Bug: Energy Conservation Issue in Krebs Cycle Simulation

[csmangum]

Bug: Energy Conservation Issue in Krebs Cycle Simulation

Description

The current Krebs Cycle simulation produces plausible metabolite changes but does not conserve energy correctly. The simulation shows an increase in energy state from 4935.00 kJ/mol to 7251.00 kJ/mol, with a discrepancy of +2316.00 kJ/mol. This energy imbalance indicates that energy is not being properly accounted for, which contradicts the principles of the Krebs Cycle.

Expected Behavior

The energy state should remain balanced throughout the simulation, with no net energy gain or loss, aside from expected energy transformations (e.g., ATP synthesis, NADH/FADH₂ production). The final energy state should align with biochemical expectations.

Steps to Reproduce

1. Run the simulate_kerbs_cycle.py script.
2. Observe the final energy state and compare with the initial energy state.

Observed Behavior

• Initial energy state: 4935.00 kJ/mol
• Final energy state: 7251.00 kJ/mol
• Energy difference: +2316.00 kJ/mol
• Metabolite changes (ATP, NADH, FADH₂, CO₂) seem correct, but energy is not conserved.

Metabolite Levels:

• Initial Metabolite Levels: {'atp': 100.0, 'adp': 10.0, 'bisphosphoglycerate_1_3': 10.0, ...}
• Final Metabolite Levels: {'atp': 104.0, 'adp': 6.0, 'bisphosphoglycerate_1_3': 10.0, ...}

Possible Causes

• Incorrect energy calculations for certain reactions.
• Missing or incorrectly modeled reactions that may affect energy balance.
• Scaling factors or unit mismatches in energy computation.
• Incomplete metabolite interaction models that could affect energy accounting.

Investigation Checklist

1. Review Energy Calculations: Ensure the energy changes for each reaction step are accurate and biologically realistic.
2. Validate Reaction Stoichiometry: Confirm that all reactions and metabolite changes are correctly balanced.
3. Verify Simulation Parameters: Ensure that initial conditions and parameters (e.g., cofactor concentrations) are accurate.
4. Implement Energy Conservation Checks: Add constraints or checks to the simulation to enforce energy conservation and detect discrepancies earlier.
5. Compare with Known Data: Cross-reference with known biochemical data to validate the simulation results.

Environment

• Python Version: x.x.x
• Simulation Script: simulate_kerbs_cycle.py
• Version: Commit SHA or Tag

Additional Context

The issue might be related to how reactions involving NAD⁺, NADH, FAD, and FADH₂ are modeled, as well as the conversion of energy in the form of ATP. This requires further debugging and validation with reference to biochemical standards.

Logs

2024-10-23 17:34:14,295 - pyology.reporter - INFO - Initial metabolite levels: {'atp': 100.0, 'adp': 10.0, ...}
2024-10-23 17:34:14,311 - pyology.reporter - INFO - Krebs Cycle completed. Produced 8 CO2.
2024-10-23 17:34:14,311 - pyology.reporter - WARNING - Energy not conserved in Krebs Cycle. Difference: 2316.0

---

Priority

High – Energy conservation is a critical component of biochemical simulations, and resolving this issue is essential for simulation accuracy.

[csmangum]

1. Energy Conservation Tests

a. Total Energy Balance Test

Purpose:
Ensure that the total energy in the system remains conserved after the simulation, accounting for energy transformations (e.g., ATP synthesis, NADH/FADH₂ production).

Implementation:

import unittest
from simulate_kerbs_cycle import simulate_krebs_cycle, calculate_total_energy

class TestEnergyConservation(unittest.TestCase):
    def test_total_energy_conservation(self):
        initial_metabolites, initial_energy = simulate_krebs_cycle(initial=True)
        final_metabolites, final_energy = simulate_krebs_cycle(initial=False)

        energy_difference = final_energy - initial_energy

        # Allow a small tolerance for floating-point calculations
        self.assertAlmostEqual(energy_difference, 0, delta=1e-2,
                               msg=f"Energy not conserved: Difference = {energy_difference} kJ/mol")

b. Per-Reaction Energy Change Test

Purpose:
Verify that each individual reaction within the Krebs Cycle conserves energy correctly.

