
                # Interation 0

                ## Task Info
                You are investigating a biological system where all species are observable. Your goal is to discover the missing reactions. You do not need to add new species.


                ## Incomplete SBML Model. You can assess it as input_sbml_string.

                <?xml version="1.0" encoding="UTF-8"?>
<sbml xmlns="http://www.sbml.org/sbml/level2/version4" level="2" version="4">
  <model id="id_jxv5">
    <listOfUnitDefinitions>
      <unitDefinition id="id_ucr3">
        <listOfUnits>
          <unit kind="litre" exponent="1" scale="-3" multiplier="1"/>
        </listOfUnits>
      </unitDefinition>
      <unitDefinition id="id_6k3o">
        <listOfUnits>
          <unit kind="mole" exponent="1" scale="-3" multiplier="1"/>
        </listOfUnits>
      </unitDefinition>
    </listOfUnitDefinitions>
    <listOfCompartments>
      <compartment id="id_y038" spatialDimensions="3" size="1" constant="true"/>
    </listOfCompartments>
    <listOfSpecies>
      <species id="id_59ww" compartment="id_y038" initialConcentration="0" boundaryCondition="false" constant="false"/>
      <species id="id_0lud" compartment="id_y038" initialConcentration="0" boundaryCondition="false" constant="false"/>
      <species id="id_70w8" compartment="id_y038" initialConcentration="0" boundaryCondition="false" constant="false"/>
      <species id="id_5lmp" compartment="id_y038" initialConcentration="0" boundaryCondition="false" constant="false"/>
      <species id="id_u8uf" compartment="id_y038" initialConcentration="7.4e-12" boundaryCondition="false" constant="false"/>
      <species id="id_ckd0" compartment="id_y038" initialConcentration="0" boundaryCondition="false" constant="false"/>
      <species id="id_3nd3" compartment="id_y038" initialConcentration="5.5e-14" boundaryCondition="false" constant="false"/>
      <species id="id_dadg" compartment="id_y038" initialConcentration="0" boundaryCondition="false" constant="false"/>
      <species id="id_erlz" compartment="id_y038" initialConcentration="0" boundaryCondition="false" constant="false"/>
      <species id="id_xsbr" compartment="id_y038" initialConcentration="3.6e-12" boundaryCondition="false" constant="false"/>
    </listOfSpecies>
  </model>
</sbml>


                ## Max iterations
                20

                Format your response according to the instructions in the system message.
                
## Thoughts
The initial SBML model defines several species with their initial concentrations but contains no reactions. My goal is to identify the missing reactions.
The first step is to observe the system's behavior without any perturbations. This will provide a baseline dataset to understand the natural dynamics of the species.
After obtaining the baseline data, I will analyze it to infer potential reactions. For example, if one species decreases while another increases, it might suggest a conversion. If a species' concentration increases from zero, it's likely a product of some reaction.

## Action

### Experiment
```json
{
   "action": "observe",
   "meta_data": {}
}
```
# Observation

## Experiment Result
Experiment ID: iteration_0
Your requested experiment has finished. The details results are saved as a dataframe in experiment_history[iteration_0]

Total size: 201 rows × 11 columns. 

Below shows a subset of the table for your convenience. You should access experiment_history[iteration_0] for the full data

Time   id_59ww  id_0lud  id_70w8  id_5lmp  id_u8uf  id_ckd0  id_3nd3  id_dadg  id_erlz  id_xsbr
0         0        0        0        0 7.40e-12        0 5.50e-14        0        0 3.60e-12
...
4.00e+00  1.05e-13 7.75e-16 2.54e-12 2.09e-16 1.40e-12 7.35e-18 2.95e-14 2.37e-14 4.58e-13 3.37e-12
...
8.00e+00  1.05e-13 7.49e-16 2.23e-12 7.65e-16 1.31e-12 2.51e-17 2.92e-14 2.35e-14 8.14e-13 3.37e-12
...
1.20e+01  1.05e-13 7.28e-16 1.99e-12 1.53e-15 1.24e-12 4.92e-17 2.88e-14 2.31e-14 1.09e-12 3.37e-12
...
1.60e+01  1.05e-13 7.04e-16 1.81e-12 2.41e-15 1.18e-12 7.64e-17 2.83e-14 2.27e-14 1.29e-12 3.36e-12
...
2.00e+01  1.05e-13 6.79e-16 1.68e-12 3.33e-15 1.14e-12 1.04e-16 2.78e-14 2.23e-14 1.45e-12 3.36e-12
...
2.40e+01  1.05e-13 6.54e-16 1.57e-12 4.24e-15 1.10e-12 1.32e-16 2.73e-14 2.19e-14 1.58e-12 3.36e-12
...
2.80e+01  1.05e-13 6.31e-16 1.49e-12 5.13e-15 1.07e-12 1.57e-16 2.68e-14 2.15e-14 1.67e-12 3.36e-12
...
3.20e+01  1.05e-13 6.09e-16 1.43e-12 5.96e-15 1.05e-12 1.81e-16 2.63e-14 2.11e-14 1.74e-12 3.36e-12
...
3.60e+01  1.05e-13 5.89e-16 1.39e-12 6.73e-15 1.03e-12 2.02e-16 2.59e-14 2.08e-14 1.80e-12 3.36e-12
...
4.00e+01  1.05e-13 5.72e-16 1.35e-12 7.44e-15 1.02e-12 2.20e-16 2.55e-14 2.05e-14 1.84e-12 3.36e-12
...
4.40e+01  1.05e-13 5.56e-16 1.32e-12 8.08e-15 1.01e-12 2.36e-16 2.51e-14 2.02e-14 1.87e-12 3.36e-12
...
4.80e+01  1.05e-13 5.42e-16 1.30e-12 8.66e-15 1.00e-12 2.50e-16 2.48e-14 1.99e-14 1.90e-12 3.36e-12
...
5.20e+01  1.05e-13 5.29e-16 1.29e-12 9.17e-15 9.95e-13 2.63e-16 2.45e-14 1.97e-14 1.92e-12 3.36e-12
...
5.60e+01  1.05e-13 5.18e-16 1.27e-12 9.63e-15 9.90e-13 2.73e-16 2.43e-14 1.95e-14 1.93e-12 3.36e-12
...
6.00e+01  1.05e-13 5.09e-16 1.26e-12 1.00e-14 9.86e-13 2.82e-16 2.40e-14 1.93e-14 1.94e-12 3.36e-12
...
6.40e+01  1.05e-13 5.00e-16 1.26e-12 1.04e-14 9.83e-13 2.89e-16 2.38e-14 1.92e-14 1.95e-12 3.36e-12
...
6.80e+01  1.05e-13 4.93e-16 1.25e-12 1.07e-14 9.81e-13 2.95e-16 2.37e-14 1.91e-14 1.96e-12 3.36e-12
...
7.20e+01  1.05e-13 4.87e-16 1.25e-12 1.10e-14 9.79e-13 3.00e-16 2.35e-14 1.89e-14 1.96e-12 3.36e-12
...
7.60e+01  1.05e-13 4.81e-16 1.24e-12 1.12e-14 9.78e-13 3.04e-16 2.34e-14 1.88e-14 1.97e-12 3.36e-12
...
8.00e+01  1.05e-13 4.76e-16 1.24e-12 1.14e-14 9.77e-13 3.08e-16 2.33e-14 1.87e-14 1.97e-12 3.36e-12
...
8.40e+01  1.05e-13 4.72e-16 1.24e-12 1.16e-14 9.76e-13 3.11e-16 2.32e-14 1.87e-14 1.97e-12 3.36e-12
...
8.80e+01  1.05e-13 4.69e-16 1.24e-12 1.18e-14 9.76e-13 3.13e-16 2.31e-14 1.86e-14 1.97e-12 3.36e-12
...
9.20e+01  1.05e-13 4.66e-16 1.24e-12 1.19e-14 9.75e-13 3.15e-16 2.30e-14 1.85e-14 1.98e-12 3.36e-12
...
9.60e+01  1.05e-13 4.63e-16 1.24e-12 1.20e-14 9.75e-13 3.16e-16 2.29e-14 1.85e-14 1.98e-12 3.36e-12
...
1.00e+02  1.05e-13 4.61e-16 1.23e-12 1.21e-14 9.75e-13 3.17e-16 2.29e-14 1.84e-14 1.98e-12 3.36e-12
...
1.04e+02  1.05e-13 4.58e-16 1.23e-12 1.22e-14 9.74e-13 3.18e-16 2.28e-14 1.84e-14 1.98e-12 3.36e-12
...
1.08e+02  1.05e-13 4.57e-16 1.23e-12 1.23e-14 9.74e-13 3.18e-16 2.28e-14 1.84e-14 1.98e-12 3.36e-12
...
1.12e+02  1.05e-13 4.55e-16 1.23e-12 1.24e-14 9.74e-13 3.19e-16 2.27e-14 1.83e-14 1.98e-12 3.36e-12
...
1.16e+02  1.05e-13 4.54e-16 1.23e-12 1.24e-14 9.74e-13 3.19e-16 2.27e-14 1.83e-14 1.98e-12 3.36e-12
...
1.20e+02  1.05e-13 4.53e-16 1.23e-12 1.25e-14 9.74e-13 3.19e-16 2.27e-14 1.83e-14 1.98e-12 3.36e-12
...
1.24e+02  1.05e-13 4.52e-16 1.23e-12 1.25e-14 9.74e-13 3.19e-16 2.27e-14 1.83e-14 1.98e-12 3.36e-12
...
1.28e+02  1.05e-13 4.51e-16 1.23e-12 1.26e-14 9.74e-13 3.19e-16 2.26e-14 1.82e-14 1.98e-12 3.36e-12
...
1.32e+02  1.05e-13 4.50e-16 1.23e-12 1.26e-14 9.74e-13 3.19e-16 2.26e-14 1.82e-14 1.98e-12 3.36e-12
...
1.36e+02  1.05e-13 4.49e-16 1.23e-12 1.26e-14 9.74e-13 3.19e-16 2.26e-14 1.82e-14 1.98e-12 3.36e-12
...
1.40e+02  1.05e-13 4.49e-16 1.23e-12 1.27e-14 9.74e-13 3.19e-16 2.26e-14 1.82e-14 1.98e-12 3.36e-12
...
1.44e+02  1.05e-13 4.48e-16 1.23e-12 1.27e-14 9.74e-13 3.19e-16 2.26e-14 1.82e-14 1.98e-12 3.36e-12
...
1.48e+02  1.05e-13 4.48e-16 1.23e-12 1.27e-14 9.74e-13 3.19e-16 2.26e-14 1.82e-14 1.98e-12 3.36e-12
...
1.52e+02  1.05e-13 4.48e-16 1.23e-12 1.27e-14 9.74e-13 3.18e-16 2.26e-14 1.82e-14 1.98e-12 3.36e-12
...
1.56e+02  1.05e-13 4.47e-16 1.23e-12 1.27e-14 9.74e-13 3.18e-16 2.25e-14 1.82e-14 1.98e-12 3.36e-12
...
1.60e+02  1.05e-13 4.47e-16 1.23e-12 1.28e-14 9.74e-13 3.18e-16 2.25e-14 1.82e-14 1.98e-12 3.36e-12
...
1.64e+02  1.05e-13 4.47e-16 1.23e-12 1.28e-14 9.74e-13 3.18e-16 2.25e-14 1.82e-14 1.98e-12 3.36e-12
...
1.68e+02  1.05e-13 4.47e-16 1.23e-12 1.28e-14 9.74e-13 3.18e-16 2.25e-14 1.82e-14 1.98e-12 3.36e-12
...
1.72e+02  1.05e-13 4.46e-16 1.23e-12 1.28e-14 9.74e-13 3.17e-16 2.25e-14 1.82e-14 1.98e-12 3.36e-12
...
1.76e+02  1.05e-13 4.46e-16 1.23e-12 1.28e-14 9.74e-13 3.17e-16 2.25e-14 1.82e-14 1.98e-12 3.36e-12
...
1.80e+02  1.05e-13 4.46e-16 1.23e-12 1.28e-14 9.74e-13 3.17e-16 2.25e-14 1.82e-14 1.98e-12 3.36e-12
...
1.84e+02  1.05e-13 4.46e-16 1.23e-12 1.28e-14 9.74e-13 3.17e-16 2.25e-14 1.82e-14 1.98e-12 3.36e-12
...
1.88e+02  1.05e-13 4.46e-16 1.23e-12 1.28e-14 9.74e-13 3.17e-16 2.25e-14 1.82e-14 1.98e-12 3.36e-12
...
1.92e+02  1.05e-13 4.46e-16 1.23e-12 1.28e-14 9.74e-13 3.17e-16 2.25e-14 1.82e-14 1.98e-12 3.36e-12
...
2.00e+02  1.05e-13 4.46e-16 1.23e-12 1.28e-14 9.74e-13 3.16e-16 2.25e-14 1.82e-14 1.98e-12 3.36e-12

## Reminder

 ### Remaining Iterations for Interactions:
- Iterations: 20/20



### Available Global Variables
- `input_sbml_string`: Original incomplete model
- `experiment_history`: Results from all previous experiments (iteration_0)
- `shared_variables`: all variables you have saved from the previous iterations. ()

### Allowed libraires
['numpy', 'pandas', 'libsbml', 'math', 'scipy', 'jax', 'sklearn', 'io', 'traceback']


Please construct your response according to the thoughts-action markdown format.



# Iteration 1
## Thoughts

The analysis of the first experiment (`iteration_0`) revealed several key dynamics:
1.  `id_u8uf` and `id_3nd3` are consumed. `id_xsbr` is also consumed, but to a lesser extent.
2.  `id_70w8` shows characteristics of an intermediate: it increases initially and then decreases.
3.  `id_erlz`, `id_59ww`, `id_dadg`, `id_5lmp`, `id_0lud`, and `id_ckd0` are produced.
4.  A mass balance analysis for `id_u8uf` suggested that its conversion to major products (`id_70w8`-pathway and `id_59ww`) likely involves a stoichiometry of `2 id_u8uf -> Product`.
    -   Consumed `id_u8uf` ~ 6.426e-12.
    -   Sum of (final `id_70w8` + final `id_erlz`) + final `id_59ww` ~ 3.315e-12.
    -   The ratio is approximately 2:1 (6.426 / 3.315 ~ 1.94).
5.  The consumption of `id_3nd3` (~3.25e-14) closely matches the sum of produced `id_dadg` (~1.82e-14) and `id_5lmp` (~1.28e-14), suggesting `id_3nd3 -> id_dadg + id_5lmp`.
6.  The very small amounts of `id_0lud` and `id_ckd0` could arise from minor side reactions, possibly from `id_3nd3`.
7.  The decrease in `id_xsbr` can be modeled as a simple degradation reaction.

Based on these observations, I propose the following set of reactions:
*   R1: `2 id_u8uf -> id_70w8`
*   R2: `id_70w8 -> id_erlz`
*   R3: `id_3nd3 -> id_dadg + id_5lmp`
*   R4: `2 id_u8uf -> id_59ww`
*   R5: `id_xsbr -> {}` (degradation)
*   R6: `id_3nd3 -> id_0lud` (minor pathway)
*   R7: `id_3nd3 -> id_ckd0` (minor pathway)

I will first plot the experimental data to visually confirm these dynamics, particularly for `id_70w8`. Then, I will add these reactions to the SBML model using `libsbml`, assuming mass action kinetics for each reaction and assigning placeholder kinetic constants.

## Action

### Code
```python
import pandas as pd
import matplotlib.pyplot as plt
import libsbml

# Access the experimental data
data = experiment_history['iteration_0']

# Plot the data for visualization
plt.figure(figsize=(12, 8))
for species_id in data.columns:
    if species_id != 'Time':
        plt.plot(data['Time'], data[species_id], label=species_id)
plt.xlabel('Time')
plt.ylabel('Concentration')
plt.title('Species Concentrations Over Time (Experiment iteration_0)')
plt.legend()
plt.grid(True)
plt.yscale('log') # Use log scale due to large differences in concentrations
plt.savefig("species_plot.png") # Save plot to file
print("Plot saved as species_plot.png")
# plt.show() # Cannot show plots directly in this environment

# Confirming intermediate behavior of id_70w8 from data
print("\nData for id_70w8 to confirm intermediate behavior:")
print(data[['Time', 'id_70w8']].head(10))
print(data[['Time', 'id_70w8']].tail(10))
max_70w8 = data['id_70w8'].max()
time_max_70w8 = data.loc[data['id_70w8'].idxmax(), 'Time']
print(f"Max concentration of id_70w8: {max_70w8} at Time: {time_max_70w8}")

# Load the SBML model
sbml_doc = libsbml.readSBMLFromString(input_sbml_string)
model = sbml_doc.getModel()

if model is None:
    print("Error: Could not get model from SBML string.")
else:
    # Helper function to add a reaction
    def add_reaction(model, r_id, reactants_stoich, products_stoich, k_val, k_id_suffix):
        reaction = model.createReaction()
        reaction.setId(r_id)
        reaction.setReversible(False)
        reaction.setFast(False)

        for species_id, stoich in reactants_stoich.items():
            reactant = reaction.createReactant()
            reactant.setSpecies(species_id)
            reactant.setStoichiometry(stoich)
            reactant.setConstant(False) 

        for species_id, stoich in products_stoich.items():
            product = reaction.createProduct()
            product.setSpecies(species_id)
            product.setStoichiometry(stoich)
            product.setConstant(False)

        kinetic_law = reaction.createKineticLaw()
        
        # Create parameter for the rate constant
        k_param = kinetic_law.createParameter()
        k_param.setId(f"k_{k_id_suffix}")
        k_param.setValue(k_val) # Placeholder value
        k_param.setConstant(True)

        # Create math formula for mass action
        math_str = f"k_{k_id_suffix}"
        for species_id, stoich in reactants_stoich.items():
            if stoich == 1:
                math_str += f" * {species_id}"
            elif stoich > 1:
                math_str += f" * {species_id}^{stoich}" # libsbml math uses ^ for power
        
        # A more robust way for powers in SBML MathML: pow(species, stoich)
        # For simplicity with common tools, species^stoich often works for display,
        # but proper MathML would be <apply><power/><ci>species</ci><cn>stoich</cn></apply>
        # Here, we construct the formula string that will be parsed by libSBML.
        # For A + A -> P (or 2A -> P), rate = k * A * A or k * A^2
        # If reactant is 'A' with stoich 2, formula is k * A * A
        
        formula_parts = [f"k_{k_id_suffix}"]
        for species_id, stoich in reactants_stoich.items():
            for _ in range(int(stoich)): # Add species_id 'stoich' times for mass-action
                 formula_parts.append(species_id)
        math_str = " * ".join(formula_parts)
        
        math_ast = libsbml.parseL3Formula(math_str)
        kinetic_law.setMath(math_ast)

    # Add Reaction 1: 2 id_u8uf -> id_70w8
    add_reaction(model, "R1_u8uf_to_70w8", {"id_u8uf": 2}, {"id_70w8": 1}, 0.1, "r1")

    # Add Reaction 2: id_70w8 -> id_erlz
    add_reaction(model, "R2_70w8_to_erlz", {"id_70w8": 1}, {"id_erlz": 1}, 0.1, "r2")

    # Add Reaction 3: id_3nd3 -> id_dadg + id_5lmp
    add_reaction(model, "R3_3nd3_to_dadg_5lmp", {"id_3nd3": 1}, {"id_dadg": 1, "id_5lmp": 1}, 0.1, "r3")

    # Add Reaction 4: 2 id_u8uf -> id_59ww
    add_reaction(model, "R4_u8uf_to_59ww", {"id_u8uf": 2}, {"id_59ww": 1}, 0.1, "r4")

    # Add Reaction 5: id_xsbr -> {} (degradation)
    add_reaction(model, "R5_xsbr_degradation", {"id_xsbr": 1}, {}, 0.01, "r5")
    
    # Add Reaction 6: id_3nd3 -> id_0lud
    add_reaction(model, "R6_3nd3_to_0lud", {"id_3nd3": 1}, {"id_0lud": 1}, 0.01, "r6")

    # Add Reaction 7: id_3nd3 -> id_ckd0
    add_reaction(model, "R7_3nd3_to_ckd0", {"id_3nd3": 1}, {"id_ckd0": 1}, 0.01, "r7")

    # Write the updated SBML model to a string
    writer = libsbml.SBMLWriter()
    updated_sbml_string = writer.writeSBMLToString(sbml_doc)
    shared_variables.add("current_sbml_model", updated_sbml_string)
    print("\nUpdated SBML model with 7 reactions stored in shared_variables['current_sbml_model']")
    # For debugging, print a part of the model
    # print(updated_sbml_string[:1000]) # Print first 1000 characters

    # Simulate the new model (if possible, though parameters are guesses)
    # This is just to check if the model is valid and runs.
    # The output will not match experimental data well yet.
    try:
        sim_df = simulate(updated_sbml_string)
        print("\nSimulation with the new model (guessed parameters) ran successfully.")
        print("Simulated data head:")
        print(sim_df.head())
        shared_variables.add("sim_df_iteration_1", sim_df)
    except Exception as e:
        print(f"\nError simulating the new model: {e}")

```
# Observation

## Code Stderror
 Unauthorized modules matplotlib detected. Only these modules are allowed: numpy, pandas, libsbml, math, scipy, jax, sklearn, io, traceback.

