios, or add new substrates to explore metabolic energetics.

Metabolic Energy Calculator

Python

Complete ATP accounting for glucose oxidation, fatty acid beta-oxidation, and macronutrient energy density comparison

metabolism_calculator.py134 lines

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

print("=" * 60)
print("  METABOLIC ENERGY CALCULATOR")
print("=" * 60)

# === GLUCOSE OXIDATION ===
print("\n--- Complete Glucose Oxidation ---")
print(f"{'Step':<35} {'NADH':>6} {'FADH2':>6} {'ATP':>6} {'GTP':>6}")
print("-" * 60)

steps = [
    ("Glycolysis (substrate level)", 0, 0, 2, 0),
    ("Glycolysis (2 NADH)", 2, 0, 0, 0),
    ("Pyruvate dehydrogenase (x2)", 2, 0, 0, 0),
    ("TCA cycle NADH (x2)", 6, 0, 0, 0),
    ("TCA cycle FADH2 (x2)", 0, 2, 0, 0),
    ("TCA cycle GTP (x2)", 0, 0, 0, 2),
]

total_nadh = total_fadh2 = total_atp = total_gtp = 0
for name, nadh, fadh2, atp, gtp in steps:
    print(f"{name:<35} {nadh:>6} {fadh2:>6} {atp:>6} {gtp:>6}")
    total_nadh += nadh
    total_fadh2 += fadh2
    total_atp += atp
    total_gtp += gtp

print("-" * 60)
print(f"{'Totals':<35} {total_nadh:>6} {total_fadh2:>6} {total_atp:>6} {total_gtp:>6}")

atp_from_nadh = total_nadh * 2.5
atp_from_fadh2 = total_fadh2 * 1.5
grand_total = total_atp + total_gtp + atp_from_nadh + atp_from_fadh2
print(f"\nATP from NADH: {total_nadh} x 2.5 = {atp_from_nadh:.1f}")
print(f"ATP from FADH2: {total_fadh2} x 1.5 = {atp_from_fadh2:.1f}")
print(f"Substrate-level ATP: {total_atp}")
print(f"GTP (approx ATP): {total_gtp}")
print(f"GRAND TOTAL: {grand_total:.1f} ATP per glucose")

# === FATTY ACID OXIDATION ===
print("\n--- Fatty Acid Beta-Oxidation ---")
print(f"{'Chain':>6} {'Cycles':>7} {'AcCoA':>7} {'NADH':>6} {'FADH2':>6} {'ATP':>6}")
print("-" * 45)
fa_data = []
for carbons in [12, 14, 16, 18, 20, 22]:
    cycles = carbons // 2 - 1
    acetyl = carbons // 2
    nadh_bo = cycles       # NADH from beta-oxidation
    fadh2_bo = cycles      # FADH2 from beta-oxidation
    nadh_tca = acetyl * 3  # NADH from TCA (3 per acetyl-CoA)
    fadh2_tca = acetyl * 1 # FADH2 from TCA (1 per acetyl-CoA)
    gtp_tca = acetyl * 1   # GTP from TCA
    atp_sub = 0            # no substrate-level ATP in beta-ox
    total_fa_atp = (nadh_bo + nadh_tca) * 2.5 + (fadh2_bo + fadh2_tca) * 1.5 + gtp_tca - 2  # -2 for activation
    print(f"C{carbons:>4} {cycles:>7} {acetyl:>7} {nadh_bo + nadh_tca:>6} {fadh2_bo + fadh2_tca:>6} {total_fa_atp:>6.1f}")
    fa_data.append((f"C{carbons}", total_fa_atp))

print("\n  * ATP values include TCA cycle processing of acetyl-CoA")
print("  * 2 ATP subtracted for fatty acid activation (to acyl-CoA)")

