
                # 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_q4ga">
    <listOfCompartments>
      <compartment id="id_o0va" size="1"/>
    </listOfCompartments>
    <listOfSpecies>
      <species id="id_dlm5" compartment="id_o0va" initialConcentration="0"/>
      <species id="id_v9bo" compartment="id_o0va" initialConcentration="100" boundaryCondition="true" constant="true"/>
      <species id="id_acno" compartment="id_o0va" initialConcentration="0"/>
      <species id="id_wgvc" compartment="id_o0va" initialConcentration="0"/>
    </listOfSpecies>
  </model>
</sbml>


                ## Max iterations
                20

                Format your response according to the instructions in the system message.
                
## Thoughts
The initial model contains four species:
- id_dlm5 (initial concentration 0)
- id_v9bo (initial concentration 100, boundary and constant species)
- id_acno (initial concentration 0)
- id_wgvc (initial concentration 0)

There are no reactions currently defined in the model. The species id_v9bo is a boundary and constant species, so it likely serves as an input or source species.

To understand the system's dynamics and infer missing reactions, I will first observe the system's behavior with the current model (which has no reactions). This will give me baseline data on how species concentrations change over time (likely no change since no reactions).

After that, I will perturb the initial concentrations of some species to see if there are any spontaneous changes or if the system remains static. This will help me understand if any implicit reactions or processes are missing.

## 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 × 5 columns. 

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

Time   id_dlm5  id_acno  id_wgvc  id_v9bo
0         0        0        0 1.00e+02
...
1.63e+01  4.20e+00 9.85e+01 1.16e+05 1.00e+02
...
3.26e+01  8.49e+00 1.20e+02 1.73e+05 1.00e+02
...
4.90e+01  1.15e+01 1.30e+02 2.01e+05 1.00e+02
...
6.53e+01  1.33e+01 1.34e+02 2.14e+05 1.00e+02
...
8.16e+01  1.45e+01 1.36e+02 2.21e+05 1.00e+02
...
9.79e+01  1.51e+01 1.37e+02 2.24e+05 1.00e+02
...
1.14e+02  1.55e+01 1.38e+02 2.26e+05 1.00e+02
...
1.31e+02  1.57e+01 1.38e+02 2.27e+05 1.00e+02
...
1.47e+02  1.58e+01 1.38e+02 2.27e+05 1.00e+02
...
1.63e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
1.80e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
1.96e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
2.12e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
2.28e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
2.45e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
2.61e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
2.77e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
2.94e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
3.10e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
3.26e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
3.43e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
3.59e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
3.75e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
3.92e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
4.08e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
4.24e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
4.41e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
4.57e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
4.73e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
4.90e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
5.06e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
5.22e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
5.39e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
5.55e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
5.71e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
5.88e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
6.04e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
6.20e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
6.36e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
6.53e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
6.69e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
6.85e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
7.02e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
7.18e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
7.34e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
7.51e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
7.67e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
7.83e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
8.00e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+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 experiment shows that the concentrations of species id_dlm5, id_acno, and id_wgvc increase over time, while the concentration of id_v9bo remains constant at 100 (as expected since it is a boundary and constant species). The increase in the other species suggests that id_v9bo is likely a source species that is converted into the other species through some reactions.

The large increase in id_wgvc compared to id_acno and id_dlm5 suggests that there might be a reaction pathway from id_v9bo to id_wgvc, possibly with intermediate steps involving id_acno and id_dlm5.

To better understand the relationships and reaction pathways, I will perturb the initial concentration of id_v9bo (the source species) to a lower value and observe how the system responds. This will help confirm if id_v9bo is indeed the source and how the other species depend on it.

## Action

### Experiment
```json
{
  "action": "change_initial_concentration",
  "meta_data": {
    "id_v9bo": 50
  }
}
```
# Observation

## Experiment error
We were not able to run the experiment with your set experiment actions. Cannot modify a constant species id_v9bo

## Reminder

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



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

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


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



# Iteration 2
## Thoughts
The species id_v9bo is a boundary and constant species, so its initial concentration cannot be changed. This confirms it acts as a constant source or input to the system.

Since we cannot perturb id_v9bo, I will try perturbing the initial concentrations of the other species (id_dlm5, id_acno, id_wgvc) to see how the system responds. This will help reveal if there are reversible reactions or feedback loops involving these species.

I will start by increasing the initial concentration of id_dlm5 and observe the system's response.

## Action

### Experiment
```json
{
  "action": "change_initial_concentration",
  "meta_data": {
    "id_dlm5": 10
  }
}
```
# 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 × 5 columns. 

