
                # 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_8hn8">
    <listOfUnitDefinitions>
      <unitDefinition id="id_cm39">
        <listOfUnits>
          <unit kind="litre" exponent="1" scale="-3" multiplier="1"/>
        </listOfUnits>
      </unitDefinition>
      <unitDefinition id="id_g8kt">
        <listOfUnits>
          <unit kind="mole" exponent="1" scale="-3" multiplier="1"/>
        </listOfUnits>
      </unitDefinition>
    </listOfUnitDefinitions>
    <listOfCompartments>
      <compartment id="id_6msh" spatialDimensions="3" size="1" constant="true"/>
    </listOfCompartments>
    <listOfSpecies>
      <species id="id_j5qx" compartment="id_6msh" initialConcentration="0.2" boundaryCondition="false" constant="false"/>
      <species id="id_o483" compartment="id_6msh" initialConcentration="0.1" boundaryCondition="false" constant="false"/>
      <species id="id_lti5" compartment="id_6msh" initialConcentration="0.55" boundaryCondition="false" constant="false"/>
    </listOfSpecies>
  </model>
</sbml>


                ## Max iterations
                20

                Format your response according to the instructions in the system message.
                
## Thoughts

The provided SBML model defines three species (`id_j5qx`, `id_o483`, `id_lti5`) but does not include any reactions. My goal is to discover the missing reactions that govern the dynamics of these species. Since all species are observable, I can request time-course data to infer the system's behavior and hypothesize possible reactions.

The first step should be to observe the system under default conditions to get a baseline of how the concentrations of the three species change over time. This will help me identify which species are being consumed or produced and suggest possible reaction types (e.g., conversion, synthesis, degradation).

## 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: 2797 rows × 4 columns. 

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

Time   id_j5qx  id_o483  id_lti5
0  2.00e-01 1.00e-01 5.50e-01
...
6.12e+00  1.91e-01 3.81e-02 8.69e-01
...
1.22e+01  1.83e-01 1.49e-02 9.60e-01
...
1.83e+01  1.75e-01 6.53e-03 9.83e-01
...
2.45e+01  1.67e-01 3.26e-03 9.92e-01
...
3.06e+01  1.60e-01 1.87e-03 9.95e-01
...
3.67e+01  1.53e-01 1.22e-03 9.97e-01
...
4.28e+01  1.46e-01 9.04e-04 9.98e-01
...
4.89e+01  1.40e-01 7.58e-04 9.98e-01
...
5.50e+01  1.33e-01 7.15e-04 9.99e-01
...
6.12e+01  1.27e-01 7.53e-04 9.99e-01
...
6.73e+01  1.22e-01 8.84e-04 9.98e-01
...
7.34e+01  1.16e-01 1.15e-03 9.98e-01
...
7.95e+01  1.11e-01 1.65e-03 9.97e-01
...
8.56e+01  1.06e-01 2.60e-03 9.96e-01
...
9.17e+01  1.02e-01 4.48e-03 9.94e-01
...
9.79e+01  9.71e-02 8.40e-03 9.90e-01
...
1.04e+02  9.28e-02 1.71e-02 9.83e-01
...
1.10e+02  8.87e-02 3.74e-02 9.73e-01
...
1.16e+02  8.49e-02 8.57e-02 9.62e-01
...
1.22e+02  8.17e-02 1.93e-01 9.53e-01
...
1.28e+02  7.99e-02 3.65e-01 9.48e-01
...
1.35e+02  8.00e-02 5.13e-01 9.45e-01
...
1.41e+02  8.13e-02 5.76e-01 9.45e-01
...
1.47e+02  8.30e-02 5.90e-01 9.44e-01
...
1.53e+02  8.48e-02 5.88e-01 9.44e-01
...
1.59e+02  8.65e-02 5.80e-01 9.44e-01
...
1.65e+02  8.82e-02 5.72e-01 9.44e-01
...
1.71e+02  8.99e-02 5.63e-01 9.44e-01
...
1.77e+02  9.14e-02 5.54e-01 9.44e-01
...
1.83e+02  9.28e-02 5.46e-01 9.44e-01
...
1.90e+02  9.42e-02 5.38e-01 9.44e-01
...
1.96e+02  9.55e-02 5.30e-01 9.45e-01
...
2.02e+02  9.67e-02 5.23e-01 9.45e-01
...
2.08e+02  9.78e-02 5.16e-01 9.45e-01
...
2.14e+02  9.88e-02 5.09e-01 9.45e-01
...
2.20e+02  9.97e-02 5.03e-01 9.45e-01
...
2.26e+02  1.01e-01 4.97e-01 9.45e-01
...
2.32e+02  1.01e-01 4.92e-01 9.45e-01
...
2.39e+02  1.02e-01 4.87e-01 9.45e-01
...
2.45e+02  1.03e-01 4.82e-01 9.45e-01
...
2.51e+02  1.03e-01 4.78e-01 9.45e-01
...
2.57e+02  1.04e-01 4.74e-01 9.45e-01
...
2.63e+02  1.04e-01 4.70e-01 9.45e-01
...
2.69e+02  1.05e-01 4.67e-01 9.45e-01
...
2.75e+02  1.05e-01 4.64e-01 9.45e-01
...
2.81e+02  1.06e-01 4.61e-01 9.45e-01
...
2.87e+02  1.06e-01 4.59e-01 9.45e-01
...
2.94e+02  1.06e-01 4.56e-01 9.45e-01
...
3.00e+02  1.06e-01 4.54e-01 9.45e-01

## 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

From the observation data, the following trends are apparent:
- `id_j5qx` starts at 0.2 and gradually decreases, then stabilizes at a lower value.
- `id_o483` starts at 0.1, decreases to a very low value, then increases and stabilizes at a higher value.
- `id_lti5` starts at 0.55, increases to a higher value, then stabilizes.

This suggests a cyclic or reversible process, possibly involving interconversion between the species. The non-monotonic behavior of `id_o483` (decreases, then increases) is particularly interesting and suggests that it is both produced and consumed in the network.

To better understand the relationships, I should visualize the time courses and look for conservation relationships or patterns that suggest specific reaction types (e.g., A → B, B → C, C → A, or reversible reactions).

## Action

### Code

```python
import matplotlib.pyplot as plt

# Load the data
df = experiment_history['iteration_0']

# Plot the time courses
plt.figure(figsize=(10,6))
plt.plot(df['Time'], df['id_j5qx'], label='id_j5qx')
plt.plot(df['Time'], df['id_o483'], label='id_o483')
plt.plot(df['Time'], df['id_lti5'], label='id_lti5')
plt.xlabel('Time')
plt.ylabel('Concentration')
plt.legend()
plt.title('Time courses of all species')
plt.show()

# Check for conservation: sum of all species
df['total'] = df['id_j5qx'] + df['id_o483'] + df['id_lti5']
plt.figure(figsize=(6,4))
plt.plot(df['Time'], df['total'])
plt.xlabel('Time')
plt.ylabel('Total concentration')
plt.title('Total concentration over time')
plt.show()

# Print final and initial values for reference
print("Initial values:", df.iloc[0][['id_j5qx', 'id_o483', 'id_lti5']])
print("Final values:", df.iloc[-1][['id_j5qx', 'id_o483', 'id_lti5']])
print("Change in total concentration:", df['total'].iloc[-1] - df['total'].iloc[0])
```
# 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

I attempted to use matplotlib, which is not allowed. Instead, I will use numpy and pandas to analyze the data numerically. I will:
- Check the initial and final concentrations of each species.
- Calculate the total concentration at each time point to see if there is conservation.
- Examine the minimum and maximum values for each species to understand their dynamic range.
- Look for time points where the direction of change for `id_o483` switches, as this may indicate a reversible or cyclic reaction.

This analysis will help me hypothesize the underlying reaction network.

## Action

### Code

```python
import numpy as np
import pandas as pd

df = experiment_history['iteration_0']

# Initial and final values
initial = df.iloc[0][['id_j5qx', 'id_o483', 'id_lti5']]
final = df.iloc[-1][['id_j5qx', 'id_o483', 'id_lti5']]

# Total concentration over time
df['total'] = df['id_j5qx'] + df['id_o483'] + df['id_lti5']
total_change = df['total'].iloc[-1] - df['total'].iloc[0]
total_min = df['total'].min()
total_max = df['total'].max()

# Min and max for each species
min_vals = df[['id_j5qx', 'id_o483', 'id_lti5']].min()
max_vals = df[['id_j5qx', 'id_o483', 'id_lti5']].max()

# Find time points where id_o483 switches from decreasing to increasing
diffs = np.diff(df['id_o483'])
switch_indices = np.where(np.sign(diffs[1:]) != np.sign(diffs[:-1]))[0]
switch_times = df['Time'].iloc[switch_indices + 1].values

print("Initial concentrations:\n", initial)
print("Final concentrations:\n", final)
print("Total concentration change:", total_change)
print("Total concentration min/max:", total_min, total_max)
print("Min concentrations:\n", min_vals)
print("Max concentrations:\n", max_vals)
print("Time points where id_o483 switches direction:", switch_times)
```
# Observation

## Code Stdout
Initial concentrations:
 id_j5qx    0.20
id_o483    0.10
id_lti5    0.55
Name: 0, dtype: float64
Final concentrations:
 id_j5qx    0.106380
id_o483    0.454252
id_lti5    0.945029
Name: 2796, dtype: float64
Total concentration change: 0.6556604928873537
Total concentration min/max: 0.8500000000000001 1.6184077207881211
Min concentrations:
 id_j5qx    0.079774
id_o483    0.000715
id_lti5    0.550000
dtype: float64
Max concentrations:
 id_j5qx    0.200000
id_o483    0.590660
id_lti5    0.998641
dtype: float64
Time points where id_o483 switches direction: [ 55.15021459 147.96137339]

## 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. ()

### 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

Key findings from the analysis:
- The total concentration increases significantly over time (from 0.85 to ~1.62), indicating that the system is not closed and there is likely an input or synthesis reaction.
- `id_j5qx` and `id_o483` both reach low values and then increase again, while `id_lti5` increases steadily and dominates at late times.
- The switch in the direction of `id_o483` occurs at two distinct times, suggesting a cyclic or oscillatory process, or a reversible reaction network.

Given the increase in total concentration, at least one species is being synthesized from an external source. The non-monotonic behavior of `id_o483` suggests it is both produced and consumed, possibly as an intermediate.

A plausible initial hypothesis is:
- An external source produces `id_j5qx`.
- `id_j5qx` is converted to `id_o483`.
- `id_o483` is converted to `id_lti5`.
- There may be reversibility in one or more steps, or feedback.

To test the system's response, I will perturb the initial concentration of `id_j5qx` and observe the effect on the dynamics. This will help clarify the directionality and connectivity of the reactions.

## Action

### Experiment

```json
{
    "action": "change_initial_concentration",
    "meta_data": {
        "id_j5qx": 0.5
    }
}
```
# Observation

## Experiment Result
Experiment ID: iteration_3
Your requested experiment has finished. The details results are saved as a dataframe in experiment_history[iteration_3]

Total size: 2797 rows × 4 columns. 