## Reminder

 ### Remaining Iterations for Interactions:
- Iterations: 19/20



### Available Global Variables
- `input_sbml_string`: Original incomplete model
- `experiment_history`: Results from all previous experiments (iteration_0)
- `shared_variables`: all variables you have saved from the previous iterations. ()

### Allowed libraires
['numpy', 'pandas', 'libsbml', 'math', 'scipy', 'jax', 'sklearn', 'io', 'traceback']


Please construct your response according to the thoughts-action markdown format.



# Iteration 2
## Thoughts
The previous attempt to plot data using `matplotlib` failed due to library restrictions. I will remove the plotting code and focus on the data analysis and model construction. The core logic of identifying reactions based on species dynamics and building the SBML model remains the same.

The key observations from `experiment_history['iteration_0']` were:
1.  `id_u8uf`, `id_3nd3`, and `id_xsbr` are consumed.
2.  `id_70w8` acts as an intermediate (increases then decreases).
3.  `id_erlz`, `id_59ww`, `id_dadg`, `id_5lmp`, `id_0lud`, and `id_ckd0` are produced.
4.  Stoichiometry guesses: `2 id_u8uf` involved in producing `id_70w8` and `id_59ww`. `id_3nd3` seems to produce `id_dadg` and `id_5lmp` in roughly 1:1:1 ratio, with minor products `id_0lud` and `id_ckd0`.

Proposed reactions:
*   R1: `2 id_u8uf -> id_70w8` (k1)
*   R2: `id_70w8 -> id_erlz` (k2)
*   R3: `id_3nd3 -> id_dadg + id_5lmp` (k3)
*   R4: `2 id_u8uf -> id_59ww` (k4)
*   R5: `id_xsbr -> ` (degradation) (k5)
*   R6: `id_3nd3 -> id_0lud` (k6)
*   R7: `id_3nd3 -> id_ckd0` (k7)

I will now implement these reactions into the SBML model with placeholder kinetic constants and then simulate the model to see if it runs and to get a first look at its behavior.

## Action

### Code
```python
import pandas as pd
import libsbml

# Access the experimental data
data = experiment_history['iteration_0']

# --- Numerical Analysis (retained from previous thought process) ---
initial_concentrations = data.iloc[0]
final_concentrations = data.iloc[-1]

delta_concentrations = final_concentrations - initial_concentrations
print("Initial Concentrations:\n", initial_concentrations)
print("\nFinal Concentrations:\n", final_concentrations)
print("\nChange in Concentrations (Final - Initial):\n", delta_concentrations)

# Analysis for id_u8uf consumption and product formation
consumed_u8uf = initial_concentrations['id_u8uf'] - final_concentrations['id_u8uf']
produced_70w8_path = final_concentrations['id_70w8'] + final_concentrations['id_erlz'] # Assuming 70w8 converts to erlz
produced_59ww = final_concentrations['id_59ww']

print(f"\nConsumed id_u8uf: {consumed_u8uf:.3e}")
print(f"Produced via 70w8 pathway (id_70w8_final + id_erlz_final): {produced_70w8_path:.3e}")
print(f"Produced id_59ww: {produced_59ww:.3e}")
if produced_70w8_path + produced_59ww > 0:
    ratio_u8uf_to_products = consumed_u8uf / (produced_70w8_path + produced_59ww)
    print(f"Ratio of consumed id_u8uf to (70w8_path_products + 59ww_products): {ratio_u8uf_to_products:.2f}")

# Analysis for id_3nd3 consumption and product formation
consumed_3nd3 = initial_concentrations['id_3nd3'] - final_concentrations['id_3nd3']
produced_dadg = final_concentrations['id_dadg']
produced_5lmp = final_concentrations['id_5lmp']
produced_0lud = final_concentrations['id_0lud']
produced_ckd0 = final_concentrations['id_ckd0']

print(f"\nConsumed id_3nd3: {consumed_3nd3:.3e}")
print(f"Produced id_dadg: {produced_dadg:.3e}")
print(f"Produced id_5lmp: {produced_5lmp:.3e}")
print(f"Produced id_0lud: {produced_0lud:.3e}")
print(f"Produced id_ckd0: {produced_ckd0:.3e}")
if produced_dadg + produced_5lmp > 0: # Avoid division by zero for minor products
    ratio_3nd3_to_major_products = consumed_3nd3 / (produced_dadg + produced_5lmp)
    print(f"Ratio of consumed id_3nd3 to (dadg + 5lmp): {ratio_3nd3_to_major_products:.2f}")

# Confirming intermediate behavior of id_70w8 from data
print("\nData for id_70w8 to confirm intermediate behavior:")
print(data[['Time', 'id_70w8']].head(10))
max_70w8 = data['id_70w8'].max()
time_max_70w8 = data.loc[data['id_70w8'].idxmax(), 'Time']
print(f"Max concentration of id_70w8: {max_70w8:.3e} at Time: {time_max_70w8}")
# --- End of Numerical Analysis ---

# Load the SBML model
sbml_doc = libsbml.readSBMLFromString(input_sbml_string)
model = sbml_doc.getModel()

if model is None:
    print("Error: Could not get model from SBML string.")
else:
    # Helper function to add a reaction
    def add_reaction_with_mass_action(model, r_id, reactants_stoich, products_stoich, k_val, k_id_suffix):
        reaction = model.createReaction()
        reaction.setId(r_id)
        reaction.setReversible(False)
        reaction.setFast(False)

        for species_id, stoich in reactants_stoich.items():
            reactant_ref = reaction.createReactant()
            reactant_ref.setSpecies(species_id)
            reactant_ref.setStoichiometry(stoich)
            reactant_ref.setConstant(False) 

        for species_id, stoich in products_stoich.items():
            product_ref = reaction.createProduct()
            product_ref.setSpecies(species_id)
            product_ref.setStoichiometry(stoich)
            product_ref.setConstant(False)

        kinetic_law = reaction.createKineticLaw()
        
        k_param = kinetic_law.createParameter()
        k_param.setId(f"k_{k_id_suffix}")
        k_param.setValue(k_val) 
        k_param.setConstant(True)

        formula_parts = [f"k_{k_id_suffix}"]
        for species_id, stoich in reactants_stoich.items():
            for _ in range(int(stoich)): 
                 formula_parts.append(species_id)
        math_str = " * ".join(formula_parts)
        
        math_ast = libsbml.parseL3Formula(math_str)
        if math_ast is None:
            print(f"Error parsing formula for reaction {r_id}: {math_str}")
            # Add a very simple kinetic law if parsing fails, to allow model to be created
            # This should not happen with simple mass action
            math_ast = libsbml.parseL3Formula(f"k_{k_id_suffix}")
        kinetic_law.setMath(math_ast)

    # Add Reaction 1: 2 id_u8uf -> id_70w8
    add_reaction_with_mass_action(model, "R1_u8uf_to_70w8", {"id_u8uf": 2}, {"id_70w8": 1}, 1e7, "r1") # Adjusted k based on concentrations

    # Add Reaction 2: id_70w8 -> id_erlz
    add_reaction_with_mass_action(model, "R2_70w8_to_erlz", {"id_70w8": 1}, {"id_erlz": 1}, 0.1, "r2")

    # Add Reaction 3: id_3nd3 -> id_dadg + id_5lmp
    add_reaction_with_mass_action(model, "R3_3nd3_to_dadg_5lmp", {"id_3nd3": 1}, {"id_dadg": 1, "id_5lmp": 1}, 0.1, "r3")

    # Add Reaction 4: 2 id_u8uf -> id_59ww
    add_reaction_with_mass_action(model, "R4_u8uf_to_59ww", {"id_u8uf": 2}, {"id_59ww": 1}, 1e6, "r4") # Adjusted k

    # Add Reaction 5: id_xsbr -> {} (degradation)
    add_reaction_with_mass_action(model, "R5_xsbr_degradation", {"id_xsbr": 1}, {}, 0.001, "r5")
    
    # Add Reaction 6: id_3nd3 -> id_0lud
    add_reaction_with_mass_action(model, "R6_3nd3_to_0lud", {"id_3nd3": 1}, {"id_0lud": 1}, 0.001, "r6")

    # Add Reaction 7: id_3nd3 -> id_ckd0
    add_reaction_with_mass_action(model, "R7_3nd3_to_ckd0", {"id_3nd3": 1}, {"id_ckd0": 1}, 0.001, "r7")

    writer = libsbml.SBMLWriter()
    updated_sbml_string = writer.writeSBMLToString(sbml_doc)
    shared_variables.add("current_sbml_model_iter2", updated_sbml_string)
    print("\nUpdated SBML model with 7 reactions stored in shared_variables['current_sbml_model_iter2']")

    try:
        sim_df = simulate(updated_sbml_string)
        print("\nSimulation with the new model (guessed parameters) ran successfully.")
        print("Simulated data head:")
        print(sim_df.head())
        shared_variables.add("sim_df_iteration_2", sim_df)
    except Exception as e:
        print(f"\nError simulating the new model: {e}")
        # Print the SBML string if simulation fails, for debugging
        # print("\nProblematic SBML model string:")
        # print(updated_sbml_string)

```
# Observation

## Code Stdout
Initial Concentrations:
 id_59ww    0.000000e+00
id_0lud    0.000000e+00
id_70w8    0.000000e+00
id_5lmp    0.000000e+00
id_u8uf    7.400000e-12
id_ckd0    0.000000e+00
id_3nd3    5.500000e-14
id_dadg    0.000000e+00
id_erlz    0.000000e+00
id_xsbr    3.600000e-12
Time       0.000000e+00
Name: 0, dtype: float64

Final Concentrations:
 id_59ww    1.046815e-13
id_0lud    4.457780e-16
id_70w8    1.232450e-12
id_5lmp    1.281449e-14
id_u8uf    9.736727e-13
id_ckd0    3.163974e-16
id_3nd3    2.251072e-14
id_dadg    1.815044e-14
id_erlz    1.980713e-12
id_xsbr    3.359672e-12
Time       2.000000e+02
Name: 200, dtype: float64

Change in Concentrations (Final - Initial):
 id_59ww    1.046815e-13
id_0lud    4.457780e-16
id_70w8    1.232450e-12
id_5lmp    1.281449e-14
id_u8uf   -6.426327e-12
id_ckd0    3.163974e-16
id_3nd3   -3.248928e-14
id_dadg    1.815044e-14
id_erlz    1.980713e-12
id_xsbr   -2.403280e-13
Time       2.000000e+02
dtype: float64

Consumed id_u8uf: 6.426e-12
Produced via 70w8 pathway (id_70w8_final + id_erlz_final): 3.213e-12
Produced id_59ww: 1.047e-13
Ratio of consumed id_u8uf to (70w8_path_products + 59ww_products): 1.94

Consumed id_3nd3: 3.249e-14
Produced id_dadg: 1.815e-14
Produced id_5lmp: 1.281e-14
Produced id_0lud: 4.458e-16
Produced id_ckd0: 3.164e-16
Ratio of consumed id_3nd3 to (dadg + 5lmp): 1.05

Data for id_70w8 to confirm intermediate behavior:
   Time       id_70w8
0   0.0  0.000000e+00
1   1.0  2.839069e-12
2   2.0  2.732972e-12
3   3.0  2.633881e-12
4   4.0  2.541478e-12
5   5.0  2.455293e-12
6   6.0  2.374901e-12
7   7.0  2.299902e-12
8   8.0  2.229926e-12
9   9.0  2.164629e-12
Max concentration of id_70w8: 2.839e-12 at Time: 1.0

Updated SBML model with 7 reactions stored in shared_variables['current_sbml_model_iter2']

Simulation with the new model (guessed parameters) ran successfully.
Simulated data head:
        id_59ww       id_0lud       id_70w8       id_5lmp       id_u8uf       id_ckd0       id_3nd3       id_dadg       id_erlz       id_xsbr  Time
0  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  7.400000e-12  0.000000e+00  5.500000e-14  0.000000e+00  0.000000e+00  3.600000e-12   0.0
1  5.475108e-17  5.228947e-17  5.210379e-16  5.228947e-15  7.398795e-12  5.228947e-17  4.966647e-14  5.228947e-15  2.647285e-17  3.596402e-12   1.0
2  1.094843e-16  9.950967e-17  9.923356e-16  9.950967e-15  7.397591e-12  9.950967e-17  4.485001e-14  9.950967e-15  1.025079e-16  3.592807e-12   2.0
3  1.641998e-16  1.421465e-16  1.418572e-15  1.421465e-14  7.396388e-12  1.421465e-16  4.050105e-14  1.421465e-14  2.234262e-16  3.589216e-12   3.0
4  2.188974e-16  1.806488e-16  1.804076e-15  1.806488e-14  7.395184e-12  1.806488e-16  3.657382e-14  1.806488e-14  3.848982e-16  3.585629e-12   4.0

## Reminder

 ### Remaining Iterations for Interactions:
- Iterations: 18/20



### Available Global Variables
- `input_sbml_string`: Original incomplete model
- `experiment_history`: Results from all previous experiments (iteration_0)
- `shared_variables`: all variables you have saved from the previous iterations. (current_sbml_model_iter2, sim_df_iteration_2)

### Allowed libraires
['numpy', 'pandas', 'libsbml', 'math', 'scipy', 'jax', 'sklearn', 'io', 'traceback']


Please construct your response according to the thoughts-action markdown format.



# Iteration 3
## Thoughts

The previous step successfully created an SBML model with 7 hypothesized reactions and placeholder kinetic constants. The simulation ran, which is a good sign.
The numerical analysis confirmed:
-   `id_u8uf` consumption is roughly twice the sum of its major products (`id_70w8` pathway and `id_59ww`), supporting the `2 id_u8uf -> ...` stoichiometry.
-   `id_3nd3` consumption is roughly equal to the sum of `id_dadg` and `id_5lmp`, supporting `id_3nd3 -> id_dadg + id_5lmp`.
-   `id_70w8` shows intermediate behavior, peaking early and then declining.

The simulated data (`sim_df_iteration_2`) from the model with guessed parameters will likely not match the experimental data (`experiment_history['iteration_0']`) closely. The next step is to compare these two datasets to understand how the model needs to be improved. Specifically, I need to adjust the kinetic parameters (k_r1, k_r2, ..., k_r7) to better fit the experimental observations.

Since I cannot use optimization libraries like `scipy.optimize.minimize` directly for parameter fitting in this environment (or it's not explicitly stated I can), I will perform a qualitative comparison and then manually adjust parameters. If I had access to more sophisticated tools, I'd use them here.

Let's compare the final concentrations and the dynamics of key species:

**Experimental Data (iteration_0) at T=200:**
*   id_u8uf: 9.74e-13 (Initial: 7.40e-12)
*   id_3nd3: 2.25e-14 (Initial: 5.50e-14)
*   id_xsbr: 3.36e-12 (Initial: 3.60e-12)
*   id_70w8: 1.23e-12 (Peak ~2.84e-12 at T=1)
*   id_erlz: 1.98e-12
*   id_59ww: 1.05e-13
*   id_dadg: 1.82e-14
*   id_5lmp: 1.28e-14
*   id_0lud: 4.46e-16
*   id_ckd0: 3.16e-16

**Simulated Data (sim_df_iteration_2) at T=4 (from head(), need full sim):**
The `sim_df.head()` only shows up to T=4. I need to look at the end of the simulation to compare final values. I will retrieve `sim_df_iteration_2` from `shared_variables` and print its tail.

The placeholder kinetic constants were:
*   k_r1 (2 id_u8uf -> id_70w8): 1e7
*   k_r2 (id_70w8 -> id_erlz): 0.1
*   k_r3 (id_3nd3 -> id_dadg + id_5lmp): 0.1
*   k_r4 (2 id_u8uf -> id_59ww): 1e6
*   k_r5 (id_xsbr -> {}): 0.001
*   k_r6 (id_3nd3 -> id_0lud): 0.001
*   k_r7 (id_3nd3 -> id_ckd0): 0.001

**Strategy for parameter adjustment:**
1.  **`id_u8uf` consumption:** If too fast/slow, adjust k_r1 and k_r4.
2.  **`id_3nd3` consumption:** If too fast/slow, adjust k_r3, k_r6, k_r7.
3.  **`id_xsbr` degradation:** If too fast/slow, adjust k_r5.
4.  **`id_70w8` peak and `id_erlz` formation:** The balance between k_r1 and k_r2 determines the peak of `id_70w8` and the rate of `id_erlz` formation.
5.  **Ratio of `id_59ww` to `id_70w8` pathway:** The relative values of k_r1 and k_r4.
6.  **Ratio of major (`id_dadg`, `id_5lmp`) to minor (`id_0lud`, `id_ckd0`) products from `id_3nd3`:** Relative values of k_r3 vs k_r6, k_r7.

I will first print the tail of the simulation data to compare with experimental final values. Then, I'll make some educated guesses for new parameters and re-simulate. This is an iterative process.

Let's start by examining the full simulation output from the previous step.

## Action

### Code
```python
import pandas as pd
import libsbml

# Access experimental data and previous simulation results
exp_data = experiment_history['iteration_0']
sim_data_prev = shared_variables.access('sim_df_iteration_2')
current_sbml_model_str = shared_variables.access('current_sbml_model_iter2')

print("Experimental Data (iteration_0) - Final State (T=200):")
print(exp_data.iloc[-1])

print("\nPrevious Simulation Data (sim_df_iteration_2) - Final State (T=200 if simulated long enough):")
# The default simulation time for the `simulate` function is 100.
# Let's check the actual final time of the simulation.
# If it's not 200, we need to be mindful of that.
# For now, assume it simulates for a comparable duration or reaches steady state.
print(sim_data_prev.iloc[-1])


# --- Qualitative Comparison ---
# Experimental final values (approx):
# id_u8uf: 9.7e-13
# id_3nd3: 2.2e-14
# id_xsbr: 3.36e-12
# id_70w8: 1.2e-12
# id_erlz: 2.0e-12
# id_59ww: 1.0e-13
# id_dadg: 1.8e-14
# id_5lmp: 1.3e-14

# Simulated final values (from sim_data_prev.iloc[-1], assuming T_sim_end is comparable):
# (Need to run the code to see these values)

# Based on the head of the previous simulation:
# id_u8uf was 7.395e-12 at T=4 (initial 7.4e-12). Consumption seems very slow.
# This suggests k_r1 (2 id_u8uf -> id_70w8, current 1e7) and k_r4 (2 id_u8uf -> id_59ww, current 1e6) are too low.
# The concentrations are pM to fM. For a bimolecular reaction A+A -> P, rate = k[A]^2.
# If [A] is ~1e-12, [A]^2 is ~1e-24. To get a reasonable rate (e.g., 1e-15 M/s), k needs to be large (e.g., 1e9 M^-1 s^-1).
# My initial guesses for k_r1 and k_r4 might have been orders of magnitude too small.

# id_3nd3 was 3.65e-14 at T=4 (initial 5.5e-14). Consumption seems okay, maybe a bit slow.
# k_r3 (id_3nd3 -> P, current 0.1), k_r6, k_r7 (current 0.001).
# For a unimolecular reaction A -> P, rate = k[A]. If [A] ~ 1e-14, rate ~ k * 1e-14.
# If k=0.1, rate ~ 1e-15 M/s. This might be reasonable.

# id_xsbr was 3.585e-12 at T=4 (initial 3.6e-12). Degradation is very slow.
# k_r5 (id_xsbr -> {}, current 0.001). Needs to be higher.

# Let's try increasing k_r1, k_r4 significantly and k_r5 moderately.
# The relative rates also matter.
# Exp: consumed_u8uf ~6.4e-12. Produced (70w8_path + 59ww) ~3.3e-12.
# Exp: id_erlz ~2e-12, id_59ww ~1e-13. So 70w8 pathway is dominant over 59ww pathway. k_r1 should be > k_r4.
# My current k_r1=1e7, k_r4=1e6. This ratio (10:1) might be okay.

# Exp: consumed_3nd3 ~3.2e-14. Produced (dadg+5lmp) ~3.1e-14. Minor products ~7.6e-16.
# Ratio of major to minor products from 3nd3 is ~3.1e-14 / 7.6e-16 ~ 40.
# So k_r3 should be ~40 times k_r6 and k_r7.
# My current k_r3=0.1, k_r6=0.001, k_r7=0.001. Ratio is 100:1. This is in the right ballpark, maybe k_r6/k_r7 slightly higher.

# Parameter adjustment plan:
# k_r1: 1e7 -> 5e8 (increase significantly for id_u8uf consumption)
# k_r2: 0.1 (id_70w8 -> id_erlz, let's see effect of k_r1 change first)
# k_r3: 0.1 (id_3nd3 -> id_dadg + id_5lmp, seems okay for now, maybe slightly increase) -> 0.2
# k_r4: 1e6 -> 5e7 (increase significantly, keeping k_r1/k_r4 ratio at 10)
# k_r5: 0.001 -> 0.01 (id_xsbr degradation, increase)
# k_r6: 0.001 -> 0.005 (id_3nd3 -> id_0lud, increase slightly relative to k_r3)
# k_r7: 0.001 -> 0.005 (id_3nd3 -> id_ckd0, increase slightly relative to k_r3)

# Load the SBML model string from shared_variables
sbml_doc = libsbml.readSBMLFromString(current_sbml_model_str)
model = sbml_doc.getModel()

if model is None:
    print("Error: Could not get model from SBML string in shared_variables.")
else:
    # Update parameters
    reaction_params_update = {
        "R1_u8uf_to_70w8": {"k_r1": 5e8},
        "R2_70w8_to_erlz": {"k_r2": 0.1}, # No change yet
        "R3_3nd3_to_dadg_5lmp": {"k_r3": 0.2},
        "R4_u8uf_to_59ww": {"k_r4": 5e7},
        "R5_xsbr_degradation": {"k_r5": 0.01},
        "R6_3nd3_to_0lud": {"k_r6": 0.005},
        "R7_3nd3_to_ckd0": {"k_r7": 0.005}
    }

    for r_idx in range(model.getNumReactions()):
        reaction = model.getReaction(r_idx)
        r_id = reaction.getId()
        if r_id in reaction_params_update:
            kl = reaction.getKineticLaw()
            if kl:
                param_updates_for_reaction = reaction_params_update[r_id]
                for p_idx in range(kl.getNumParameters()):
                    param = kl.getParameter(p_idx)
                    p_id = param.getId()
                    if p_id in param_updates_for_reaction:
                        param.setValue(param_updates_for_reaction[p_id])
                        print(f"Updated parameter {p_id} in reaction {r_id} to {param_updates_for_reaction[p_id]}")

    writer = libsbml.SBMLWriter()
    updated_sbml_string_iter3 = writer.writeSBMLToString(sbml_doc)
    shared_variables.add("current_sbml_model_iter3", updated_sbml_string_iter3)
    print("\nUpdated SBML model with new parameters stored in shared_variables['current_sbml_model_iter3']")

    try:
        sim_df_iter3 = simulate(updated_sbml_string_iter3)
        print("\nSimulation with updated parameters ran successfully.")
        print("Simulated data head (iter3):")
        print(sim_df_iter3.head())
        print("\nSimulated data tail (iter3):")
        print(sim_df_iter3.tail())
        shared_variables.add("sim_df_iteration_3", sim_df_iter3)
    except Exception as e:
        print(f"\nError simulating the updated model: {e}")

