
                # 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_3mz6">
    <listOfUnitDefinitions>
      <unitDefinition id="id_j587">
        <listOfUnits>
          <unit kind="second" multiplier="60"/>
        </listOfUnits>
      </unitDefinition>
      <unitDefinition id="id_l8fv">
        <listOfUnits>
          <unit kind="mole" scale="-9"/>
        </listOfUnits>
      </unitDefinition>
    </listOfUnitDefinitions>
    <listOfCompartments>
      <compartment id="id_yjjr" size="1"/>
    </listOfCompartments>
    <listOfSpecies>
      <species id="id_l0rt" compartment="id_yjjr" initialConcentration="170"/>
      <species id="id_v6ig" compartment="id_yjjr" initialConcentration="1"/>
      <species id="id_4k49" compartment="id_yjjr" initialConcentration="0"/>
      <species id="id_ljzz" compartment="id_yjjr" initialConcentration="0"/>
      <species id="id_btma" compartment="id_yjjr" initialConcentration="0"/>
      <species id="id_we08" compartment="id_yjjr" initialConcentration="0"/>
      <species id="id_rzml" compartment="id_yjjr" initialConcentration="2.4"/>
      <species id="id_pq2o" compartment="id_yjjr" initialConcentration="0"/>
      <species id="id_nfl6" compartment="id_yjjr" initialConcentration="0"/>
    </listOfSpecies>
  </model>
</sbml>


                ## Max iterations
                20

                Format your response according to the instructions in the system message.
                
## Thoughts
The initial incomplete SBML model contains 9 species with their initial concentrations but no reactions. To understand the biological system and discover the missing reactions, I need to observe the system's behavior over time under default conditions. This will provide time-series data for all species, which I can analyze to infer possible reactions and interactions.

The first step is to run an observation experiment with the default initial concentrations to gather baseline dynamic data.

## 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: 10001 rows × 10 columns. 

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

Time   id_l0rt  id_v6ig  id_4k49  id_ljzz  id_btma  id_we08  id_rzml  id_pq2o  id_nfl6
0  1.70e+02 1.00e+00        0        0        0        0 2.40e+00        0        0
...
3.75e+01  1.24e+02 1.30e-03 9.55e-01 1.38e+00 6.59e-04 7.55e-04 1.97e-02 4.20e-02 4.40e+01
...
7.50e+01  1.13e+02 1.36e-03 9.25e-01 1.39e+00 6.34e-04 8.49e-04 1.58e-02 7.24e-02 5.42e+01
...
1.12e+02  1.04e+02 1.43e-03 8.93e-01 1.39e+00 6.08e-04 9.49e-04 1.32e-02 1.04e-01 6.40e+01
...
1.50e+02  9.43e+01 1.50e-03 8.59e-01 1.39e+00 5.81e-04 1.05e-03 1.14e-02 1.38e-01 7.34e+01
...
1.87e+02  8.53e+01 1.58e-03 8.23e-01 1.39e+00 5.54e-04 1.17e-03 9.97e-03 1.73e-01 8.23e+01
...
2.25e+02  7.68e+01 1.66e-03 7.86e-01 1.39e+00 5.24e-04 1.29e-03 8.89e-03 2.11e-01 9.08e+01
...
2.62e+02  6.88e+01 1.75e-03 7.46e-01 1.40e+00 4.94e-04 1.41e-03 8.02e-03 2.50e-01 9.88e+01
...
3.00e+02  6.13e+01 1.84e-03 7.05e-01 1.40e+00 4.62e-04 1.54e-03 7.31e-03 2.91e-01 1.06e+02
...
3.37e+02  5.43e+01 1.92e-03 6.62e-01 1.40e+00 4.29e-04 1.67e-03 6.72e-03 3.34e-01 1.13e+02
...
3.75e+02  4.78e+01 2.01e-03 6.16e-01 1.40e+00 3.94e-04 1.79e-03 6.22e-03 3.80e-01 1.20e+02
...
4.12e+02  4.19e+01 2.08e-03 5.69e-01 1.40e+00 3.58e-04 1.91e-03 5.79e-03 4.27e-01 1.26e+02
...
4.50e+02  3.65e+01 2.15e-03 5.20e-01 1.40e+00 3.22e-04 2.02e-03 5.41e-03 4.76e-01 1.31e+02
...
4.87e+02  3.17e+01 2.19e-03 4.70e-01 1.40e+00 2.85e-04 2.10e-03 5.09e-03 5.26e-01 1.36e+02
...
5.25e+02  2.75e+01 2.20e-03 4.19e-01 1.40e+00 2.49e-04 2.15e-03 4.80e-03 5.77e-01 1.40e+02
...
5.62e+02  2.39e+01 2.17e-03 3.68e-01 1.40e+00 2.13e-04 2.15e-03 4.55e-03 6.27e-01 1.44e+02
...
6.00e+02  2.08e+01 2.10e-03 3.19e-01 1.40e+00 1.80e-04 2.11e-03 4.33e-03 6.77e-01 1.47e+02
...
6.37e+02  1.82e+01 1.99e-03 2.72e-01 1.40e+00 1.49e-04 2.02e-03 4.14e-03 7.24e-01 1.49e+02
...
6.75e+02  1.61e+01 1.83e-03 2.28e-01 1.40e+00 1.21e-04 1.88e-03 3.97e-03 7.68e-01 1.51e+02
...
7.12e+02  1.44e+01 1.65e-03 1.88e-01 1.40e+00 9.72e-05 1.69e-03 3.83e-03 8.08e-01 1.53e+02
...
7.50e+02  1.30e+01 1.44e-03 1.53e-01 1.40e+00 7.69e-05 1.49e-03 3.72e-03 8.44e-01 1.55e+02
...
7.87e+02  1.20e+01 1.23e-03 1.23e-01 1.40e+00 6.01e-05 1.27e-03 3.62e-03 8.75e-01 1.56e+02
...
8.25e+02  1.11e+01 1.02e-03 9.71e-02 1.40e+00 4.66e-05 1.07e-03 3.54e-03 9.01e-01 1.56e+02
...
8.62e+02  1.05e+01 8.34e-04 7.62e-02 1.40e+00 3.58e-05 8.72e-04 3.47e-03 9.22e-01 1.57e+02
...
9.00e+02  9.97e+00 6.70e-04 5.92e-02 1.40e+00 2.74e-05 7.02e-04 3.42e-03 9.39e-01 1.58e+02
...
9.37e+02  9.59e+00 5.30e-04 4.57e-02 1.40e+00 2.09e-05 5.57e-04 3.38e-03 9.53e-01 1.58e+02
...
9.75e+02  9.31e+00 4.15e-04 3.51e-02 1.40e+00 1.59e-05 4.37e-04 3.35e-03 9.64e-01 1.58e+02
...
1.01e+03  9.09e+00 3.22e-04 2.68e-02 1.40e+00 1.20e-05 3.39e-04 3.33e-03 9.72e-01 1.59e+02
...
1.05e+03  8.92e+00 2.49e-04 2.04e-02 1.40e+00 9.11e-06 2.62e-04 3.31e-03 9.79e-01 1.59e+02
...
1.09e+03  8.80e+00 1.91e-04 1.55e-02 1.40e+00 6.88e-06 2.01e-04 3.29e-03 9.84e-01 1.59e+02
...
1.12e+03  8.70e+00 1.45e-04 1.18e-02 1.40e+00 5.20e-06 1.53e-04 3.28e-03 9.88e-01 1.59e+02
...
1.16e+03  8.63e+00 1.11e-04 8.91e-03 1.40e+00 3.92e-06 1.17e-04 3.28e-03 9.91e-01 1.59e+02
...
1.20e+03  8.58e+00 8.40e-05 6.74e-03 1.40e+00 2.96e-06 8.85e-05 3.27e-03 9.93e-01 1.59e+02
...
1.24e+03  8.54e+00 6.36e-05 5.09e-03 1.40e+00 2.23e-06 6.71e-05 3.26e-03 9.95e-01 1.59e+02
...
1.27e+03  8.51e+00 4.82e-05 3.84e-03 1.40e+00 1.68e-06 5.08e-05 3.26e-03 9.96e-01 1.59e+02
...
1.31e+03  8.48e+00 3.64e-05 2.90e-03 1.40e+00 1.27e-06 3.84e-05 3.26e-03 9.97e-01 1.59e+02
...
1.35e+03  8.47e+00 2.75e-05 2.19e-03 1.40e+00 9.55e-07 2.90e-05 3.26e-03 9.98e-01 1.59e+02
...
1.39e+03  8.45e+00 2.07e-05 1.65e-03 1.40e+00 7.20e-07 2.19e-05 3.25e-03 9.98e-01 1.59e+02
...
1.42e+03  8.44e+00 1.56e-05 1.24e-03 1.40e+00 5.42e-07 1.65e-05 3.25e-03 9.99e-01 1.59e+02
...
1.46e+03  8.44e+00 1.18e-05 9.36e-04 1.40e+00 4.08e-07 1.24e-05 3.25e-03 9.99e-01 1.59e+02
...
1.50e+03  8.43e+00 8.89e-06 7.05e-04 1.40e+00 3.08e-07 9.37e-06 3.25e-03 9.99e-01 1.59e+02
...
1.54e+03  8.43e+00 6.70e-06 5.31e-04 1.40e+00 2.32e-07 7.06e-06 3.25e-03 9.99e-01 1.59e+02
...
1.57e+03  8.42e+00 5.05e-06 4.00e-04 1.40e+00 1.75e-07 5.32e-06 3.25e-03 1.00e+00 1.59e+02
...
1.61e+03  8.42e+00 3.80e-06 3.01e-04 1.40e+00 1.32e-07 4.01e-06 3.25e-03 1.00e+00 1.59e+02
...
1.65e+03  8.42e+00 2.87e-06 2.27e-04 1.40e+00 9.91e-08 3.02e-06 3.25e-03 1.00e+00 1.59e+02
...
1.69e+03  8.42e+00 2.16e-06 1.71e-04 1.40e+00 7.46e-08 2.28e-06 3.25e-03 1.00e+00 1.59e+02
...
1.72e+03  8.42e+00 1.63e-06 1.29e-04 1.40e+00 5.62e-08 1.72e-06 3.25e-03 1.00e+00 1.59e+02
...
1.76e+03  8.42e+00 1.23e-06 9.71e-05 1.40e+00 4.23e-08 1.29e-06 3.25e-03 1.00e+00 1.59e+02
...
1.80e+03  8.42e+00 9.23e-07 7.31e-05 1.40e+00 3.19e-08 9.73e-07 3.25e-03 1.00e+00 1.59e+02
...
1.84e+03  8.42e+00 6.91e-07 5.48e-05 1.40e+00 2.39e-08 7.29e-07 3.25e-03 1.00e+00 1.59e+02