# === ENERGY DENSITY ===
print("\n--- Energy Density Comparison ---")
fuels_info = [
    ("Carbohydrate", 17, 4.0),
    ("Fat", 37, 9.0),
    ("Protein", 17, 4.0),
    ("Ethanol", 29, 7.1),
]
print(f"{'Fuel':<15} {'kJ/g':>8} {'kcal/g':>8}")
print("-" * 35)
for name, kj, kcal in fuels_info:
    print(f"{name:<15} {kj:>8} {kcal:>8.1f}")

# === PLOTTING ===
fig, axes = plt.subplots(1, 3, figsize=(18, 6))
fig.patch.set_facecolor('#0a0a0a')
fig.suptitle('Metabolic Energy Analysis', fontsize=16, color='#6ee7b7', fontweight='bold', y=1.02)

for ax in axes:
    ax.set_facecolor('#0a0a0a')
    ax.tick_params(colors='white', labelsize=9)
    ax.xaxis.label.set_color('white')
    ax.yaxis.label.set_color('white')
    ax.title.set_color('#6ee7b7')
    for spine in ax.spines.values():
        spine.set_color('#333')

# Plot 1: Glucose ATP sources (stacked bar)
sources = ['Substrate\nATP', 'Glycolysis\nNADH', 'PDH\nNADH', 'TCA\nNADH', 'TCA\nFADH\u2082', 'TCA\nGTP']
values = [2, 2*2.5, 2*2.5, 6*2.5, 2*1.5, 2]
colors_bar = ['#10b981', '#34d399', '#6ee7b7', '#a7f3d0', '#d1fae5', '#059669']
bars = axes[0].bar(sources, values, color=colors_bar, edgecolor='#0a0a0a', linewidth=0.5)
axes[0].set_ylabel('ATP equivalents', fontsize=11)
axes[0].set_title('Glucose: ATP Sources', fontsize=13, fontweight='bold')
for bar, val in zip(bars, values):
    axes[0].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.3,
                f'{val:.1f}', ha='center', color='white', fontsize=9)
axes[0].set_ylim(0, max(values) + 3)

# Plot 2: Substrate comparison
substrates = ['Glucose', 'C12:0\n(Lauric)', 'C16:0\n(Palmitic)', 'C18:0\n(Stearic)', 'C18:1\n(Oleic)']
oleate_atp = fa_data[3][1] - 1.5  # one less FADH2 for the double bond
atp_vals = [grand_total, fa_data[0][1], fa_data[2][1], fa_data[3][1], oleate_atp]
bar_colors = ['#10b981', '#34d399', '#6ee7b7', '#a7f3d0', '#d1fae5']
bars2 = axes[1].bar(substrates, atp_vals, color=bar_colors, edgecolor='#0a0a0a', linewidth=0.5)
axes[1].set_ylabel('Total ATP', fontsize=11)
axes[1].set_title('ATP Yield Comparison', fontsize=13, fontweight='bold')
for i, v in enumerate(atp_vals):
    axes[1].text(i, v + 2, f'{v:.0f}', ha='center', color='white', fontsize=10, fontweight='bold')
axes[1].set_ylim(0, max(atp_vals) + 15)

# Plot 3: Energy density
fuels = ['Carbohydrate', 'Fat', 'Protein', 'Ethanol']
kj_per_g = [17, 37, 17, 29]
hbar_colors = ['#10b981', '#34d399', '#6ee7b7', '#a7f3d0']
hbars = axes[2].barh(fuels, kj_per_g, color=hbar_colors, edgecolor='#0a0a0a', linewidth=0.5)
axes[2].set_xlabel('Energy (kJ/g)', fontsize=11)
axes[2].set_title('Energy Density by Macronutrient', fontsize=13, fontweight='bold')
for i, v in enumerate(kj_per_g):
    axes[2].text(v + 0.5, i, f'{v} kJ/g', va='center', color='white', fontsize=10)
axes[2].set_xlim(0, max(kj_per_g) + 8)
axes[2].tick_params(axis='y', labelsize=10)

plt.tight_layout(pad=3.0)
plt.savefig('metabolism_energy.png', dpi=150, bbox_inches='tight', facecolor='#0a0a0a')
print("\nPlot saved: metabolism_energy.png")
print("\n" + "=" * 60)
print("  Calculator complete. Modify parameters above to explore!")
print("=" * 60)

Click Run to execute the Python code

Code will be executed with Python 3 on the server

Interpretation Guide

Plot 1: Glucose ATP Sources

The stacked bar chart breaks down the ~30-32 ATP produced per glucose molecule by source. Notice that the vast majority of ATP comes from oxidative phosphorylation (NADH and FADH$_2$), not from substrate-level phosphorylation. The TCA cycle NADH (6 molecules per glucose) is the single largest contributor, yielding 15 ATP equivalents.