```
# Observation

## Code Stdout
Experimental Data (iteration_0) - Final State (T=200):
id_59ww    1.046815e-13
id_0lud    4.457780e-16
id_70w8    1.232450e-12
id_5lmp    1.281449e-14
id_u8uf    9.736727e-13
id_ckd0    3.163974e-16
id_3nd3    2.251072e-14
id_dadg    1.815044e-14
id_erlz    1.980713e-12
id_xsbr    3.359672e-12
Time       2.000000e+02
Name: 200, dtype: float64

Previous Simulation Data (sim_df_iteration_2) - Final State (T=200 if simulated long enough):
id_59ww    1.060665e-14
id_0lud    5.392157e-16
id_70w8    5.152365e-15
id_5lmp    5.392157e-14
id_u8uf    7.166654e-12
id_ckd0    5.392157e-16
id_3nd3    7.614631e-23
id_dadg    5.392157e-14
id_erlz    1.009141e-13
id_xsbr    2.947431e-12
Time       2.000000e+02
Name: 200, dtype: float64
Updated parameter k_r1 in reaction R1_u8uf_to_70w8 to 500000000.0
Updated parameter k_r2 in reaction R2_70w8_to_erlz to 0.1
Updated parameter k_r3 in reaction R3_3nd3_to_dadg_5lmp to 0.2
Updated parameter k_r4 in reaction R4_u8uf_to_59ww to 50000000.0
Updated parameter k_r5 in reaction R5_xsbr_degradation to 0.01
Updated parameter k_r6 in reaction R6_3nd3_to_0lud to 0.005
Updated parameter k_r7 in reaction R7_3nd3_to_ckd0 to 0.005

Updated SBML model with new parameters stored in shared_variables['current_sbml_model_iter3']

Simulation with updated parameters ran successfully.
Simulated data head (iter3):
        id_59ww       id_0lud       id_70w8       id_5lmp       id_u8uf       id_ckd0       id_3nd3       id_dadg       id_erlz       id_xsbr  Time
0  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  7.400000e-12  0.000000e+00  5.500000e-14  0.000000e+00  0.000000e+00  3.600000e-12   0.0
1  2.715890e-15  2.480543e-16  2.584179e-14  9.922171e-15  7.340250e-12  2.480543e-16  4.458172e-14  9.922171e-15  1.317102e-15  3.564179e-12   1.0
2  5.388276e-15  4.491053e-16  4.881019e-14  1.796421e-14  7.281458e-12  4.491053e-16  3.613758e-14  1.796421e-14  5.072567e-15  3.528715e-12   2.0
3  8.018192e-15  6.120762e-16  6.918882e-14  2.448305e-14  7.223600e-12  6.120762e-16  2.929280e-14  2.448305e-14  1.099310e-14  3.493604e-12   3.0
4  1.060664e-14  7.441782e-16  8.723365e-14  2.976713e-14  7.166654e-12  7.441782e-16  2.374452e-14  2.976713e-14  1.883279e-14  3.458842e-12   4.0

Simulated data tail (iter3):
          id_59ww       id_0lud       id_70w8       id_5lmp       id_u8uf       id_ckd0       id_3nd3       id_dadg       id_erlz       id_xsbr   Time
196  2.067659e-13  1.309524e-15  4.347051e-14  5.238095e-14  2.851150e-12  1.309524e-15  1.468120e-27  5.238095e-14  2.024189e-12  5.070889e-13  196.0
197  2.071711e-13  1.309524e-15  4.318934e-14  5.238095e-14  2.842236e-12  1.309524e-15  1.546193e-27  5.238095e-14  2.028522e-12  5.020433e-13  197.0
198  2.075738e-13  1.309524e-15  4.291090e-14  5.238095e-14  2.833377e-12  1.309524e-15  1.542200e-27  5.238095e-14  2.032827e-12  4.970478e-13  198.0
199  2.079739e-13  1.309524e-15  4.263515e-14  5.238095e-14  2.824574e-12  1.309524e-15  1.435068e-27  5.238095e-14  2.037104e-12  4.921021e-13  199.0
200  2.083716e-13  1.309524e-15  4.236206e-14  5.238095e-14  2.815825e-12  1.309524e-15  1.286316e-27  5.238095e-14  2.041354e-12  4.872056e-13  200.0

## Reminder

 ### Remaining Iterations for Interactions:
- Iterations: 17/20



### Available Global Variables
- `input_sbml_string`: Original incomplete model
- `experiment_history`: Results from all previous experiments (iteration_0)
- `shared_variables`: all variables you have saved from the previous iterations. (current_sbml_model_iter2, sim_df_iteration_2, current_sbml_model_iter3, sim_df_iteration_3)

### Allowed libraires
['numpy', 'pandas', 'libsbml', 'math', 'scipy', 'jax', 'sklearn', 'io', 'traceback']


Please construct your response according to the thoughts-action markdown format.



# Iteration 4
## Thoughts

The parameter adjustment in Iteration 3 led to a new simulation (`sim_df_iteration_3`). Let's compare its final state (T=200) with the experimental data.

**Experimental Data (iteration_0) at T=200:**
*   id_u8uf:  0.97e-12 (Initial: 7.40e-12) -> Consumed: 6.43e-12
*   id_3nd3:  2.25e-14 (Initial: 5.50e-14) -> Consumed: 3.25e-14
*   id_xsbr:  3.36e-12 (Initial: 3.60e-12) -> Consumed: 0.24e-12
*   id_70w8:  1.23e-12
*   id_erlz:  1.98e-12
*   id_59ww:  0.105e-12 (1.05e-13)
*   id_dadg:  1.82e-14
*   id_5lmp:  1.28e-14
*   id_0lud:  0.45e-15 (4.46e-16)
*   id_ckd0:  0.32e-15 (3.16e-16)

**Simulated Data (sim_df_iteration_3) at T=200:**
*   id_u8uf:  2.82e-12 -> Consumed: 7.40e-12 - 2.82e-12 = 4.58e-12
*   id_3nd3:  ~1.3e-27 (effectively 0) -> Consumed: 5.50e-14
*   id_xsbr:  0.49e-12 -> Consumed: 3.60e-12 - 0.49e-12 = 3.11e-12
*   id_70w8:  0.042e-12 (4.24e-14)
*   id_erlz:  2.04e-12
*   id_59ww:  0.208e-12 (2.08e-13)
*   id_dadg:  5.24e-14
*   id_5lmp:  5.24e-14
*   id_0lud:  1.31e-15
*   id_ckd0:  1.31e-15

**Comparison and Parameter Adjustment Strategy:**

1.  **`id_u8uf` Consumption:**
    *   Exp: Consumed 6.43e-12. Sim: Consumed 4.58e-12.
    *   Simulation consumes less `id_u8uf` than experiment.
    *   Need to increase k_r1 (2 id_u8uf -> id_70w8) and/or k_r4 (2 id_u8uf -> id_59ww).
    *   Current: k_r1=5e8, k_r4=5e7.

2.  **`id_3nd3` Consumption:**
    *   Exp: Consumed 3.25e-14. Sim: Consumed 5.50e-14 (all of it).
    *   Simulation consumes `id_3nd3` too fast/completely.
    *   Need to decrease k_r3, k_r6, k_r7.
    *   Current: k_r3=0.2, k_r6=0.005, k_r7=0.005.

3.  **`id_xsbr` Degradation:**
    *   Exp: Consumed 0.24e-12. Sim: Consumed 3.11e-12.
    *   Simulation degrades `id_xsbr` too much/too fast.
    *   Need to decrease k_r5.
    *   Current: k_r5=0.01.

4.  **`id_70w8` and `id_erlz`:**
    *   Exp: id_70w8 = 1.23e-12, id_erlz = 1.98e-12. Sum = 3.21e-12.
    *   Sim: id_70w8 = 0.042e-12, id_erlz = 2.04e-12. Sum = 2.082e-12.
    *   Simulated `id_70w8` is too low, `id_erlz` is about right. This suggests `id_70w8` is converted to `id_erlz` too quickly OR not produced enough.
    *   Since `id_u8uf` consumption is low, `id_70w8` production (via R1) is also low.
    *   k_r2 (id_70w8 -> id_erlz) might be too high relative to k_r1. Current k_r2=0.1.

5.  **`id_59ww` Production:**
    *   Exp: 0.105e-12. Sim: 0.208e-12.
    *   Simulated `id_59ww` is about 2x too high.
    *   This is linked to k_r4.

6.  **`id_dadg`, `id_5lmp` Production (from `id_3nd3`):**
    *   Exp: id_dadg = 1.82e-14, id_5lmp = 1.28e-14. Sum = 3.1e-14.
    *   Sim: id_dadg = 5.24e-14, id_5lmp = 5.24e-14. Sum = 10.48e-14. (Note: `id_3nd3` was fully consumed in sim, producing more than exp).
    *   The simulation produces too much `id_dadg` and `id_5lmp` because `id_3nd3` is consumed too fast.
    *   The ratio `id_dadg`:`id_5lmp` is 1:1 in sim (due to R3: `id_3nd3 -> id_dadg + id_5lmp`). Exp ratio is ~1.82:1.28 = 1.42:1. This suggests the simple 1:1 stoichiometry might be an oversimplification or there are other reactions. For now, I'll stick to the 1:1 and adjust overall rate.

7.  **`id_0lud`, `id_ckd0` Production (minor from `id_3nd3`):**
    *   Exp: id_0lud = 0.45e-15, id_ckd0 = 0.32e-15.
    *   Sim: id_0lud = 1.31e-15, id_ckd0 = 1.31e-15.
    *   Simulated minor products are also too high, due to `id_3nd3` over-consumption.

**Revised Parameter Adjustment Plan:**

*   **k_r1 (2 id_u8uf -> id_70w8):** Was 5e8. Increase to get more `id_u8uf` consumption and more `id_70w8` production. Try 8e8.
*   **k_r4 (2 id_u8uf -> id_59ww):** Was 5e7. `id_59ww` is too high. Decrease. Try 2.5e7. (This changes k_r1/k_r4 ratio from 10 to 32, favoring `id_70w8` pathway more).
*   **k_r2 (id_70w8 -> id_erlz):** Was 0.1. `id_70w8` is too low, `id_erlz` is okay. If k_r1 increases, `id_70w8` production increases. Let's try decreasing k_r2 slightly to allow `id_70w8` to accumulate more. Try 0.07.
*   **k_r3 (id_3nd3 -> id_dadg + id_5lmp):** Was 0.2. `id_3nd3` consumed too fast. Decrease significantly. Try 0.05.
*   **k_r6 (id_3nd3 -> id_0lud):** Was 0.005. Decrease proportionally to k_r3. Try 0.00125.
*   **k_r7 (id_3nd3 -> id_ckd0):** Was 0.005. Decrease proportionally to k_r3. Try 0.00125.
    *   (Ratio k_r3 / (k_r6 or k_r7) = 0.05 / 0.00125 = 40. This matches the desired product ratio better).
*   **k_r5 (id_xsbr -> {}):** Was 0.01. `id_xsbr` degraded too fast. Decrease. Try 0.0015. (Exp consumed 0.24/3.6 = ~6.7%. Sim consumed 3.11/3.6 = ~86%. Need a large reduction).

Let's implement these changes.

## Action

### Code
```python
import pandas as pd
import libsbml

# Access previous SBML model and simulation results
current_sbml_model_str = shared_variables.access('current_sbml_model_iter3')
exp_data = experiment_history['iteration_0']
sim_data_prev = shared_variables.access('sim_df_iteration_3')

print("Experimental Data (iteration_0) - Final State (T=200):")
print(exp_data.iloc[-1])
print("\nPrevious Simulation Data (sim_df_iteration_3) - Final State (T=200):")
print(sim_data_prev.iloc[-1])

# Load the SBML model string
sbml_doc = libsbml.readSBMLFromString(current_sbml_model_str)
model = sbml_doc.getModel()

if model is None:
    print("Error: Could not get model from SBML string in shared_variables.")
else:
    # New parameter values
    # k_r1 (2 id_u8uf -> id_70w8): 5e8 -> 8e8
    # k_r2 (id_70w8 -> id_erlz): 0.1 -> 0.07
    # k_r3 (id_3nd3 -> id_dadg + id_5lmp): 0.2 -> 0.05
    # k_r4 (2 id_u8uf -> id_59ww): 5e7 -> 2.5e7
    # k_r5 (id_xsbr -> {}): 0.01 -> 0.0015
    # k_r6 (id_3nd3 -> id_0lud): 0.005 -> 0.00125
    # k_r7 (id_3nd3 -> id_ckd0): 0.005 -> 0.00125

    reaction_params_update = {
        "R1_u8uf_to_70w8": {"k_r1": 8e8},
        "R2_70w8_to_erlz": {"k_r2": 0.07},
        "R3_3nd3_to_dadg_5lmp": {"k_r3": 0.05},
        "R4_u8uf_to_59ww": {"k_r4": 2.5e7},
        "R5_xsbr_degradation": {"k_r5": 0.0015},
        "R6_3nd3_to_0lud": {"k_r6": 0.00125},
        "R7_3nd3_to_ckd0": {"k_r7": 0.00125}
    }

    for r_idx in range(model.getNumReactions()):
        reaction = model.getReaction(r_idx)
        r_id = reaction.getId()
        if r_id in reaction_params_update:
            kl = reaction.getKineticLaw()
            if kl:
                param_updates_for_reaction = reaction_params_update[r_id]
                for p_idx in range(kl.getNumParameters()):
                    param = kl.getParameter(p_idx)
                    p_id = param.getId()
                    if p_id in param_updates_for_reaction:
                        param.setValue(param_updates_for_reaction[p_id])
                        print(f"Updated parameter {p_id} in reaction {r_id} to {param_updates_for_reaction[p_id]}")

    writer = libsbml.SBMLWriter()
    updated_sbml_string_iter4 = writer.writeSBMLToString(sbml_doc)
    shared_variables.add("current_sbml_model_iter4", updated_sbml_string_iter4)
    print("\nUpdated SBML model with new parameters stored in shared_variables['current_sbml_model_iter4']")

    try:
        sim_df_iter4 = simulate(updated_sbml_string_iter4)
        print("\nSimulation with updated parameters (iter4) ran successfully.")
        print("Simulated data head (iter4):")
        print(sim_df_iter4.head())
        print("\nSimulated data tail (iter4):")
        print(sim_df_iter4.tail())
        shared_variables.add("sim_df_iteration_4", sim_df_iter4)
    except Exception as e:
        print(f"\nError simulating the updated model (iter4): {e}")

```
# Observation

## Code Stdout
Experimental Data (iteration_0) - Final State (T=200):
id_59ww    1.046815e-13
id_0lud    4.457780e-16
id_70w8    1.232450e-12
id_5lmp    1.281449e-14
id_u8uf    9.736727e-13
id_ckd0    3.163974e-16
id_3nd3    2.251072e-14
id_dadg    1.815044e-14
id_erlz    1.980713e-12
id_xsbr    3.359672e-12
Time       2.000000e+02
Name: 200, dtype: float64

Previous Simulation Data (sim_df_iteration_3) - Final State (T=200):
id_59ww    2.083716e-13
id_0lud    1.309524e-15
id_70w8    4.236206e-14
id_5lmp    5.238095e-14
id_u8uf    2.815825e-12
id_ckd0    1.309524e-15
id_3nd3    1.286316e-27
id_dadg    5.238095e-14
id_erlz    2.041354e-12
id_xsbr    4.872056e-13
Time       2.000000e+02
Name: 200, dtype: float64
Updated parameter k_r1 in reaction R1_u8uf_to_70w8 to 800000000.0
Updated parameter k_r2 in reaction R2_70w8_to_erlz to 0.07
Updated parameter k_r3 in reaction R3_3nd3_to_dadg_5lmp to 0.05
Updated parameter k_r4 in reaction R4_u8uf_to_59ww to 25000000.0
Updated parameter k_r5 in reaction R5_xsbr_degradation to 0.0015
Updated parameter k_r6 in reaction R6_3nd3_to_0lud to 0.00125
Updated parameter k_r7 in reaction R7_3nd3_to_ckd0 to 0.00125

Updated SBML model with new parameters stored in shared_variables['current_sbml_model_iter4']

Simulation with updated parameters (iter4) ran successfully.
Simulated data head (iter4):
        id_59ww       id_0lud       id_70w8       id_5lmp       id_u8uf       id_ckd0       id_3nd3       id_dadg       id_erlz       id_xsbr  Time
0  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  7.400000e-12  0.000000e+00  5.500000e-14  0.000000e+00  0.000000e+00  3.600000e-12   0.0
1  1.352450e-15  6.697354e-17  4.179192e-14  2.678942e-15  7.310738e-12  6.697354e-17  5.218711e-14  2.678942e-15  1.486472e-15  3.594604e-12   1.0
2  2.672742e-15  1.305316e-16  7.977559e-14  5.221264e-15  7.223599e-12  1.305316e-16  4.951767e-14  5.221264e-15  5.752167e-15  3.589216e-12   2.0
3  3.961923e-15  1.908374e-16  1.142274e-13  7.633497e-15  7.138513e-12  1.908374e-16  4.698483e-14  7.633497e-15  1.255410e-14  3.583836e-12   3.0
4  5.221056e-15  2.480541e-16  1.454147e-13  9.922165e-15  7.055410e-12  2.480541e-16  4.458173e-14  9.922165e-15  2.165913e-14  3.578465e-12   4.0

Simulated data tail (iter4):
          id_59ww       id_0lud       id_70w8       id_5lmp       id_u8uf       id_ckd0       id_3nd3       id_dadg       id_erlz       id_xsbr   Time
196  7.907791e-14  1.309479e-15  6.105125e-14  5.237917e-14  2.180858e-12  1.309479e-15  1.868188e-18  5.237917e-14  2.469442e-12  2.682995e-12  196.0
197  7.919639e-14  1.309482e-15  6.058527e-14  5.237926e-14  2.173038e-12  1.309482e-15  1.772637e-18  5.237926e-14  2.473699e-12  2.678974e-12  197.0
198  7.931402e-14  1.309484e-15  6.012464e-14  5.237935e-14  2.165275e-12  1.309484e-15  1.681973e-18  5.237935e-14  2.477924e-12  2.674958e-12  198.0
199  7.943081e-14  1.309486e-15  5.966926e-14  5.237943e-14  2.157566e-12  1.309486e-15  1.595946e-18  5.237943e-14  2.482117e-12  2.670949e-12  199.0
200  7.954678e-14  1.309488e-15  5.921906e-14  5.237951e-14  2.149913e-12  1.309488e-15  1.514320e-18  5.237951e-14  2.486278e-12  2.666946e-12  200.0

## Reminder

 ### Remaining Iterations for Interactions:
- Iterations: 16/20



### Available Global Variables
- `input_sbml_string`: Original incomplete model
- `experiment_history`: Results from all previous experiments (iteration_0)
- `shared_variables`: all variables you have saved from the previous iterations. (current_sbml_model_iter2, sim_df_iteration_2, current_sbml_model_iter3, sim_df_iteration_3, current_sbml_model_iter4, sim_df_iteration_4)

### Allowed libraires
['numpy', 'pandas', 'libsbml', 'math', 'scipy', 'jax', 'sklearn', 'io', 'traceback']


Please construct your response according to the thoughts-action markdown format.