## 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 observation data shows dynamic changes in species concentrations over time. Notably:
- id_l0rt decreases steadily from 170 to about 8.4.
- id_v6ig decreases from 1 to near zero.
- id_4k49, id_ljzz, id_btma, id_we08, id_pq2o, and id_nfl6 increase from zero to various levels.
- id_rzml decreases from 2.4 to about 0.00325.

This suggests a network of reactions where id_l0rt and id_v6ig are consumed, and other species are produced. The species id_ljzz remains almost constant after an initial increase, indicating it might be a product or intermediate with slow turnover.

To infer the missing reactions, I want to understand the relationships between species. A useful next step is to perturb the initial concentration of one key species and observe the system's response. I will perturb id_l0rt (initially 170) to a lower value (e.g., 50) and observe how the system dynamics change. This will help identify which species depend on id_l0rt and clarify reaction pathways.

## Action

### Experiment
```json
{
  "action": "change_initial_concentration",
  "meta_data": {
    "id_l0rt": 50
  }
}
```
# Observation

## Experiment Result
Experiment ID: iteration_1
Your requested experiment has finished. The details results are saved as a dataframe in experiment_history[iteration_1]

Total size: 10001 rows × 10 columns. 

Below shows a subset of the table for your convenience. You should access experiment_history[iteration_1] for the full data

Time   id_l0rt  id_v6ig  id_4k49  id_ljzz  id_btma  id_we08  id_rzml  id_pq2o  id_nfl6
0  5.00e+01 1.00e+00        0        0        0        0 2.40e+00        0        0
...
3.75e+01  1.70e+01 6.23e-03 8.05e-01 1.38e+00 4.36e-04 1.50e-03 2.64e-02 1.87e-01 3.06e+01
...
7.50e+01  1.11e+01 6.83e-03 6.54e-01 1.39e+00 3.12e-04 1.80e-03 2.07e-02 3.37e-01 3.65e+01
...
1.12e+02  7.14e+00 6.82e-03 4.96e-01 1.39e+00 2.00e-04 1.91e-03 1.72e-02 4.95e-01 4.05e+01
...
1.50e+02  4.70e+00 5.96e-03 3.49e-01 1.39e+00 1.15e-04 1.73e-03 1.50e-02 6.44e-01 4.29e+01
...
1.87e+02  3.34e+00 4.55e-03 2.29e-01 1.39e+00 6.24e-05 1.35e-03 1.35e-02 7.65e-01 4.43e+01
...
2.25e+02  2.61e+00 3.12e-03 1.43e-01 1.39e+00 3.34e-05 9.34e-04 1.26e-02 8.53e-01 4.50e+01
...
2.62e+02  2.22e+00 2.00e-03 8.68e-02 1.39e+00 1.82e-05 6.00e-04 1.20e-02 9.11e-01 4.54e+01
...
3.00e+02  2.00e+00 1.22e-03 5.15e-02 1.39e+00 1.00e-05 3.69e-04 1.17e-02 9.47e-01 4.56e+01
...
3.37e+02  1.88e+00 7.30e-04 3.03e-02 1.39e+00 5.64e-06 2.21e-04 1.15e-02 9.69e-01 4.57e+01
...
3.75e+02  1.81e+00 4.30e-04 1.76e-02 1.39e+00 3.20e-06 1.30e-04 1.14e-02 9.82e-01 4.58e+01
...
4.12e+02  1.77e+00 2.51e-04 1.02e-02 1.39e+00 1.83e-06 7.61e-05 1.13e-02 9.89e-01 4.58e+01
...
4.50e+02  1.75e+00 1.46e-04 5.94e-03 1.39e+00 1.05e-06 4.42e-05 1.13e-02 9.94e-01 4.59e+01
...
4.87e+02  1.74e+00 8.47e-05 3.43e-03 1.39e+00 6.05e-07 2.56e-05 1.12e-02 9.96e-01 4.59e+01
...
5.25e+02  1.73e+00 4.90e-05 1.99e-03 1.39e+00 3.49e-07 1.48e-05 1.12e-02 9.98e-01 4.59e+01
...
5.62e+02  1.73e+00 2.84e-05 1.15e-03 1.39e+00 2.01e-07 8.59e-06 1.12e-02 9.99e-01 4.59e+01
...
6.00e+02  1.73e+00 1.64e-05 6.63e-04 1.39e+00 1.16e-07 4.96e-06 1.12e-02 9.99e-01 4.59e+01
...
6.37e+02  1.72e+00 9.47e-06 3.83e-04 1.39e+00 6.71e-08 2.87e-06 1.12e-02 1.00e+00 4.59e+01
...
6.75e+02  1.72e+00 5.47e-06 2.21e-04 1.39e+00 3.87e-08 1.66e-06 1.12e-02 1.00e+00 4.59e+01
...
7.12e+02  1.72e+00 3.16e-06 1.28e-04 1.39e+00 2.24e-08 9.58e-07 1.12e-02 1.00e+00 4.59e+01
...
7.50e+02  1.72e+00 1.83e-06 7.39e-05 1.39e+00 1.29e-08 5.53e-07 1.12e-02 1.00e+00 4.59e+01
...
7.87e+02  1.72e+00 1.05e-06 4.27e-05 1.39e+00 7.46e-09 3.20e-07 1.12e-02 1.00e+00 4.59e+01
...
8.25e+02  1.72e+00 6.09e-07 2.46e-05 1.39e+00 4.31e-09 1.85e-07 1.12e-02 1.00e+00 4.59e+01
...
8.62e+02  1.72e+00 3.52e-07 1.42e-05 1.39e+00 2.49e-09 1.07e-07 1.12e-02 1.00e+00 4.59e+01
...
9.00e+02  1.72e+00 2.03e-07 8.22e-06 1.39e+00 1.44e-09 6.16e-08 1.12e-02 1.00e+00 4.59e+01
...
9.37e+02  1.72e+00 1.17e-07 4.75e-06 1.39e+00 8.31e-10 3.56e-08 1.12e-02 1.00e+00 4.59e+01
...
9.75e+02  1.72e+00 6.78e-08 2.74e-06 1.39e+00 4.80e-10 2.05e-08 1.12e-02 1.00e+00 4.59e+01
...
1.01e+03  1.72e+00 3.92e-08 1.58e-06 1.39e+00 2.77e-10 1.19e-08 1.12e-02 1.00e+00 4.59e+01
...
1.05e+03  1.72e+00 2.26e-08 9.15e-07 1.39e+00 1.60e-10 6.85e-09 1.12e-02 1.00e+00 4.59e+01
...
1.09e+03  1.72e+00 1.31e-08 5.29e-07 1.39e+00 9.25e-11 3.96e-09 1.12e-02 1.00e+00 4.59e+01
...
1.12e+03  1.72e+00 7.55e-09 3.05e-07 1.39e+00 5.34e-11 2.29e-09 1.12e-02 1.00e+00 4.59e+01
...
1.16e+03  1.72e+00 4.36e-09 1.76e-07 1.39e+00 3.08e-11 1.32e-09 1.12e-02 1.00e+00 4.59e+01
...
1.20e+03  1.72e+00 2.52e-09 1.02e-07 1.39e+00 1.78e-11 7.63e-10 1.12e-02 1.00e+00 4.59e+01
...
1.24e+03  1.72e+00 1.46e-09 5.88e-08 1.39e+00 1.03e-11 4.41e-10 1.12e-02 1.00e+00 4.59e+01
...
1.27e+03  1.72e+00 8.41e-10 3.40e-08 1.39e+00 5.94e-12 2.55e-10 1.12e-02 1.00e+00 4.59e+01
...
1.31e+03  1.72e+00 4.86e-10 1.96e-08 1.39e+00 3.43e-12 1.47e-10 1.12e-02 1.00e+00 4.59e+01
...
1.35e+03  1.72e+00 2.80e-10 1.13e-08 1.39e+00 1.98e-12 8.49e-11 1.12e-02 1.00e+00 4.59e+01
...
1.39e+03  1.72e+00 1.62e-10 6.55e-09 1.39e+00 1.15e-12 4.91e-11 1.12e-02 1.00e+00 4.59e+01
...
1.42e+03  1.72e+00 9.36e-11 3.79e-09 1.39e+00 6.62e-13 2.84e-11 1.12e-02 1.00e+00 4.59e+01
...
1.46e+03  1.72e+00 5.41e-11 2.19e-09 1.39e+00 3.83e-13 1.64e-11 1.12e-02 1.00e+00 4.59e+01
...
1.50e+03  1.72e+00 3.12e-11 1.26e-09 1.39e+00 2.21e-13 9.46e-12 1.12e-02 1.00e+00 4.59e+01
...
1.54e+03  1.72e+00 1.81e-11 7.30e-10 1.39e+00 1.28e-13 5.47e-12 1.12e-02 1.00e+00 4.59e+01
...
1.57e+03  1.72e+00 1.05e-11 4.23e-10 1.39e+00 7.40e-14 3.17e-12 1.12e-02 1.00e+00 4.59e+01
...
1.61e+03  1.72e+00 6.08e-12 2.46e-10 1.39e+00 4.30e-14 1.84e-12 1.12e-02 1.00e+00 4.59e+01
...
1.65e+03  1.72e+00 3.53e-12 1.43e-10 1.39e+00 2.50e-14 1.07e-12 1.12e-02 1.00e+00 4.59e+01
...
1.69e+03  1.72e+00 2.04e-12 8.26e-11 1.39e+00 1.44e-14 6.19e-13 1.12e-02 1.00e+00 4.59e+01
...
1.72e+03  1.72e+00 1.20e-12 4.87e-11 1.39e+00 8.52e-15 3.65e-13 1.12e-02 1.00e+00 4.59e+01
...
1.76e+03  1.72e+00 8.00e-13 3.24e-11 1.39e+00 5.66e-15 2.42e-13 1.12e-02 1.00e+00 4.59e+01
...
1.80e+03  1.72e+00 5.45e-13 2.20e-11 1.39e+00 3.85e-15 1.65e-13 1.12e-02 1.00e+00 4.59e+01
...
1.84e+03  1.72e+00 3.35e-13 1.36e-11 1.39e+00 2.37e-15 1.01e-13 1.12e-02 1.00e+00 4.59e+01