Implementation:

class TestPerReactionEnergy(unittest.TestCase):
    def test_reaction_energy_conservation(self):
        reactions = get_all_reactions()  # Function to retrieve all reactions
        for reaction in reactions:
            delta_energy = reaction.calculate_energy_change()
            self.assertAlmostEqual(delta_energy, 0, delta=1e-2,
                                   msg=f"Energy not conserved in reaction {reaction.name}: ΔE = {delta_energy} kJ/mol")

---

2. Metabolite Level Tests

a. Final Metabolite Levels Validation

Purpose:
Ensure that the final concentrations of key metabolites match expected biochemical outcomes.

Implementation:

class TestMetaboliteLevels(unittest.TestCase):
    def test_final_metabolite_levels(self):
        final_metabolites, _ = simulate_krebs_cycle()

        expected_levels = {
            'atp': 104.0,
            'adp': 6.0,
            'nad+': 8.0,
            'nadh': 22.0,
            'fad': 6.0,
            'fadh2': 4.0,
            'coa': 14.0,
            'acetyl_coa': 0.0,
            'co2': 8.0,
            # Add other key metabolites as needed
        }

        for metabolite, expected in expected_levels.items():
            self.assertAlmostEqual(final_metabolites.get(metabolite, 0), expected, delta=0.1,
                                   msg=f"Metabolite {metabolite} level incorrect: Expected {expected}, Got {final_metabolites.get(metabolite, 0)}")

b. Metabolite Conservation Test

Purpose:
Check that the total amount of specific metabolite pools (e.g., NAD⁺/NADH, FAD/FADH₂) remains consistent, accounting for their interconversions.

Implementation:

class TestMetaboliteConservation(unittest.TestCase):
    def test_nad_conservation(self):
        final_metabolites, _ = simulate_krebs_cycle()

        total_nad = final_metabolites.get('nad+', 0) + final_metabolites.get('nadh', 0)
        expected_total_nad = 30.0  # Example initial total

        self.assertAlmostEqual(total_nad, expected_total_nad, delta=1e-2,
                               msg=f"NAD/NADH not conserved: Expected {expected_total_nad}, Got {total_nad}")

---

3. Reaction Stoichiometry Tests

Purpose:
Ensure that each reaction step adheres to correct stoichiometry, preventing unintended accumulation or depletion of metabolites.

Implementation:

class TestReactionStoichiometry(unittest.TestCase):
    def test_reaction_stoichiometry(self):
        reactions = get_all_reactions()
        for reaction in reactions:
            reactants, products = reaction.get_stoichiometry()
            for metabolite, coeff in reactants.items():
                self.assertGreaterEqual(final_metabolites.get(metabolite, 0), coeff,
                                        msg=f"Insufficient {metabolite} for reaction {reaction.name}")
            # Similarly, verify products if needed

---

4. Specific Functional Tests

a. ATP Production Test

Purpose:
Confirm that the simulation produces the expected amount of ATP based on the Krebs Cycle's biochemical pathways.

Implementation:

class TestATPProduction(unittest.TestCase):
    def test_atp_production(self):
        final_metabolites, _ = simulate_krebs_cycle()

        expected_atp = 104.0  # Example expected value
        self.assertAlmostEqual(final_metabolites.get('atp', 0), expected_atp, delta=1.0,
                               msg=f"ATP level incorrect: Expected {expected_atp}, Got {final_metabolites.get('atp', 0)}")

b. CO₂ Production Consistency Test

Purpose:
Ensure that the amount of CO₂ produced aligns with the number of Krebs Cycle turns.

Implementation:

class TestCO2Production(unittest.TestCase):
    def test_co2_production(self):
        final_metabolites, _ = simulate_krebs_cycle()

        expected_co2 = 8.0  # Example for 4 turns (2 CO2 per turn)
        self.assertEqual(final_metabolites.get('co2', 0), expected_co2,
                         msg=f"CO2 production incorrect: Expected {expected_co2}, Got {final_metabolites.get('co2', 0)}")

---

5. Edge Case Tests

a. Zero Initial Metabolites Test

Purpose:
Verify that the simulation handles scenarios with zero initial metabolite concentrations gracefully without producing unexpected results.