Below shows a subset of the table for your convenience. You should access experiment_history[iteration_3] for the full data

Time   id_j5qx   id_o483  id_lti5
0  5.00e-01  1.00e-01 5.50e-01
...
6.12e+00  4.78e-01  1.69e-04 8.95e-01
...
1.22e+01  4.57e-01  3.47e-07 9.85e-01
...
1.83e+01  4.37e-01  1.02e-09 9.98e-01
...
2.45e+01  4.17e-01  4.13e-12 1.00e+00
...
3.06e+01  3.99e-01  3.17e-14 1.00e+00
...
3.67e+01  3.81e-01  6.91e-15 1.00e+00
...
4.28e+01  3.64e-01 -1.21e-14 1.00e+00
...
4.89e+01  3.48e-01  4.97e-15 1.00e+00
...
5.50e+01  3.33e-01 -1.99e-14 1.00e+00
...
6.12e+01  3.18e-01 -1.80e-14 1.00e+00
...
6.73e+01  3.04e-01  4.99e-15 1.00e+00
...
7.34e+01  2.91e-01 -3.47e-15 1.00e+00
...
7.95e+01  2.78e-01 -7.20e-16 1.00e+00
...
8.56e+01  2.65e-01  3.18e-15 1.00e+00
...
9.17e+01  2.54e-01 -1.69e-15 1.00e+00
...
9.79e+01  2.42e-01 -9.61e-15 1.00e+00
...
1.04e+02  2.32e-01 -1.40e-14 1.00e+00
...
1.10e+02  2.21e-01 -7.19e-15 1.00e+00
...
1.16e+02  2.12e-01  6.91e-15 1.00e+00
...
1.22e+02  2.02e-01  2.16e-14 1.00e+00
...
1.28e+02  1.93e-01  2.21e-14 1.00e+00
...
1.35e+02  1.85e-01  1.68e-15 1.00e+00
...
1.41e+02  1.77e-01 -3.96e-14 1.00e+00
...
1.47e+02  1.69e-01 -8.55e-14 1.00e+00
...
1.53e+02  1.61e-01 -1.08e-13 1.00e+00
...
1.59e+02  1.54e-01 -8.21e-14 1.00e+00
...
1.65e+02  1.47e-01  1.31e-14 1.00e+00
...
1.71e+02  1.41e-01  1.67e-13 1.00e+00
...
1.77e+02  1.35e-01  3.52e-13 1.00e+00
...
1.83e+02  1.29e-01  5.15e-13 1.00e+00
...
1.90e+02  1.23e-01  6.03e-13 1.00e+00
...
1.96e+02  1.18e-01  5.78e-13 1.00e+00
...
2.02e+02  1.12e-01  4.26e-13 1.00e+00
...
2.08e+02  1.07e-01  1.82e-13 1.00e+00
...
2.14e+02  1.03e-01 -1.18e-13 1.00e+00
...
2.20e+02  9.81e-02 -4.04e-13 1.00e+00
...
2.26e+02  9.37e-02 -5.98e-13 1.00e+00
...
2.32e+02  8.96e-02 -6.38e-13 1.00e+00
...
2.39e+02  8.56e-02 -4.17e-13 1.00e+00
...
2.45e+02  8.18e-02  2.23e-13 1.00e+00
...
2.51e+02  7.82e-02  1.44e-12 1.00e+00
...
2.57e+02  7.47e-02  3.72e-12 1.00e+00
...
2.63e+02  7.14e-02  1.15e-11 1.00e+00
...
2.69e+02  6.83e-02  3.73e-11 1.00e+00
...
2.75e+02  6.53e-02  1.29e-10 1.00e+00
...
2.81e+02  6.24e-02  4.71e-10 1.00e+00
...
2.87e+02  5.96e-02  1.80e-09 1.00e+00
...
2.94e+02  5.70e-02  7.25e-09 1.00e+00
...
3.00e+02  5.43e-02  3.31e-08 1.00e+00

## 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, iteration_3)
- `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 4
## Thoughts

With the initial concentration of `id_j5qx` increased to 0.5:
- `id_j5qx` decreases over time but remains much higher than before.
- `id_o483` rapidly drops to near zero and stays there (with only numerical noise).
- `id_lti5` increases and saturates at 1.0.

This suggests:
- `id_j5qx` is not efficiently converted to `id_o483` under these conditions, or the conversion is extremely rapid and then `id_o483` is immediately converted to `id_lti5`.
- The system seems to "prefer" accumulating `id_lti5` at the expense of the other two species.
- The total concentration is not reported, but since `id_lti5` saturates at 1.0, it seems there is a strong drive toward `id_lti5` accumulation.

Given the previous experiment (default initial conditions), `id_o483` showed a non-monotonic profile, but with high `id_j5qx`, it is almost absent. This suggests that the conversion from `id_j5qx` to `id_o483` is not limiting, but the conversion from `id_o483` to `id_lti5` is very fast or irreversible.

To further probe the system, I will set the initial concentration of `id_lti5` to zero and observe the dynamics. This will help clarify if `id_lti5` is produced from the other species and whether its accumulation is dependent on their presence.

## Action

### Experiment

```json
{
    "action": "change_initial_concentration",
    "meta_data": {
        "id_lti5": 0.0
    }
}
```
# Observation

## Experiment Result
Experiment ID: iteration_4
Your requested experiment has finished. The details results are saved as a dataframe in experiment_history[iteration_4]

Total size: 2797 rows × 4 columns. 

Below shows a subset of the table for your convenience. You should access experiment_history[iteration_4] for the full data

Time   id_j5qx  id_o483 id_lti5
0  2.00e-01 1.00e-01       0
...
6.12e+00  1.92e-01 5.31e-02       0
...
1.22e+01  1.83e-01 3.21e-02       0
...
1.83e+01  1.75e-01 2.23e-02       0
...
2.45e+01  1.67e-01 1.78e-02       0
...
3.06e+01  1.60e-01 1.63e-02       0
...
3.67e+01  1.53e-01 1.71e-02       0
...
4.28e+01  1.46e-01 2.02e-02       0
...
4.89e+01  1.40e-01 2.71e-02       0
...
5.50e+01  1.34e-01 4.04e-02       0
...
6.12e+01  1.28e-01 6.66e-02       0
...
6.73e+01  1.23e-01 1.18e-01       0
...
7.34e+01  1.18e-01 2.09e-01       0
...
7.95e+01  1.16e-01 3.37e-01       0
...
8.56e+01  1.15e-01 4.51e-01       0
...
9.17e+01  1.16e-01 5.11e-01       0
...
9.79e+01  1.17e-01 5.30e-01       0
...
1.04e+02  1.19e-01 5.32e-01       0
...
1.10e+02  1.20e-01 5.26e-01       0
...
1.16e+02  1.22e-01 5.18e-01       0
...
1.22e+02  1.23e-01 5.11e-01       0
...
1.28e+02  1.24e-01 5.03e-01       0
...
1.35e+02  1.25e-01 4.96e-01       0
...
1.41e+02  1.26e-01 4.89e-01       0
...
1.47e+02  1.27e-01 4.83e-01       0
...
1.53e+02  1.28e-01 4.77e-01       0
...
1.59e+02  1.28e-01 4.73e-01       0
...
1.65e+02  1.29e-01 4.68e-01       0
...
1.71e+02  1.29e-01 4.64e-01       0
...
1.77e+02  1.30e-01 4.61e-01       0
...
1.83e+02  1.30e-01 4.58e-01       0
...
1.90e+02  1.30e-01 4.55e-01       0
...
1.96e+02  1.31e-01 4.53e-01       0
...
2.02e+02  1.31e-01 4.51e-01       0
...
2.08e+02  1.31e-01 4.49e-01       0
...
2.14e+02  1.31e-01 4.47e-01       0
...
2.20e+02  1.31e-01 4.46e-01       0
...
2.26e+02  1.32e-01 4.45e-01       0
...
2.32e+02  1.32e-01 4.44e-01       0
...
2.39e+02  1.32e-01 4.43e-01       0
...
2.45e+02  1.32e-01 4.43e-01       0
...
2.51e+02  1.32e-01 4.42e-01       0
...
2.57e+02  1.32e-01 4.41e-01       0
...
2.63e+02  1.32e-01 4.41e-01       0
...
2.69e+02  1.32e-01 4.41e-01       0
...
2.75e+02  1.32e-01 4.40e-01       0
...
2.81e+02  1.32e-01 4.40e-01       0
...
2.87e+02  1.32e-01 4.40e-01       0
...
2.94e+02  1.32e-01 4.40e-01       0
...
3.00e+02  1.32e-01 4.40e-01       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, iteration_3, iteration_4)
- `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 5
## Thoughts

With `id_lti5` initialized to zero:
- `id_lti5` remains at zero throughout the experiment.
- `id_j5qx` and `id_o483` reach new steady-state values, with `id_o483` increasing and `id_j5qx` decreasing slightly.
- There is no production of `id_lti5` from the other species, suggesting that either the reaction producing `id_lti5` requires its own presence (autocatalytic), or the reaction is missing a necessary component or condition.

This is a strong indication that the production of `id_lti5` is not a simple conversion from `id_o483` or `id_j5qx` alone, or that the reaction is autocatalytic (i.e., requires `id_lti5` as a catalyst or reactant).

To further test this, I will set the initial concentration of `id_o483` to zero and observe if `id_lti5` can still be produced. This will help determine if `id_o483` is necessary for `id_lti5` production, or if there is an alternative pathway.

## Action

### Experiment

```json
{
    "action": "change_initial_concentration",
    "meta_data": {
        "id_o483": 0.0
    }
}
```
# Observation

## Experiment Result
Experiment ID: iteration_5
Your requested experiment has finished. The details results are saved as a dataframe in experiment_history[iteration_5]

Total size: 2797 rows × 4 columns. 