## 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, iteration_1)
- `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 perturbation of lowering the initial concentration of id_l0rt from 170 to 50 caused a proportional decrease in the final concentrations of several species, especially id_nfl6, id_pq2o, id_4k49, and id_btma. This suggests these species are downstream products or intermediates dependent on id_l0rt. The species id_ljzz remains almost constant in both experiments, indicating it might be a stable intermediate or product.

The species id_v6ig decreases rapidly to near zero in both experiments, suggesting it is consumed in reactions, possibly as a catalyst or cofactor.

The species id_rzml decreases but remains at low levels, indicating it might be involved in a slow reaction or degradation.

To further clarify the reaction network, I want to perturb another species that shows interesting dynamics and is likely involved in the reaction network. id_v6ig is a good candidate since it starts at 1 and rapidly decreases to near zero. I will increase its initial concentration to 5 and observe the system response. This will help determine if id_v6ig is a limiting factor or involved in catalytic reactions.

## Action

### Experiment
```json
{
  "action": "change_initial_concentration",
  "meta_data": {
    "id_v6ig": 5
  }
}
```
# Observation

## Experiment Result
Experiment ID: iteration_2
Your requested experiment has finished. The details results are saved as a dataframe in experiment_history[iteration_2]

Total size: 10001 rows × 10 columns. 

Below shows a subset of the table for your convenience. You should access experiment_history[iteration_2] for the full data

Time    id_l0rt  id_v6ig  id_4k49  id_ljzz   id_btma  id_we08  id_rzml  id_pq2o  id_nfl6
0   1.70e+02 5.00e+00        0        0         0        0 2.40e+00        0        0
...
3.75e+01  -2.43e-13 1.27e+00 1.61e+00 3.03e-02 -1.39e-15 1.37e+00 1.92e-03 7.54e-01 1.66e+02
...
7.50e+01  -2.96e-15 1.26e+00 1.10e+00 2.08e-02 -1.44e-17 1.36e+00 1.31e-03 1.28e+00 1.66e+02
...
1.12e+02   4.60e-17 1.26e+00 7.46e-01 1.42e-02  3.54e-19 1.36e+00 8.89e-04 1.64e+00 1.66e+02
...
1.50e+02  -1.09e-16 1.26e+00 5.07e-01 9.65e-03  3.12e-19 1.36e+00 6.04e-04 1.88e+00 1.66e+02
...
1.87e+02   5.45e-17 1.25e+00 3.44e-01 6.57e-03  1.08e-19 1.35e+00 4.11e-04 2.05e+00 1.66e+02
...
2.25e+02   1.40e-17 1.25e+00 2.34e-01 4.47e-03  6.85e-20 1.35e+00 2.79e-04 2.16e+00 1.66e+02
...
2.62e+02   2.48e-17 1.25e+00 1.59e-01 3.04e-03  5.79e-20 1.35e+00 1.90e-04 2.24e+00 1.66e+02
...
3.00e+02   3.59e-17 1.25e+00 1.08e-01 2.06e-03  4.57e-20 1.35e+00 1.29e-04 2.29e+00 1.66e+02
...
3.37e+02   3.24e-17 1.25e+00 7.33e-02 1.40e-03  2.65e-20 1.35e+00 8.75e-05 2.33e+00 1.66e+02
...
3.75e+02   4.76e-18 1.25e+00 4.97e-02 9.52e-04  1.39e-20 1.35e+00 5.94e-05 2.35e+00 1.66e+02
...
4.12e+02   2.99e-19 1.25e+00 3.38e-02 6.47e-04  1.62e-21 1.35e+00 4.03e-05 2.37e+00 1.66e+02
...
4.50e+02   2.63e-20 1.25e+00 2.29e-02 4.39e-04  1.65e-22 1.35e+00 2.74e-05 2.38e+00 1.66e+02
...
4.87e+02   3.85e-21 1.25e+00 1.56e-02 2.98e-04  2.85e-23 1.35e+00 1.86e-05 2.38e+00 1.66e+02
...
5.25e+02   7.86e-22 1.25e+00 1.06e-02 2.02e-04  5.63e-24 1.35e+00 1.26e-05 2.39e+00 1.66e+02
...
5.62e+02   7.54e-23 1.25e+00 7.18e-03 1.37e-04  5.11e-25 1.35e+00 8.57e-06 2.39e+00 1.66e+02
...
6.00e+02  -1.35e-23 1.25e+00 4.87e-03 9.33e-05 -6.87e-26 1.35e+00 5.82e-06 2.40e+00 1.66e+02
...
6.37e+02  -6.68e-24 1.25e+00 3.31e-03 6.34e-05 -3.09e-26 1.35e+00 3.95e-06 2.40e+00 1.66e+02
...
6.75e+02  -1.57e-24 1.25e+00 2.25e-03 4.30e-05 -7.54e-27 1.35e+00 2.68e-06 2.40e+00 1.66e+02
...
7.12e+02  -2.60e-25 1.25e+00 1.52e-03 2.92e-05 -1.20e-27 1.35e+00 1.82e-06 2.40e+00 1.66e+02
...
7.50e+02  -2.67e-26 1.25e+00 1.04e-03 1.98e-05 -8.86e-29 1.35e+00 1.24e-06 2.40e+00 1.66e+02
...
7.87e+02  -7.44e-27 1.25e+00 7.03e-04 1.35e-05 -9.49e-30 1.35e+00 8.40e-07 2.40e+00 1.66e+02
...
8.25e+02  -3.81e-27 1.25e+00 4.77e-04 9.14e-06 -2.52e-30 1.35e+00 5.70e-07 2.40e+00 1.66e+02
...
8.62e+02  -2.08e-27 1.25e+00 3.24e-04 6.21e-06 -6.47e-30 1.35e+00 3.87e-07 2.40e+00 1.66e+02
...
9.00e+02  -1.67e-27 1.25e+00 2.20e-04 4.21e-06  2.11e-31 1.35e+00 2.63e-07 2.40e+00 1.66e+02
...
9.37e+02  -1.19e-27 1.25e+00 1.49e-04 2.86e-06 -1.62e-32 1.35e+00 1.78e-07 2.40e+00 1.66e+02
...
9.75e+02  -8.12e-28 1.25e+00 1.01e-04 1.94e-06 -1.65e-32 1.35e+00 1.21e-07 2.40e+00 1.66e+02
...
1.01e+03  -5.51e-28 1.25e+00 6.88e-05 1.32e-06 -1.35e-32 1.35e+00 8.22e-08 2.40e+00 1.66e+02
...
1.05e+03  -3.74e-28 1.25e+00 4.67e-05 8.95e-07 -6.48e-33 1.35e+00 5.58e-08 2.40e+00 1.66e+02
...
1.09e+03  -7.96e-29 1.25e+00 3.17e-05 6.08e-07  1.95e-31 1.35e+00 3.79e-08 2.40e+00 1.66e+02
...
1.12e+03  -7.21e-30 1.25e+00 2.15e-05 4.13e-07 -3.78e-32 1.35e+00 2.57e-08 2.40e+00 1.66e+02
...
1.16e+03  -5.04e-31 1.25e+00 1.46e-05 2.80e-07 -2.65e-33 1.35e+00 1.75e-08 2.40e+00 1.66e+02
...
1.20e+03  -1.16e-31 1.25e+00 9.93e-06 1.90e-07 -6.12e-34 1.35e+00 1.19e-08 2.40e+00 1.66e+02
...
1.24e+03  -2.46e-32 1.25e+00 6.74e-06 1.29e-07 -1.29e-34 1.35e+00 8.05e-09 2.40e+00 1.66e+02
...
1.27e+03  -4.14e-33 1.25e+00 4.58e-06 8.77e-08 -2.18e-35 1.35e+00 5.47e-09 2.40e+00 1.66e+02
...
1.31e+03  -3.89e-34 1.25e+00 3.11e-06 5.95e-08 -2.05e-36 1.35e+00 3.71e-09 2.40e+00 1.66e+02
...
1.35e+03  -1.46e-35 1.25e+00 2.11e-06 4.04e-08 -7.68e-38 1.35e+00 2.52e-09 2.40e+00 1.66e+02
...
1.39e+03  -7.43e-37 1.25e+00 1.43e-06 2.74e-08 -3.91e-39 1.35e+00 1.71e-09 2.40e+00 1.66e+02
...
1.42e+03  -5.10e-38 1.25e+00 9.73e-07 1.86e-08 -2.69e-40 1.35e+00 1.16e-09 2.40e+00 1.66e+02
...
1.46e+03  -2.15e-38 1.25e+00 6.60e-07 1.27e-08 -1.13e-40 1.35e+00 7.89e-10 2.40e+00 1.66e+02
...
1.50e+03  -4.38e-39 1.25e+00 4.48e-07 8.59e-09 -2.31e-41 1.35e+00 5.36e-10 2.40e+00 1.66e+02
...
1.54e+03   2.45e-40 1.25e+00 3.04e-07 5.83e-09  1.28e-42 1.35e+00 3.64e-10 2.40e+00 1.66e+02
...
1.57e+03   1.97e-40 1.25e+00 2.07e-07 3.96e-09  1.03e-42 1.35e+00 2.47e-10 2.40e+00 1.66e+02
...
1.61e+03  -3.26e-41 1.25e+00 1.40e-07 2.69e-09 -1.83e-43 1.35e+00 1.68e-10 2.40e+00 1.66e+02
...
1.65e+03  -5.50e-41 1.25e+00 9.53e-08 1.83e-09 -2.93e-43 1.35e+00 1.14e-10 2.40e+00 1.66e+02
...
1.69e+03   5.46e-42 1.25e+00 6.47e-08 1.24e-09  2.73e-45 1.35e+00 7.73e-11 2.40e+00 1.66e+02
...
1.72e+03   1.04e-41 1.25e+00 4.39e-08 8.41e-10  1.90e-44 1.35e+00 5.25e-11 2.40e+00 1.66e+02
...
1.76e+03   6.36e-42 1.25e+00 2.98e-08 5.71e-10 -3.00e-45 1.35e+00 3.56e-11 2.40e+00 1.66e+02
...
1.80e+03   2.48e-42 1.25e+00 2.02e-08 3.88e-10 -1.46e-45 1.35e+00 2.42e-11 2.40e+00 1.66e+02
...
1.84e+03   2.13e-42 1.25e+00 1.36e-08 2.61e-10 -1.02e-45 1.35e+00 1.63e-11 2.40e+00 1.66e+02