Implementation:

class TestZeroInitialMetabolites(unittest.TestCase):
    def test_zero_initial_metabolites(self):
        final_metabolites, final_energy = simulate_krebs_cycle(initial_metabolites={})

        # Expect no reactions to occur
        expected_final_metabolites = {met:0 for met in all_metabolites}
        self.assertDictEqual(final_metabolites, expected_final_metabolites,
                             msg="Simulation should not alter metabolites when initialized with zero concentrations")

        # Energy should remain the same
        self.assertEqual(final_energy, initial_energy,
                         msg="Energy should remain unchanged when no reactions occur")

b. Maximum Capacity Test

Purpose:
Test the simulation's behavior under maximum metabolite concentrations to ensure stability and correctness.

Implementation:

class TestMaximumCapacity(unittest.TestCase):
    def test_maximum_metabolite_concentration(self):
        max_metabolites = {met: 1e6 for met in all_metabolites}
        final_metabolites, final_energy = simulate_krebs_cycle(initial_metabolites=max_metabolites)

        # Add assertions based on expected behavior under high concentrations
        # For example, check that no metabolite exceeds logical bounds
        for metabolite, concentration in final_metabolites.items():
            self.assertLessEqual(concentration, 1e6 + tolerance,
                                 msg=f"Metabolite {metabolite} exceeded maximum expected concentration")

---

6. Integration Tests

Purpose:
Run the entire simulation and compare the output against a known reference or baseline to ensure overall system integrity.

Implementation:

class TestIntegration(unittest.TestCase):
    def test_full_simulation_output(self):
        final_metabolites, final_energy = simulate_krebs_cycle()

        # Load reference data
        reference_metabolites = load_reference_metabolites()
        reference_energy = load_reference_energy()

        for metabolite, expected in reference_metabolites.items():
            self.assertAlmostEqual(final_metabolites.get(metabolite, 0), expected, delta=1.0,
                                   msg=f"Metabolite {metabolite} differs from reference: Expected {expected}, Got {final_metabolites.get(metabolite, 0)}")

        self.assertAlmostEqual(final_energy, reference_energy, delta=1e-2,
                               msg=f"Final energy differs from reference: Expected {reference_energy}, Got {final_energy}")

---

7. Validation Against Biochemical Data

Purpose:
Cross-validate simulation results with established biochemical data to ensure biological accuracy.

Implementation:

class TestBiochemicalValidation(unittest.TestCase):
    def test_biochemical_accuracy(self):
        final_metabolites, final_energy = simulate_krebs_cycle()

        # Example: Verify that for each turn of the Krebs Cycle, specific products are formed
        expected_nadh = 3 * number_of_turns
        expected_fadh2 = 1 * number_of_turns
        expected_atp = 1 * number_of_turns  # Or 1 GTP per turn, equivalent to ATP

        self.assertEqual(final_metabolites.get('nadh', 0), expected_nadh,
                         msg=f"NADH production incorrect: Expected {expected_nadh}, Got {final_metabolites.get('nadh', 0)}")
        self.assertEqual(final_metabolites.get('fadh2', 0), expected_fadh2,
                         msg=f"FADH₂ production incorrect: Expected {expected_fadh2}, Got {final_metabolites.get('fadh2', 0)}")
        self.assertEqual(final_metabolites.get('atp', 0), expected_atp,
                         msg=f"ATP production incorrect: Expected {expected_atp}, Got {final_metabolites.get('atp', 0)}")

---

8. Continuous Integration (CI) Integration

Purpose:
Automate the execution of all unit tests upon code changes to promptly detect and address issues.

Implementation Steps:

1. Choose a CI Tool:
   Use platforms like GitHub Actions, Travis CI, or Jenkins.

2. Configure the CI Pipeline:
   Create a configuration file (e.g., .github/workflows/python-app.yml for GitHub Actions) that sets up the environment, installs dependencies, and runs the test suite.