Below shows a subset of the table for your convenience. You should access experiment_history[iteration_5] for the full data

Time   id_j5qx   id_o483  id_lti5
0  2.00e-01         0 5.50e-01
...
6.12e+00  1.91e-01         0 9.02e-01
...
1.22e+01  1.83e-01         0 9.86e-01
...
1.83e+01  1.75e-01         0 9.98e-01
...
2.45e+01  1.67e-01         0 1.00e+00
...
3.06e+01  1.59e-01         0 1.00e+00
...
3.67e+01  1.52e-01         0 1.00e+00
...
4.28e+01  1.46e-01         0 1.00e+00
...
4.89e+01  1.39e-01 -5.05e-23 1.00e+00
...
5.50e+01  1.33e-01 -3.93e-23 1.00e+00
...
6.12e+01  1.27e-01 -6.50e-23 1.00e+00
...
6.73e+01  1.22e-01 -4.60e-22 1.00e+00
...
7.34e+01  1.16e-01 -1.03e-21 1.00e+00
...
7.95e+01  1.11e-01 -1.68e-21 1.00e+00
...
8.56e+01  1.06e-01 -2.56e-21 1.00e+00
...
9.17e+01  1.01e-01 -4.13e-21 1.00e+00
...
9.79e+01  9.70e-02 -7.33e-21 1.00e+00
...
1.04e+02  9.27e-02 -1.42e-20 1.00e+00
...
1.10e+02  8.86e-02 -2.92e-20 1.00e+00
...
1.16e+02  8.46e-02 -6.38e-20 1.00e+00
...
1.22e+02  8.09e-02 -1.59e-19 1.00e+00
...
1.28e+02  7.73e-02 -4.09e-19 1.00e+00
...
1.35e+02  7.39e-02 -1.05e-18 1.00e+00
...
1.41e+02  7.06e-02 -2.73e-18 1.00e+00
...
1.47e+02  6.75e-02 -6.77e-18 1.00e+00
...
1.53e+02  6.45e-02 -1.68e-17 1.00e+00
...
1.59e+02  6.17e-02 -5.47e-17 1.00e+00
...
1.65e+02  5.89e-02 -1.46e-16 1.00e+00
...
1.71e+02  5.63e-02 -5.21e-16 1.00e+00
...
1.77e+02  5.38e-02 -1.39e-15 1.00e+00
...
1.83e+02  5.14e-02 -4.03e-15 1.00e+00
...
1.90e+02  4.92e-02 -9.69e-15 1.00e+00
...
1.96e+02  4.70e-02 -2.65e-14 1.00e+00
...
2.02e+02  4.49e-02 -6.37e-14 1.00e+00
...
2.08e+02  4.29e-02 -1.74e-13 1.00e+00
...
2.14e+02  4.10e-02 -4.30e-13 1.00e+00
...
2.20e+02  3.92e-02 -1.19e-12 1.00e+00
...
2.26e+02  3.75e-02 -3.01e-12 1.00e+00
...
2.32e+02  3.58e-02 -8.34e-12 1.00e+00
...
2.39e+02  3.42e-02 -2.46e-11 1.00e+00
...
2.45e+02  3.27e-02 -1.60e-10 1.00e+00
...
2.51e+02  3.13e-02 -1.06e-09 1.00e+00
...
2.57e+02  2.99e-02 -7.19e-09 1.00e+00
...
2.63e+02  2.86e-02 -4.97e-08 1.00e+00
...
2.69e+02  2.73e-02 -3.51e-07 1.00e+00
...
2.75e+02  2.61e-02 -2.54e-06 1.00e+00
...
2.81e+02  2.49e-02 -1.88e-05 1.00e+00
...
2.87e+02  2.38e-02 -1.42e-04 1.00e+00
...
2.94e+02  2.28e-02 -1.10e-03 1.00e+00
...
3.00e+02  2.17e-02 -9.66e-03 1.01e+00

## 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, iteration_3, iteration_4, iteration_5)
- `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 6
## Thoughts

With `id_o483` initialized to zero:
- `id_lti5` increases and saturates at 1.0, just as in previous experiments.
- `id_j5qx` decreases over time.
- `id_o483` remains at zero (with only numerical noise).

This suggests:
- `id_o483` is not required for the production of `id_lti5`.
- `id_j5qx` can be directly converted to `id_lti5` without passing through `id_o483`, or there is a parallel pathway.
- The system strongly favors the accumulation of `id_lti5` regardless of the initial conditions for the other two species, unless `id_lti5` is initialized at zero, in which case it remains at zero.

The experiment where `id_lti5` was initialized to zero and stayed at zero suggests that the production of `id_lti5` is autocatalytic: it requires its own presence to be produced. This is a hallmark of an autocatalytic reaction, such as `id_j5qx + id_lti5 → 2 id_lti5`.

To further test this hypothesis, I will set both `id_j5qx` and `id_lti5` to zero and see if any `id_lti5` is produced. If not, this will confirm that `id_lti5` production is strictly autocatalytic.

## Action

### Experiment

```json
{
    "action": "change_initial_concentration",
    "meta_data": {
        "id_j5qx": 0.0,
        "id_lti5": 0.0
    }
}
```
# Observation

## Experiment Result
Experiment ID: iteration_6
Your requested experiment has finished. The details results are saved as a dataframe in experiment_history[iteration_6]

Total size: 2797 rows × 4 columns. 

Below shows a subset of the table for your convenience. You should access experiment_history[iteration_6] for the full data

Time  id_j5qx  id_o483 id_lti5
0        0 1.00e-01       0
...
6.12e+00        0 6.80e-01       0
...
1.22e+01        0 9.76e-01       0
...
1.83e+01        0 9.99e-01       0
...
2.45e+01        0 1.00e+00       0
...
3.06e+01        0 1.00e+00       0
...
3.67e+01        0 1.00e+00       0
...
4.28e+01        0 1.00e+00       0
...
4.89e+01        0 1.00e+00       0
...
5.50e+01        0 1.00e+00       0
...
6.12e+01        0 1.00e+00       0
...
6.73e+01        0 1.00e+00       0
...
7.34e+01        0 1.00e+00       0
...
7.95e+01        0 1.00e+00       0
...
8.56e+01        0 1.00e+00       0
...
9.17e+01        0 1.00e+00       0
...
9.79e+01        0 1.00e+00       0
...
1.04e+02        0 1.00e+00       0
...
1.10e+02        0 1.00e+00       0
...
1.16e+02        0 1.00e+00       0
...
1.22e+02        0 1.00e+00       0
...
1.28e+02        0 1.00e+00       0
...
1.35e+02        0 1.00e+00       0
...
1.41e+02        0 1.00e+00       0
...
1.47e+02        0 1.00e+00       0
...
1.53e+02        0 1.00e+00       0
...
1.59e+02        0 1.00e+00       0
...
1.65e+02        0 1.00e+00       0
...
1.71e+02        0 1.00e+00       0
...
1.77e+02        0 1.00e+00       0
...
1.83e+02        0 1.00e+00       0
...
1.90e+02        0 1.00e+00       0
...
1.96e+02        0 1.00e+00       0
...
2.02e+02        0 1.00e+00       0
...
2.08e+02        0 1.00e+00       0
...
2.14e+02        0 1.00e+00       0
...
2.20e+02        0 1.00e+00       0
...
2.26e+02        0 1.00e+00       0
...
2.32e+02        0 1.00e+00       0
...
2.39e+02        0 1.00e+00       0
...
2.45e+02        0 1.00e+00       0
...
2.51e+02        0 1.00e+00       0
...
2.57e+02        0 1.00e+00       0
...
2.63e+02        0 1.00e+00       0
...
2.69e+02        0 1.00e+00       0
...
2.75e+02        0 1.00e+00       0
...
2.81e+02        0 1.00e+00       0
...
2.87e+02        0 1.00e+00       0
...
2.94e+02        0 1.00e+00       0
...
3.00e+02        0 1.00e+00       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, iteration_3, iteration_4, iteration_5, iteration_6)
- `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 7
## Thoughts

With both `id_j5qx` and `id_lti5` initialized to zero:
- `id_j5qx` remains at zero.
- `id_lti5` remains at zero.
- `id_o483` increases and saturates at 1.0.

This suggests:
- When `id_j5qx` is absent, `id_o483` accumulates, likely from another source or by conversion from something not present in the model (possibly an implicit source).
- `id_lti5` is not produced at all without its own initial presence, supporting the hypothesis that its production is autocatalytic.
- The system seems to have a strong drive to accumulate either `id_lti5` or `id_o483` depending on the initial conditions.

From all experiments so far, the following hypotheses are supported:
- `id_lti5` is produced autocatalytically, likely via a reaction like `id_j5qx + id_lti5 → 2 id_lti5`.
- `id_j5qx` can be depleted, and its depletion is associated with the rise of either `id_lti5` or `id_o483`.
- `id_o483` can accumulate in the absence of `id_j5qx` and `id_lti5`, suggesting a possible conversion from an external source or a reversible reaction with `id_lti5`.

To further clarify the relationship between `id_o483` and `id_lti5`, I will set both to nonzero values and set `id_j5qx` to zero, to see if `id_lti5` can be produced from `id_o483` alone.

## Action

### Experiment

```json
{
    "action": "change_initial_concentration",
    "meta_data": {
        "id_j5qx": 0.0,
        "id_o483": 0.5,
        "id_lti5": 0.1
    }
}
```
# Observation

## Experiment Result
Experiment ID: iteration_7
Your requested experiment has finished. The details results are saved as a dataframe in experiment_history[iteration_7]

Total size: 2797 rows × 4 columns. 

Below shows a subset of the table for your convenience. You should access experiment_history[iteration_7] for the full data

Time  id_j5qx  id_o483  id_lti5
0        0 5.00e-01 1.00e-01
...
6.12e+00        0 9.26e-01 4.19e-01
...
1.22e+01        0 9.44e-01 7.96e-01
...
1.83e+01        0 9.30e-01 9.18e-01
...
2.45e+01        0 9.26e-01 9.39e-01
...
3.06e+01        0 9.25e-01 9.43e-01
...
3.67e+01        0 9.25e-01 9.43e-01
...
4.28e+01        0 9.25e-01 9.43e-01
...
4.89e+01        0 9.25e-01 9.43e-01
...
5.50e+01        0 9.25e-01 9.43e-01
...
6.12e+01        0 9.25e-01 9.43e-01
...
6.73e+01        0 9.25e-01 9.43e-01
...
7.34e+01        0 9.25e-01 9.43e-01
...
7.95e+01        0 9.25e-01 9.43e-01
...
8.56e+01        0 9.25e-01 9.43e-01
...
9.17e+01        0 9.25e-01 9.43e-01
...
9.79e+01        0 9.25e-01 9.43e-01
...
1.04e+02        0 9.25e-01 9.43e-01
...
1.10e+02        0 9.25e-01 9.43e-01
...
1.16e+02        0 9.25e-01 9.43e-01
...
1.22e+02        0 9.25e-01 9.43e-01
...
1.28e+02        0 9.25e-01 9.43e-01
...
1.35e+02        0 9.25e-01 9.43e-01
...
1.41e+02        0 9.25e-01 9.43e-01
...
1.47e+02        0 9.25e-01 9.43e-01
...
1.53e+02        0 9.25e-01 9.43e-01
...
1.59e+02        0 9.25e-01 9.43e-01
...
1.65e+02        0 9.25e-01 9.43e-01
...
1.71e+02        0 9.25e-01 9.43e-01
...
1.77e+02        0 9.25e-01 9.43e-01
...
1.83e+02        0 9.25e-01 9.43e-01
...
1.90e+02        0 9.25e-01 9.43e-01
...
1.96e+02        0 9.25e-01 9.43e-01
...
2.02e+02        0 9.25e-01 9.43e-01
...
2.08e+02        0 9.25e-01 9.43e-01
...
2.14e+02        0 9.25e-01 9.43e-01
...
2.20e+02        0 9.25e-01 9.43e-01
...
2.26e+02        0 9.25e-01 9.43e-01
...
2.32e+02        0 9.25e-01 9.43e-01
...
2.39e+02        0 9.25e-01 9.43e-01
...
2.45e+02        0 9.25e-01 9.43e-01
...
2.51e+02        0 9.25e-01 9.43e-01
...
2.57e+02        0 9.25e-01 9.43e-01
...
2.63e+02        0 9.25e-01 9.43e-01
...
2.69e+02        0 9.25e-01 9.43e-01
...
2.75e+02        0 9.25e-01 9.43e-01
...
2.81e+02        0 9.25e-01 9.43e-01
...
2.87e+02        0 9.25e-01 9.43e-01
...
2.94e+02        0 9.25e-01 9.43e-01
...
3.00e+02        0 9.25e-01 9.43e-01

## 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, iteration_3, iteration_4, iteration_5, iteration_6, iteration_7)
- `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 8
## Thoughts

With `id_j5qx` set to zero and both `id_o483` and `id_lti5` nonzero:
- `id_j5qx` remains at zero.
- `id_o483` increases and saturates at ~0.925.
- `id_lti5` increases from 0.1 to ~0.943, then plateaus.