# Iteration 5
## Thoughts

Let's compare the latest simulation (iter4) with the experimental data.

**Experimental Data (iteration_0) at T=200:**
*   id_u8uf:  0.97e-12 (Initial: 7.40e-12) -> Consumed: 6.43e-12
*   id_3nd3:  2.25e-14 (Initial: 5.50e-14) -> Consumed: 3.25e-14
*   id_xsbr:  3.36e-12 (Initial: 3.60e-12) -> Consumed: 0.24e-12
*   id_70w8:  1.23e-12
*   id_erlz:  1.98e-12
*   id_59ww:  1.05e-13
*   id_dadg:  1.82e-14
*   id_5lmp:  1.28e-14
*   id_0lud:  4.46e-16
*   id_ckd0:  3.16e-16

**Simulated Data (sim_df_iteration_4) at T=200:**
*   id_u8uf:  2.15e-12 -> Consumed: 7.40e-12 - 2.15e-12 = 5.25e-12
*   id_3nd3:  ~1.5e-18 (effectively 0, but less than before) -> Consumed: ~5.50e-14
*   id_xsbr:  2.67e-12 -> Consumed: 3.60e-12 - 2.67e-12 = 0.93e-12
*   id_70w8:  0.059e-12 (5.92e-14)
*   id_erlz:  2.49e-12
*   id_59ww:  0.080e-12 (7.95e-14)
*   id_dadg:  5.24e-14
*   id_5lmp:  5.24e-14
*   id_0lud:  1.31e-15
*   id_ckd0:  1.31e-15

**Comparison and Parameter Adjustment Strategy (Iter 4 vs Exp):**

1.  **`id_u8uf` Consumption:**
    *   Exp: Consumed 6.43e-12. Sim: Consumed 5.25e-12.
    *   Still consuming less `id_u8uf`. Need to increase k_r1 and/or k_r4.
    *   Current: k_r1=8e8, k_r4=2.5e7. (Ratio 32:1)

2.  **`id_3nd3` Consumption:**
    *   Exp: Consumed 3.25e-14 (final 2.25e-14). Sim: Consumed ~5.50e-14 (final ~0).
    *   Still consumes `id_3nd3` too fast/completely. The rates (k_r3, k_r6, k_r7) are still too high.
    *   Current: k_r3=0.05, k_r6=0.00125, k_r7=0.00125.

3.  **`id_xsbr` Degradation:**
    *   Exp: Consumed 0.24e-12 (final 3.36e-12). Sim: Consumed 0.93e-12 (final 2.67e-12).
    *   Still degrades `id_xsbr` too much/too fast (though better than iter3).
    *   Need to decrease k_r5 further.
    *   Current: k_r5=0.0015.

4.  **`id_70w8` and `id_erlz`:**
    *   Exp: id_70w8 = 1.23e-12, id_erlz = 1.98e-12. Sum = 3.21e-12.
    *   Sim: id_70w8 = 0.059e-12, id_erlz = 2.49e-12. Sum = 2.55e-12.
    *   Simulated `id_70w8` is far too low. `id_erlz` is a bit high.
    *   This strongly suggests k_r2 (id_70w8 -> id_erlz) is too high relative to k_r1.
    *   Current k_r1=8e8, k_r2=0.07.

5.  **`id_59ww` Production:**
    *   Exp: 1.05e-13. Sim: 0.80e-13 (7.95e-14).
    *   This is much closer! Good. k_r4=2.5e7 seems to be in a good range relative to k_r1.

6.  **`id_dadg`, `id_5lmp` Production (from `id_3nd3`):**
    *   Exp: id_dadg = 1.82e-14, id_5lmp = 1.28e-14.
    *   Sim: id_dadg = 5.24e-14, id_5lmp = 5.24e-14.
    *   Still too high due to `id_3nd3` over-consumption. The 1:1 ratio in sim vs ~1.4:1 in exp is still an issue I'm deferring.

7.  **`id_0lud`, `id_ckd0` Production (minor from `id_3nd3`):**
    *   Exp: id_0lud = 4.46e-16, id_ckd0 = 3.16e-16.
    *   Sim: id_0lud = 1.31e-15, id_ckd0 = 1.31e-15.
    *   Still too high.

**Key issues to address:**
*   `id_u8uf` needs more consumption.
*   `id_3nd3` needs much slower consumption.
*   `id_xsbr` needs much slower degradation.
*   `id_70w8` needs to be much higher, meaning k_r2 needs to be significantly lower.

**Revised Parameter Adjustment Plan (Iter 5):**

*   **k_r1 (2 id_u8uf -> id_70w8):** Was 8e8. Increase. Try 1.2e9.
*   **k_r4 (2 id_u8uf -> id_59ww):** Was 2.5e7. `id_59ww` is good. To maintain its level while increasing total `id_u8uf` consumption via k_r1, k_r4 might need a slight proportional increase or stay. Let's keep it for now and see the effect of k_r1. If `id_59ww` becomes too low, we can increase k_r4 later.
*   **k_r2 (id_70w8 -> id_erlz):** Was 0.07. `id_70w8` is critically low. Decrease k_r2 significantly. Try 0.01. This should allow `id_70w8` to accumulate.
*   **k_r3 (id_3nd3 -> id_dadg + id_5lmp):** Was 0.05. `id_3nd3` consumed too fast. Decrease by factor of ~2-3. Try 0.02.
*   **k_r6 (id_3nd3 -> id_0lud):** Was 0.00125. Decrease proportionally. Try 0.0005. (k_r3/k_r6 = 0.02/0.0005 = 40).
*   **k_r7 (id_3nd3 -> id_ckd0):** Was 0.00125. Decrease proportionally. Try 0.0005.
*   **k_r5 (id_xsbr -> {}):** Was 0.0015. `id_xsbr` degraded too fast (target consumption ~0.24e-12, current sim ~0.93e-12). Needs to be about 1/4th of current. Try 0.0004.

Let's implement these. The large change in k_r2 is the most critical one for `id_70w8`.

## Action

### Code
```python
import pandas as pd
import libsbml

# Access previous SBML model and simulation results
current_sbml_model_str = shared_variables.access('current_sbml_model_iter4')
exp_data = experiment_history['iteration_0']
sim_data_prev = shared_variables.access('sim_df_iteration_4')

print("Experimental Data (iteration_0) - Final State (T=200):")
print(exp_data.iloc[-1])
print("\nPrevious Simulation Data (sim_df_iteration_4) - Final State (T=200):")
print(sim_data_prev.iloc[-1])

# Load the SBML model string
sbml_doc = libsbml.readSBMLFromString(current_sbml_model_str)
model = sbml_doc.getModel()

if model is None:
    print("Error: Could not get model from SBML string in shared_variables.")
else:
    # New parameter values for Iteration 5
    # k_r1 (2 id_u8uf -> id_70w8): 8e8 -> 1.2e9
    # k_r2 (id_70w8 -> id_erlz): 0.07 -> 0.01
    # k_r3 (id_3nd3 -> id_dadg + id_5lmp): 0.05 -> 0.02
    # k_r4 (2 id_u8uf -> id_59ww): 2.5e7 (no change for now)
    # k_r5 (id_xsbr -> {}): 0.0015 -> 0.0004
    # k_r6 (id_3nd3 -> id_0lud): 0.00125 -> 0.0005
    # k_r7 (id_3nd3 -> id_ckd0): 0.00125 -> 0.0005

    reaction_params_update = {
        "R1_u8uf_to_70w8": {"k_r1": 1.2e9},
        "R2_70w8_to_erlz": {"k_r2": 0.01},
        "R3_3nd3_to_dadg_5lmp": {"k_r3": 0.02},
        "R4_u8uf_to_59ww": {"k_r4": 2.5e7}, # No change from iter4
        "R5_xsbr_degradation": {"k_r5": 0.0004},
        "R6_3nd3_to_0lud": {"k_r6": 0.0005},
        "R7_3nd3_to_ckd0": {"k_r7": 0.0005}
    }

    for r_idx in range(model.getNumReactions()):
        reaction = model.getReaction(r_idx)
        r_id = reaction.getId()
        if r_id in reaction_params_update:
            kl = reaction.getKineticLaw()
            if kl:
                param_updates_for_reaction = reaction_params_update[r_id]
                for p_idx in range(kl.getNumParameters()):
                    param = kl.getParameter(p_idx)
                    p_id = param.getId()
                    if p_id in param_updates_for_reaction:
                        param.setValue(param_updates_for_reaction[p_id])
                        print(f"Updated parameter {p_id} in reaction {r_id} to {param_updates_for_reaction[p_id]}")

    writer = libsbml.SBMLWriter()
    updated_sbml_string_iter5 = writer.writeSBMLToString(sbml_doc)
    shared_variables.add("current_sbml_model_iter5", updated_sbml_string_iter5)
    print("\nUpdated SBML model with new parameters stored in shared_variables['current_sbml_model_iter5']")

    try:
        sim_df_iter5 = simulate(updated_sbml_string_iter5)
        print("\nSimulation with updated parameters (iter5) ran successfully.")
        print("Simulated data head (iter5):")
        print(sim_df_iter5.head())
        print("\nSimulated data tail (iter5):")
        print(sim_df_iter5.tail())
        shared_variables.add("sim_df_iteration_5", sim_df_iter5)
    except Exception as e:
        print(f"\nError simulating the updated model (iter5): {e}")

```
# Observation

## Code Stdout
Experimental Data (iteration_0) - Final State (T=200):
id_59ww    1.046815e-13
id_0lud    4.457780e-16
id_70w8    1.232450e-12
id_5lmp    1.281449e-14
id_u8uf    9.736727e-13
id_ckd0    3.163974e-16
id_3nd3    2.251072e-14
id_dadg    1.815044e-14
id_erlz    1.980713e-12
id_xsbr    3.359672e-12
Time       2.000000e+02
Name: 200, dtype: float64

Previous Simulation Data (sim_df_iteration_4) - Final State (T=200):
id_59ww    7.954678e-14
id_0lud    1.309488e-15
id_70w8    5.921906e-14
id_5lmp    5.237951e-14
id_u8uf    2.149913e-12
id_ckd0    1.309488e-15
id_3nd3    1.514320e-18
id_dadg    5.237951e-14
id_erlz    2.486278e-12
id_xsbr    2.666946e-12
Time       2.000000e+02
Name: 200, dtype: float64
Updated parameter k_r1 in reaction R1_u8uf_to_70w8 to 1200000000.0
Updated parameter k_r2 in reaction R2_70w8_to_erlz to 0.01
Updated parameter k_r3 in reaction R3_3nd3_to_dadg_5lmp to 0.02
Updated parameter k_r4 in reaction R4_u8uf_to_59ww to 25000000.0
Updated parameter k_r5 in reaction R5_xsbr_degradation to 0.0004
Updated parameter k_r6 in reaction R6_3nd3_to_0lud to 0.0005
Updated parameter k_r7 in reaction R7_3nd3_to_ckd0 to 0.0005

Updated SBML model with new parameters stored in shared_variables['current_sbml_model_iter5']

Simulation with updated parameters (iter5) ran successfully.
Simulated data head (iter5):
        id_59ww       id_0lud       id_70w8       id_5lmp       id_u8uf       id_ckd0       id_3nd3       id_dadg       id_erlz       id_xsbr  Time
0  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  7.400000e-12  0.000000e+00  5.500000e-14  0.000000e+00  0.000000e+00  3.600000e-12   0.0
1  1.344421e-15  2.721045e-17  6.420588e-14  1.088418e-15  7.268247e-12  2.721045e-17  5.385716e-14  1.088418e-15  3.263095e-16  3.598560e-12   1.0
2  2.642290e-15  5.385956e-17  1.255558e-13  2.154382e-15  7.141056e-12  5.385956e-17  5.273790e-14  2.154382e-15  1.274072e-15  3.597121e-12   2.0
3  3.895536e-15  7.995506e-17  1.841644e-13  3.198202e-15  7.018238e-12  7.995506e-17  5.164189e-14  3.198202e-15  2.821310e-15  3.595683e-12   3.0
4  5.106370e-15  1.055081e-16  2.401638e-13  4.220325e-15  6.899576e-12  1.055081e-16  5.056866e-14  4.220325e-15  4.941921e-15  3.594245e-12   4.0

Simulated data tail (iter5):
          id_59ww       id_0lud       id_70w8       id_5lmp       id_u8uf       id_ckd0       id_3nd3       id_dadg       id_erlz       id_xsbr   Time
196  5.892706e-14  1.288166e-15  7.705878e-13  5.152663e-14  1.625148e-12  1.288166e-15  8.970417e-16  5.152663e-14  2.057911e-12  3.328540e-12  196.0
197  5.899282e-14  1.288610e-15  7.660614e-13  5.154438e-14  1.618703e-12  1.288610e-15  8.784004e-16  5.154438e-14  2.065594e-12  3.327209e-12  197.0
198  5.905807e-14  1.289044e-15  7.615552e-13  5.156177e-14  1.612309e-12  1.289044e-15  8.601464e-16  5.156177e-14  2.073232e-12  3.325878e-12  198.0
199  5.912280e-14  1.289470e-15  7.570693e-13  5.157879e-14  1.605965e-12  1.289470e-15  8.422717e-16  5.157879e-14  2.080825e-12  3.324548e-12  199.0
200  5.918703e-14  1.289886e-15  7.526037e-13  5.159546e-14  1.599671e-12  1.289886e-15  8.247685e-16  5.159546e-14  2.088374e-12  3.323219e-12  200.0

## Reminder

 ### Remaining Iterations for Interactions:
- Iterations: 15/20



### Available Global Variables
- `input_sbml_string`: Original incomplete model
- `experiment_history`: Results from all previous experiments (iteration_0)
- `shared_variables`: all variables you have saved from the previous iterations. (current_sbml_model_iter2, sim_df_iteration_2, current_sbml_model_iter3, sim_df_iteration_3, current_sbml_model_iter4, sim_df_iteration_4, current_sbml_model_iter5, sim_df_iteration_5)

### Allowed libraires
['numpy', 'pandas', 'libsbml', 'math', 'scipy', 'jax', 'sklearn', 'io', 'traceback']


Please construct your response according to the thoughts-action markdown format.



# Iteration 6
## Thoughts

Let's compare the latest simulation (iter5) with the experimental data.

**Experimental Data (iteration_0) at T=200:**
*   id_u8uf:  0.97e-12 (Initial: 7.40e-12) -> Consumed: 6.43e-12
*   id_3nd3:  2.25e-14 (Initial: 5.50e-14) -> Consumed: 3.25e-14
*   id_xsbr:  3.36e-12 (Initial: 3.60e-12) -> Consumed: 0.24e-12
*   id_70w8:  1.23e-12
*   id_erlz:  1.98e-12
*   id_59ww:  1.05e-13
*   id_dadg:  1.82e-14
*   id_5lmp:  1.28e-14
*   id_0lud:  4.46e-16
*   id_ckd0:  3.16e-16

**Simulated Data (sim_df_iteration_5) at T=200:**
*   id_u8uf:  1.60e-12 -> Consumed: 7.40e-12 - 1.60e-12 = 5.80e-12
*   id_3nd3:  0.82e-16 (8.25e-17) -> Consumed: ~5.49e-14 (almost all)
*   id_xsbr:  3.32e-12 -> Consumed: 3.60e-12 - 3.32e-12 = 0.28e-12
*   id_70w8:  0.75e-12 (7.53e-13)
*   id_erlz:  2.09e-12
*   id_59ww:  0.059e-12 (5.92e-14)
*   id_dadg:  5.16e-14
*   id_5lmp:  5.16e-14
*   id_0lud:  1.29e-15
*   id_ckd0:  1.29e-15

**Comparison and Parameter Adjustment Strategy (Iter 5 vs Exp):**

1.  **`id_u8uf` Consumption:**
    *   Exp: Consumed 6.43e-12. Sim: Consumed 5.80e-12.
    *   Getting closer. Still slightly under-consumed.
    *   Current: k_r1=1.2e9, k_r4=2.5e7.

2.  **`id_3nd3` Consumption:**
    *   Exp: Consumed 3.25e-14 (final 2.25e-14). Sim: Consumed ~5.49e-14 (final ~0).
    *   Still consumes `id_3nd3` too fast/completely. Rates (k_r3, k_r6, k_r7) are still too high.
    *   Current: k_r3=0.02, k_r6=0.0005, k_r7=0.0005.

3.  **`id_xsbr` Degradation:**
    *   Exp: Consumed 0.24e-12 (final 3.36e-12). Sim: Consumed 0.28e-12 (final 3.32e-12).
    *   This is very close! k_r5=0.0004 seems good.

4.  **`id_70w8` and `id_erlz`:**
    *   Exp: id_70w8 = 1.23e-12, id_erlz = 1.98e-12.
    *   Sim: id_70w8 = 0.75e-12, id_erlz = 2.09e-12.
    *   `id_70w8` is much better (was 0.059e-12), but still a bit low. `id_erlz` is good.
    *   This suggests k_r2 (id_70w8 -> id_erlz) might still be a tad high, or k_r1 (2 id_u8uf -> id_70w8) a tad low.
    *   Current k_r1=1.2e9, k_r2=0.01.

5.  **`id_59ww` Production:**
    *   Exp: 1.05e-13. Sim: 0.59e-13.
    *   Now `id_59ww` is a bit low (was 0.80e-13 in iter4). This is because k_r1 was increased but k_r4 was not, so more `id_u8uf` flux went to `id_70w8`.
    *   Current k_r4=2.5e7.

6.  **`id_dadg`, `id_5lmp` Production (from `id_3nd3`):**
    *   Exp: id_dadg = 1.82e-14, id_5lmp = 1.28e-14.
    *   Sim: id_dadg = 5.16e-14, id_5lmp = 5.16e-14.
    *   Still too high due to `id_3nd3` over-consumption.

7.  **`id_0lud`, `id_ckd0` Production (minor from `id_3nd3`):**
    *   Exp: id_0lud = 4.46e-16, id_ckd0 = 3.16e-16.
    *   Sim: id_0lud = 1.29e-15, id_ckd0 = 1.29e-15.
    *   Still too high.

**Key issues to address:**
*   `id_u8uf` needs slightly more consumption.
*   `id_3nd3` needs significantly slower consumption. This is the main remaining problem for its products.
*   `id_70w8` needs to be a bit higher.
*   `id_59ww` needs to be a bit higher.

**Revised Parameter Adjustment Plan (Iter 6):**

*   **k_r1 (2 id_u8uf -> id_70w8):** Was 1.2e9. Increase slightly to boost `id_u8uf` consumption and `id_70w8` production. Try 1.5e9.
*   **k_r4 (2 id_u8uf -> id_59ww):** Was 2.5e7. `id_59ww` is low. Increase to get more flux. Try 3.5e7.
    *   New k_r1/k_r4 ratio = 1.5e9 / 3.5e7 = ~42.8 (was 1.2e9 / 2.5e7 = 48). This slightly reduces preference for `id_70w8` path over `id_59ww` path compared to the change in k_r1 alone.
*   **k_r2 (id_70w8 -> id_erlz):** Was 0.01. `id_70w8` is 0.75e-12 (target 1.23e-12). `id_erlz` is 2.09e-12 (target 1.98e-12, good). If k_r1 increases, `id_70w8` production increases. Let's try a small decrease in k_r2 to help `id_70w8` accumulate. Try 0.008.
*   **k_r3 (id_3nd3 -> id_dadg + id_5lmp):** Was 0.02. `id_3nd3` consumed too fast. Target final `id_3nd3` is 2.25e-14 from 5.5e-14 initial (consumes ~59%). Sim consumes almost 100%. Need to reduce rates by about half again. Try 0.01.
*   **k_r6 (id_3nd3 -> id_0lud):** Was 0.0005. Reduce proportionally. Try 0.00025. (k_r3/k_r6 = 0.01/0.00025 = 40).
*   **k_r7 (id_3nd3 -> id_ckd0):** Was 0.0005. Reduce proportionally. Try 0.00025.
*   **k_r5 (id_xsbr -> {}):** Was 0.0004. This is good. No change.

The most significant change is for the `id_3nd3` consumption rates.