Key insight: Only ~6.5% of the total ATP comes from substrate-level phosphorylation in glycolysis. This underscores why aerobic metabolism is so much more efficient than anaerobic glycolysis (which yields only 2 ATP per glucose).

Plot 2: Substrate ATP Yield Comparison

This chart compares total ATP yield per mole of substrate. Palmitate (C16:0) yields roughly 3.3 times more ATP than glucose, reflecting its greater number of oxidizable carbon atoms and higher reduction state. Longer-chain fatty acids yield progressively more ATP.

Modifiable parameters: Change the chain lengths in the code, add odd-chain fatty acids (which produce propionyl-CoA), or adjust the P/O ratios (NADH: 2.5 vs. older value of 3.0; FADH$_2$: 1.5 vs. older value of 2.0) to see how different assumptions affect total yield.

Plot 3: Energy Density

The horizontal bar chart shows physiological energy density in kJ per gram. Fat provides more than double the energy of carbohydrates or proteins per gram. Ethanol falls between the two, which explains its significant caloric contribution despite not being a macronutrient in the traditional sense.

Clinical relevance: These values are the Atwater factors used in nutrition labeling worldwide. They account for incomplete digestion and absorption in vivo, and represent metabolizable energy rather than total combustion energy (bomb calorimeter values).

Important Caveats

P/O ratios are approximate: The values 2.5 (NADH) and 1.5 (FADH$_2$) reflect the current consensus based on the chemiosmotic mechanism, but the actual ratio depends on proton leak, shuttle systems (malate-aspartate vs. glycerol-3-phosphate), and mitochondrial efficiency.
Glycolytic NADH transport cost: Cytoplasmic NADH from glycolysis must be shuttled into mitochondria. The malate-aspartate shuttle preserves the full 2.5 ATP/NADH, but the glycerol-3-phosphate shuttle yields only 1.5 ATP/NADH.
Fatty acid activation cost: 2 ATP equivalents are consumed to activate a fatty acid to acyl-CoA (ATP $\rightarrow$ AMP + PP$_i$, equivalent to 2 ATP).
Unsaturated fatty acids: Oleate (C18:1) yields slightly less ATP than stearate (C18:0) because one $\beta$-oxidation cycle is bypassed at the existing double bond (no FADH$_2$ produced at that step).

Quick Reference: ATP Accounting

Glucose (Aerobic)

Glycolysis: 2 ATP + 2 NADH
PDH: 2 NADH
TCA cycle: 6 NADH + 2 FADH$_2$ + 2 GTP
Total: ~30-32 ATP per glucose

Palmitate (C16:0)

$\beta$-oxidation: 7 cycles $\rightarrow$ 8 acetyl-CoA
7 NADH + 7 FADH$_2$ from $\beta$-ox
8 turns TCA: 24 NADH + 8 FADH$_2$ + 8 GTP
Total: ~106 ATP (minus 2 for activation)

Electron Carrier Values

NADH: ~2.5 ATP (P/O ratio)
FADH$_2$: ~1.5 ATP (P/O ratio)
GTP $\approx$ ATP (nucleoside diphosphate kinase)
Older textbooks: 3.0 and 2.0 respectively

Efficiency

ATP hydrolysis: $\Delta G \approx -30.5$ kJ/mol
32 ATP = ~976 kJ captured
Glucose combustion: $-2840$ kJ/mol
Thermodynamic efficiency: ~34%

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