This suggests:
- Both `id_o483` and `id_lti5` can increase in the absence of `id_j5qx`, but their total is less than 2, so there is no net creation of mass.
- The increase in `id_lti5` comes at the expense of `id_o483` (and vice versa), suggesting a reversible reaction between these two species.
- The system does not require `id_j5qx` for the interconversion between `id_o483` and `id_lti5`.

From all experiments, a plausible minimal reaction network is:
1. `id_j5qx` is a substrate that can be converted to either `id_o483` or `id_lti5`.
2. `id_o483` and `id_lti5` are interconvertible via a reversible reaction.
3. The production of `id_lti5` from `id_j5qx` is autocatalytic (requires `id_lti5`).

To confirm the reversibility between `id_o483` and `id_lti5`, I will set `id_o483` to a high value and `id_lti5` to a low value, with `id_j5qx` at zero, and observe if `id_lti5` increases at the expense of `id_o483`.

## Action

### Experiment

```json
{
    "action": "change_initial_concentration",
    "meta_data": {
        "id_j5qx": 0.0,
        "id_o483": 1.0,
        "id_lti5": 0.0
    }
}
```
# Observation

## Experiment Result
Experiment ID: iteration_8
Your requested experiment has finished. The details results are saved as a dataframe in experiment_history[iteration_8]

Total size: 2797 rows × 4 columns. 

Below shows a subset of the table for your convenience. You should access experiment_history[iteration_8] for the full data

Time  id_j5qx  id_o483 id_lti5
0        0 1.00e+00       0
...
6.12e+00        0 1.00e+00       0
...
1.22e+01        0 1.00e+00       0
...
1.83e+01        0 1.00e+00       0
...
2.45e+01        0 1.00e+00       0
...
3.06e+01        0 1.00e+00       0
...
3.67e+01        0 1.00e+00       0
...
4.28e+01        0 1.00e+00       0
...
4.89e+01        0 1.00e+00       0
...
5.50e+01        0 1.00e+00       0
...
6.12e+01        0 1.00e+00       0
...
6.73e+01        0 1.00e+00       0
...
7.34e+01        0 1.00e+00       0
...
7.95e+01        0 1.00e+00       0
...
8.56e+01        0 1.00e+00       0
...
9.17e+01        0 1.00e+00       0
...
9.79e+01        0 1.00e+00       0
...
1.04e+02        0 1.00e+00       0
...
1.10e+02        0 1.00e+00       0
...
1.16e+02        0 1.00e+00       0
...
1.22e+02        0 1.00e+00       0
...
1.28e+02        0 1.00e+00       0
...
1.35e+02        0 1.00e+00       0
...
1.41e+02        0 1.00e+00       0
...
1.47e+02        0 1.00e+00       0
...
1.53e+02        0 1.00e+00       0
...
1.59e+02        0 1.00e+00       0
...
1.65e+02        0 1.00e+00       0
...
1.71e+02        0 1.00e+00       0
...
1.77e+02        0 1.00e+00       0
...
1.83e+02        0 1.00e+00       0
...
1.90e+02        0 1.00e+00       0
...
1.96e+02        0 1.00e+00       0
...
2.02e+02        0 1.00e+00       0
...
2.08e+02        0 1.00e+00       0
...
2.14e+02        0 1.00e+00       0
...
2.20e+02        0 1.00e+00       0
...
2.26e+02        0 1.00e+00       0
...
2.32e+02        0 1.00e+00       0
...
2.39e+02        0 1.00e+00       0
...
2.45e+02        0 1.00e+00       0
...
2.51e+02        0 1.00e+00       0
...
2.57e+02        0 1.00e+00       0
...
2.63e+02        0 1.00e+00       0
...
2.69e+02        0 1.00e+00       0
...
2.75e+02        0 1.00e+00       0
...
2.81e+02        0 1.00e+00       0
...
2.87e+02        0 1.00e+00       0
...
2.94e+02        0 1.00e+00       0
...
3.00e+02        0 1.00e+00       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, iteration_3, iteration_4, iteration_5, iteration_6, iteration_7, iteration_8)
- `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 9
## Thoughts

With `id_j5qx` at zero, `id_o483` at 1.0, and `id_lti5` at zero:
- `id_o483` remains at 1.0, and `id_lti5` remains at zero throughout the experiment.
- There is no conversion between `id_o483` and `id_lti5` in the absence of `id_lti5`.

This strongly supports the hypothesis that the conversion from `id_o483` to `id_lti5` is autocatalytic, i.e., it requires the presence of `id_lti5` to proceed. This is consistent with a reaction of the form:  
`id_o483 + id_lti5 → 2 id_lti5`

Similarly, previous experiments suggest:
- `id_j5qx` can be depleted, and its depletion is associated with the rise of `id_lti5` (when `id_lti5` is present).
- When both `id_j5qx` and `id_lti5` are zero, `id_o483` accumulates, suggesting a possible conversion from `id_j5qx` to `id_o483` (possibly spontaneous or via another autocatalytic process).

To clarify the relationship between `id_j5qx` and `id_o483`, I will set `id_j5qx` to a high value, `id_o483` to zero, and `id_lti5` to a small nonzero value, to see if `id_o483` is produced from `id_j5qx` in the presence of `id_lti5`.

## Action

### Experiment

```json
{
    "action": "change_initial_concentration",
    "meta_data": {
        "id_j5qx": 1.0,
        "id_o483": 0.0,
        "id_lti5": 0.01
    }
}
```
# Observation

## Experiment Result
Experiment ID: iteration_9
Your requested experiment has finished. The details results are saved as a dataframe in experiment_history[iteration_9]

Total size: 2797 rows × 4 columns. 

Below shows a subset of the table for your convenience. You should access experiment_history[iteration_9] for the full data

Time   id_j5qx id_o483  id_lti5
0  1.00e+00       0 1.00e-02
...
6.12e+00  9.56e-01       0 7.09e-02
...
1.22e+01  9.13e-01       0 3.66e-01
...
1.83e+01  8.73e-01       0 8.13e-01
...
2.45e+01  8.34e-01       0 9.71e-01
...
3.06e+01  7.97e-01       0 9.96e-01
...
3.67e+01  7.62e-01       0 9.99e-01
...
4.28e+01  7.28e-01       0 1.00e+00
...
4.89e+01  6.96e-01       0 1.00e+00
...
5.50e+01  6.65e-01       0 1.00e+00
...
6.12e+01  6.36e-01       0 1.00e+00
...
6.73e+01  6.08e-01       0 1.00e+00
...
7.34e+01  5.81e-01       0 1.00e+00
...
7.95e+01  5.55e-01       0 1.00e+00
...
8.56e+01  5.31e-01       0 1.00e+00
...
9.17e+01  5.07e-01       0 1.00e+00
...
9.79e+01  4.85e-01       0 1.00e+00
...
1.04e+02  4.63e-01       0 1.00e+00
...
1.10e+02  4.43e-01       0 1.00e+00
...
1.16e+02  4.23e-01       0 1.00e+00
...
1.22e+02  4.04e-01       0 1.00e+00
...
1.28e+02  3.87e-01       0 1.00e+00
...
1.35e+02  3.69e-01       0 1.00e+00
...
1.41e+02  3.53e-01       0 1.00e+00
...
1.47e+02  3.38e-01       0 1.00e+00
...
1.53e+02  3.23e-01       0 1.00e+00
...
1.59e+02  3.08e-01       0 1.00e+00
...
1.65e+02  2.95e-01       0 1.00e+00
...
1.71e+02  2.82e-01       0 1.00e+00
...
1.77e+02  2.69e-01       0 1.00e+00
...
1.83e+02  2.57e-01       0 1.00e+00
...
1.90e+02  2.46e-01       0 1.00e+00
...
1.96e+02  2.35e-01       0 1.00e+00
...
2.02e+02  2.25e-01       0 1.00e+00
...
2.08e+02  2.15e-01       0 1.00e+00
...
2.14e+02  2.05e-01       0 1.00e+00
...
2.20e+02  1.96e-01       0 1.00e+00
...
2.26e+02  1.87e-01       0 1.00e+00
...
2.32e+02  1.79e-01       0 1.00e+00
...
2.39e+02  1.71e-01       0 1.00e+00
...
2.45e+02  1.64e-01       0 1.00e+00
...
2.51e+02  1.56e-01       0 1.00e+00
...
2.57e+02  1.49e-01       0 1.00e+00
...
2.63e+02  1.43e-01       0 1.00e+00
...
2.69e+02  1.37e-01       0 1.00e+00
...
2.75e+02  1.30e-01       0 1.00e+00
...
2.81e+02  1.25e-01       0 1.00e+00
...
2.87e+02  1.19e-01       0 1.00e+00
...
2.94e+02  1.14e-01       0 1.00e+00
...
3.00e+02  1.09e-01       0 1.00e+00

## 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, iteration_3, iteration_4, iteration_5, iteration_6, iteration_7, iteration_8, iteration_9)
- `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 10
## Thoughts

With `id_j5qx` at 1.0, `id_o483` at 0, and a small amount of `id_lti5` (0.01):
- `id_lti5` increases rapidly and saturates at 1.0.
- `id_j5qx` decreases steadily.
- `id_o483` remains at zero throughout.

This supports the hypothesis that:
- `id_lti5` is produced autocatalytically from `id_j5qx` and itself: `id_j5qx + id_lti5 → 2 id_lti5`.
- There is no significant production of `id_o483` in this scenario, suggesting that `id_o483` is not a direct product of `id_j5qx` under these conditions, or its production is suppressed in the presence of `id_lti5`.

From all experiments, the minimal set of reactions that can explain the data are:
1. Autocatalytic production: `id_j5qx + id_lti5 → 2 id_lti5`
2. In the absence of `id_lti5`, `id_j5qx` is converted to `id_o483`: `id_j5qx → id_o483`
3. There is no evidence for direct interconversion between `id_o483` and `id_lti5` unless `id_lti5` is present.

To confirm the second reaction, I will set `id_j5qx` to a high value, and both `id_o483` and `id_lti5` to zero, and observe if `id_o483` is produced.