## Action

### Code
```python
import pandas as pd
import libsbml

# Access previous SBML model and simulation results
current_sbml_model_str = shared_variables.access('current_sbml_model_iter5')
exp_data = experiment_history['iteration_0']
sim_data_prev = shared_variables.access('sim_df_iteration_5')

print("Experimental Data (iteration_0) - Final State (T=200):")
print(exp_data.iloc[-1])
print("\nPrevious Simulation Data (sim_df_iteration_5) - Final State (T=200):")
print(sim_data_prev.iloc[-1])

# Load the SBML model string
sbml_doc = libsbml.readSBMLFromString(current_sbml_model_str)
model = sbml_doc.getModel()

if model is None:
    print("Error: Could not get model from SBML string in shared_variables.")
else:
    # New parameter values for Iteration 6
    # k_r1 (2 id_u8uf -> id_70w8): 1.2e9 -> 1.5e9
    # k_r2 (id_70w8 -> id_erlz): 0.01 -> 0.008
    # k_r3 (id_3nd3 -> id_dadg + id_5lmp): 0.02 -> 0.01
    # k_r4 (2 id_u8uf -> id_59ww): 2.5e7 -> 3.5e7
    # k_r5 (id_xsbr -> {}): 0.0004 (no change)
    # k_r6 (id_3nd3 -> id_0lud): 0.0005 -> 0.00025
    # k_r7 (id_3nd3 -> id_ckd0): 0.0005 -> 0.00025

    reaction_params_update = {
        "R1_u8uf_to_70w8": {"k_r1": 1.5e9},
        "R2_70w8_to_erlz": {"k_r2": 0.008},
        "R3_3nd3_to_dadg_5lmp": {"k_r3": 0.01},
        "R4_u8uf_to_59ww": {"k_r4": 3.5e7},
        "R5_xsbr_degradation": {"k_r5": 0.0004}, # No change from iter5
        "R6_3nd3_to_0lud": {"k_r6": 0.00025},
        "R7_3nd3_to_ckd0": {"k_r7": 0.00025}
    }

    for r_idx in range(model.getNumReactions()):
        reaction = model.getReaction(r_idx)
        r_id = reaction.getId()
        if r_id in reaction_params_update: # Check if reaction_id is in our update list
            kl = reaction.getKineticLaw()
            if kl:
                param_updates_for_reaction = reaction_params_update[r_id]
                for p_idx in range(kl.getNumParameters()):
                    param = kl.getParameter(p_idx)
                    p_id = param.getId()
                    if p_id in param_updates_for_reaction: # Check if param_id is in our update list for this reaction
                        param.setValue(param_updates_for_reaction[p_id])
                        print(f"Updated parameter {p_id} in reaction {r_id} to {param_updates_for_reaction[p_id]}")
                    # If a parameter (e.g. k_r5) is not in reaction_params_update[r_id] because it's unchanged, it won't be touched.
                    # This is fine, but ensure all intended changes are listed.
                    # For k_r5, it's listed with its existing value to be explicit, or could be omitted from param_updates_for_reaction["R5_xsbr_degradation"]
                    # The current structure correctly handles this: if "k_r5" is not in reaction_params_update["R5_xsbr_degradation"], it's not updated.
                    # To be safe, ensure all parameters for a reaction are specified if any one is.
                    # My current reaction_params_update structure is {reaction_id: {param_id: value}}. This is fine.

    writer = libsbml.SBMLWriter()
    updated_sbml_string_iter6 = writer.writeSBMLToString(sbml_doc)
    shared_variables.add("current_sbml_model_iter6", updated_sbml_string_iter6)
    print("\nUpdated SBML model with new parameters stored in shared_variables['current_sbml_model_iter6']")

    try:
        sim_df_iter6 = simulate(updated_sbml_string_iter6)
        print("\nSimulation with updated parameters (iter6) ran successfully.")
        print("Simulated data head (iter6):")
        print(sim_df_iter6.head())
        print("\nSimulated data tail (iter6):")
        print(sim_df_iter6.tail())
        shared_variables.add("sim_df_iteration_6", sim_df_iter6)
    except Exception as e:
        print(f"\nError simulating the updated model (iter6): {e}")

```
# Observation

## Code Stdout
Experimental Data (iteration_0) - Final State (T=200):
id_59ww    1.046815e-13
id_0lud    4.457780e-16
id_70w8    1.232450e-12
id_5lmp    1.281449e-14
id_u8uf    9.736727e-13
id_ckd0    3.163974e-16
id_3nd3    2.251072e-14
id_dadg    1.815044e-14
id_erlz    1.980713e-12
id_xsbr    3.359672e-12
Time       2.000000e+02
Name: 200, dtype: float64

Previous Simulation Data (sim_df_iteration_5) - Final State (T=200):
id_59ww    5.918703e-14
id_0lud    1.289886e-15
id_70w8    7.526037e-13
id_5lmp    5.159546e-14
id_u8uf    1.599671e-12
id_ckd0    1.289886e-15
id_3nd3    8.247685e-16
id_dadg    5.159546e-14
id_erlz    2.088374e-12
id_xsbr    3.323219e-12
Time       2.000000e+02
Name: 200, dtype: float64
Updated parameter k_r1 in reaction R1_u8uf_to_70w8 to 1500000000.0
Updated parameter k_r2 in reaction R2_70w8_to_erlz to 0.008
Updated parameter k_r3 in reaction R3_3nd3_to_dadg_5lmp to 0.01
Updated parameter k_r4 in reaction R4_u8uf_to_59ww to 35000000.0
Updated parameter k_r5 in reaction R5_xsbr_degradation to 0.0004
Updated parameter k_r6 in reaction R6_3nd3_to_0lud to 0.00025
Updated parameter k_r7 in reaction R7_3nd3_to_ckd0 to 0.00025

Updated SBML model with new parameters stored in shared_variables['current_sbml_model_iter6']

Simulation with updated parameters (iter6) ran successfully.
Simulated data head (iter6):
        id_59ww       id_0lud       id_70w8       id_5lmp       id_u8uf       id_ckd0       id_3nd3       id_dadg       id_erlz       id_xsbr  Time
0  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  7.400000e-12  0.000000e+00  5.500000e-14  0.000000e+00  0.000000e+00  3.600000e-12   0.0
1  1.873838e-15  1.367752e-17  7.998289e-14  5.471007e-16  7.235638e-12  1.367752e-17  5.442554e-14  5.471007e-16  3.244472e-16  3.598560e-12   1.0
2  3.666867e-15  2.721283e-17  1.558836e-13  1.088513e-15  7.078363e-12  2.721283e-17  5.385706e-14  1.088513e-15  1.267820e-15  3.597121e-12   2.0
3  5.383685e-15  4.060680e-17  2.279270e-13  1.624272e-15  6.927774e-12  4.060680e-17  5.329451e-14  1.624272e-15  2.802354e-15  3.595683e-12   3.0
4  7.028706e-15  5.386080e-17  2.963299e-13  2.154432e-15  6.783482e-12  5.386080e-17  5.273785e-14  2.154432e-15  4.900330e-15  3.594245e-12   4.0

Simulated data tail (iter6):
          id_59ww       id_0lud       id_70w8       id_5lmp       id_u8uf       id_ckd0       id_3nd3       id_dadg       id_erlz       id_xsbr   Time
196  6.889309e-14  1.142287e-15  9.763669e-13  4.569146e-14  1.357091e-12  1.142287e-15  7.023965e-15  4.569146e-14  1.976194e-12  3.328540e-12  196.0
197  6.895729e-14  1.144033e-15  9.713274e-13  4.576133e-14  1.351461e-12  1.144033e-15  6.950598e-15  4.576133e-14  1.983985e-12  3.327209e-12  197.0
198  6.902095e-14  1.145762e-15  9.663054e-13  4.583048e-14  1.345876e-12  1.145762e-15  6.877998e-15  4.583048e-14  1.991735e-12  3.325878e-12  198.0
199  6.908409e-14  1.147472e-15  9.613010e-13  4.589890e-14  1.340338e-12  1.147472e-15  6.806156e-15  4.589890e-14  1.999446e-12  3.324548e-12  199.0
200  6.914671e-14  1.149165e-15  9.563143e-13  4.596660e-14  1.334845e-12  1.149165e-15  6.735065e-15  4.596660e-14  2.007116e-12  3.323219e-12  200.0

## Reminder

 ### Remaining Iterations for Interactions:
- Iterations: 14/20



### Available Global Variables
- `input_sbml_string`: Original incomplete model
- `experiment_history`: Results from all previous experiments (iteration_0)
- `shared_variables`: all variables you have saved from the previous iterations. (current_sbml_model_iter2, sim_df_iteration_2, current_sbml_model_iter3, sim_df_iteration_3, current_sbml_model_iter4, sim_df_iteration_4, current_sbml_model_iter5, sim_df_iteration_5, current_sbml_model_iter6, sim_df_iteration_6)

### Allowed libraires
['numpy', 'pandas', 'libsbml', 'math', 'scipy', 'jax', 'sklearn', 'io', 'traceback']


Please construct your response according to the thoughts-action markdown format.



# Iteration 7
## Thoughts

Let's compare the latest simulation (iter6) with the experimental data.

**Experimental Data (iteration_0) at T=200:**
*   id_u8uf:  0.97e-12 (Initial: 7.40e-12) -> Consumed: 6.43e-12
*   id_3nd3:  2.25e-14 (Initial: 5.50e-14) -> Consumed: 3.25e-14
*   id_xsbr:  3.36e-12 (Initial: 3.60e-12) -> Consumed: 0.24e-12
*   id_70w8:  1.23e-12
*   id_erlz:  1.98e-12
*   id_59ww:  1.05e-13
*   id_dadg:  1.82e-14
*   id_5lmp:  1.28e-14
*   id_0lud:  4.46e-16
*   id_ckd0:  3.16e-16

**Simulated Data (sim_df_iteration_6) at T=200:**
*   id_u8uf:  1.33e-12 -> Consumed: 7.40e-12 - 1.33e-12 = 6.07e-12
*   id_3nd3:  6.74e-15 -> Consumed: 5.50e-14 - 0.67e-14 = 4.83e-14
*   id_xsbr:  3.32e-12 -> Consumed: 0.28e-12 (This is good)
*   id_70w8:  0.96e-12 (9.56e-13)
*   id_erlz:  2.01e-12
*   id_59ww:  0.069e-12 (6.91e-14)
*   id_dadg:  4.60e-14
*   id_5lmp:  4.60e-14
*   id_0lud:  1.15e-15
*   id_ckd0:  1.15e-15

**Comparison and Parameter Adjustment Strategy (Iter 6 vs Exp):**

1.  **`id_u8uf` Consumption:**
    *   Exp: Consumed 6.43e-12. Sim: Consumed 6.07e-12.
    *   Very close! Slightly under.
    *   Current: k_r1=1.5e9, k_r4=3.5e7.

2.  **`id_3nd3` Consumption:**
    *   Exp: Consumed 3.25e-14 (final 2.25e-14). Sim: Consumed 4.83e-14 (final 0.67e-14).
    *   Still consumes `id_3nd3` too much, but much better. Target consumption is ~59%, sim is ~88%.
    *   Rates (k_r3, k_r6, k_r7) need to be reduced further.
    *   Current: k_r3=0.01, k_r6=0.00025, k_r7=0.00025.

3.  **`id_xsbr` Degradation:**
    *   Exp: Consumed 0.24e-12. Sim: Consumed 0.28e-12.
    *   Excellent. k_r5=0.0004 is good.

4.  **`id_70w8` and `id_erlz`:**
    *   Exp: id_70w8 = 1.23e-12, id_erlz = 1.98e-12.
    *   Sim: id_70w8 = 0.96e-12, id_erlz = 2.01e-12.
    *   `id_70w8` is getting closer (was 0.75e-12). `id_erlz` is excellent.
    *   Slightly more `id_u8uf` consumption (via k_r1) and perhaps a tiny decrease in k_r2 might get `id_70w8` there.
    *   Current k_r1=1.5e9, k_r2=0.008.

5.  **`id_59ww` Production:**
    *   Exp: 1.05e-13. Sim: 0.69e-13.
    *   Still a bit low. Needs more flux via k_r4.
    *   Current k_r4=3.5e7.

6.  **`id_dadg`, `id_5lmp` Production (from `id_3nd3`):**
    *   Exp: id_dadg = 1.82e-14, id_5lmp = 1.28e-14. Sum = 3.1e-14.
    *   Sim: id_dadg = 4.60e-14, id_5lmp = 4.60e-14. Sum = 9.2e-14.
    *   Still too high due to `id_3nd3` over-consumption. The 1:1 ratio issue persists.

7.  **`id_0lud`, `id_ckd0` Production (minor from `id_3nd3`):**
    *   Exp: id_0lud = 4.46e-16, id_ckd0 = 3.16e-16. Sum = 0.76e-15.
    *   Sim: id_0lud = 1.15e-15, id_ckd0 = 1.15e-15. Sum = 2.3e-15.
    *   Too high.

**Revised Parameter Adjustment Plan (Iter 7):**

*   **k_r1 (2 id_u8uf -> id_70w8):** Was 1.5e9. Slight increase. Try 1.7e9.
*   **k_r4 (2 id_u8uf -> id_59ww):** Was 3.5e7. `id_59ww` is low (0.69e-13 vs 1.05e-13). Increase. Try 5.0e7.
    *   New k_r1/k_r4 ratio = 1.7e9 / 5.0e7 = 34. (Was ~42.8). This shifts more flux to `id_59ww`.
*   **k_r2 (id_70w8 -> id_erlz):** Was 0.008. `id_70w8` is 0.96e-12 (target 1.23e-12). `id_erlz` is good. If k_r1 increases, `id_70w8` production increases. Let's try a small decrease in k_r2. Try 0.007.
*   **k_r3 (id_3nd3 -> id_dadg + id_5lmp):** Was 0.01. `id_3nd3` consumed ~88%, target ~59%. Need to reduce rate by (1-0.59)/(1-0.88) * current_rate, roughly. (0.41/0.12)*0.01 is too much. Let's try reducing by about 40%. 0.01 * 0.6 = 0.006.
*   **k_r6 (id_3nd3 -> id_0lud):** Was 0.00025. Reduce proportionally. 0.006 / 40 = 0.00015.
*   **k_r7 (id_3nd3 -> id_ckd0):** Was 0.00025. Reduce proportionally. 0.00015.
*   **k_r5 (id_xsbr -> {}):** Was 0.0004. No change.

The main focus is still on `id_3nd3` pathway rates and fine-tuning `id_u8uf` pathway.
The 1:1 stoichiometry for `id_dadg`:`id_5lmp` from R3 (`id_3nd3 -> id_dadg + id_5lmp`) means their simulated final concentrations will always be equal if R3 is the only source. Experimental values are `id_dadg`: 1.82e-14, `id_5lmp`: 1.28e-14. This is a ratio of ~1.42.
If the current model structure is correct, this discrepancy cannot be fixed by rate constants alone. It might imply:
    a) `id_dadg` or `id_5lmp` are involved in other reactions (e.g., one is consumed).
    b) The stoichiometry of R3 is not 1:1 for both.
    c) There are separate reactions producing them from `id_3nd3` with different rates.
For now, I'm sticking to the simplest model (R3 as is) and trying to get overall `id_3nd3` consumption right. If other things match well, this might be a point for a structural model change or a new experiment.

Let's proceed with parameter tuning.

## Action

### Code
```python
import pandas as pd
import libsbml

# Access previous SBML model and simulation results
current_sbml_model_str = shared_variables.access('current_sbml_model_iter6')
exp_data = experiment_history['iteration_0']
sim_data_prev = shared_variables.access('sim_df_iteration_6')

print("Experimental Data (iteration_0) - Final State (T=200):")
print(exp_data.iloc[-1])
print("\nPrevious Simulation Data (sim_df_iteration_6) - Final State (T=200):")
print(sim_data_prev.iloc[-1])

# Load the SBML model string
sbml_doc = libsbml.readSBMLFromString(current_sbml_model_str)
model = sbml_doc.getModel()

if model is None:
    print("Error: Could not get model from SBML string in shared_variables.")
else:
    # New parameter values for Iteration 7
    # k_r1 (2 id_u8uf -> id_70w8): 1.5e9 -> 1.7e9
    # k_r2 (id_70w8 -> id_erlz): 0.008 -> 0.007
    # k_r3 (id_3nd3 -> id_dadg + id_5lmp): 0.01 -> 0.006
    # k_r4 (2 id_u8uf -> id_59ww): 3.5e7 -> 5.0e7
    # k_r5 (id_xsbr -> {}): 0.0004 (no change)
    # k_r6 (id_3nd3 -> id_0lud): 0.00025 -> 0.00015
    # k_r7 (id_3nd3 -> id_ckd0): 0.00025 -> 0.00015

    reaction_params_update = {
        "R1_u8uf_to_70w8": {"k_r1": 1.7e9},
        "R2_70w8_to_erlz": {"k_r2": 0.007},
        "R3_3nd3_to_dadg_5lmp": {"k_r3": 0.006},
        "R4_u8uf_to_59ww": {"k_r4": 5.0e7},
        "R5_xsbr_degradation": {"k_r5": 0.0004}, # No change
        "R6_3nd3_to_0lud": {"k_r6": 0.00015},
        "R7_3nd3_to_ckd0": {"k_r7": 0.00015}
    }

    for r_idx in range(model.getNumReactions()):
        reaction = model.getReaction(r_idx)
        r_id = reaction.getId()
        if r_id in reaction_params_update:
            kl = reaction.getKineticLaw()
            if kl:
                param_updates_for_reaction = reaction_params_update[r_id]
                for p_idx in range(kl.getNumParameters()):
                    param = kl.getParameter(p_idx)
                    p_id = param.getId()
                    if p_id in param_updates_for_reaction:
                        param.setValue(param_updates_for_reaction[p_id])
                        print(f"Updated parameter {p_id} in reaction {r_id} to {param_updates_for_reaction[p_id]}")

    writer = libsbml.SBMLWriter()
    updated_sbml_string_iter7 = writer.writeSBMLToString(sbml_doc)
    shared_variables.add("current_sbml_model_iter7", updated_sbml_string_iter7)
    print("\nUpdated SBML model with new parameters stored in shared_variables['current_sbml_model_iter7']")

    try:
        sim_df_iter7 = simulate(updated_sbml_string_iter7)
        print("\nSimulation with updated parameters (iter7) ran successfully.")
        print("Simulated data head (iter7):")
        print(sim_df_iter7.head())
        print("\nSimulated data tail (iter7):")
        print(sim_df_iter7.tail())
        shared_variables.add("sim_df_iteration_7", sim_df_iter7)
    except Exception as e:
        print(f"\nError simulating the updated model (iter7): {e}")

```
# Observation

## Code Stdout
Experimental Data (iteration_0) - Final State (T=200):
id_59ww    1.046815e-13
id_0lud    4.457780e-16
id_70w8    1.232450e-12
id_5lmp    1.281449e-14
id_u8uf    9.736727e-13
id_ckd0    3.163974e-16
id_3nd3    2.251072e-14
id_dadg    1.815044e-14
id_erlz    1.980713e-12
id_xsbr    3.359672e-12
Time       2.000000e+02
Name: 200, dtype: float64

Previous Simulation Data (sim_df_iteration_6) - Final State (T=200):
id_59ww    6.914671e-14
id_0lud    1.149165e-15
id_70w8    9.563143e-13
id_5lmp    4.596660e-14
id_u8uf    1.334845e-12
id_ckd0    1.149165e-15
id_3nd3    6.735065e-15
id_dadg    4.596660e-14
id_erlz    2.007116e-12
id_xsbr    3.323219e-12
Time       2.000000e+02
Name: 200, dtype: float64
Updated parameter k_r1 in reaction R1_u8uf_to_70w8 to 1700000000.0
Updated parameter k_r2 in reaction R2_70w8_to_erlz to 0.007
Updated parameter k_r3 in reaction R3_3nd3_to_dadg_5lmp to 0.006
Updated parameter k_r4 in reaction R4_u8uf_to_59ww to 50000000.0
Updated parameter k_r5 in reaction R5_xsbr_degradation to 0.0004
Updated parameter k_r6 in reaction R6_3nd3_to_0lud to 0.00015
Updated parameter k_r7 in reaction R7_3nd3_to_ckd0 to 0.00015

Updated SBML model with new parameters stored in shared_variables['current_sbml_model_iter7']

Simulation with updated parameters (iter7) ran successfully.
Simulated data head (iter7):
        id_59ww       id_0lud       id_70w8       id_5lmp       id_u8uf       id_ckd0       id_3nd3       id_dadg       id_erlz       id_xsbr  Time
0  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  7.400000e-12  0.000000e+00  5.500000e-14  0.000000e+00  0.000000e+00  3.600000e-12   0.0
1  2.668717e-15  8.223909e-18  9.041582e-14  3.289564e-16  7.213190e-12  8.223909e-18  5.465460e-14  3.289564e-16  3.205694e-16  3.598560e-12   1.0
2  5.206867e-15  1.639635e-17  1.757810e-13  6.558539e-16  7.035519e-12  1.639635e-17  5.431135e-14  6.558539e-16  1.252518e-15  3.597121e-12   2.0
3  7.622907e-15  2.451746e-17  2.564132e-13  9.806986e-16  6.866397e-12  2.451746e-17  5.397027e-14  9.806986e-16  2.765608e-15  3.595683e-12   3.0
4  9.925052e-15  3.258756e-17  3.326225e-13  1.303502e-15  6.705246e-12  3.258756e-17  5.363132e-14  1.303502e-15  4.829305e-15  3.594245e-12   4.0

Simulated data tail (iter7):
          id_59ww       id_0lud       id_70w8       id_5lmp       id_u8uf       id_ckd0       id_3nd3       id_dadg       id_erlz       id_xsbr   Time
196  8.831671e-14  9.285925e-16  1.105623e-12  3.714370e-14  1.217830e-12  9.285925e-16  1.599911e-14  3.714370e-14  1.897145e-12  3.328540e-12  196.0
197  8.839055e-14  9.309849e-16  1.100412e-12  3.723939e-14  1.212661e-12  9.309849e-16  1.589864e-14  3.723939e-14  1.904866e-12  3.327209e-12  197.0
198  8.846377e-14  9.333622e-16  1.095217e-12  3.733449e-14  1.207536e-12  9.333622e-16  1.579879e-14  3.733449e-14  1.912551e-12  3.325878e-12  198.0
199  8.853637e-14  9.357245e-16  1.090037e-12  3.742898e-14  1.202454e-12  9.357245e-16  1.569957e-14  3.742898e-14  1.920199e-12  3.324548e-12  199.0
200  8.860836e-14  9.380721e-16  1.084873e-12  3.752288e-14  1.197415e-12  9.380721e-16  1.560097e-14  3.752288e-14  1.927812e-12  3.323219e-12  200.0

## Reminder

 ### Remaining Iterations for Interactions:
- Iterations: 13/20



### Available Global Variables
- `input_sbml_string`: Original incomplete model
- `experiment_history`: Results from all previous experiments (iteration_0)
- `shared_variables`: all variables you have saved from the previous iterations. (current_sbml_model_iter2, sim_df_iteration_2, current_sbml_model_iter3, sim_df_iteration_3, current_sbml_model_iter4, sim_df_iteration_4, current_sbml_model_iter5, sim_df_iteration_5, current_sbml_model_iter6, sim_df_iteration_6, current_sbml_model_iter7, sim_df_iteration_7)

### Allowed libraires
['numpy', 'pandas', 'libsbml', 'math', 'scipy', 'jax', 'sklearn', 'io', 'traceback']


Please construct your response according to the thoughts-action markdown format.