## 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, iteration_1, iteration_2)
- `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
The perturbation increasing id_v6ig initial concentration from 1 to 5 resulted in id_v6ig stabilizing around 1.25, which is higher than near zero in previous experiments. The species id_l0rt is almost completely depleted (near zero) very early, indicating rapid consumption. The species id_nfl6 increased dramatically to about 166, much higher than previous experiments, suggesting it is a major product downstream of id_v6ig and id_l0rt.

The species id_4k49 and id_we08 also increased significantly, while id_btma remained near zero or slightly negative (likely numerical noise). The species id_rzml remained stable around 1.35, similar to previous experiments.

This suggests that id_v6ig acts catalytically or as a cofactor, enabling rapid conversion of id_l0rt into downstream products like id_nfl6, id_4k49, and id_we08. The near-zero id_l0rt and high id_nfl6 indicate a reaction where id_l0rt is converted to id_nfl6 with id_v6ig involvement.

Next, to further clarify the role of id_rzml, which remains stable, I will perturb its initial concentration by increasing it from 2.4 to 10 and observe the system response. This will help determine if id_rzml is involved in reactions or acts as a constant or boundary species.

## Action

### Experiment
```json
{
  "action": "change_initial_concentration",
  "meta_data": {
    "id_rzml": 10
  }
}
```
# 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: 10001 rows × 10 columns. 

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

Time   id_l0rt  id_v6ig  id_4k49  id_ljzz  id_btma  id_we08  id_rzml  id_pq2o  id_nfl6
0  1.70e+02 1.00e+00        0        0        0        0 1.00e+01        0        0
...
3.75e+01  1.56e+02 1.93e-04 9.71e-01 8.26e+00 1.11e-04 9.11e-05 7.38e-01 2.87e-02 4.78e+00
...
7.50e+01  1.54e+02 1.87e-04 9.45e-01 8.44e+00 1.06e-04 8.96e-05 5.61e-01 5.49e-02 6.31e+00
...
1.12e+02  1.53e+02 1.82e-04 9.19e-01 8.55e+00 1.02e-04 8.85e-05 4.52e-01 8.07e-02 7.84e+00
...
1.50e+02  1.51e+02 1.78e-04 8.93e-01 8.62e+00 9.86e-05 8.75e-05 3.80e-01 1.06e-01 9.34e+00
...
1.87e+02  1.50e+02 1.74e-04 8.68e-01 8.67e+00 9.53e-05 8.65e-05 3.28e-01 1.31e-01 1.08e+01
...
2.25e+02  1.48e+02 1.70e-04 8.44e-01 8.71e+00 9.23e-05 8.56e-05 2.90e-01 1.56e-01 1.23e+01
...
2.62e+02  1.47e+02 1.66e-04 8.19e-01 8.74e+00 8.93e-05 8.46e-05 2.60e-01 1.80e-01 1.37e+01
...
3.00e+02  1.45e+02 1.63e-04 7.96e-01 8.76e+00 8.65e-05 8.36e-05 2.36e-01 2.04e-01 1.50e+01
...
3.37e+02  1.44e+02 1.59e-04 7.72e-01 8.78e+00 8.38e-05 8.25e-05 2.17e-01 2.27e-01 1.63e+01
...
3.75e+02  1.43e+02 1.56e-04 7.50e-01 8.80e+00 8.12e-05 8.14e-05 2.01e-01 2.50e-01 1.76e+01
...
4.12e+02  1.41e+02 1.52e-04 7.27e-01 8.81e+00 7.87e-05 8.03e-05 1.87e-01 2.72e-01 1.89e+01
...
4.50e+02  1.40e+02 1.49e-04 7.05e-01 8.82e+00 7.62e-05 7.92e-05 1.76e-01 2.94e-01 2.01e+01
...
4.87e+02  1.39e+02 1.45e-04 6.84e-01 8.83e+00 7.38e-05 7.80e-05 1.66e-01 3.16e-01 2.12e+01
...
5.25e+02  1.38e+02 1.42e-04 6.63e-01 8.84e+00 7.15e-05 7.67e-05 1.57e-01 3.37e-01 2.24e+01
...
5.62e+02  1.37e+02 1.38e-04 6.43e-01 8.85e+00 6.92e-05 7.54e-05 1.50e-01 3.57e-01 2.35e+01
...
6.00e+02  1.36e+02 1.35e-04 6.23e-01 8.86e+00 6.70e-05 7.42e-05 1.43e-01 3.77e-01 2.45e+01
...
6.37e+02  1.35e+02 1.32e-04 6.03e-01 8.86e+00 6.48e-05 7.28e-05 1.37e-01 3.97e-01 2.56e+01
...
6.75e+02  1.34e+02 1.28e-04 5.84e-01 8.87e+00 6.27e-05 7.15e-05 1.32e-01 4.16e-01 2.66e+01
...
7.12e+02  1.33e+02 1.25e-04 5.66e-01 8.87e+00 6.07e-05 7.01e-05 1.27e-01 4.34e-01 2.75e+01
...
7.50e+02  1.32e+02 1.22e-04 5.48e-01 8.88e+00 5.87e-05 6.87e-05 1.23e-01 4.52e-01 2.85e+01
...
7.87e+02  1.31e+02 1.19e-04 5.30e-01 8.88e+00 5.68e-05 6.73e-05 1.19e-01 4.70e-01 2.94e+01
...
8.25e+02  1.30e+02 1.16e-04 5.13e-01 8.89e+00 5.49e-05 6.59e-05 1.15e-01 4.87e-01 3.03e+01
...
8.62e+02  1.29e+02 1.13e-04 4.96e-01 8.89e+00 5.31e-05 6.45e-05 1.12e-01 5.04e-01 3.11e+01
...
9.00e+02  1.28e+02 1.10e-04 4.80e-01 8.89e+00 5.13e-05 6.31e-05 1.09e-01 5.20e-01 3.19e+01
...
9.37e+02  1.27e+02 1.07e-04 4.64e-01 8.89e+00 4.96e-05 6.17e-05 1.06e-01 5.36e-01 3.27e+01
...
9.75e+02  1.27e+02 1.04e-04 4.48e-01 8.90e+00 4.79e-05 6.03e-05 1.03e-01 5.51e-01 3.35e+01
...
1.01e+03  1.26e+02 1.01e-04 4.34e-01 8.90e+00 4.63e-05 5.88e-05 1.01e-01 5.66e-01 3.42e+01
...
1.05e+03  1.25e+02 9.78e-05 4.19e-01 8.90e+00 4.47e-05 5.74e-05 9.88e-02 5.81e-01 3.49e+01
...
1.09e+03  1.24e+02 9.50e-05 4.05e-01 8.90e+00 4.32e-05 5.60e-05 9.68e-02 5.95e-01 3.56e+01
...
1.12e+03  1.24e+02 9.22e-05 3.91e-01 8.91e+00 4.17e-05 5.46e-05 9.49e-02 6.09e-01 3.63e+01
...
1.16e+03  1.23e+02 8.95e-05 3.78e-01 8.91e+00 4.03e-05 5.32e-05 9.31e-02 6.22e-01 3.69e+01
...
1.20e+03  1.23e+02 8.68e-05 3.65e-01 8.91e+00 3.89e-05 5.18e-05 9.14e-02 6.35e-01 3.76e+01
...
1.24e+03  1.22e+02 8.42e-05 3.52e-01 8.91e+00 3.75e-05 5.04e-05 8.99e-02 6.48e-01 3.82e+01
...
1.27e+03  1.21e+02 8.17e-05 3.40e-01 8.91e+00 3.62e-05 4.91e-05 8.85e-02 6.60e-01 3.87e+01
...
1.31e+03  1.21e+02 7.92e-05 3.28e-01 8.91e+00 3.49e-05 4.78e-05 8.71e-02 6.72e-01 3.93e+01
...
1.35e+03  1.20e+02 7.67e-05 3.17e-01 8.91e+00 3.37e-05 4.64e-05 8.58e-02 6.83e-01 3.98e+01
...
1.39e+03  1.20e+02 7.43e-05 3.06e-01 8.92e+00 3.25e-05 4.51e-05 8.46e-02 6.94e-01 4.04e+01
...
1.42e+03  1.19e+02 7.20e-05 2.95e-01 8.92e+00 3.14e-05 4.38e-05 8.35e-02 7.05e-01 4.09e+01
...
1.46e+03  1.19e+02 6.97e-05 2.84e-01 8.92e+00 3.02e-05 4.26e-05 8.24e-02 7.15e-01 4.13e+01
...
1.50e+03  1.18e+02 6.75e-05 2.74e-01 8.92e+00 2.92e-05 4.13e-05 8.14e-02 7.25e-01 4.18e+01
...
1.54e+03  1.18e+02 6.53e-05 2.65e-01 8.92e+00 2.81e-05 4.01e-05 8.05e-02 7.35e-01 4.23e+01
...
1.57e+03  1.17e+02 6.32e-05 2.55e-01 8.92e+00 2.71e-05 3.89e-05 7.96e-02 7.45e-01 4.27e+01
...
1.61e+03  1.17e+02 6.11e-05 2.46e-01 8.92e+00 2.61e-05 3.77e-05 7.88e-02 7.54e-01 4.31e+01
...
1.65e+03  1.17e+02 5.91e-05 2.37e-01 8.92e+00 2.52e-05 3.66e-05 7.80e-02 7.63e-01 4.35e+01
...
1.69e+03  1.16e+02 5.72e-05 2.29e-01 8.92e+00 2.43e-05 3.55e-05 7.72e-02 7.71e-01 4.39e+01
...
1.72e+03  1.16e+02 5.53e-05 2.20e-01 8.92e+00 2.34e-05 3.44e-05 7.65e-02 7.80e-01 4.43e+01
...
1.76e+03  1.15e+02 5.34e-05 2.12e-01 8.92e+00 2.25e-05 3.33e-05 7.58e-02 7.88e-01 4.46e+01
...
1.80e+03  1.15e+02 5.16e-05 2.05e-01 8.92e+00 2.17e-05 3.22e-05 7.52e-02 7.95e-01 4.50e+01
...
1.84e+03  1.15e+02 4.98e-05 1.97e-01 8.93e+00 2.09e-05 3.12e-05 7.45e-02 8.03e-01 4.53e+01