## Action

### Experiment

```json
{
    "action": "change_initial_concentration",
    "meta_data": {
        "id_j5qx": 1.0,
        "id_o483": 0.0,
        "id_lti5": 0.0
    }
}
```
# Observation

## Experiment Result
Experiment ID: iteration_10
Your requested experiment has finished. The details results are saved as a dataframe in experiment_history[iteration_10]

Total size: 2797 rows × 4 columns. 

Below shows a subset of the table for your convenience. You should access experiment_history[iteration_10] for the full data

Time   id_j5qx id_o483 id_lti5
0  1.00e+00       0       0
...
6.12e+00  9.56e-01       0       0
...
1.22e+01  9.13e-01       0       0
...
1.83e+01  8.73e-01       0       0
...
2.45e+01  8.34e-01       0       0
...
3.06e+01  7.97e-01       0       0
...
3.67e+01  7.62e-01       0       0
...
4.28e+01  7.28e-01       0       0
...
4.89e+01  6.96e-01       0       0
...
5.50e+01  6.65e-01       0       0
...
6.12e+01  6.36e-01       0       0
...
6.73e+01  6.08e-01       0       0
...
7.34e+01  5.81e-01       0       0
...
7.95e+01  5.55e-01       0       0
...
8.56e+01  5.31e-01       0       0
...
9.17e+01  5.07e-01       0       0
...
9.79e+01  4.85e-01       0       0
...
1.04e+02  4.63e-01       0       0
...
1.10e+02  4.43e-01       0       0
...
1.16e+02  4.23e-01       0       0
...
1.22e+02  4.04e-01       0       0
...
1.28e+02  3.87e-01       0       0
...
1.35e+02  3.69e-01       0       0
...
1.41e+02  3.53e-01       0       0
...
1.47e+02  3.38e-01       0       0
...
1.53e+02  3.23e-01       0       0
...
1.59e+02  3.08e-01       0       0
...
1.65e+02  2.95e-01       0       0
...
1.71e+02  2.82e-01       0       0
...
1.77e+02  2.69e-01       0       0
...
1.83e+02  2.57e-01       0       0
...
1.90e+02  2.46e-01       0       0
...
1.96e+02  2.35e-01       0       0
...
2.02e+02  2.25e-01       0       0
...
2.08e+02  2.15e-01       0       0
...
2.14e+02  2.05e-01       0       0
...
2.20e+02  1.96e-01       0       0
...
2.26e+02  1.87e-01       0       0
...
2.32e+02  1.79e-01       0       0
...
2.39e+02  1.71e-01       0       0
...
2.45e+02  1.64e-01       0       0
...
2.51e+02  1.56e-01       0       0
...
2.57e+02  1.49e-01       0       0
...
2.63e+02  1.43e-01       0       0
...
2.69e+02  1.37e-01       0       0
...
2.75e+02  1.30e-01       0       0
...
2.81e+02  1.25e-01       0       0
...
2.87e+02  1.19e-01       0       0
...
2.94e+02  1.14e-01       0       0
...
3.00e+02  1.09e-01       0       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, iteration_3, iteration_4, iteration_5, iteration_6, iteration_7, iteration_8, iteration_9, iteration_10)
- `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 11
## Thoughts

With `id_j5qx` at 1.0 and both `id_o483` and `id_lti5` at zero:
- `id_j5qx` decreases steadily.
- `id_o483` and `id_lti5` remain at zero throughout.

This is surprising, as previous experiments suggested that in the absence of `id_lti5`, `id_j5qx` would be converted to `id_o483`. However, here, `id_o483` does not increase at all. This suggests that either:
- There is a threshold or requirement for `id_o483` to be present initially for its own production (autocatalysis), or
- The conversion from `id_j5qx` to `id_o483` is not spontaneous and requires a catalyst or another species.

Given that in previous experiments, when both `id_j5qx` and `id_lti5` were zero, `id_o483` increased to 1.0, it seems that `id_o483` can be produced from something, but not from `id_j5qx` alone. This points to a possible autocatalytic reaction for `id_o483` as well, such as `id_j5qx + id_o483 → 2 id_o483`.

To test this, I will set `id_j5qx` to a high value and `id_o483` to a small nonzero value, with `id_lti5` at zero, and observe if `id_o483` increases at the expense of `id_j5qx`.

## Action

### Experiment

```json
{
    "action": "change_initial_concentration",
    "meta_data": {
        "id_j5qx": 1.0,
        "id_o483": 0.01,
        "id_lti5": 0.0
    }
}
```
# Observation

## Experiment Result
Experiment ID: iteration_11
Your requested experiment has finished. The details results are saved as a dataframe in experiment_history[iteration_11]

Total size: 2797 rows × 4 columns. 

Below shows a subset of the table for your convenience. You should access experiment_history[iteration_11] for the full data

Time   id_j5qx   id_o483 id_lti5
0  1.00e+00  1.00e-02       0
...
6.12e+00  9.56e-01  2.47e-09       0
...
1.22e+01  9.13e-01 -8.75e-15       0
...
1.83e+01  8.73e-01  6.26e-17       0
...
2.45e+01  8.34e-01 -1.04e-16       0
...
3.06e+01  7.97e-01 -7.32e-16       0
...
3.67e+01  7.62e-01 -1.24e-14       0
...
4.28e+01  7.28e-01  7.78e-15       0
...
4.89e+01  6.96e-01 -1.75e-15       0
...
5.50e+01  6.65e-01  2.99e-14       0
...
6.12e+01  6.36e-01 -1.49e-14       0
...
6.73e+01  6.08e-01  3.81e-14       0
...
7.34e+01  5.81e-01  5.06e-14       0
...
7.95e+01  5.55e-01 -6.78e-15       0
...
8.56e+01  5.31e-01 -3.66e-15       0
...
9.17e+01  5.07e-01  1.02e-15       0
...
9.79e+01  4.85e-01 -1.35e-15       0
...
1.04e+02  4.63e-01  1.13e-16       0
...
1.10e+02  4.43e-01 -4.40e-15       0
...
1.16e+02  4.23e-01 -3.26e-15       0
...
1.22e+02  4.04e-01  4.13e-15       0
...
1.28e+02  3.87e-01  7.65e-15       0
...
1.35e+02  3.69e-01  4.96e-15       0
...
1.41e+02  3.53e-01  2.62e-16       0
...
1.47e+02  3.38e-01  6.49e-16       0
...
1.53e+02  3.23e-01  1.27e-14       0
...
1.59e+02  3.08e-01  3.33e-14       0
...
1.65e+02  2.95e-01  4.67e-14       0
...
1.71e+02  2.82e-01  3.97e-14       0
...
1.77e+02  2.69e-01 -5.88e-15       0
...
1.83e+02  2.57e-01 -6.38e-14       0
...
1.90e+02  2.46e-01 -3.35e-14       0
...
1.96e+02  2.35e-01  8.94e-15       0
...
2.02e+02  2.25e-01  5.25e-15       0
...
2.08e+02  2.15e-01 -1.67e-14       0
...
2.14e+02  2.05e-01  1.62e-14       0
...
2.20e+02  1.96e-01  7.07e-14       0
...
2.26e+02  1.87e-01  2.04e-14       0
...
2.32e+02  1.79e-01 -1.34e-13       0
...
2.39e+02  1.71e-01 -1.53e-13       0
...
2.45e+02  1.64e-01  5.38e-14       0
...
2.51e+02  1.56e-01  3.77e-14       0
...
2.57e+02  1.49e-01  8.36e-14       0
...
2.63e+02  1.43e-01  1.26e-13       0
...
2.69e+02  1.37e-01  1.74e-13       0
...
2.75e+02  1.30e-01  2.94e-13       0
...
2.81e+02  1.25e-01  5.54e-13       0
...
2.87e+02  1.19e-01  1.09e-12       0
...
2.94e+02  1.14e-01  2.32e-12       0
...
3.00e+02  1.09e-01  5.80e-12       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, iteration_3, iteration_4, iteration_5, iteration_6, iteration_7, iteration_8, iteration_9, iteration_10, iteration_11)
- `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 12
## Thoughts

With `id_j5qx` at 1.0, `id_o483` at 0.01, and `id_lti5` at 0:
- `id_j5qx` decreases steadily.
- `id_o483` starts at 0.01, but quickly drops to near zero and stays there (with only numerical noise).
- `id_lti5` remains at zero.

This suggests:
- There is no autocatalytic production of `id_o483` from `id_j5qx` and `id_o483` (i.e., no reaction like `id_j5qx + id_o483 → 2 id_o483`).
- The only way for `id_o483` to accumulate is when both `id_j5qx` and `id_lti5` are zero, as seen in previous experiments.
- The system is dominated by the autocatalytic production of `id_lti5` when it is present, and otherwise, `id_j5qx` simply depletes without producing `id_o483` or `id_lti5`.

Given all the data, the most consistent minimal reaction network is:
1. Autocatalytic production: `id_j5qx + id_lti5 → 2 id_lti5`
2. When both `id_j5qx` and `id_lti5` are zero, `id_o483` accumulates, possibly from an external source or as a default state.

To finalize the model, I will now analyze the time courses to estimate the kinetic parameters for the autocatalytic reaction. I will use the default experiment (iteration_0) for this purpose.

## Action

### Code

```python
import numpy as np
import pandas as pd

df = experiment_history['iteration_0']

# Focus on early time points where id_j5qx and id_lti5 are both present
early = df[(df['Time'] < 50) & (df['id_j5qx'] > 0.1) & (df['id_lti5'] > 0.55)]

# Estimate rate: d[id_lti5]/dt ≈ k * [id_j5qx] * [id_lti5]
# Use finite differences
dt = np.diff(early['Time'])
dL = np.diff(early['id_lti5'])
J5 = early['id_j5qx'].values[:-1]
L = early['id_lti5'].values[:-1]
rate = dL / dt
k_est = rate / (J5 * L)

print("Estimated k (mean, std):", np.mean(k_est), np.std(k_est))
print("k values (first 10):", k_est[:10])
```
# Observation

## Code Stdout
Estimated k (mean, std): 0.060637998042897864 0.1309282736341456
k values (first 10): [0.65570205 0.64333963 0.63106275 0.61885751 0.60673398 0.59470447
 0.58273736 0.57091591 0.55915614 0.54750301]

## Reminder

 ### Remaining Iterations for Interactions:
- Iterations: 8/20



### Available Global Variables
- `input_sbml_string`: Original incomplete model
- `experiment_history`: Results from all previous experiments (iteration_0, iteration_3, iteration_4, iteration_5, iteration_6, iteration_7, iteration_8, iteration_9, iteration_10, iteration_11)
- `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 13
## Thoughts

The estimated rate constant `k` for the autocatalytic reaction `id_j5qx + id_lti5 → 2 id_lti5` is around 0.06 (mean), but the first 10 values are much higher (~0.65 to ~0.55), then drop off, suggesting the rate slows as substrate is depleted or as the system approaches a new regime. This is typical for autocatalytic reactions, where the rate is highest when both reactants are abundant.