3. Example GitHub Actions Workflow:

   name: Python application
   
   on:
    push:
      branches: [ main ]
    pull_request:
      branches: [ main ]
   
   jobs:
    build:
   
      runs-on: ubuntu-latest
   
      strategy:
        matrix:
          python-version: [3.8, 3.9, 3.10]
   
      steps:
      - uses: actions/checkout@v2
      - name: Set up Python ${{ matrix.python-version }}
        uses: actions/setup-python@v2
        with:
          python-version: ${{ matrix.python-version }}
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install -r requirements.txt
      - name: Run tests
        run: |
          python -m unittest discover -s tests

4. Ensure Tests Are Comprehensive:
   Make sure all recommended unit tests are included in the test suite to be executed by the CI pipeline.

---

9. Mocking and Isolation

Purpose:
Isolate components to test them independently, ensuring that each part of the simulation functions correctly without interference from others.

Implementation:

from unittest.mock import patch

class TestIsolatedComponents(unittest.TestCase):
    @patch('simulate_kerbs_cycle.Reaction.calculate_energy_change')
    def test_reaction_isolation(self, mock_calculate_energy):
        # Mock the energy change to return a controlled value
        mock_calculate_energy.return_value = 0.0

        # Run the simulation
        final_metabolites, final_energy = simulate_krebs_cycle()

        # Verify that the mocked method was called
        mock_calculate_energy.assert_called()

        # Additional assertions as needed

---

10. Example Test Cases

Below are some concrete examples of how you might implement these tests using unittest. You can expand upon these based on your specific simulation details.

import unittest
from simulate_kerbs_cycle import simulate_krebs_cycle, calculate_total_energy, get_all_reactions

class TestKrebsCycleSimulation(unittest.TestCase):
    def test_energy_conservation(self):
        initial_metabolites, initial_energy = simulate_krebs_cycle(initial=True)
        final_metabolites, final_energy = simulate_krebs_cycle(initial=False)
        energy_diff = final_energy - initial_energy
        self.assertAlmostEqual(energy_diff, 0.0, delta=1e-2, msg=f"Energy not conserved: {energy_diff} kJ/mol")

    def test_co2_production(self):
        final_metabolites, _ = simulate_krebs_cycle()
        expected_co2 = 8.0
        self.assertEqual(final_metabolites.get('co2'), expected_co2, "Incorrect CO2 production")

    def test_nadh_production(self):
        final_metabolites, _ = simulate_krebs_cycle()
        expected_nadh = 22.0
        self.assertEqual(final_metabolites.get('nadh'), expected_nadh, "Incorrect NADH production")

    def test_fadh2_production(self):
        final_metabolites, _ = simulate_krebs_cycle()
        expected_fadh2 = 4.0
        self.assertEqual(final_metabolites.get('fadh2'), expected_fadh2, "Incorrect FADH2 production")

    def test_atp_change(self):
        final_metabolites, _ = simulate_krebs_cycle()
        expected_atp = 104.0
        self.assertEqual(final_metabolites.get('atp'), expected_atp, "Incorrect ATP level")

    def test_energy_state_after_simulation(self):
        _, final_energy = simulate_krebs_cycle()
        expected_final_energy = 7251.00
        self.assertAlmostEqual(final_energy, expected_final_energy, delta=1.0, "Final energy state mismatch")

if __name__ == '__main__':
    unittest.main()

---

11. Best Practices for Future Testing

• Modular Design:
  Structure your simulation code into modular components (e.g., separate classes or functions for reactions, energy calculations, metabolite management) to facilitate isolated testing.

• Consistent Naming Conventions:
  Use clear and consistent naming for metabolites, reactions, and variables to avoid confusion and make tests more readable.

• Comprehensive Coverage:
  Strive for high test coverage, ensuring that all critical paths and edge cases are tested.

• Automated Testing:
  Integrate automated testing into your development workflow to catch issues early and frequently.

• Documentation:
  Document your tests and their purposes to aid future maintenance and onboarding of new contributors.

• Use Assertions Effectively:
  Utilize a variety of assertions (assertEqual, assertAlmostEqual, assertGreater, etc.) to validate different aspects of your simulation.

• Handle Floating-Point Precision:
  Be mindful of floating-point precision issues by using appropriate delta values in assertAlmostEqual assertions.