## 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_1, iteration_2, 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
Increasing the initial concentration of id_rzml from 2.4 to 10 caused only a moderate decrease in id_rzml over time (from 10 to about 0.07), and the other species showed dynamics similar to the original experiment with default initial concentrations. This suggests id_rzml is involved in a slow reaction or acts as a substrate or cofactor with slow turnover.

The species id_ljzz increased significantly and stabilized around 8.9, which is much higher than in previous experiments, indicating it is likely a product or intermediate related to id_rzml.

The species id_l0rt decreased slightly from 170 to about 115, indicating it is consumed but not as rapidly as in the id_v6ig perturbation experiment.

Based on these observations, I hypothesize the following reaction network:
- id_l0rt is converted to id_4k49, id_ljzz, id_btma, id_we08, id_pq2o, and id_nfl6 through multiple reactions.
- id_v6ig acts catalytically or as a cofactor in the conversion of id_l0rt to id_nfl6 and other products.
- id_rzml is involved in a slow reaction producing id_ljzz.
- id_ljzz is a stable intermediate or product.
- id_btma and id_we08 are minor products or intermediates.
- id_pq2o and id_nfl6 are downstream products.

Next, I will attempt to add reactions to the SBML model to represent these hypotheses and simulate the model to compare with the observed data.

## Action

### Code
```python
import libsbml

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

# Clear any existing reactions (there are none, but just in case)
for i in range(model.getNumReactions()):
    model.removeReaction(0)

# Add reaction: id_l0rt -> id_4k49 (simple conversion)
reaction1 = model.createReaction()
reaction1.setId("r1")
reaction1.setReversible(False)
reaction1.setFast(False)
reactant1 = reaction1.createReactant()
reactant1.setSpecies("id_l0rt")
reactant1.setStoichiometry(1)
reactant1.setConstant(False)
product1 = reaction1.createProduct()
product1.setSpecies("id_4k49")
product1.setStoichiometry(1)
product1.setConstant(False)

# Add reaction: id_l0rt + id_v6ig -> id_nfl6 + id_v6ig (catalytic conversion)
reaction2 = model.createReaction()
reaction2.setId("r2")
reaction2.setReversible(False)
reaction2.setFast(False)
reactant2_1 = reaction2.createReactant()
reactant2_1.setSpecies("id_l0rt")
reactant2_1.setStoichiometry(1)
reactant2_1.setConstant(False)
reactant2_2 = reaction2.createReactant()
reactant2_2.setSpecies("id_v6ig")
reactant2_2.setStoichiometry(1)
reactant2_2.setConstant(False)
product2_1 = reaction2.createProduct()
product2_1.setSpecies("id_nfl6")
product2_1.setStoichiometry(1)
product2_1.setConstant(False)
product2_2 = reaction2.createProduct()
product2_2.setSpecies("id_v6ig")
product2_2.setStoichiometry(1)
product2_2.setConstant(False)

# Add reaction: id_rzml -> id_ljzz (slow conversion)
reaction3 = model.createReaction()
reaction3.setId("r3")
reaction3.setReversible(False)
reaction3.setFast(False)
reactant3 = reaction3.createReactant()
reactant3.setSpecies("id_rzml")
reactant3.setStoichiometry(1)
reactant3.setConstant(False)
product3 = reaction3.createProduct()
product3.setSpecies("id_ljzz")
product3.setStoichiometry(1)
product3.setConstant(False)

# Add reaction: id_l0rt -> id_btma
reaction4 = model.createReaction()
reaction4.setId("r4")
reaction4.setReversible(False)
reaction4.setFast(False)
reactant4 = reaction4.createReactant()
reactant4.setSpecies("id_l0rt")
reactant4.setStoichiometry(1)
reactant4.setConstant(False)
product4 = reaction4.createProduct()
product4.setSpecies("id_btma")
product4.setStoichiometry(1)
product4.setConstant(False)

# Add reaction: id_l0rt -> id_we08
reaction5 = model.createReaction()
reaction5.setId("r5")
reaction5.setReversible(False)
reaction5.setFast(False)
reactant5 = reaction5.createReactant()
reactant5.setSpecies("id_l0rt")
reactant5.setStoichiometry(1)
reactant5.setConstant(False)
product5 = reaction5.createProduct()
product5.setSpecies("id_we08")
product5.setStoichiometry(1)
product5.setConstant(False)

# Add reaction: id_l0rt -> id_pq2o
reaction6 = model.createReaction()
reaction6.setId("r6")
reaction6.setReversible(False)
reaction6.setFast(False)
reactant6 = reaction6.createReactant()
reactant6.setSpecies("id_l0rt")
reactant6.setStoichiometry(1)
reactant6.setConstant(False)
product6 = reaction6.createProduct()
product6.setSpecies("id_pq2o")
product6.setStoichiometry(1)
product6.setConstant(False)

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

# Save updated SBML for future use
shared_variables.add("updated_sbml", updated_sbml)

print("Reactions added to the model.")
```
# Observation

## Code Stdout
Reactions added to the model.

## 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_1, iteration_2, iteration_3)
- `shared_variables`: all variables you have saved from the previous iterations. (updated_sbml)

### 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
I have added six reactions to the model based on the hypotheses from the perturbation experiments:
- Conversion of id_l0rt to id_4k49.
- Catalytic conversion of id_l0rt to id_nfl6 with id_v6ig as catalyst.
- Slow conversion of id_rzml to id_ljzz.
- Conversion of id_l0rt to id_btma.
- Conversion of id_l0rt to id_we08.
- Conversion of id_l0rt to id_pq2o.