Given all experimental results, the minimal model that fits the data is:
- Autocatalytic reaction: `id_j5qx + id_lti5 → 2 id_lti5` (with k ≈ 0.6)
- No direct conversion between `id_j5qx` and `id_o483` unless both `id_j5qx` and `id_lti5` are zero, in which case `id_o483` accumulates (possibly from an implicit source or as a default state).

To capture the observed behavior, I will:
- Add the autocatalytic reaction with k = 0.6.
- Add a zero-order production of `id_o483` (e.g., `→ id_o483` with a small rate) to account for its accumulation when the other species are absent.
- No other reactions are needed based on the data.

I will now implement this model in SBML.

## Action

### Code

```python
import libsbml

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

# Add autocatalytic reaction: id_j5qx + id_lti5 -> 2 id_lti5
reaction1 = model.createReaction()
reaction1.setId("autocat_lti5")
reaction1.setReversible(False)
reaction1.setFast(False)

# Reactants
reactant1 = reaction1.createReactant()
reactant1.setSpecies("id_j5qx")
reactant1.setStoichiometry(1.0)
reactant1.setConstant(False)

reactant2 = reaction1.createReactant()
reactant2.setSpecies("id_lti5")
reactant2.setStoichiometry(1.0)
reactant2.setConstant(False)

# Product
product1 = reaction1.createProduct()
product1.setSpecies("id_lti5")
product1.setStoichiometry(2.0)
product1.setConstant(False)

# Kinetic law for autocatalytic reaction
klaw1 = reaction1.createKineticLaw()
param_k1 = klaw1.createParameter()
param_k1.setId("k_autocat")
param_k1.setValue(0.6)
klaw1.setFormula("k_autocat * id_j5qx * id_lti5")

# Add zero-order production of id_o483: -> id_o483
reaction2 = model.createReaction()
reaction2.setId("zero_order_o483")
reaction2.setReversible(False)
reaction2.setFast(False)

# Product
product2 = reaction2.createProduct()
product2.setSpecies("id_o483")
product2.setStoichiometry(1.0)
product2.setConstant(False)

# Kinetic law for zero-order production
klaw2 = reaction2.createKineticLaw()
param_k2 = klaw2.createParameter()
param_k2.setId("k_zero_o483")
param_k2.setValue(0.01)
klaw2.setFormula("k_zero_o483")

# Write the updated SBML
writer = libsbml.SBMLWriter()
final_sbml = writer.writeSBMLToString(sbml_doc)
print(final_sbml[:1000])  # Print the first 1000 characters for verification
```
# Observation

## Code Stdout
<?xml version="1.0" encoding="UTF-8"?>
<sbml xmlns="http://www.sbml.org/sbml/level2/version4" level="2" version="4">
  <model id="id_8hn8">
    <listOfUnitDefinitions>
      <unitDefinition id="id_cm39">
        <listOfUnits>
          <unit kind="litre" exponent="1" scale="-3" multiplier="1"/>
        </listOfUnits>
      </unitDefinition>
      <unitDefinition id="id_g8kt">
        <listOfUnits>
          <unit kind="mole" exponent="1" scale="-3" multiplier="1"/>
        </listOfUnits>
      </unitDefinition>
    </listOfUnitDefinitions>
    <listOfCompartments>
      <compartment id="id_6msh" spatialDimensions="3" size="1" constant="true"/>
    </listOfCompartments>
    <listOfSpecies>
      <species id="id_j5qx" compartment="id_6msh" initialConcentration="0.2" boundaryCondition="false" constant="false"/>
      <species id="id_o483" compartment="id_6msh" initialConcentration="0.1" boundaryCondition="false" constant="false"/>
      <species id="id_lti5" compartment="id_6msh" initialC

## Reminder

 ### Remaining Iterations for Interactions:
- Iterations: 7/20



### Available Global Variables
- `input_sbml_string`: Original incomplete model
- `experiment_history`: Results from all previous experiments (iteration_0, iteration_3, iteration_4, iteration_5, iteration_6, iteration_7, iteration_8, iteration_9, iteration_10, iteration_11)
- `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 14
## Thoughts

The SBML model now includes:
- An autocatalytic reaction: `id_j5qx + id_lti5 → 2 id_lti5` with k = 0.6.
- A zero-order production of `id_o483` to account for its accumulation when the other species are absent.

Before submitting, I should simulate this model and compare its output to the experimental data, especially for the default and key perturbation experiments. This will help verify if the model captures the essential dynamics, particularly:
- The autocatalytic growth of `id_lti5` when present.
- The accumulation of `id_o483` when both `id_j5qx` and `id_lti5` are absent.
- The lack of `id_o483` production when only `id_j5qx` is present.

I will simulate the model and compare it to the default experiment.

## Action

### Code

```python
# Simulate the new model and compare to the default experiment
sim_df = simulate(final_sbml)
exp_df = experiment_history['iteration_0']

# Print head of both for quick comparison
print("Simulated (head):\n", sim_df.head())
print("Experimental (head):\n", exp_df.head())

# Print final values for comparison
print("Simulated (final):\n", sim_df.iloc[-1])
print("Experimental (final):\n", exp_df.iloc[-1])
```
# Observation

## Code Stderror
 name 'final_sbml' is not defined

## Reminder

 ### Remaining Iterations for Interactions:
- Iterations: 6/20



### Available Global Variables
- `input_sbml_string`: Original incomplete model
- `experiment_history`: Results from all previous experiments (iteration_0, iteration_3, iteration_4, iteration_5, iteration_6, iteration_7, iteration_8, iteration_9, iteration_10, iteration_11)
- `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 15
## Thoughts

I neglected to save the `final_sbml` variable from the previous code block, so it is not available for simulation. I need to reconstruct the SBML model with the reactions and parameters as before, assign it to `final_sbml`, and then proceed with the simulation and comparison.

## Action

### Code

```python
import libsbml

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

# Remove any existing reactions (if any)
while model.getNumReactions() > 0:
    model.removeReaction(0)

# Add autocatalytic reaction: id_j5qx + id_lti5 -> 2 id_lti5
reaction1 = model.createReaction()
reaction1.setId("autocat_lti5")
reaction1.setReversible(False)
reaction1.setFast(False)

# Reactants
reactant1 = reaction1.createReactant()
reactant1.setSpecies("id_j5qx")
reactant1.setStoichiometry(1.0)
reactant1.setConstant(False)

reactant2 = reaction1.createReactant()
reactant2.setSpecies("id_lti5")
reactant2.setStoichiometry(1.0)
reactant2.setConstant(False)

# Product
product1 = reaction1.createProduct()
product1.setSpecies("id_lti5")
product1.setStoichiometry(2.0)
product1.setConstant(False)

# Kinetic law for autocatalytic reaction
klaw1 = reaction1.createKineticLaw()
param_k1 = klaw1.createParameter()
param_k1.setId("k_autocat")
param_k1.setValue(0.6)
klaw1.setFormula("k_autocat * id_j5qx * id_lti5")

# Add zero-order production of id_o483: -> id_o483
reaction2 = model.createReaction()
reaction2.setId("zero_order_o483")
reaction2.setReversible(False)
reaction2.setFast(False)

# Product
product2 = reaction2.createProduct()
product2.setSpecies("id_o483")
product2.setStoichiometry(1.0)
product2.setConstant(False)

# Kinetic law for zero-order production
klaw2 = reaction2.createKineticLaw()
param_k2 = klaw2.createParameter()
param_k2.setId("k_zero_o483")
param_k2.setValue(0.01)
klaw2.setFormula("k_zero_o483")

# Write the updated SBML
writer = libsbml.SBMLWriter()
final_sbml = writer.writeSBMLToString(sbml_doc)

# Simulate the new model and compare to the default experiment
sim_df = simulate(final_sbml)
exp_df = experiment_history['iteration_0']

# Print head of both for quick comparison
print("Simulated (head):\n", sim_df.head())
print("Experimental (head):\n", exp_df.head())

# Print final values for comparison
print("Simulated (final):\n", sim_df.iloc[-1])
print("Experimental (final):\n", exp_df.iloc[-1])
```
# Observation

## Code Stdout
Simulated (head):
     id_j5qx   id_o483   id_lti5      Time
0  0.200000  0.100000  0.550000  0.000000
1  0.192999  0.101073  0.557001  0.107296
2  0.186160  0.102146  0.563840  0.214592
3  0.179485  0.103219  0.570515  0.321888
4  0.172976  0.104292  0.577024  0.429185
Experimental (head):
     id_j5qx   id_o483   id_lti5      Time
0  0.200000  0.100000  0.550000  0.000000
1  0.199855  0.098384  0.557886  0.107296
2  0.199710  0.096791  0.565730  0.214592
3  0.199564  0.095220  0.573529  0.321888
4  0.199418  0.093672  0.581279  0.429185
Simulated (final):
 id_j5qx   -1.000312e-16
id_o483    3.100000e+00
id_lti5    7.500000e-01
Time       3.000000e+02
Name: 2796, dtype: float64
Experimental (final):
 id_j5qx      0.106380
id_o483      0.454252
id_lti5      0.945029
Time       300.000000
Name: 2796, dtype: float64

## Reminder

 ### Remaining Iterations for Interactions:
- Iterations: 5/20



### Available Global Variables
- `input_sbml_string`: Original incomplete model
- `experiment_history`: Results from all previous experiments (iteration_0, iteration_3, iteration_4, iteration_5, iteration_6, iteration_7, iteration_8, iteration_9, iteration_10, iteration_11)
- `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 16
## Thoughts

The simulation with the current model shows:
- `id_j5qx` is depleted to zero (as expected for an autocatalytic reaction).
- `id_lti5` increases but only to 0.75, not matching the experimental final value (~0.95).
- `id_o483` increases linearly and reaches 3.1, which is much higher than the experimental value (~0.45).

This suggests:
- The zero-order production of `id_o483` is too strong and not consistent with the experimental system, where `id_o483` only accumulates under specific conditions.
- The autocatalytic reaction alone does not fully account for the observed dynamics, especially the final steady-state values.
- In the experiment, `id_j5qx` is not fully depleted, and `id_lti5` reaches a higher value.

A more plausible mechanism is:
- `id_j5qx` can be converted to `id_o483` (first-order or via a reversible reaction), and
- `id_o483` + `id_lti5` → 2 `id_lti5` (autocatalytic conversion).

This would allow for the observed non-monotonic behavior of `id_o483` and the incomplete depletion of `id_j5qx`.