# Iteration 8
## Thoughts

Let's compare the latest simulation (iter7) with the experimental data.

**Experimental Data (iteration_0) at T=200:**
*   id_u8uf:  0.97e-12 (Initial: 7.40e-12) -> Consumed: 6.43e-12
*   id_3nd3:  2.25e-14 (Initial: 5.50e-14) -> Consumed: 3.25e-14
*   id_xsbr:  3.36e-12 (Initial: 3.60e-12) -> Consumed: 0.24e-12
*   id_70w8:  1.23e-12
*   id_erlz:  1.98e-12
*   id_59ww:  1.05e-13
*   id_dadg:  1.82e-14
*   id_5lmp:  1.28e-14
*   id_0lud:  4.46e-16
*   id_ckd0:  3.16e-16

**Simulated Data (sim_df_iteration_7) at T=200:**
*   id_u8uf:  1.20e-12 -> Consumed: 7.40e-12 - 1.20e-12 = 6.20e-12
*   id_3nd3:  1.56e-14 -> Consumed: 5.50e-14 - 1.56e-14 = 3.94e-14
*   id_xsbr:  3.32e-12 -> Consumed: 0.28e-12 (Excellent)
*   id_70w8:  1.08e-12
*   id_erlz:  1.93e-12
*   id_59ww:  0.89e-13 (8.86e-14)
*   id_dadg:  3.75e-14
*   id_5lmp:  3.75e-14
*   id_0lud:  0.94e-15 (9.38e-16)
*   id_ckd0:  0.94e-15

**Comparison and Parameter Adjustment Strategy (Iter 7 vs Exp):**

1.  **`id_u8uf` Consumption:**
    *   Exp: Consumed 6.43e-12. Sim: Consumed 6.20e-12.
    *   Very close! Slightly under.
    *   Current: k_r1=1.7e9, k_r4=5.0e7.

2.  **`id_3nd3` Consumption:**
    *   Exp: Consumed 3.25e-14 (final 2.25e-14). Sim: Consumed 3.94e-14 (final 1.56e-14).
    *   This is much better! Target consumption ~59%, sim is ~71.6%. Closer.
    *   Rates (k_r3, k_r6, k_r7) might still be a little high.
    *   Current: k_r3=0.006, k_r6=0.00015, k_r7=0.00015.

3.  **`id_xsbr` Degradation:**
    *   Excellent. k_r5=0.0004.

4.  **`id_70w8` and `id_erlz`:**
    *   Exp: id_70w8 = 1.23e-12, id_erlz = 1.98e-12.
    *   Sim: id_70w8 = 1.08e-12, id_erlz = 1.93e-12.
    *   `id_70w8` is close (target 1.23). `id_erlz` is excellent (target 1.98).
    *   A bit more `id_u8uf` consumption (via k_r1) might push `id_70w8` up.
    *   Current k_r1=1.7e9, k_r2=0.007.

5.  **`id_59ww` Production:**
    *   Exp: 1.05e-13. Sim: 0.89e-13.
    *   Close, but a bit low.
    *   Current k_r4=5.0e7.

6.  **`id_dadg`, `id_5lmp` Production (from `id_3nd3`):**
    *   Exp: id_dadg = 1.82e-14, id_5lmp = 1.28e-14. Sum = 3.1e-14.
    *   Sim: id_dadg = 3.75e-14, id_5lmp = 3.75e-14. Sum = 7.5e-14.
    *   Still too high, because `id_3nd3` is still slightly over-consumed, and the products reflect this.
    *   Target `id_dadg` + `id_5lmp` is 3.1e-14. Sim produces 3.94e-14 * (k_r3 / (k_r3+k_r6+k_r7)) * 2 (stoich).
    *   Consumed `id_3nd3` in sim is 3.94e-14.
    *   Fraction to R3 = 0.006 / (0.006 + 0.00015 + 0.00015) = 0.006 / 0.0063 = 0.952.
    *   Amount to R3 = 3.94e-14 * 0.952 = 3.75e-14. This produces 3.75e-14 of `id_dadg` and `id_5lmp` each. This matches.
    *   If `id_3nd3` consumption was 3.25e-14, then R3 products would be 3.25e-14 * 0.952 = 3.09e-14 each. This is closer to the sum, but individual values would be off.

7.  **`id_0lud`, `id_ckd0` Production (minor from `id_3nd3`):**
    *   Exp: id_0lud = 4.46e-16, id_ckd0 = 3.16e-16. Sum = 0.76e-15.
    *   Sim: id_0lud = 0.94e-15, id_ckd0 = 0.94e-15. Sum = 1.88e-15.
    *   Still too high.

**Revised Parameter Adjustment Plan (Iter 8):**

*   **k_r1 (2 id_u8uf -> id_70w8):** Was 1.7e9. Slight increase. Try 1.8e9.
*   **k_r4 (2 id_u8uf -> id_59ww):** Was 5.0e7. `id_59ww` is 0.89e-13 (target 1.05e-13). Increase slightly. Try 5.5e7.
    *   New k_r1/k_r4 ratio = 1.8e9 / 5.5e7 = ~32.7. (Was 34). This shifts more flux to `id_59ww`.
*   **k_r2 (id_70w8 -> id_erlz):** Was 0.007. `id_70w8` is 1.08e-12 (target 1.23e-12). `id_erlz` is good. If k_r1 increases, `id_70w8` production increases. Let's keep k_r2 at 0.007 for now, or tiny decrease to 0.0068.
*   **k_r3 (id_3nd3 -> id_dadg + id_5lmp):** Was 0.006. `id_3nd3` consumed ~71.6%, target ~59%.
    *   (Target remaining fraction / Sim remaining fraction) * current_rate = ((1-0.59)/(1-0.716)) * 0.006 = (0.41/0.284)*0.006 = 1.44 * 0.006 = 0.0086 (this is wrong logic, should decrease rate).
    *   Need to decrease rate. (Sim_consumption_fraction / Target_consumption_fraction) = (0.716/0.59) = 1.21. So current rate is ~21% too high.
    *   New k_r3 = 0.006 / 1.21 = ~0.00495. Let's try 0.005.
*   **k_r6 (id_3nd3 -> id_0lud):** Was 0.00015. New k_r3/40 = 0.005 / 40 = 0.000125.
*   **k_r7 (id_3nd3 -> id_ckd0):** Was 0.00015. New k_r3/40 = 0.000125.
*   **k_r5 (id_xsbr -> {}):** Was 0.0004. No change.

The model is getting quite close for many species. The `id_dadg`/`id_5lmp` ratio remains an issue tied to model structure.

One more iteration of parameter tuning. If the results are very close, I might consider submitting.
The discrepancy in `id_dadg` and `id_5lmp` (1.82e-14 vs 1.28e-14) is noticeable.
If R3 is `id_3nd3 -> id_dadg + id_5lmp`, they must be produced equally.
Perhaps one of them is consumed? Or R3 is actually two reactions:
`R3a: id_3nd3 -> id_dadg` (k3a)
`R3b: id_3nd3 -> id_5lmp` (k3b)
Then k3a/k3b could be ~1.42.
And k3a + k3b would be the old k3.
And k_r6, k_r7 would be relative to (k3a+k3b).
This is a structural change. Let's try one more parameter refinement first. If `id_3nd3` consumption and its minor products get better, but `id_dadg`/`id_5lmp` are still off but equal to each other, then this structural change is warranted.

## Action

### Code
```python
import pandas as pd
import libsbml

# Access previous SBML model and simulation results
current_sbml_model_str = shared_variables.access('current_sbml_model_iter7')
exp_data = experiment_history['iteration_0']
sim_data_prev = shared_variables.access('sim_df_iteration_7')

print("Experimental Data (iteration_0) - Final State (T=200):")
print(exp_data.iloc[-1])
print("\nPrevious Simulation Data (sim_df_iteration_7) - Final State (T=200):")
print(sim_data_prev.iloc[-1])

# Load the SBML model string
sbml_doc = libsbml.readSBMLFromString(current_sbml_model_str)
model = sbml_doc.getModel()

if model is None:
    print("Error: Could not get model from SBML string in shared_variables.")
else:
    # New parameter values for Iteration 8
    # k_r1 (2 id_u8uf -> id_70w8): 1.7e9 -> 1.8e9
    # k_r2 (id_70w8 -> id_erlz): 0.007 -> 0.0068
    # k_r3 (id_3nd3 -> id_dadg + id_5lmp): 0.006 -> 0.005
    # k_r4 (2 id_u8uf -> id_59ww): 5.0e7 -> 5.5e7
    # k_r5 (id_xsbr -> {}): 0.0004 (no change)
    # k_r6 (id_3nd3 -> id_0lud): 0.00015 -> 0.000125
    # k_r7 (id_3nd3 -> id_ckd0): 0.00015 -> 0.000125

    reaction_params_update = {
        "R1_u8uf_to_70w8": {"k_r1": 1.8e9},
        "R2_70w8_to_erlz": {"k_r2": 0.0068},
        "R3_3nd3_to_dadg_5lmp": {"k_r3": 0.005},
        "R4_u8uf_to_59ww": {"k_r4": 5.5e7},
        "R5_xsbr_degradation": {"k_r5": 0.0004}, # No change
        "R6_3nd3_to_0lud": {"k_r6": 0.000125},
        "R7_3nd3_to_ckd0": {"k_r7": 0.000125}
    }

    for r_idx in range(model.getNumReactions()):
        reaction = model.getReaction(r_idx)
        r_id = reaction.getId()
        if r_id in reaction_params_update:
            kl = reaction.getKineticLaw()
            if kl:
                param_updates_for_reaction = reaction_params_update[r_id]
                for p_idx in range(kl.getNumParameters()):
                    param = kl.getParameter(p_idx)
                    p_id = param.getId()
                    if p_id in param_updates_for_reaction:
                        param.setValue(param_updates_for_reaction[p_id])
                        print(f"Updated parameter {p_id} in reaction {r_id} to {param_updates_for_reaction[p_id]}")

    writer = libsbml.SBMLWriter()
    updated_sbml_string_iter8 = writer.writeSBMLToString(sbml_doc)
    shared_variables.add("current_sbml_model_iter8", updated_sbml_string_iter8)
    print("\nUpdated SBML model with new parameters stored in shared_variables['current_sbml_model_iter8']")

    try:
        sim_df_iter8 = simulate(updated_sbml_string_iter8)
        print("\nSimulation with updated parameters (iter8) ran successfully.")
        # print("Simulated data head (iter8):")
        # print(sim_df_iter8.head())
        print("\nSimulated data tail (iter8):")
        print(sim_df_iter8.tail())
        shared_variables.add("sim_df_iteration_8", sim_df_iter8)
    except Exception as e:
        print(f"\nError simulating the updated model (iter8): {e}")

```
# Observation

## Code Stdout
Experimental Data (iteration_0) - Final State (T=200):
id_59ww    1.046815e-13
id_0lud    4.457780e-16
id_70w8    1.232450e-12
id_5lmp    1.281449e-14
id_u8uf    9.736727e-13
id_ckd0    3.163974e-16
id_3nd3    2.251072e-14
id_dadg    1.815044e-14
id_erlz    1.980713e-12
id_xsbr    3.359672e-12
Time       2.000000e+02
Name: 200, dtype: float64

Previous Simulation Data (sim_df_iteration_7) - Final State (T=200):
id_59ww    8.860836e-14
id_0lud    9.380721e-16
id_70w8    1.084873e-12
id_5lmp    3.752288e-14
id_u8uf    1.197415e-12
id_ckd0    9.380721e-16
id_3nd3    1.560097e-14
id_dadg    3.752288e-14
id_erlz    1.927812e-12
id_xsbr    3.323219e-12
Time       2.000000e+02
Name: 200, dtype: float64
Updated parameter k_r1 in reaction R1_u8uf_to_70w8 to 1800000000.0
Updated parameter k_r2 in reaction R2_70w8_to_erlz to 0.0068
Updated parameter k_r3 in reaction R3_3nd3_to_dadg_5lmp to 0.005
Updated parameter k_r4 in reaction R4_u8uf_to_59ww to 55000000.0
Updated parameter k_r5 in reaction R5_xsbr_degradation to 0.0004
Updated parameter k_r6 in reaction R6_3nd3_to_0lud to 0.000125
Updated parameter k_r7 in reaction R7_3nd3_to_ckd0 to 0.000125

Updated SBML model with new parameters stored in shared_variables['current_sbml_model_iter8']

Simulation with updated parameters (iter8) ran successfully.

Simulated data tail (iter8):
          id_59ww       id_0lud       id_70w8       id_5lmp       id_u8uf       id_ckd0       id_3nd3       id_dadg       id_erlz       id_xsbr   Time
196  9.251082e-14  8.415469e-16  1.134776e-12  3.366188e-14  1.159724e-12  8.415469e-16  1.965503e-14  3.366188e-14  1.892851e-12  3.328540e-12  196.0
197  9.258448e-14  8.439974e-16  1.129488e-12  3.375989e-14  1.154756e-12  8.439974e-16  1.955211e-14  3.375989e-14  1.900550e-12  3.327209e-12  197.0
198  9.265750e-14  8.464350e-16  1.124215e-12  3.385740e-14  1.149830e-12  8.464350e-16  1.944973e-14  3.385740e-14  1.908212e-12  3.325878e-12  198.0
199  9.272991e-14  8.488598e-16  1.118958e-12  3.395439e-14  1.144946e-12  8.488598e-16  1.934789e-14  3.395439e-14  1.915839e-12  3.324548e-12  199.0
200  9.280171e-14  8.512720e-16  1.113717e-12  3.405088e-14  1.140103e-12  8.512720e-16  1.924658e-14  3.405088e-14  1.923430e-12  3.323219e-12  200.0

## Reminder

 ### Remaining Iterations for Interactions:
- Iterations: 12/20



### Available Global Variables
- `input_sbml_string`: Original incomplete model
- `experiment_history`: Results from all previous experiments (iteration_0)
- `shared_variables`: all variables you have saved from the previous iterations. (current_sbml_model_iter2, sim_df_iteration_2, current_sbml_model_iter3, sim_df_iteration_3, current_sbml_model_iter4, sim_df_iteration_4, current_sbml_model_iter5, sim_df_iteration_5, current_sbml_model_iter6, sim_df_iteration_6, current_sbml_model_iter7, sim_df_iteration_7, current_sbml_model_iter8, sim_df_iteration_8)

### Allowed libraires
['numpy', 'pandas', 'libsbml', 'math', 'scipy', 'jax', 'sklearn', 'io', 'traceback']


Please construct your response according to the thoughts-action markdown format.



# Iteration 9
## Thoughts

Let's compare the latest simulation (iter8) with the experimental data.

**Experimental Data (iteration_0) at T=200:**
*   id_u8uf:  0.97e-12 (Initial: 7.40e-12) -> Consumed: 6.43e-12
*   id_3nd3:  2.25e-14 (Initial: 5.50e-14) -> Consumed: 3.25e-14
*   id_xsbr:  3.36e-12 (Initial: 3.60e-12) -> Consumed: 0.24e-12
*   id_70w8:  1.23e-12
*   id_erlz:  1.98e-12
*   id_59ww:  1.05e-13
*   id_dadg:  1.82e-14
*   id_5lmp:  1.28e-14
*   id_0lud:  4.46e-16
*   id_ckd0:  3.16e-16

**Simulated Data (sim_df_iteration_8) at T=200:**
*   id_u8uf:  1.14e-12 -> Consumed: 7.40e-12 - 1.14e-12 = 6.26e-12
*   id_3nd3:  1.92e-14 -> Consumed: 5.50e-14 - 1.92e-14 = 3.58e-14
*   id_xsbr:  3.32e-12 -> Consumed: 0.28e-12 (Excellent)
*   id_70w8:  1.11e-12
*   id_erlz:  1.92e-12
*   id_59ww:  0.93e-13 (9.28e-14)
*   id_dadg:  3.41e-14
*   id_5lmp:  3.41e-14
*   id_0lud:  0.85e-15 (8.51e-16)
*   id_ckd0:  0.85e-15

**Comparison and Parameter Adjustment Strategy (Iter 8 vs Exp):**

This is very close for most species.
1.  **`id_u8uf` Consumption:** Exp: 6.43e-12. Sim: 6.26e-12. (Sim needs ~3% more consumption).
2.  **`id_3nd3` Consumption:** Exp: 3.25e-14 (final 2.25e-14). Sim: 3.58e-14 (final 1.92e-14). (Sim consumes ~10% more `id_3nd3` than exp). This is a good match for final concentration.
3.  **`id_xsbr` Degradation:** Excellent.
4.  **`id_70w8` & `id_erlz`:** Exp: id_70w8=1.23e-12, id_erlz=1.98e-12. Sim: id_70w8=1.11e-12, id_erlz=1.92e-12. Both are very good. `id_70w8` is ~10% low.
5.  **`id_59ww` Production:** Exp: 1.05e-13. Sim: 0.93e-13. (~11% low).
6.  **`id_dadg`, `id_5lmp` Production:**
    *   Exp: id_dadg = 1.82e-14, id_5lmp = 1.28e-14. Sum = 3.10e-14.
    *   Sim: id_dadg = 3.41e-14, id_5lmp = 3.41e-14. Sum = 6.82e-14.
    *   The amount of `id_3nd3` consumed in sim (3.58e-14) is very close to experimental (3.25e-14).
    *   Fraction of consumed `id_3nd3` going to R3 = k_r3 / (k_r3 + k_r6 + k_r7) = 0.005 / (0.005 + 0.000125 + 0.000125) = 0.005 / 0.00525 = ~0.952.
    *   So, from 3.58e-14 of `id_3nd3` consumed, R3 gets 3.58e-14 * 0.952 = 3.41e-14. This produces 3.41e-14 of `id_dadg` and `id_5lmp` each. This matches the simulation.
    *   The experimental sum of `id_dadg` + `id_5lmp` is 3.10e-14. The simulation produces 3.41e-14 of each, which is more than the experimental sum for *both combined*. This means the flux through R3 is too high, even if `id_3nd3` consumption is about right. This implies the proportion of `id_3nd3` going to R3 vs R6/R7 is too high.
    *   Target R3 products sum = 3.10e-14. Sim R3 products sum = 6.82e-14. Sim is ~2.2x too high.
    *   This means the ratio k_r3 / (k_r6+k_r7) needs to be lower. Currently 0.005 / 0.00025 = 20.
    *   If total `id_3nd3` consumed is ~3.4e-14, and we want ~3.1e-14 to be (dadg+5lmp), then ~0.3e-14 should be (0lud+ckd0).
    *   This means the flux to R3 should be ~ (3.1/3.4) = 91% of total `id_3nd3` flux, and flux to (R6+R7) should be ~9%.
    *   So k_r3 / (k_r6+k_r7) should be roughly 0.91 / 0.09 ~ 10.
    *   Current k_r3=0.005. If k_r6+k_r7 = 0.0005 (so k_r6=k_r7=0.00025), then k_r3/(k_r6+k_r7) = 0.005/0.0005 = 10. This is what we want.
    *   My previous calculation of k_r3/(k_r6 or k_r7) = 40 was for individual minor products. The ratio of k_r3 to the *sum* of k_r6 and k_r7 is what matters for partitioning.
    *   So, if k_r3=0.005, then k_r6 and k_r7 should each be 0.00025. This is what I have.
    *   The issue is that R3 produces `id_dadg` + `id_5lmp`. So if X amount of `id_3nd3` goes through R3, it produces X of `id_dadg` and X of `id_5lmp`.
    *   Exp: `id_dadg`+`id_5lmp` = 3.1e-14. This means 3.1e-14 of `id_3nd3` should have gone into *producing these two combined*.
    *   If R3 is `id_3nd3 -> id_dadg + id_5lmp`, then if 3.1e-14 of `id_3nd3` is consumed *by this reaction*, it produces 3.1e-14 of `id_dadg` AND 3.1e-14 of `id_5lmp`. This is not what is observed.
    *   The stoichiometry of R3 must be different, or there's another sink for `id_dadg` or `id_5lmp`.
    *   If R3 is `id_3nd3 -> id_dadg` and R3b is `id_3nd3 -> id_5lmp`.
        *   Then `id_3nd3` consumed by (R3a+R3b) = 3.1e-14.
        *   k3a would make 1.82e-14 of `id_dadg`. k3b would make 1.28e-14 of `id_5lmp`.
        *   k3a / k3b = 1.82 / 1.28 = 1.42.
        *   This is the structural change I was considering.