Next, I will simulate this updated model under the default initial conditions and compare the simulation results with the observed data from iteration_0 to evaluate how well the model captures the system dynamics.

## Action

### Code
```python
# Retrieve the updated SBML model
updated_sbml = shared_variables.access("updated_sbml")

# Simulate the updated model
sim_data = simulate(updated_sbml)

# Access the observed data from iteration_0
obs_data = experiment_history["iteration_0"]

# Compare simulation and observation for key species by printing the last time point concentrations
print("Simulation final concentrations:")
print(sim_data.iloc[-1])

print("\nObserved final concentrations (iteration_0):")
print(obs_data.iloc[-1])
```
# Observation

## Code Stdout
Simulation final concentrations:
id_l0rt     170.0
id_v6ig       1.0
id_4k49       0.0
id_ljzz       0.0
id_btma       0.0
id_we08       0.0
id_rzml       2.4
id_pq2o       0.0
id_nfl6       0.0
Time       1838.0
Name: 10000, dtype: float64

Observed final concentrations (iteration_0):
id_l0rt    8.415245e+00
id_v6ig    6.912660e-07
id_4k49    5.476307e-05
id_ljzz    1.396752e+00
id_btma    2.388145e-08
id_we08    7.289910e-07
id_rzml    3.249839e-03
id_pq2o    9.999438e-01
id_nfl6    1.591880e+02
Time       1.838000e+03
Name: 10000, dtype: float64

## 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_1, iteration_2, iteration_3)
- `shared_variables`: all variables you have saved from the previous iterations. (updated_sbml)

### 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
The simulation results show no change in species concentrations over time, indicating the reactions added are not active or lack kinetic laws. The observed data shows significant dynamics, with id_l0rt decreasing and id_nfl6 increasing substantially.

The missing piece is the kinetic laws for the reactions. Without kinetic laws, the reactions do not proceed, so the model does not reproduce the observed dynamics.

Next, I will add simple mass-action kinetic laws with rate constants to the reactions to enable the system to evolve. I will assign arbitrary rate constants initially and later refine them if needed.

## Action

### Code
```python
import libsbml

# Load the updated SBML model with reactions but no kinetics
sbml_doc = libsbml.readSBMLFromString(shared_variables.access("updated_sbml"))
model = sbml_doc.getModel()

# Remove existing kinetic laws if any (should be none)
for i in range(model.getNumReactions()):
    reaction = model.getReaction(i)
    if reaction.isSetKineticLaw():
        reaction.unsetKineticLaw()

# Add kinetic laws with mass-action rate constants
rate_constants = {
    "r1": 0.01,
    "r2": 0.05,
    "r3": 0.001,
    "r4": 0.005,
    "r5": 0.005,
    "r6": 0.005
}

for i in range(model.getNumReactions()):
    reaction = model.getReaction(i)
    rid = reaction.getId()
    kl = reaction.createKineticLaw()
    # Create parameter for rate constant
    param = kl.createParameter()
    param.setId("k_" + rid)
    param.setValue(rate_constants[rid])
    param.setConstant(True)
    # Create kinetic law formula (mass action)
    # For r2, which is catalytic, rate = k_r2 * [id_l0rt] * [id_v6ig]
    if rid == "r2":
        formula = "k_r2 * id_l0rt * id_v6ig"
    else:
        # For other reactions, rate = k * [substrate]
        reactant = reaction.getReactant(0)
        formula = f"k_{rid} * {reactant.getSpecies()}"
    kl.setFormula(formula)

# Write the updated SBML string with kinetics
updated_sbml_with_kinetics = libsbml.SBMLWriter().writeSBMLToString(sbml_doc)

# Save updated SBML for future use
shared_variables.add("updated_sbml_with_kinetics", updated_sbml_with_kinetics)

print("Kinetic laws added to reactions.")
```
# Observation

## Code Stdout
Kinetic laws added to reactions.

## 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_1, iteration_2, iteration_3)
- `shared_variables`: all variables you have saved from the previous iterations. (updated_sbml, updated_sbml_with_kinetics)

### 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
I have added mass-action kinetic laws with rate constants to all reactions. The next step is to simulate this updated model with kinetics under default initial conditions and compare the simulation results with the observed data from iteration_0. This will help evaluate if the added reactions and kinetics capture the system dynamics.

If the model still does not fit well, I can consider refining rate constants or adding additional regulatory mechanisms.

## Action

### Code
```python
# Retrieve the updated SBML model with kinetics
updated_sbml_with_kinetics = shared_variables.access("updated_sbml_with_kinetics")

# Simulate the updated model with kinetics
sim_data_kinetics = simulate(updated_sbml_with_kinetics)

# Access the observed data from iteration_0
obs_data = experiment_history["iteration_0"]

# Compare simulation and observation for key species by printing the last time point concentrations
print("Simulation final concentrations with kinetics:")
print(sim_data_kinetics.iloc[-1])

print("\nObserved final concentrations (iteration_0):")
print(obs_data.iloc[-1])
```
# Observation

## Code Stdout
Simulation final concentrations with kinetics:
id_l0rt    4.383593e-13
id_v6ig    1.000000e+00
id_4k49    2.266667e+01
id_ljzz    2.018076e+00
id_btma    1.133333e+01
id_we08    1.133333e+01
id_rzml    3.819241e-01
id_pq2o    1.133333e+01
id_nfl6    1.133333e+02
Time       1.838000e+03
Name: 10000, dtype: float64

Observed final concentrations (iteration_0):
id_l0rt    8.415245e+00
id_v6ig    6.912660e-07
id_4k49    5.476307e-05
id_ljzz    1.396752e+00
id_btma    2.388145e-08
id_we08    7.289910e-07
id_rzml    3.249839e-03
id_pq2o    9.999438e-01
id_nfl6    1.591880e+02
Time       1.838000e+03
Name: 10000, dtype: float64

## 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_1, iteration_2, iteration_3)
- `shared_variables`: all variables you have saved from the previous iterations. (updated_sbml, updated_sbml_with_kinetics)

### 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
The simulation with kinetics shows that id_l0rt is almost completely consumed, which is consistent with the observed data, but the final concentration of id_l0rt in the simulation is much lower than observed. The product species id_4k49, id_btma, id_we08, id_pq2o, and id_nfl6 are much higher in simulation than observed, except for id_nfl6 which is somewhat close. The species id_ljzz and id_rzml are also higher in simulation.

This suggests that the current model overestimates the conversion rates or the stoichiometry of the reactions producing these species. The model assumes all id_l0rt is converted into these products in a 1:1 stoichiometry, which may not be accurate.

To improve the model, I will:
- Adjust stoichiometry coefficients to reflect partial conversion.
- Add degradation or consumption reactions for some products to prevent their accumulation.
- Possibly add reversible reactions or feedback inhibition if needed.

First, I will add degradation reactions for id_4k49, id_btma, id_we08, id_pq2o, and id_nfl6 to simulate their removal or consumption.

## Action

### Code
```python
import libsbml

# Load the current SBML model with kinetics
sbml_doc = libsbml.readSBMLFromString(shared_variables.access("updated_sbml_with_kinetics"))
model = sbml_doc.getModel()

# Add degradation reactions for product species
product_species = ["id_4k49", "id_btma", "id_we08", "id_pq2o", "id_nfl6"]
for species_id in product_species:
    reaction = model.createReaction()
    reaction_id = "deg_" + species_id
    reaction.setId(reaction_id)
    reaction.setReversible(False)
    reaction.setFast(False)
    reactant = reaction.createReactant()
    reactant.setSpecies(species_id)
    reactant.setStoichiometry(1)
    reactant.setConstant(False)
    # No products (degradation to nothing)
    # Add kinetic law with rate constant
    kl = reaction.createKineticLaw()
    param = kl.createParameter()
    param.setId("k_" + reaction_id)
    param.setValue(0.01)  # Arbitrary degradation rate
    param.setConstant(True)
    kl.setFormula(f"k_{reaction_id} * {species_id}")

# Write the updated SBML string with degradation reactions
updated_sbml_with_degradation = libsbml.SBMLWriter().writeSBMLToString(sbml_doc)

# Save updated SBML for future use
shared_variables.add("updated_sbml_with_degradation", updated_sbml_with_degradation)

print("Degradation reactions added for product species.")
```
# Observation

## Code Stdout
Degradation reactions added for product species.

## 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_1, iteration_2, iteration_3)
- `shared_variables`: all variables you have saved from the previous iterations. (updated_sbml, updated_sbml_with_kinetics, updated_sbml_with_degradation)

### 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
I have added degradation reactions for the product species to simulate their removal or consumption, which should prevent their over-accumulation and better match observed data.

Next, I will simulate the updated model with degradation reactions under default initial conditions and compare the simulation results with the observed data from iteration_0. This will help evaluate if the degradation reactions improve the model fit.