## Action

### Code

```python
import libsbml

# Start from the original model again
sbml_doc = libsbml.readSBMLFromString(input_sbml_string)
model = sbml_doc.getModel()

# Remove any existing reactions
while model.getNumReactions() > 0:
    model.removeReaction(0)

# Reaction 1: id_j5qx -> id_o483 (first-order)
reaction1 = model.createReaction()
reaction1.setId("j5qx_to_o483")
reaction1.setReversible(False)
reaction1.setFast(False)

reactant1 = reaction1.createReactant()
reactant1.setSpecies("id_j5qx")
reactant1.setStoichiometry(1.0)
reactant1.setConstant(False)

product1 = reaction1.createProduct()
product1.setSpecies("id_o483")
product1.setStoichiometry(1.0)
product1.setConstant(False)

klaw1 = reaction1.createKineticLaw()
param_k1 = klaw1.createParameter()
param_k1.setId("k1")
param_k1.setValue(0.02)
klaw1.setFormula("k1 * id_j5qx")

# Reaction 2: id_o483 + id_lti5 -> 2 id_lti5 (autocatalytic)
reaction2 = model.createReaction()
reaction2.setId("o483_lti5_autocat")
reaction2.setReversible(False)
reaction2.setFast(False)

reactant2a = reaction2.createReactant()
reactant2a.setSpecies("id_o483")
reactant2a.setStoichiometry(1.0)
reactant2a.setConstant(False)

reactant2b = reaction2.createReactant()
reactant2b.setSpecies("id_lti5")
reactant2b.setStoichiometry(1.0)
reactant2b.setConstant(False)

product2 = reaction2.createProduct()
product2.setSpecies("id_lti5")
product2.setStoichiometry(2.0)
product2.setConstant(False)

klaw2 = reaction2.createKineticLaw()
param_k2 = klaw2.createParameter()
param_k2.setId("k2")
param_k2.setValue(0.6)
klaw2.setFormula("k2 * id_o483 * id_lti5")

# Write the updated SBML
writer = libsbml.SBMLWriter()
final_sbml = writer.writeSBMLToString(sbml_doc)

# Simulate and compare to experiment
sim_df = simulate(final_sbml)
exp_df = experiment_history['iteration_0']

print("Simulated (final):\n", sim_df.iloc[-1])
print("Experimental (final):\n", exp_df.iloc[-1])
```
# Observation

## Code Stdout
Simulated (final):
 id_j5qx      0.000496
id_o483      0.000020
id_lti5      0.849484
Time       300.000000
Name: 2796, dtype: float64
Experimental (final):
 id_j5qx      0.106380
id_o483      0.454252
id_lti5      0.945029
Time       300.000000
Name: 2796, dtype: float64

## Reminder

 ### Remaining Iterations for Interactions:
- Iterations: 4/20



### Available Global Variables
- `input_sbml_string`: Original incomplete model
- `experiment_history`: Results from all previous experiments (iteration_0, iteration_3, iteration_4, iteration_5, iteration_6, iteration_7, iteration_8, iteration_9, iteration_10, iteration_11)
- `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 17
## Thoughts

The new model with:
- `id_j5qx → id_o483` (first-order, k1=0.02)
- `id_o483 + id_lti5 → 2 id_lti5` (autocatalytic, k2=0.6)

produces:
- `id_j5qx` nearly depleted (0.0005 vs. 0.106 in experiment)
- `id_o483` almost zero (0.00002 vs. 0.45 in experiment)
- `id_lti5` increases but not as much as in experiment (0.85 vs. 0.95)

This suggests the autocatalytic reaction is too strong or the conversion from `id_j5qx` to `id_o483` is too fast, causing `id_o483` to be rapidly consumed. In the experiment, a significant amount of `id_o483` remains at steady state, indicating a balance between its production and consumption.

A possible solution:
- Lower the autocatalytic rate (k2).
- Lower the conversion rate (k1).
- Add a reversible step: `id_lti5 → id_o483` to allow for a steady-state pool of `id_o483`.

I will try adding the reverse reaction and adjust the rates to better match the experimental steady states.

## Action

### Code

```python
import libsbml

# Start from the original model again
sbml_doc = libsbml.readSBMLFromString(input_sbml_string)
model = sbml_doc.getModel()

# Remove any existing reactions
while model.getNumReactions() > 0:
    model.removeReaction(0)

# Reaction 1: id_j5qx -> id_o483 (first-order)
reaction1 = model.createReaction()
reaction1.setId("j5qx_to_o483")
reaction1.setReversible(False)
reaction1.setFast(False)

reactant1 = reaction1.createReactant()
reactant1.setSpecies("id_j5qx")
reactant1.setStoichiometry(1.0)
reactant1.setConstant(False)

product1 = reaction1.createProduct()
product1.setSpecies("id_o483")
product1.setStoichiometry(1.0)
product1.setConstant(False)

klaw1 = reaction1.createKineticLaw()
param_k1 = klaw1.createParameter()
param_k1.setId("k1")
param_k1.setValue(0.01)
klaw1.setFormula("k1 * id_j5qx")

# Reaction 2: id_o483 + id_lti5 -> 2 id_lti5 (autocatalytic)
reaction2 = model.createReaction()
reaction2.setId("o483_lti5_autocat")
reaction2.setReversible(False)
reaction2.setFast(False)

reactant2a = reaction2.createReactant()
reactant2a.setSpecies("id_o483")
reactant2a.setStoichiometry(1.0)
reactant2a.setConstant(False)

reactant2b = reaction2.createReactant()
reactant2b.setSpecies("id_lti5")
reactant2b.setStoichiometry(1.0)
reactant2b.setConstant(False)

product2 = reaction2.createProduct()
product2.setSpecies("id_lti5")
product2.setStoichiometry(2.0)
product2.setConstant(False)

klaw2 = reaction2.createKineticLaw()
param_k2 = klaw2.createParameter()
param_k2.setId("k2")
param_k2.setValue(0.2)
klaw2.setFormula("k2 * id_o483 * id_lti5")

# Reaction 3: id_lti5 -> id_o483 (first-order, reversible step)
reaction3 = model.createReaction()
reaction3.setId("lti5_to_o483")
reaction3.setReversible(False)
reaction3.setFast(False)

reactant3 = reaction3.createReactant()
reactant3.setSpecies("id_lti5")
reactant3.setStoichiometry(1.0)
reactant3.setConstant(False)

product3 = reaction3.createProduct()
product3.setSpecies("id_o483")
product3.setStoichiometry(1.0)
product3.setConstant(False)

klaw3 = reaction3.createKineticLaw()
param_k3 = klaw3.createParameter()
param_k3.setId("k3")
param_k3.setValue(0.01)
klaw3.setFormula("k3 * id_lti5")

# Write the updated SBML
writer = libsbml.SBMLWriter()
final_sbml = writer.writeSBMLToString(sbml_doc)

# Simulate and compare to experiment
sim_df = simulate(final_sbml)
exp_df = experiment_history['iteration_0']

print("Simulated (final):\n", sim_df.iloc[-1])
print("Experimental (final):\n", exp_df.iloc[-1])
```
# Observation

## Code Stdout
Simulated (final):
 id_j5qx      0.009957
id_o483      0.050674
id_lti5      0.789369
Time       300.000000
Name: 2796, dtype: float64
Experimental (final):
 id_j5qx      0.106380
id_o483      0.454252
id_lti5      0.945029
Time       300.000000
Name: 2796, dtype: float64

## Reminder

 ### Remaining Iterations for Interactions:
- Iterations: 3/20



### Available Global Variables
- `input_sbml_string`: Original incomplete model
- `experiment_history`: Results from all previous experiments (iteration_0, iteration_3, iteration_4, iteration_5, iteration_6, iteration_7, iteration_8, iteration_9, iteration_10, iteration_11)
- `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 18
## Thoughts

The model now produces:
- `id_j5qx` final: 0.01 (vs. 0.11 in experiment)
- `id_o483` final: 0.05 (vs. 0.45 in experiment)
- `id_lti5` final: 0.79 (vs. 0.95 in experiment)

The trends are correct, but the steady-state values for `id_o483` and `id_lti5` are still too low, and `id_j5qx` is too depleted. This suggests:
- The conversion from `id_j5qx` to `id_o483` is still too fast, or
- The autocatalytic conversion of `id_o483` to `id_lti5` is too strong, or
- The reverse reaction from `id_lti5` to `id_o483` is too weak.

To better match the experimental steady states, I will:
- Decrease the autocatalytic rate (k2) further.
- Decrease the conversion rate from `id_j5qx` to `id_o483` (k1).
- Increase the reverse rate from `id_lti5` to `id_o483` (k3).

## Action

### Code

```python
import libsbml

# Start from the original model again
sbml_doc = libsbml.readSBMLFromString(input_sbml_string)
model = sbml_doc.getModel()

# Remove any existing reactions
while model.getNumReactions() > 0:
    model.removeReaction(0)

# Reaction 1: id_j5qx -> id_o483 (first-order)
reaction1 = model.createReaction()
reaction1.setId("j5qx_to_o483")
reaction1.setReversible(False)
reaction1.setFast(False)

reactant1 = reaction1.createReactant()
reactant1.setSpecies("id_j5qx")
reactant1.setStoichiometry(1.0)
reactant1.setConstant(False)

product1 = reaction1.createProduct()
product1.setSpecies("id_o483")
product1.setStoichiometry(1.0)
product1.setConstant(False)

klaw1 = reaction1.createKineticLaw()
param_k1 = klaw1.createParameter()
param_k1.setId("k1")
param_k1.setValue(0.005)
klaw1.setFormula("k1 * id_j5qx")

# Reaction 2: id_o483 + id_lti5 -> 2 id_lti5 (autocatalytic)
reaction2 = model.createReaction()
reaction2.setId("o483_lti5_autocat")
reaction2.setReversible(False)
reaction2.setFast(False)

reactant2a = reaction2.createReactant()
reactant2a.setSpecies("id_o483")
reactant2a.setStoichiometry(1.0)
reactant2a.setConstant(False)

reactant2b = reaction2.createReactant()
reactant2b.setSpecies("id_lti5")
reactant2b.setStoichiometry(1.0)
reactant2b.setConstant(False)

product2 = reaction2.createProduct()
product2.setSpecies("id_lti5")
product2.setStoichiometry(2.0)
product2.setConstant(False)

klaw2 = reaction2.createKineticLaw()
param_k2 = klaw2.createParameter()
param_k2.setId("k2")
param_k2.setValue(0.05)
klaw2.setFormula("k2 * id_o483 * id_lti5")

# Reaction 3: id_lti5 -> id_o483 (first-order, reversible step)
reaction3 = model.createReaction()
reaction3.setId("lti5_to_o483")
reaction3.setReversible(False)
reaction3.setFast(False)

reactant3 = reaction3.createReactant()
reactant3.setSpecies("id_lti5")
reactant3.setStoichiometry(1.0)
reactant3.setConstant(False)

product3 = reaction3.createProduct()
product3.setSpecies("id_o483")
product3.setStoichiometry(1.0)
product3.setConstant(False)

klaw3 = reaction3.createKineticLaw()
param_k3 = klaw3.createParameter()
param_k3.setId("k3")
param_k3.setValue(0.02)
klaw3.setFormula("k3 * id_lti5")

# Write the updated SBML
writer = libsbml.SBMLWriter()
final_sbml = writer.writeSBMLToString(sbml_doc)

# Simulate and compare to experiment
sim_df = simulate(final_sbml)
exp_df = experiment_history['iteration_0']

print("Simulated (final):\n", sim_df.iloc[-1])
print("Experimental (final):\n", exp_df.iloc[-1])
```
# Observation

## Code Stdout
Simulated (final):
 id_j5qx      0.044626
id_o483      0.414998
id_lti5      0.390376
Time       300.000000
Name: 2796, dtype: float64
Experimental (final):
 id_j5qx      0.106380
id_o483      0.454252
id_lti5      0.945029
Time       300.000000
Name: 2796, dtype: float64

## Reminder

 ### Remaining Iterations for Interactions:
- Iterations: 2/20



### Available Global Variables
- `input_sbml_string`: Original incomplete model
- `experiment_history`: Results from all previous experiments (iteration_0, iteration_3, iteration_4, iteration_5, iteration_6, iteration_7, iteration_8, iteration_9, iteration_10, iteration_11)
- `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 19
## Thoughts

The model now produces:
- `id_j5qx` final: 0.045 (vs. 0.106 in experiment)
- `id_o483` final: 0.415 (vs. 0.454 in experiment)
- `id_lti5` final: 0.39 (vs. 0.95 in experiment)

The fit for `id_o483` is quite good, and `id_j5qx` is closer, but `id_lti5` is still much lower than in the experiment. This suggests that the conversion from `id_o483` to `id_lti5` is still too weak, or the reverse reaction is too strong, or there is an additional source of `id_lti5` (e.g., a basal production or a direct conversion from `id_j5qx`).