7.  **`id_0lud`, `id_ckd0` Production:**
    *   Exp: id_0lud = 4.46e-16, id_ckd0 = 3.16e-16. Sum = 0.76e-15.
    *   Sim: id_0lud = 0.85e-15, id_ckd0 = 0.85e-15. Sum = 1.70e-15.
    *   Sim is ~2.2x too high. This is consistent with the R3 flux issue. If total `id_3nd3` consumption is about right, but R3 products are too high, then R6/R7 products must also be too high by a similar factor if their rates relative to k_r3 are fixed.
    *   Consumed `id_3nd3` (sim) = 3.58e-14. Flux to R6+R7 = 3.58e-14 * (0.00025 / 0.00525) = 3.58e-14 * 0.0476 = 1.70e-15. This produces 0.85e-15 of each, matching sim.
    *   Target R6+R7 products sum = 0.76e-15.

**Decision Point:** The current model structure for R3 (`id_3nd3 -> id_dadg + id_5lmp`) cannot match the experimental `id_dadg` and `id_5lmp` levels simultaneously if they are unequal.
I need to change the model structure for `id_3nd3`'s main products.

**Proposed Structural Change:**
Replace R3: `id_3nd3 -> id_dadg + id_5lmp` (k_r3)
With:
R3a: `id_3nd3 -> id_dadg` (k_r3a)
R3b: `id_3nd3 -> id_5lmp` (k_r3b)

The other reactions (R1, R2, R4, R5, R6, R7) remain.
The parameters for R1, R2, R4, R5 are quite good.
The parameters for R6, R7 (minor products of `id_3nd3`) will need to be adjusted relative to the new (k_r3a + k_r3b).

**Parameter estimation for new structure:**
*   Total `id_3nd3` consumed (exp) = 3.25e-14.
*   `id_dadg` produced (exp) = 1.82e-14.
*   `id_5lmp` produced (exp) = 1.28e-14.
*   `id_0lud` produced (exp) = 0.446e-15.
*   `id_ckd0` produced (exp) = 0.316e-15.
*   Sum of products from `id_3nd3` = (1.82+1.28)e-14 + (0.0446+0.0316)e-14 = 3.10e-14 + 0.0762e-14 = 3.1762e-14. This matches consumed `id_3nd3` (3.25e-14) well.

Let K_total_3nd3 = k_r3a + k_r3b + k_r6 + k_r7. This is the overall rate constant for `id_3nd3` consumption.
From sim_iter8, `id_3nd3` final was 1.92e-14 (target 2.25e-14). So consumption rate is slightly too high.
The sum of rates (0.005 + 0.000125 + 0.000125 = 0.00525) led to this.
We want slightly lower total consumption. Let's aim for K_total_3nd3 around 0.0045-0.005.

Relative fluxes:
Flux_r3a / Flux_total = 1.82 / 3.1762 = 0.573
Flux_r3b / Flux_total = 1.28 / 3.1762 = 0.403
Flux_r6 / Flux_total  = 0.0446 / 3.1762 = 0.014
Flux_r7 / Flux_total  = 0.0316 / 3.1762 = 0.010
Sum of ratios = 0.573 + 0.403 + 0.014 + 0.010 = 1.000.

Let K_total_3nd3 = 0.005 (similar to current overall rate for `id_3nd3`).
k_r3a = 0.005 * 0.573 = 0.002865
k_r3b = 0.005 * 0.403 = 0.002015
k_r6  = 0.005 * 0.014 = 0.00007
k_r7  = 0.005 * 0.010 = 0.00005
Sum = 0.002865 + 0.002015 + 0.00007 + 0.00005 = 0.005. This looks consistent.

Fine-tuning for `id_u8uf` pathway:
*   k_r1 (2 id_u8uf -> id_70w8): Was 1.8e9. `id_u8uf` consumption 6.26e-12 (target 6.43e-12). `id_70w8` 1.11e-12 (target 1.23e-12). Increase k_r1. Try 1.9e9.
*   k_r4 (2 id_u8uf -> id_59ww): Was 5.5e7. `id_59ww` 0.93e-13 (target 1.05e-13). Increase k_r4. Try 6.0e7.
*   k_r2 (id_70w8 -> id_erlz): Was 0.0068. `id_erlz` 1.92e-12 (target 1.98e-12). This is good. If k_r1 increases, `id_70w8` increases. Maybe k_r2 can stay or slightly increase to compensate. Let's keep 0.0068.

This is a significant change.

## Action

### Code
```python
import pandas as pd
import libsbml

# Access previous SBML model (iter8) and experimental data
# current_sbml_model_str_iter8 = shared_variables.access('current_sbml_model_iter8') # This model has R3
exp_data = experiment_history['iteration_0']
# sim_data_prev = shared_variables.access('sim_df_iteration_8')

# We need to build a new model structure from an earlier point or modify iter8's structure.
# It's easier to take the input_sbml_string (empty model) and add all reactions with new structure.
# Or, carefully remove R3 and add R3a, R3b to the model from iter8.
# Let's try modifying the model from iter8.

sbml_doc_iter8 = libsbml.readSBMLFromString(shared_variables.access('current_sbml_model_iter8'))
model = sbml_doc_iter8.getModel()

if model is None:
    print("Error: Could not get model from SBML string in shared_variables (iter8).")
else:
    # Remove existing R3 ("R3_3nd3_to_dadg_5lmp")
    r3_to_remove = model.getReaction("R3_3nd3_to_dadg_5lmp")
    if r3_to_remove:
        model.removeReaction("R3_3nd3_to_dadg_5lmp")
        print("Removed reaction R3_3nd3_to_dadg_5lmp")
    else:
        print("Reaction R3_3nd3_to_dadg_5lmp not found for removal.")

    # Helper function to add a reaction (copied from earlier iterations)
    def add_reaction_with_mass_action(model, r_id, reactants_stoich, products_stoich, k_val, k_id_suffix):
        reaction = model.createReaction()
        reaction.setId(r_id)
        reaction.setReversible(False)
        reaction.setFast(False)

        for spec_id, stoich in reactants_stoich.items():
            reactant_ref = reaction.createReactant()
            reactant_ref.setSpecies(spec_id)
            reactant_ref.setStoichiometry(stoich)
            reactant_ref.setConstant(False) 

        for spec_id, stoich in products_stoich.items():
            product_ref = reaction.createProduct()
            product_ref.setSpecies(spec_id)
            product_ref.setStoichiometry(stoich)
            product_ref.setConstant(False)

        kinetic_law = reaction.createKineticLaw()
        k_param = kinetic_law.createParameter()
        k_param.setId(f"k_{k_id_suffix}")
        k_param.setValue(k_val) 
        k_param.setConstant(True)

        formula_parts = [f"k_{k_id_suffix}"]
        for spec_id, stoich in reactants_stoich.items():
            for _ in range(int(stoich)): 
                 formula_parts.append(spec_id)
        math_str = " * ".join(formula_parts)
        math_ast = libsbml.parseL3Formula(math_str)
        kinetic_law.setMath(math_ast)

    # Add new reactions R3a and R3b
    # k_r3a = 0.002865
    # k_r3b = 0.002015
    add_reaction_with_mass_action(model, "R3a_3nd3_to_dadg", {"id_3nd3": 1}, {"id_dadg": 1}, 0.002865, "r3a")
    add_reaction_with_mass_action(model, "R3b_3nd3_to_5lmp", {"id_3nd3": 1}, {"id_5lmp": 1}, 0.002015, "r3b")
    print("Added new reactions R3a and R3b.")

    # Update parameters for other reactions
    # k_r1: 1.8e9 -> 1.9e9
    # k_r2: 0.0068 (no change from iter8 value, but ensure it's set)
    # k_r4: 5.5e7 -> 6.0e7
    # k_r5: 0.0004 (no change)
    # k_r6: 0.000125 -> 0.00007
    # k_r7: 0.000125 -> 0.00005
    
    reaction_params_update = {
        "R1_u8uf_to_70w8": {"k_r1": 1.9e9},
        "R2_70w8_to_erlz": {"k_r2": 0.0068}, # Keep current best
        "R4_u8uf_to_59ww": {"k_r4": 6.0e7},
        "R5_xsbr_degradation": {"k_r5": 0.0004}, # Keep current best
        "R6_3nd3_to_0lud": {"k_r6": 0.00007},
        "R7_3nd3_to_ckd0": {"k_r7": 0.00005}
    }

    for r_idx in range(model.getNumReactions()):
        reaction = model.getReaction(r_idx)
        r_id = reaction.getId()
        if r_id in reaction_params_update: # This will update R1, R2, R4, R5, R6, R7
            kl = reaction.getKineticLaw()
            if kl:
                param_updates_for_reaction = reaction_params_update[r_id]
                for p_idx in range(kl.getNumParameters()):
                    param = kl.getParameter(p_idx)
                    p_id = param.getId() # e.g. k_r1, k_r2 ...
                    if p_id in param_updates_for_reaction:
                        param.setValue(param_updates_for_reaction[p_id])
                        print(f"Updated parameter {p_id} in reaction {r_id} to {param_updates_for_reaction[p_id]}")
    
    writer = libsbml.SBMLWriter()
    updated_sbml_string_iter9 = writer.writeSBMLToString(sbml_doc)
    shared_variables.add("current_sbml_model_iter9", updated_sbml_string_iter9)
    print("\nUpdated SBML model with new structure and parameters stored in shared_variables['current_sbml_model_iter9']")

    try:
        sim_df_iter9 = simulate(updated_sbml_string_iter9)
        print("\nSimulation with new structure (iter9) ran successfully.")
        print("\nSimulated data tail (iter9):")
        print(sim_df_iter9.tail())
        shared_variables.add("sim_df_iteration_9", sim_df_iter9)
    except Exception as e:
        print(f"\nError simulating the updated model (iter9): {e}")
        # print(updated_sbml_string_iter9) # For debugging if simulation fails

```
# Observation

## Code Stderror
 name 'sbml_doc' is not defined

## Reminder

 ### Remaining Iterations for Interactions:
- Iterations: 11/20



### Available Global Variables
- `input_sbml_string`: Original incomplete model
- `experiment_history`: Results from all previous experiments (iteration_0)
- `shared_variables`: all variables you have saved from the previous iterations. (current_sbml_model_iter2, sim_df_iteration_2, current_sbml_model_iter3, sim_df_iteration_3, current_sbml_model_iter4, sim_df_iteration_4, current_sbml_model_iter5, sim_df_iteration_5, current_sbml_model_iter6, sim_df_iteration_6, current_sbml_model_iter7, sim_df_iteration_7, current_sbml_model_iter8, sim_df_iteration_8)

### Allowed libraires
['numpy', 'pandas', 'libsbml', 'math', 'scipy', 'jax', 'sklearn', 'io', 'traceback']


Please construct your response according to the thoughts-action markdown format.



# Iteration 10
## Thoughts
I encountered a `NameError: name 'sbml_doc' is not defined` in the previous iteration. This happened because I tried to use `sbml_doc` after the `else` block where `sbml_doc_iter8` was defined and used to get `model`. The variable `sbml_doc` was not the one I intended to use for writing the string; it should have been `sbml_doc_iter8`.

I will correct this error. The logic for structural change (splitting R3 into R3a and R3b) and parameter updates remains the same as planned in Iteration 9.

**Recap of planned changes for Iteration 9 (now Iteration 10 due to error):**
1.  **Structural Change:**
    *   Remove R3: `id_3nd3 -> id_dadg + id_5lmp`
    *   Add R3a: `id_3nd3 -> id_dadg` (k_r3a = 0.002865)
    *   Add R3b: `id_3nd3 -> id_5lmp` (k_r3b = 0.002015)
2.  **Parameter Updates for other reactions:**
    *   k_r1 (R1_u8uf_to_70w8): 1.9e9
    *   k_r2 (R2_70w8_to_erlz): 0.0068 (from iter8 best)
    *   k_r4 (R4_u8uf_to_59ww): 6.0e7
    *   k_r5 (R5_xsbr_degradation): 0.0004 (from iter8 best)
    *   k_r6 (R6_3nd3_to_0lud): 0.00007
    *   k_r7 (R7_3nd3_to_ckd0): 0.00005

I will re-implement this carefully.

## Action

### Code
```python
import pandas as pd
import libsbml

# Access experimental data
exp_data = experiment_history['iteration_0']

# Load the SBML model string from iteration 8 to modify it
sbml_doc_to_modify = libsbml.readSBMLFromString(shared_variables.access('current_sbml_model_iter8'))
model = sbml_doc_to_modify.getModel()

if model is None:
    print("Error: Could not get model from SBML string in shared_variables (iter8).")
else:
    # Remove existing R3 ("R3_3nd3_to_dadg_5lmp")
    r3_id_to_remove = "R3_3nd3_to_dadg_5lmp"
    r3_to_remove = model.getReaction(r3_id_to_remove)
    if r3_to_remove:
        model.removeReaction(r3_id_to_remove)
        print(f"Removed reaction {r3_id_to_remove}")
    else:
        print(f"Reaction {r3_id_to_remove} not found for removal.")

    # Helper function to add a reaction (copied from earlier iterations)
    def add_reaction_with_mass_action(current_model, r_id, reactants_stoich, products_stoich, k_val, k_id_suffix):
        reaction = current_model.createReaction()
        reaction.setId(r_id)
        reaction.setReversible(False)
        reaction.setFast(False)

        for spec_id, stoich in reactants_stoich.items():
            reactant_ref = reaction.createReactant()
            reactant_ref.setSpecies(spec_id)
            reactant_ref.setStoichiometry(stoich)
            reactant_ref.setConstant(False) 

        for spec_id, stoich in products_stoich.items():
            product_ref = reaction.createProduct()
            product_ref.setSpecies(spec_id)
            product_ref.setStoichiometry(stoich)
            product_ref.setConstant(False)

        kinetic_law = reaction.createKineticLaw()
        k_param = kinetic_law.createParameter()
        k_param.setId(f"k_{k_id_suffix}") # e.g. k_r3a, k_r3b
        k_param.setValue(k_val) 
        k_param.setConstant(True)

        formula_parts = [f"k_{k_id_suffix}"]
        for spec_id, stoich in reactants_stoich.items():
            for _ in range(int(stoich)): 
                 formula_parts.append(spec_id)
        math_str = " * ".join(formula_parts)
        math_ast = libsbml.parseL3Formula(math_str)
        if math_ast is None:
            print(f"Error parsing formula for reaction {r_id}: {math_str}")
            # Fallback to simple k if parsing fails (should not happen for mass action)
            math_ast = libsbml.parseL3Formula(f"k_{k_id_suffix}")
        kinetic_law.setMath(math_ast)

    # Add new reactions R3a and R3b
    # k_r3a = 0.002865
    # k_r3b = 0.002015
    add_reaction_with_mass_action(model, "R3a_3nd3_to_dadg", {"id_3nd3": 1}, {"id_dadg": 1}, 0.002865, "r3a")
    add_reaction_with_mass_action(model, "R3b_3nd3_to_5lmp", {"id_3nd3": 1}, {"id_5lmp": 1}, 0.002015, "r3b")
    print("Added new reactions R3a (id_3nd3 -> id_dadg) and R3b (id_3nd3 -> id_5lmp).")

    # Update parameters for other reactions (R1, R2, R4, R5, R6, R7)
    # These reactions already exist in the 'model' object loaded from iter8's SBML.
    # k_r1: 1.9e9
    # k_r2: 0.0068
    # k_r4: 6.0e7
    # k_r5: 0.0004
    # k_r6: 0.00007
    # k_r7: 0.00005
    
    reaction_params_update = {
        "R1_u8uf_to_70w8": {"k_r1": 1.9e9},
        "R2_70w8_to_erlz": {"k_r2": 0.0068}, 
        "R4_u8uf_to_59ww": {"k_r4": 6.0e7},
        "R5_xsbr_degradation": {"k_r5": 0.0004}, 
        "R6_3nd3_to_0lud": {"k_r6": 0.00007}, # This reaction still exists
        "R7_3nd3_to_ckd0": {"k_r7": 0.00005}  # This reaction still exists
    }

    for r_idx in range(model.getNumReactions()):
        reaction = model.getReaction(r_idx)
        r_id = reaction.getId()
        if r_id in reaction_params_update: 
            kl = reaction.getKineticLaw()
            if kl:
                param_updates_for_reaction = reaction_params_update[r_id]
                # Iterate through all parameters in the kinetic law of this reaction
                for p_idx in range(kl.getNumParameters()):
                    param = kl.getParameter(p_idx)
                    p_id_in_law = param.getId() # This is the ID of the parameter as defined in the SBML (e.g., k_r1)
                    
                    # Check if this specific parameter (by its ID) is in our update map for this reaction
                    if p_id_in_law in param_updates_for_reaction:
                        new_value = param_updates_for_reaction[p_id_in_law]
                        param.setValue(new_value)
                        print(f"Updated parameter {p_id_in_law} in existing reaction {r_id} to {new_value}")
    
    writer = libsbml.SBMLWriter()
    # Use the modified sbml_doc_to_modify to write the string
    updated_sbml_string_iter10 = writer.writeSBMLToString(sbml_doc_to_modify)
    shared_variables.add("current_sbml_model_iter10", updated_sbml_string_iter10)
    print("\nUpdated SBML model with new structure (R3a, R3b) and parameters stored in shared_variables['current_sbml_model_iter10']")

    try:
        sim_df_iter10 = simulate(updated_sbml_string_iter10)
        print("\nSimulation with new structure (iter10) ran successfully.")
        print("\nSimulated data tail (iter10):")
        print(sim_df_iter10.tail())
        shared_variables.add("sim_df_iteration_10", sim_df_iter10)
    except Exception as e:
        print(f"\nError simulating the updated model (iter10): {e}")
        # print(updated_sbml_string_iter10) # For debugging if simulation fails

```
# Observation

## Code Stdout
Removed reaction R3_3nd3_to_dadg_5lmp
Added new reactions R3a (id_3nd3 -> id_dadg) and R3b (id_3nd3 -> id_5lmp).
Updated parameter k_r1 in existing reaction R1_u8uf_to_70w8 to 1900000000.0
Updated parameter k_r2 in existing reaction R2_70w8_to_erlz to 0.0068
Updated parameter k_r4 in existing reaction R4_u8uf_to_59ww to 60000000.0
Updated parameter k_r5 in existing reaction R5_xsbr_degradation to 0.0004
Updated parameter k_r6 in existing reaction R6_3nd3_to_0lud to 7e-05
Updated parameter k_r7 in existing reaction R7_3nd3_to_ckd0 to 5e-05

Updated SBML model with new structure (R3a, R3b) and parameters stored in shared_variables['current_sbml_model_iter10']

Simulation with new structure (iter10) ran successfully.

Simulated data tail (iter10):
          id_59ww       id_0lud       id_70w8       id_5lmp       id_u8uf       id_ckd0       id_3nd3       id_dadg       id_erlz       id_xsbr   Time
196  9.632316e-14  4.810104e-16  1.133726e-12  1.384623e-14  1.106887e-12  3.435789e-16  2.064211e-14  1.968707e-14  1.916508e-12  3.328540e-12  196.0
197  9.639636e-14  4.824518e-16  1.128353e-12  1.388772e-14  1.102105e-12  3.446084e-16  2.053916e-14  1.974606e-14  1.924199e-12  3.327209e-12  197.0
198  9.646892e-14  4.838859e-16  1.122996e-12  1.392900e-14  1.097364e-12  3.456328e-16  2.043672e-14  1.980476e-14  1.931853e-12  3.325878e-12  198.0
199  9.654087e-14  4.853129e-16  1.117656e-12  1.397008e-14  1.092663e-12  3.466521e-16  2.033479e-14  1.986316e-14  1.939471e-12  3.324548e-12  199.0
200  9.661220e-14  4.867328e-16  1.112333e-12  1.401095e-14  1.088003e-12  3.476663e-16  2.023337e-14  1.992128e-14  1.947053e-12  3.323219e-12  200.0

## Reminder

 ### Remaining Iterations for Interactions:
- Iterations: 10/20



### Available Global Variables
- `input_sbml_string`: Original incomplete model
- `experiment_history`: Results from all previous experiments (iteration_0)
- `shared_variables`: all variables you have saved from the previous iterations. (current_sbml_model_iter2, sim_df_iteration_2, current_sbml_model_iter3, sim_df_iteration_3, current_sbml_model_iter4, sim_df_iteration_4, current_sbml_model_iter5, sim_df_iteration_5, current_sbml_model_iter6, sim_df_iteration_6, current_sbml_model_iter7, sim_df_iteration_7, current_sbml_model_iter8, sim_df_iteration_8, current_sbml_model_iter10, sim_df_iteration_10)

### Allowed libraires
['numpy', 'pandas', 'libsbml', 'math', 'scipy', 'jax', 'sklearn', 'io', 'traceback']


Please construct your response according to the thoughts-action markdown format.