## Action

### Code
```python
# Retrieve the updated SBML model with degradation reactions
updated_sbml_with_degradation = shared_variables.access("updated_sbml_with_degradation")

# Simulate the updated model with degradation
sim_data_degradation = simulate(updated_sbml_with_degradation)

# Access the observed data from iteration_0
obs_data = experiment_history["iteration_0"]

# Compare simulation and observation for key species by printing the last time point concentrations
print("Simulation final concentrations with degradation:")
print(sim_data_degradation.iloc[-1])

print("\nObserved final concentrations (iteration_0):")
print(obs_data.iloc[-1])
```
# Observation

## Code Stdout
Simulation final concentrations with degradation:
id_l0rt   -5.801651e-25
id_v6ig    1.000000e+00
id_4k49    2.724147e-07
id_ljzz    2.018075e+00
id_btma    1.362074e-07
id_we08    1.362074e-07
id_rzml    3.819249e-01
id_pq2o    1.362074e-07
id_nfl6    1.362074e-06
Time       1.838000e+03
Name: 10000, dtype: float64

Observed final concentrations (iteration_0):
id_l0rt    8.415245e+00
id_v6ig    6.912660e-07
id_4k49    5.476307e-05
id_ljzz    1.396752e+00
id_btma    2.388145e-08
id_we08    7.289910e-07
id_rzml    3.249839e-03
id_pq2o    9.999438e-01
id_nfl6    1.591880e+02
Time       1.838000e+03
Name: 10000, dtype: float64

## 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_1, iteration_2, iteration_3)
- `shared_variables`: all variables you have saved from the previous iterations. (updated_sbml, updated_sbml_with_kinetics, updated_sbml_with_degradation)

### 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
The addition of degradation reactions drastically reduced the concentrations of product species, making them much lower than observed. Also, id_l0rt is almost completely depleted, which is inconsistent with observed data where it remains at about 8.4.

This suggests the degradation rates are too high or the production rates are too low. The model is not balanced in terms of production and degradation.

To improve, I will:
- Reduce degradation rate constants to a smaller value.
- Add a reversible reaction for id_rzml and id_ljzz to reflect possible equilibrium.
- Adjust stoichiometry or add feedback inhibition if needed later.

I will first reduce degradation rates by an order of magnitude and add a reversible reaction between id_rzml and id_ljzz.

## Action

### Code
```python
import libsbml

# Load the current SBML model with degradation
sbml_doc = libsbml.readSBMLFromString(shared_variables.access("updated_sbml_with_degradation"))
model = sbml_doc.getModel()

# Update degradation rate constants to smaller values
for i in range(model.getNumReactions()):
    reaction = model.getReaction(i)
    rid = reaction.getId()
    if rid.startswith("deg_"):
        kl = reaction.getKineticLaw()
        param = kl.getParameter("k_" + rid)
        param.setValue(0.001)  # Reduced degradation rate

# Add reversible reaction between id_rzml and id_ljzz
reaction_rev = model.createReaction()
reaction_rev.setId("r_rev")
reaction_rev.setReversible(True)
reaction_rev.setFast(False)
reactant = reaction_rev.createReactant()
reactant.setSpecies("id_rzml")
reactant.setStoichiometry(1)
reactant.setConstant(False)
product = reaction_rev.createProduct()
product.setSpecies("id_ljzz")
product.setStoichiometry(1)
product.setConstant(False)

# Add kinetic law for reversible reaction: forward and reverse rates
kl_rev = reaction_rev.createKineticLaw()
param_fwd = kl_rev.createParameter()
param_fwd.setId("k_r_rev_fwd")
param_fwd.setValue(0.01)
param_fwd.setConstant(True)
param_rev = kl_rev.createParameter()
param_rev.setId("k_r_rev_rev")
param_rev.setValue(0.005)
param_rev.setConstant(True)
kl_rev.setFormula("k_r_rev_fwd * id_rzml - k_r_rev_rev * id_ljzz")

# Write the updated SBML string
updated_sbml_refined = libsbml.SBMLWriter().writeSBMLToString(sbml_doc)

# Save updated SBML for future use
shared_variables.add("updated_sbml_refined", updated_sbml_refined)

print("Degradation rates reduced and reversible reaction added.")
```
# Observation

## Code Stdout
Degradation rates reduced and reversible reaction added.

## 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_1, iteration_2, iteration_3)
- `shared_variables`: all variables you have saved from the previous iterations. (updated_sbml, updated_sbml_with_kinetics, updated_sbml_with_degradation, updated_sbml_refined)

### 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
I have reduced the degradation rates and added a reversible reaction between id_rzml and id_ljzz to better capture their dynamics. The next step is to simulate this refined model under default initial conditions and compare the simulation results with the observed data from iteration_0 to evaluate improvements.

## Action

### Code
```python
# Retrieve the refined SBML model
updated_sbml_refined = shared_variables.access("updated_sbml_refined")

# Simulate the refined model
sim_data_refined = simulate(updated_sbml_refined)

# Access the observed data from iteration_0
obs_data = experiment_history["iteration_0"]

# Compare simulation and observation for key species by printing the last time point concentrations
print("Simulation final concentrations with refinement:")
print(sim_data_refined.iloc[-1])

print("\nObserved final concentrations (iteration_0):")
print(obs_data.iloc[-1])
```
# Observation

## Code Stdout
Simulation final concentrations with refinement:
id_l0rt   -3.555869e-13
id_v6ig    1.000000e+00
id_4k49    3.655821e+00
id_ljzz    1.650000e+00
id_btma    1.827910e+00
id_we08    1.827910e+00
id_rzml    7.500000e-01
id_pq2o    1.827910e+00
id_nfl6    1.827910e+01
Time       1.838000e+03
Name: 10000, dtype: float64

Observed final concentrations (iteration_0):
id_l0rt    8.415245e+00
id_v6ig    6.912660e-07
id_4k49    5.476307e-05
id_ljzz    1.396752e+00
id_btma    2.388145e-08
id_we08    7.289910e-07
id_rzml    3.249839e-03
id_pq2o    9.999438e-01
id_nfl6    1.591880e+02
Time       1.838000e+03
Name: 10000, dtype: float64

## 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_1, iteration_2, iteration_3)
- `shared_variables`: all variables you have saved from the previous iterations. (updated_sbml, updated_sbml_with_kinetics, updated_sbml_with_degradation, updated_sbml_refined)

### 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 refined model shows improved dynamics for id_ljzz and id_rzml, closer to observed values. However, id_l0rt is still almost completely depleted in simulation, while it remains at about 8.4 in observed data. The product species id_4k49, id_btma, id_we08, id_pq2o, and id_nfl6 are still overproduced compared to observed data, especially id_nfl6.

This suggests the model is missing a mechanism to maintain id_l0rt at a nonzero steady state, possibly through synthesis or inflow. Also, the stoichiometry or rate constants may need further tuning.

I will test if adding a constant synthesis reaction for id_l0rt can maintain its level and improve the model fit.

## Action

### Code
```python
import libsbml

# Load the current refined SBML model
sbml_doc = libsbml.readSBMLFromString(shared_variables.access("updated_sbml_refined"))
model = sbml_doc.getModel()

# Add synthesis reaction for id_l0rt (zero order)
reaction_syn = model.createReaction()
reaction_syn.setId("synthesis_id_l0rt")
reaction_syn.setReversible(False)
reaction_syn.setFast(False)
# No reactants
product = reaction_syn.createProduct()
product.setSpecies("id_l0rt")
product.setStoichiometry(1)
product.setConstant(False)

# Add kinetic law with constant rate
kl_syn = reaction_syn.createKineticLaw()
param_syn = kl_syn.createParameter()
param_syn.setId("k_synthesis_id_l0rt")
param_syn.setValue(1.0)  # Arbitrary synthesis rate
param_syn.setConstant(True)
kl_syn.setFormula("k_synthesis_id_l0rt")

# Write the updated SBML string
updated_sbml_with_synthesis = libsbml.SBMLWriter().writeSBMLToString(sbml_doc)

# Save updated SBML for future use
shared_variables.add("updated_sbml_with_synthesis", updated_sbml_with_synthesis)

print("Synthesis reaction added for id_l0rt.")
```
# Observation

## Code Stdout
Synthesis reaction added for id_l0rt.

## 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_1, iteration_2, iteration_3)
- `shared_variables`: all variables you have saved from the previous iterations. (updated_sbml, updated_sbml_with_kinetics, updated_sbml_with_degradation, updated_sbml_refined, updated_sbml_with_synthesis)

### 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
I have added a synthesis reaction for id_l0rt to maintain its concentration and prevent complete depletion. The next step is to simulate this updated model with synthesis under default initial conditions and compare the simulation results with the observed data from iteration_0 to evaluate if this addition improves the model fit.

## Action

### Code
```python
# Retrieve the updated SBML model with synthesis
updated_sbml_with_synthesis = shared_variables.access("updated_sbml_with_synthesis")

# Simulate the updated model with synthesis
sim_data_synthesis = simulate(updated_sbml_with_synthesis)

# Access the observed data from iteration_0
obs_data = experiment_history["iteration_0"]

# Compare simulation and observation for key species by printing the last time point concentrations
print("Simulation final concentrations with synthesis:")
print(sim_data_synthesis.iloc[-1])

print("\nObserved final concentrations (iteration_0):")
print(obs_data.iloc[-1])
```
# Observation

## Code Stdout
Simulation final concentrations with synthesis:
id_l0rt      13.333333
id_v6ig       1.000000
id_4k49     115.484521
id_ljzz       1.650000
id_btma      57.742260
id_we08      57.742260
id_rzml       0.750000
id_pq2o      57.742260
id_nfl6     577.422604
Time       1838.000000
Name: 10000, dtype: float64

Observed final concentrations (iteration_0):
id_l0rt    8.415245e+00
id_v6ig    6.912660e-07
id_4k49    5.476307e-05
id_ljzz    1.396752e+00
id_btma    2.388145e-08
id_we08    7.289910e-07
id_rzml    3.249839e-03
id_pq2o    9.999438e-01
id_nfl6    1.591880e+02
Time       1.838000e+03
Name: 10000, dtype: float64

## 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_1, iteration_2, iteration_3)
- `shared_variables`: all variables you have saved from the previous iterations. (updated_sbml, updated_sbml_with_kinetics, updated_sbml_with_degradation, updated_sbml_refined, updated_sbml_with_synthesis)

### 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 synthesis reaction helped maintain id_l0rt at a nonzero level, but the product species concentrations are now much higher than observed, especially id_nfl6, id_4k49, id_btma, id_we08, and id_pq2o. This suggests the production rates or stoichiometries are too high relative to degradation and synthesis.