Given the experimental data, a direct conversion from `id_j5qx` to `id_lti5` (in addition to the autocatalytic pathway) could help increase the steady-state level of `id_lti5`. I will add this reaction and slightly increase the autocatalytic rate.

## Action

### Code

```python
import libsbml

# Start from the original model again
sbml_doc = libsbml.readSBMLFromString(input_sbml_string)
model = sbml_doc.getModel()

# Remove any existing reactions
while model.getNumReactions() > 0:
    model.removeReaction(0)

# Reaction 1: id_j5qx -> id_o483 (first-order)
reaction1 = model.createReaction()
reaction1.setId("j5qx_to_o483")
reaction1.setReversible(False)
reaction1.setFast(False)

reactant1 = reaction1.createReactant()
reactant1.setSpecies("id_j5qx")
reactant1.setStoichiometry(1.0)
reactant1.setConstant(False)

product1 = reaction1.createProduct()
product1.setSpecies("id_o483")
product1.setStoichiometry(1.0)
product1.setConstant(False)

klaw1 = reaction1.createKineticLaw()
param_k1 = klaw1.createParameter()
param_k1.setId("k1")
param_k1.setValue(0.005)
klaw1.setFormula("k1 * id_j5qx")

# Reaction 2: id_o483 + id_lti5 -> 2 id_lti5 (autocatalytic)
reaction2 = model.createReaction()
reaction2.setId("o483_lti5_autocat")
reaction2.setReversible(False)
reaction2.setFast(False)

reactant2a = reaction2.createReactant()
reactant2a.setSpecies("id_o483")
reactant2a.setStoichiometry(1.0)
reactant2a.setConstant(False)

reactant2b = reaction2.createReactant()
reactant2b.setSpecies("id_lti5")
reactant2b.setStoichiometry(1.0)
reactant2b.setConstant(False)

product2 = reaction2.createProduct()
product2.setSpecies("id_lti5")
product2.setStoichiometry(2.0)
product2.setConstant(False)

klaw2 = reaction2.createKineticLaw()
param_k2 = klaw2.createParameter()
param_k2.setId("k2")
param_k2.setValue(0.08)
klaw2.setFormula("k2 * id_o483 * id_lti5")

# Reaction 3: id_lti5 -> id_o483 (first-order, reversible step)
reaction3 = model.createReaction()
reaction3.setId("lti5_to_o483")
reaction3.setReversible(False)
reaction3.setFast(False)

reactant3 = reaction3.createReactant()
reactant3.setSpecies("id_lti5")
reactant3.setStoichiometry(1.0)
reactant3.setConstant(False)

product3 = reaction3.createProduct()
product3.setSpecies("id_o483")
product3.setStoichiometry(1.0)
product3.setConstant(False)

klaw3 = reaction3.createKineticLaw()
param_k3 = klaw3.createParameter()
param_k3.setId("k3")
param_k3.setValue(0.02)
klaw3.setFormula("k3 * id_lti5")

# Reaction 4: id_j5qx -> id_lti5 (direct conversion)
reaction4 = model.createReaction()
reaction4.setId("j5qx_to_lti5")
reaction4.setReversible(False)
reaction4.setFast(False)

reactant4 = reaction4.createReactant()
reactant4.setSpecies("id_j5qx")
reactant4.setStoichiometry(1.0)
reactant4.setConstant(False)

product4 = reaction4.createProduct()
product4.setSpecies("id_lti5")
product4.setStoichiometry(1.0)
product4.setConstant(False)

klaw4 = reaction4.createKineticLaw()
param_k4 = klaw4.createParameter()
param_k4.setId("k4")
param_k4.setValue(0.004)
klaw4.setFormula("k4 * id_j5qx")

# Write the updated SBML
writer = libsbml.SBMLWriter()
final_sbml = writer.writeSBMLToString(sbml_doc)

# Simulate and compare to experiment
sim_df = simulate(final_sbml)
exp_df = experiment_history['iteration_0']

print("Simulated (final):\n", sim_df.iloc[-1])
print("Experimental (final):\n", exp_df.iloc[-1])
```
# Observation

## Code Stdout
Simulated (final):
 id_j5qx      0.013441
id_o483      0.251797
id_lti5      0.584762
Time       300.000000
Name: 2796, dtype: float64
Experimental (final):
 id_j5qx      0.106380
id_o483      0.454252
id_lti5      0.945029
Time       300.000000
Name: 2796, dtype: float64

## Reminder

 ### Remaining Iterations for Interactions:
- Iterations: 1/20



### Available Global Variables
- `input_sbml_string`: Original incomplete model
- `experiment_history`: Results from all previous experiments (iteration_0, iteration_3, iteration_4, iteration_5, iteration_6, iteration_7, iteration_8, iteration_9, iteration_10, iteration_11)
- `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 20
## Thoughts

The model now produces:
- `id_j5qx` final: 0.013 (vs. 0.106 in experiment)
- `id_o483` final: 0.252 (vs. 0.454 in experiment)
- `id_lti5` final: 0.585 (vs. 0.945 in experiment)

The trends are correct, but the steady-state values are still lower than the experimental values, especially for `id_lti5`. This suggests that the conversion rates from `id_j5qx` to both `id_o483` and `id_lti5` may still be too high, or the reverse reaction from `id_lti5` to `id_o483` is too strong. However, the qualitative behavior is captured: all three species persist, and the system does not deplete any species to zero.

Given that this is the last iteration, I will submit the current model as the best mechanistic hypothesis, as it captures the essential features of the system: 
- Autocatalytic conversion,
- Reversible interconversion,
- Direct conversion from substrate to product.

## Action

### Submit

```python
import libsbml
final_sbml = final_sbml
```
# Observation


            ## Important Notice 
            You have used up all interactions. Please put your final model as a string variable called `final_sbml` in your code.
            ```python
            final_sbml = ....
            ```

            It is recommended using libsbml to modify `input_sbml_string` rather than write the entire xml on your own.
## Parsing error
ERROR: stat: path should be string, bytes, os.PathLike or integer, not NoneType
# Iteration 21
## Thoughts

I have reached the maximum number of iterations. I need to ensure that my final model is submitted as a string variable called `final_sbml`. I will reconstruct the model using libsbml as in the last code block and assign it to `final_sbml`.

## Action

### Submit

```python
import libsbml

# Start from the original model again
sbml_doc = libsbml.readSBMLFromString(input_sbml_string)
model = sbml_doc.getModel()

# Remove any existing reactions
while model.getNumReactions() > 0:
    model.removeReaction(0)

# Reaction 1: id_j5qx -> id_o483 (first-order)
reaction1 = model.createReaction()
reaction1.setId("j5qx_to_o483")
reaction1.setReversible(False)
reaction1.setFast(False)

reactant1 = reaction1.createReactant()
reactant1.setSpecies("id_j5qx")
reactant1.setStoichiometry(1.0)
reactant1.setConstant(False)

product1 = reaction1.createProduct()
product1.setSpecies("id_o483")
product1.setStoichiometry(1.0)
product1.setConstant(False)

klaw1 = reaction1.createKineticLaw()
param_k1 = klaw1.createParameter()
param_k1.setId("k1")
param_k1.setValue(0.005)
klaw1.setFormula("k1 * id_j5qx")

# Reaction 2: id_o483 + id_lti5 -> 2 id_lti5 (autocatalytic)
reaction2 = model.createReaction()
reaction2.setId("o483_lti5_autocat")
reaction2.setReversible(False)
reaction2.setFast(False)

reactant2a = reaction2.createReactant()
reactant2a.setSpecies("id_o483")
reactant2a.setStoichiometry(1.0)
reactant2a.setConstant(False)

reactant2b = reaction2.createReactant()
reactant2b.setSpecies("id_lti5")
reactant2b.setStoichiometry(1.0)
reactant2b.setConstant(False)

product2 = reaction2.createProduct()
product2.setSpecies("id_lti5")
product2.setStoichiometry(2.0)
product2.setConstant(False)

klaw2 = reaction2.createKineticLaw()
param_k2 = klaw2.createParameter()
param_k2.setId("k2")
param_k2.setValue(0.08)
klaw2.setFormula("k2 * id_o483 * id_lti5")

# Reaction 3: id_lti5 -> id_o483 (first-order, reversible step)
reaction3 = model.createReaction()
reaction3.setId("lti5_to_o483")
reaction3.setReversible(False)
reaction3.setFast(False)

reactant3 = reaction3.createReactant()
reactant3.setSpecies("id_lti5")
reactant3.setStoichiometry(1.0)
reactant3.setConstant(False)

product3 = reaction3.createProduct()
product3.setSpecies("id_o483")
product3.setStoichiometry(1.0)
product3.setConstant(False)

klaw3 = reaction3.createKineticLaw()
param_k3 = klaw3.createParameter()
param_k3.setId("k3")
param_k3.setValue(0.02)
klaw3.setFormula("k3 * id_lti5")

# Reaction 4: id_j5qx -> id_lti5 (direct conversion)
reaction4 = model.createReaction()
reaction4.setId("j5qx_to_lti5")
reaction4.setReversible(False)
reaction4.setFast(False)

reactant4 = reaction4.createReactant()
reactant4.setSpecies("id_j5qx")
reactant4.setStoichiometry(1.0)
reactant4.setConstant(False)

product4 = reaction4.createProduct()
product4.setSpecies("id_lti5")
product4.setStoichiometry(1.0)
product4.setConstant(False)

klaw4 = reaction4.createKineticLaw()
param_k4 = klaw4.createParameter()
param_k4.setId("k4")
param_k4.setValue(0.004)
klaw4.setFormula("k4 * id_j5qx")

# Write the updated SBML
writer = libsbml.SBMLWriter()
final_sbml = writer.writeSBMLToString(sbml_doc)
```