# Iteration 11
## Thoughts

The structural change (splitting R3 into R3a and R3b) has been implemented, and the simulation (iter10) completed. Let's compare with experimental data.

**Experimental Data (iteration_0) at T=200:**
*   id_u8uf:  0.97e-12 (Initial: 7.40e-12) -> Consumed: 6.43e-12
*   id_3nd3:  2.25e-14 (Initial: 5.50e-14) -> Consumed: 3.25e-14
*   id_xsbr:  3.36e-12 (Initial: 3.60e-12) -> Consumed: 0.24e-12
*   id_70w8:  1.23e-12
*   id_erlz:  1.98e-12
*   id_59ww:  1.05e-13
*   id_dadg:  1.82e-14
*   id_5lmp:  1.28e-14
*   id_0lud:  4.46e-16
*   id_ckd0:  3.16e-16

**Simulated Data (sim_df_iteration_10) at T=200:**
*   id_u8uf:  1.09e-12 (1.088e-12) -> Consumed: 7.40e-12 - 1.09e-12 = 6.31e-12
*   id_3nd3:  2.02e-14 (2.023e-14) -> Consumed: 5.50e-14 - 2.02e-14 = 3.48e-14
*   id_xsbr:  3.32e-12 -> Consumed: 0.28e-12 (Excellent)
*   id_70w8:  1.11e-12
*   id_erlz:  1.95e-12 (1.947e-12)
*   id_59ww:  0.97e-13 (9.66e-14)
*   id_dadg:  1.99e-14 (1.992e-14)
*   id_5lmp:  1.40e-14 (1.401e-14)
*   id_0lud:  0.49e-15 (4.87e-16)
*   id_ckd0:  0.35e-15 (3.48e-16)

**Comparison and Parameter Adjustment Strategy (Iter 10 vs Exp):**

This is looking very good! The structural change was key.
1.  **`id_u8uf` Consumption:** Exp: 6.43e-12. Sim: 6.31e-12. (Sim needs ~2% more consumption). Excellent.
2.  **`id_3nd3` Consumption:** Exp: 3.25e-14 (final 2.25e-14). Sim: 3.48e-14 (final 2.02e-14). Excellent match for final concentration.
3.  **`id_xsbr` Degradation:** Excellent.
4.  **`id_70w8` & `id_erlz`:**
    *   Exp: id_70w8=1.23e-12, id_erlz=1.98e-12.
    *   Sim: id_70w8=1.11e-12, id_erlz=1.95e-12.
    *   `id_70w8` is ~10% low. `id_erlz` is excellent.
5.  **`id_59ww` Production:** Exp: 1.05e-13. Sim: 0.97e-13. (~8% low).
6.  **`id_dadg` & `id_5lmp` Production:**
    *   Exp: id_dadg = 1.82e-14, id_5lmp = 1.28e-14.
    *   Sim: id_dadg = 1.99e-14, id_5lmp = 1.40e-14.
    *   This is a massive improvement! `id_dadg` is ~9% high. `id_5lmp` is ~9% high. The ratio is also much better.
    *   Sim ratio dadg/5lmp = 1.99/1.40 = 1.421. Exp ratio = 1.82/1.28 = 1.422. The ratio is perfect!
    *   The absolute amounts are slightly high because total `id_3nd3` consumption (3.48e-14) is slightly higher than target (3.25e-14).
7.  **`id_0lud` & `id_ckd0` Production:**
    *   Exp: id_0lud = 4.46e-16, id_ckd0 = 3.16e-16.
    *   Sim: id_0lud = 4.87e-16, id_ckd0 = 3.48e-16.
    *   `id_0lud` is ~9% high. `id_ckd0` is ~10% high. Also excellent, and follows the slight overconsumption of `id_3nd3`.

**Overall Assessment:** The model is now very accurate. The remaining discrepancies are mostly within 10%.
The main things to tweak slightly:
*   Increase `id_u8uf` consumption a tiny bit (k_r1, k_r4). This should also help `id_70w8` and `id_59ww`.
*   The `id_3nd3` pathway rates (k_r3a, k_r3b, k_r6, k_r7) are very well balanced for ratios. If `id_3nd3` consumption was slightly lower, all its products would scale down. The current total rate for `id_3nd3` consumption is k_total_3nd3 = k_r3a + k_r3b + k_r6 + k_r7 = 0.002865 + 0.002015 + 0.00007 + 0.00005 = 0.005. This resulted in final `id_3nd3` of 2.02e-14 (target 2.25e-14). So the total rate 0.005 is slightly too high. We need to reduce it slightly.

**Revised Parameter Adjustment Plan (Iter 11 - Final Tuning):**

*   **k_r1 (R1_u8uf_to_70w8):** Was 1.9e9. `id_u8uf` consumption needs to increase by (6.43-6.31)/6.31 = ~1.9%. `id_70w8` needs to increase by (1.23-1.11)/1.11 = ~10.8%. Let's try increasing k_r1 by ~5-7%. Try 2.0e9.
*   **k_r4 (R4_u8uf_to_59ww):** Was 6.0e7. `id_59ww` needs to increase by (1.05-0.97)/0.97 = ~8.2%. Increase k_r4 by ~8%. Try 6.5e7.
*   **k_r2 (R2_70w8_to_erlz):** Was 0.0068. `id_erlz` is good. If k_r1 increases `id_70w8` production, `id_70w8` level might rise enough. If it overshoots `id_erlz`, k_r2 might need slight increase. Let's keep 0.0068 for now.

*   **`id_3nd3` pathway:**
    *   Current K_total_3nd3 = 0.005. Final `id_3nd3` is 2.02e-14 (target 2.25e-14).
    *   To increase final `id_3nd3`, we need to decrease K_total_3nd3.
    *   Roughly, since it's a first-order decay, if C_final/C_initial = exp(-K*t), then -K*t = ln(C_final/C_initial).
    *   Current: ln(2.02/5.5) = ln(0.367) = -1.00. So K_total_3nd3 * t_eff = 1.00.
    *   Target: ln(2.25/5.5) = ln(0.409) = -0.89. So K_target_3nd3 * t_eff = 0.89.
    *   So K_target_3nd3 should be K_current_3nd3 * (0.89/1.00) = 0.005 * 0.89 = 0.00445.
    *   Let new K_total_3nd3 = 0.00445.
    *   Keep ratios for k_r3a, k_r3b, k_r6, k_r7 the same:
        *   k_r3a_new = 0.00445 * 0.573 = 0.002550
        *   k_r3b_new = 0.00445 * 0.403 = 0.001793
        *   k_r6_new  = 0.00445 * 0.014 = 0.0000623
        *   k_r7_new  = 0.00445 * 0.010 = 0.0000445
        *   Sum = 0.002550 + 0.001793 + 0.0000623 + 0.0000445 = 0.004450. (Rounding slightly for precision)
        Let's use:
        k_r3a = 0.00255
        k_r3b = 0.00179
        k_r6  = 0.000062
        k_r7  = 0.000045
        Sum = 0.00255 + 0.00179 + 0.000062 + 0.000045 = 0.004447. Close enough.

*   **k_r5 (R5_xsbr_degradation):** Was 0.0004. Excellent. No change.

This should be the final tuning. If this is good, I will submit.

## Action

### Code
```python
import pandas as pd
import libsbml

# Access experimental data
exp_data = experiment_history['iteration_0']
sim_data_prev = shared_variables.access('sim_df_iteration_10')

print("Experimental Data (iteration_0) - Final State (T=200):")
print(exp_data.iloc[-1])
print("\nPrevious Simulation Data (sim_df_iteration_10) - Final State (T=200):")
print(sim_data_prev.iloc[-1])

# Load the SBML model string from iteration 10 to modify it
sbml_doc_to_modify = libsbml.readSBMLFromString(shared_variables.access('current_sbml_model_iter10'))
model = sbml_doc_to_modify.getModel()

if model is None:
    print("Error: Could not get model from SBML string in shared_variables (iter10).")
else:
    # Parameter values for Iteration 11 (Final Tuning)
    # k_r1: 1.9e9 -> 2.0e9
    # k_r2: 0.0068 (no change)
    # k_r3a: 0.002865 -> 0.00255
    # k_r3b: 0.002015 -> 0.00179
    # k_r4: 6.0e7 -> 6.5e7
    # k_r5: 0.0004 (no change)
    # k_r6: 0.00007 -> 0.000062
    # k_r7: 0.00005 -> 0.000045
    
    reaction_params_update = {
        "R1_u8uf_to_70w8": {"k_r1": 2.0e9},
        "R2_70w8_to_erlz": {"k_r2": 0.0068}, 
        "R3a_3nd3_to_dadg": {"k_r3a": 0.00255}, # New reaction from iter10
        "R3b_3nd3_to_5lmp": {"k_r3b": 0.00179}, # New reaction from iter10
        "R4_u8uf_to_59ww": {"k_r4": 6.5e7},
        "R5_xsbr_degradation": {"k_r5": 0.0004}, 
        "R6_3nd3_to_0lud": {"k_r6": 0.000062},
        "R7_3nd3_to_ckd0": {"k_r7": 0.000045}
    }

    for r_idx in range(model.getNumReactions()):
        reaction = model.getReaction(r_idx)
        r_id = reaction.getId()
        if r_id in reaction_params_update: 
            kl = reaction.getKineticLaw()
            if kl:
                param_updates_for_reaction = reaction_params_update[r_id]
                for p_idx in range(kl.getNumParameters()):
                    param = kl.getParameter(p_idx)
                    p_id_in_law = param.getId() 
                    
                    if p_id_in_law in param_updates_for_reaction:
                        new_value = param_updates_for_reaction[p_id_in_law]
                        param.setValue(new_value)
                        print(f"Updated parameter {p_id_in_law} in reaction {r_id} to {new_value}")
    
    final_sbml_model_str = libsbml.writeSBMLToString(sbml_doc_to_modify)
    shared_variables.add("final_sbml_model_candidate", final_sbml_model_str) # Store before submitting
    print("\nFinal SBML model candidate prepared.")

    # Simulate this candidate model to check before submission
    try:
        sim_df_final_candidate = simulate(final_sbml_model_str)
        print("\nSimulation of final candidate model ran successfully.")
        print("\nSimulated data tail (final candidate):")
        print(sim_df_final_candidate.tail())
        shared_variables.add("sim_df_final_candidate", sim_df_final_candidate)
        
        # If this looks good, the next step would be to submit.
        # For now, I'll just print the comparison.
        # This iteration's main goal is to prepare the model for submission.
        # The actual submission will be a separate action.
        
    except Exception as e:
        print(f"\nError simulating the final candidate model: {e}")

    # Set the variable for submission
    final_sbml = final_sbml_model_str 
    # This variable `final_sbml` will be picked up if I choose the "Submit" action.
    # However, I am not submitting yet in this step, just preparing and checking.
    # To actually submit, I would need a "Submit" section in the markdown.
    # For now, I will just output the results of this final tuning.
    # If I were to submit, I would create a new action block:
    # ## Action
    # ### Submit
    # ```python
    # final_sbml = shared_variables.access("final_sbml_model_candidate")
    # ```
    # But I will wait for one more check of the simulation output.

```
# Observation

## Code Stdout
Experimental Data (iteration_0) - Final State (T=200):
id_59ww    1.046815e-13
id_0lud    4.457780e-16
id_70w8    1.232450e-12
id_5lmp    1.281449e-14
id_u8uf    9.736727e-13
id_ckd0    3.163974e-16
id_3nd3    2.251072e-14
id_dadg    1.815044e-14
id_erlz    1.980713e-12
id_xsbr    3.359672e-12
Time       2.000000e+02
Name: 200, dtype: float64

Previous Simulation Data (sim_df_iteration_10) - Final State (T=200):
id_59ww    9.661220e-14
id_0lud    4.867328e-16
id_70w8    1.112333e-12
id_5lmp    1.401095e-14
id_u8uf    1.088003e-12
id_ckd0    3.476663e-16
id_3nd3    2.023337e-14
id_dadg    1.992128e-14
id_erlz    1.947053e-12
id_xsbr    3.323219e-12
Time       2.000000e+02
Name: 200, dtype: float64
Updated parameter k_r1 in reaction R1_u8uf_to_70w8 to 2000000000.0
Updated parameter k_r2 in reaction R2_70w8_to_erlz to 0.0068
Updated parameter k_r4 in reaction R4_u8uf_to_59ww to 65000000.0
Updated parameter k_r5 in reaction R5_xsbr_degradation to 0.0004
Updated parameter k_r6 in reaction R6_3nd3_to_0lud to 6.2e-05
Updated parameter k_r7 in reaction R7_3nd3_to_ckd0 to 4.5e-05
Updated parameter k_r3a in reaction R3a_3nd3_to_dadg to 0.00255
Updated parameter k_r3b in reaction R3b_3nd3_to_5lmp to 0.00179

Final SBML model candidate prepared.

Simulation of final candidate model ran successfully.

Simulated data tail (final candidate):
          id_59ww       id_0lud       id_70w8       id_5lmp       id_u8uf       id_ckd0       id_3nd3       id_dadg       id_erlz       id_xsbr   Time
196  9.980347e-14  4.460707e-16  1.132433e-12  1.287849e-14  1.058641e-12  3.237610e-16  2.300522e-14  1.834645e-14  1.938443e-12  3.328540e-12  196.0
197  9.987600e-14  4.474938e-16  1.126983e-12  1.291958e-14  1.054033e-12  3.247939e-16  2.290314e-14  1.840499e-14  1.946125e-12  3.327209e-12  197.0
198  9.994790e-14  4.489107e-16  1.121550e-12  1.296049e-14  1.049464e-12  3.258223e-16  2.280152e-14  1.846326e-14  1.953770e-12  3.325878e-12  198.0
199  1.000192e-13  4.503212e-16  1.116135e-12  1.300121e-14  1.044936e-12  3.268460e-16  2.270035e-14  1.852128e-14  1.961378e-12  3.324548e-12  199.0
200  1.000898e-13  4.517255e-16  1.110738e-12  1.304175e-14  1.040446e-12  3.278653e-16  2.259962e-14  1.857903e-14  1.968949e-12  3.323219e-12  200.0

## Reminder

 ### Remaining Iterations for Interactions:
- Iterations: 9/20



### Available Global Variables
- `input_sbml_string`: Original incomplete model
- `experiment_history`: Results from all previous experiments (iteration_0)
- `shared_variables`: all variables you have saved from the previous iterations. (current_sbml_model_iter2, sim_df_iteration_2, current_sbml_model_iter3, sim_df_iteration_3, current_sbml_model_iter4, sim_df_iteration_4, current_sbml_model_iter5, sim_df_iteration_5, current_sbml_model_iter6, sim_df_iteration_6, current_sbml_model_iter7, sim_df_iteration_7, current_sbml_model_iter8, sim_df_iteration_8, current_sbml_model_iter10, sim_df_iteration_10, final_sbml_model_candidate, sim_df_final_candidate)

### Allowed libraires
['numpy', 'pandas', 'libsbml', 'math', 'scipy', 'jax', 'sklearn', 'io', 'traceback']


Please construct your response according to the thoughts-action markdown format.



# Iteration 12
## Thoughts

The simulation of the "final candidate model" from Iteration 11 is complete. Let's compare its output (`sim_df_final_candidate`) with the experimental data.

**Experimental Data (iteration_0) at T=200:**
*   id_u8uf:  0.9737e-12 (Initial: 7.40e-12) -> Consumed: 6.426e-12
*   id_3nd3:  2.2511e-14 (Initial: 5.50e-14) -> Consumed: 3.249e-14
*   id_xsbr:  3.3597e-12 (Initial: 3.60e-12) -> Consumed: 0.240e-12
*   id_70w8:  1.2325e-12
*   id_erlz:  1.9807e-12
*   id_59ww:  1.0468e-13
*   id_dadg:  1.8150e-14
*   id_5lmp:  1.2814e-14
*   id_0lud:  4.4578e-16
*   id_ckd0:  3.1640e-16

**Simulated Data (sim_df_final_candidate from iter11) at T=200:**
*   id_u8uf:  1.0404e-12 -> Consumed: 7.40e-12 - 1.0404e-12 = 6.3596e-12
    *   Exp: 0.9737e-12. Sim: 1.0404e-12. (Sim is ~6.8% higher than exp final value for u8uf, meaning consumption is slightly less than target)
*   id_3nd3:  2.2600e-14 -> Consumed: 5.50e-14 - 2.2600e-14 = 3.2400e-14
    *   Exp: 2.2511e-14. Sim: 2.2600e-14. (Perfect match!)
*   id_xsbr:  3.3232e-12 -> Consumed: 0.2768e-12
    *   Exp: 3.3597e-12. Sim: 3.3232e-12. (Sim is ~1% lower, consumption ~15% higher. Still very good.)
*   id_70w8:  1.1107e-12
    *   Exp: 1.2325e-12. Sim: 1.1107e-12. (Sim is ~9.9% lower)
*   id_erlz:  1.9689e-12
    *   Exp: 1.9807e-12. Sim: 1.9689e-12. (Sim is ~0.6% lower. Excellent.)
*   id_59ww:  1.0009e-13
    *   Exp: 1.0468e-13. Sim: 1.0009e-13. (Sim is ~4.4% lower. Very good.)
*   id_dadg:  1.8579e-14
    *   Exp: 1.8150e-14. Sim: 1.8579e-14. (Sim is ~2.4% higher. Excellent.)
*   id_5lmp:  1.3042e-14
    *   Exp: 1.2814e-14. Sim: 1.3042e-14. (Sim is ~1.8% higher. Excellent.)
*   id_0lud:  4.5173e-16
    *   Exp: 4.4578e-16. Sim: 4.5173e-16. (Sim is ~1.3% higher. Excellent.)
*   id_ckd0:  3.2787e-16
    *   Exp: 3.1640e-16. Sim: 3.2787e-16. (Sim is ~3.6% higher. Excellent.)

**Overall Assessment:**
This is an excellent fit. Most species are within 5% of their experimental values, and many are within 1-2%.
The largest remaining discrepancies:
*   `id_u8uf` final concentration is about 7% higher in sim (meaning consumption is a bit low).
*   `id_70w8` is about 10% lower in sim.

These two are linked. If `id_u8uf` consumption via R1 (2 id_u8uf -> id_70w8) increases slightly, `id_u8uf` will decrease, and `id_70w8` will increase. This is the main area for a tiny final adjustment.

Current parameters for this pathway:
*   k_r1 (R1_u8uf_to_70w8): 2.0e9
*   k_r4 (R4_u8uf_to_59ww): 6.5e7
*   k_r2 (R2_70w8_to_erlz): 0.0068

If I increase k_r1 slightly:
*   `id_u8uf` will decrease (good).
*   `id_70w8` will increase (good).
*   `id_erlz` might increase slightly (currently excellent, a small increase might be okay).
*   `id_59ww` is unaffected by k_r1 directly.

Let's try a small increase in k_r1, say from 2.0e9 to 2.1e9 (a 5% increase).
This should increase `id_u8uf` consumption and `id_70w8` levels.
The `id_3nd3` pathway and `id_xsbr` degradation are essentially perfect, so no changes there.

This will be the absolute final adjustment.

## Action

### Submit
```python
import pandas as pd
import libsbml

# Load the SBML model string from the previous iteration (final candidate)
sbml_doc_to_modify = libsbml.readSBMLFromString(shared_variables.access('final_sbml_model_candidate'))
model = sbml_doc_to_modify.getModel()

if model is None:
    print("Error: Could not get model from SBML string 'final_sbml_model_candidate'.")
    final_sbml = shared_variables.access('final_sbml_model_candidate') # Fallback to previous if error
else:
    # Final adjustment: Increase k_r1 slightly
    # k_r1: 2.0e9 -> 2.1e9
    
    reaction_id_to_update = "R1_u8uf_to_70w8"
    parameter_id_to_update = "k_r1"
    new_k_r1_value = 2.1e9
    
    reaction = model.getReaction(reaction_id_to_update)
    if reaction:
        kl = reaction.getKineticLaw()
        if kl:
            param = kl.getParameter(parameter_id_to_update)
            if param:
                param.setValue(new_k_r1_value)
                print(f"Final adjustment: Updated parameter {parameter_id_to_update} in reaction {reaction_id_to_update} to {new_k_r1_value}")
            else:
                print(f"Error: Parameter {parameter_id_to_update} not found in reaction {reaction_id_to_update}.")
        else:
            print(f"Error: Kinetic law not found for reaction {reaction_id_to_update}.")
    else:
        print(f"Error: Reaction {reaction_id_to_update} not found.")
        
    # Write the truly final SBML string
    final_sbml_str_for_submission = libsbml.writeSBMLToString(sbml_doc_to_modify)
    
    # This is the variable name the system expects for submission.
    final_sbml = final_sbml_str_for_submission
    
    # (Optional: can simulate one last time for verification, but not strictly needed for submission step)
    # try:
    #     sim_df_truly_final = simulate(final_sbml)
    #     print("\nSimulation of TRULY final model ran successfully.")
    #     print(sim_df_truly_final.iloc[-1])
    # except Exception as e:
    #     print(f"\nError simulating the TRULY final model: {e}")

print("\nFinal SBML model is prepared for submission.")
# The variable `final_sbml` is now set.
```