To improve the model, I will:
- Adjust stoichiometry coefficients for the reactions converting id_l0rt to products to be fractional, reflecting partial conversion.
- Reduce the synthesis rate of id_l0rt to better balance production and consumption.

I will first adjust stoichiometries of the reactions converting id_l0rt to products to 0.01 and reduce synthesis rate to 0.1.

## Action

### Code
```python
import libsbml

# Load the current SBML model with synthesis
sbml_doc = libsbml.readSBMLFromString(shared_variables.access("updated_sbml_with_synthesis"))
model = sbml_doc.getModel()

# Adjust stoichiometry of reactions converting id_l0rt to products to 0.01
reaction_ids_to_adjust = ["r1", "r4", "r5", "r6"]
for rid in reaction_ids_to_adjust:
    reaction = model.getReaction(rid)
    # Adjust product stoichiometry
    product = reaction.getProduct(0)
    product.setStoichiometry(0.01)

# Adjust stoichiometry for catalytic reaction r2 remains 1 for id_nfl6 product

# Reduce synthesis rate constant for id_l0rt
reaction_syn = model.getReaction("synthesis_id_l0rt")
kl_syn = reaction_syn.getKineticLaw()
param_syn = kl_syn.getParameter("k_synthesis_id_l0rt")
param_syn.setValue(0.1)

# Write the updated SBML string
updated_sbml_stoich_adjusted = libsbml.SBMLWriter().writeSBMLToString(sbml_doc)

# Save updated SBML for future use
shared_variables.add("updated_sbml_stoich_adjusted", updated_sbml_stoich_adjusted)

print("Stoichiometry adjusted and synthesis rate reduced.")
```
# Observation

## Code Stdout
Stoichiometry adjusted and synthesis rate reduced.

## 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_1, iteration_2, iteration_3)
- `shared_variables`: all variables you have saved from the previous iterations. (updated_sbml, updated_sbml_with_kinetics, updated_sbml_with_degradation, updated_sbml_refined, updated_sbml_with_synthesis, updated_sbml_stoich_adjusted)

### 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 have adjusted the stoichiometry of the reactions converting id_l0rt to products to 0.01 and reduced the synthesis rate of id_l0rt to 0.1. This should reduce the overproduction of product species and better balance the system.

Next, I will simulate this updated model under default initial conditions and compare the simulation results with the observed data from iteration_0 to evaluate the improvement.

## Action

### Code
```python
# Retrieve the updated SBML model with stoichiometry adjustment
updated_sbml_stoich_adjusted = shared_variables.access("updated_sbml_stoich_adjusted")

# Simulate the updated model
sim_data_stoich_adjusted = simulate(updated_sbml_stoich_adjusted)

# Access the observed data from iteration_0
obs_data = experiment_history["iteration_0"]

# Compare simulation and observation for key species by printing the last time point concentrations
print("Simulation final concentrations with stoichiometry adjustment:")
print(sim_data_stoich_adjusted.iloc[-1])

print("\nObserved final concentrations (iteration_0):")
print(obs_data.iloc[-1])
```
# Observation

## Code Stdout
Simulation final concentrations with stoichiometry adjustment:
id_l0rt       1.333333
id_v6ig       1.000000
id_4k49       0.148386
id_ljzz       1.650000
id_btma       0.074193
id_we08       0.074193
id_rzml       0.750000
id_pq2o       0.074193
id_nfl6      74.193241
Time       1838.000000
Name: 10000, dtype: float64

Observed final concentrations (iteration_0):
id_l0rt    8.415245e+00
id_v6ig    6.912660e-07
id_4k49    5.476307e-05
id_ljzz    1.396752e+00
id_btma    2.388145e-08
id_we08    7.289910e-07
id_rzml    3.249839e-03
id_pq2o    9.999438e-01
id_nfl6    1.591880e+02
Time       1.838000e+03
Name: 10000, 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_1, iteration_2, iteration_3)
- `shared_variables`: all variables you have saved from the previous iterations. (updated_sbml, updated_sbml_with_kinetics, updated_sbml_with_degradation, updated_sbml_refined, updated_sbml_with_synthesis, updated_sbml_stoich_adjusted)

### 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 stoichiometry adjustment and synthesis rate reduction improved the model fit for some species, but id_l0rt is still lower than observed, and id_nfl6 is about half the observed value. Other products are still higher than observed but closer.

To further improve, I will:
- Reduce the catalytic reaction rate constant (r2) to slow id_nfl6 production.
- Increase the degradation rates slightly to balance production.
- Possibly add feedback inhibition on synthesis if needed later.

I will first reduce the rate constant of reaction r2 and increase degradation rates moderately.

## Action

### Code
```python
import libsbml

# Load the current SBML model with stoichiometry adjustment
sbml_doc = libsbml.readSBMLFromString(shared_variables.access("updated_sbml_stoich_adjusted"))
model = sbml_doc.getModel()

# Reduce rate constant for catalytic reaction r2
reaction_r2 = model.getReaction("r2")
kl_r2 = reaction_r2.getKineticLaw()
param_r2 = kl_r2.getParameter("k_r2")
param_r2.setValue(0.01)  # Reduced from 0.05

# Increase degradation rate constants moderately
for i in range(model.getNumReactions()):
    reaction = model.getReaction(i)
    rid = reaction.getId()
    if rid.startswith("deg_"):
        kl = reaction.getKineticLaw()
        param = kl.getParameter("k_" + rid)
        param.setValue(0.005)  # Increased from 0.001

# Write the updated SBML string
updated_sbml_rate_adjusted = libsbml.SBMLWriter().writeSBMLToString(sbml_doc)

# Save updated SBML for future use
shared_variables.add("updated_sbml_rate_adjusted", updated_sbml_rate_adjusted)

print("Rate constants adjusted for catalytic and degradation reactions.")
```
# Observation

## Code Stdout
Rate constants adjusted for catalytic and degradation reactions.

## 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_1, iteration_2, iteration_3)
- `shared_variables`: all variables you have saved from the previous iterations. (updated_sbml, updated_sbml_with_kinetics, updated_sbml_with_degradation, updated_sbml_refined, updated_sbml_with_synthesis, updated_sbml_stoich_adjusted, updated_sbml_rate_adjusted)

### 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
I have adjusted the rate constants by reducing the catalytic reaction rate and increasing degradation rates moderately. This should slow down the production of id_nfl6 and increase removal of products, potentially improving the model fit.

Next, I will simulate this updated model under default initial conditions and compare the simulation results with the observed data from iteration_0 to evaluate the improvement.

## Action

### Code
```python
# Retrieve the updated SBML model with rate adjustments
updated_sbml_rate_adjusted = shared_variables.access("updated_sbml_rate_adjusted")

# Simulate the updated model
sim_data_rate_adjusted = simulate(updated_sbml_rate_adjusted)

# Access the observed data from iteration_0
obs_data = experiment_history["iteration_0"]

# Compare simulation and observation for key species by printing the last time point concentrations
print("Simulation final concentrations with rate adjustments:")
print(sim_data_rate_adjusted.iloc[-1])

print("\nObserved final concentrations (iteration_0):")
print(obs_data.iloc[-1])
```
# Observation

## Code Stdout
Simulation final concentrations with rate adjustments:
id_l0rt       2.857143
id_v6ig       1.000000
id_4k49       0.057194
id_ljzz       1.650000
id_btma       0.028597
id_we08       0.028597
id_rzml       0.750000
id_pq2o       0.028597
id_nfl6       5.719394
Time       1838.000000
Name: 10000, dtype: float64

Observed final concentrations (iteration_0):
id_l0rt    8.415245e+00
id_v6ig    6.912660e-07
id_4k49    5.476307e-05
id_ljzz    1.396752e+00
id_btma    2.388145e-08
id_we08    7.289910e-07
id_rzml    3.249839e-03
id_pq2o    9.999438e-01
id_nfl6    1.591880e+02
Time       1.838000e+03
Name: 10000, 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_1, iteration_2, iteration_3)
- `shared_variables`: all variables you have saved from the previous iterations. (updated_sbml, updated_sbml_with_kinetics, updated_sbml_with_degradation, updated_sbml_refined, updated_sbml_with_synthesis, updated_sbml_stoich_adjusted, updated_sbml_rate_adjusted)

### 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 rate adjustments slowed product formation and increased id_l0rt concentration compared to the previous iteration, but the model still underestimates id_l0rt and id_nfl6 and overestimates id_pq2o and others relative to observed data.

Given the limited remaining iterations, I will submit the current model as it captures the main features: id_l0rt consumption, catalytic role of id_v6ig, slow conversion of id_rzml to id_ljzz, and degradation of products.

## Action

### Submit
```python
import libsbml

final_sbml = shared_variables.access("updated_sbml_rate_adjusted")
```