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

Time   id_dlm5  id_acno  id_wgvc  id_v9bo
0  1.00e+01        0        0 1.00e+02
...
1.63e+01  9.32e+00 9.85e+01 1.16e+05 1.00e+02
...
3.26e+01  1.11e+01 1.20e+02 1.73e+05 1.00e+02
...
4.90e+01  1.28e+01 1.30e+02 2.01e+05 1.00e+02
...
6.53e+01  1.40e+01 1.34e+02 2.14e+05 1.00e+02
...
8.16e+01  1.48e+01 1.36e+02 2.21e+05 1.00e+02
...
9.79e+01  1.53e+01 1.37e+02 2.24e+05 1.00e+02
...
1.14e+02  1.56e+01 1.38e+02 2.26e+05 1.00e+02
...
1.31e+02  1.57e+01 1.38e+02 2.27e+05 1.00e+02
...
1.47e+02  1.58e+01 1.38e+02 2.27e+05 1.00e+02
...
1.63e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
1.80e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
1.96e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
2.12e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
2.28e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
2.45e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
2.61e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
2.77e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
2.94e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
3.10e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
3.26e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
3.43e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
3.59e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
3.75e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
3.92e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
4.08e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
4.24e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
4.41e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
4.57e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
4.73e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
4.90e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
5.06e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
5.22e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
5.39e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
5.55e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
5.71e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
5.88e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
6.04e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
6.20e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
6.36e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
6.53e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
6.69e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
6.85e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
7.02e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
7.18e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
7.34e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
7.51e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
7.67e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
7.83e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
8.00e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+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_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 of increasing the initial concentration of id_dlm5 to 10 shows that id_dlm5 concentration slightly decreases initially but then increases over time, similar to the previous observation. The other species (id_acno and id_wgvc) still increase significantly, and id_v9bo remains constant.

This suggests that id_dlm5 might be involved in a reaction that consumes it initially but is also produced later, possibly from id_v9bo or other species. The large increase in id_wgvc and id_acno suggests they are downstream products.

To further understand the system, I will now perturb the initial concentration of id_acno to see how it affects the system dynamics.

## Action

### Experiment
```json
{
  "action": "change_initial_concentration",
  "meta_data": {
    "id_acno": 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 × 5 columns. 

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

Time   id_dlm5  id_acno  id_wgvc  id_v9bo
0         0 1.00e+01        0 1.00e+02
...
1.63e+01  4.20e+00 9.85e+01 1.16e+05 1.00e+02
...
3.26e+01  8.49e+00 1.20e+02 1.73e+05 1.00e+02
...
4.90e+01  1.15e+01 1.30e+02 2.01e+05 1.00e+02
...
6.53e+01  1.33e+01 1.34e+02 2.14e+05 1.00e+02
...
8.16e+01  1.45e+01 1.36e+02 2.21e+05 1.00e+02
...
9.79e+01  1.51e+01 1.37e+02 2.24e+05 1.00e+02
...
1.14e+02  1.55e+01 1.38e+02 2.26e+05 1.00e+02
...
1.31e+02  1.57e+01 1.38e+02 2.27e+05 1.00e+02
...
1.47e+02  1.58e+01 1.38e+02 2.27e+05 1.00e+02
...
1.63e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
1.80e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
1.96e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
2.12e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
2.28e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
2.45e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
2.61e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
2.77e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
2.94e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
3.10e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
3.26e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
3.43e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
3.59e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
3.75e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
3.92e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
4.08e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
4.24e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
4.41e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
4.57e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
4.73e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
4.90e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
5.06e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
5.22e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
5.39e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
5.55e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
5.71e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
5.88e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
6.04e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
6.20e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
6.36e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
6.53e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
6.69e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
6.85e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
7.02e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
7.18e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
7.34e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
7.51e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
7.67e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
7.83e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
8.00e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02

## 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_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
The perturbation of increasing id_acno initial concentration to 10 shows that id_acno concentration decreases initially but then increases over time, similar to previous observations. The other species (id_dlm5 and id_wgvc) increase over time, and id_v9bo remains constant.

This suggests that id_acno might be consumed initially but is also produced later, possibly from id_v9bo or other species. The large increase in id_wgvc suggests it is a downstream product.

Given the data, it seems likely that id_v9bo is converted into id_acno and id_dlm5, which in turn are converted into id_wgvc. There might be reactions like:
- id_v9bo → id_acno
- id_v9bo → id_dlm5
- id_acno → id_wgvc
- id_dlm5 → id_wgvc

I will now try to perturb the initial concentration of id_wgvc to see if it affects the system, which will help confirm if id_wgvc is only a product or also involved in other reactions.

## Action

### Experiment
```json
{
  "action": "change_initial_concentration",
  "meta_data": {
    "id_wgvc": 10
  }
}
```
# 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: 10001 rows × 5 columns. 

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

Time   id_dlm5  id_acno  id_wgvc  id_v9bo
0         0        0 1.00e+01 1.00e+02
...
1.63e+01  4.20e+00 9.85e+01 1.16e+05 1.00e+02
...
3.26e+01  8.49e+00 1.20e+02 1.73e+05 1.00e+02
...
4.90e+01  1.15e+01 1.30e+02 2.01e+05 1.00e+02
...
6.53e+01  1.33e+01 1.34e+02 2.14e+05 1.00e+02
...
8.16e+01  1.45e+01 1.36e+02 2.21e+05 1.00e+02
...
9.79e+01  1.51e+01 1.37e+02 2.24e+05 1.00e+02
...
1.14e+02  1.55e+01 1.38e+02 2.26e+05 1.00e+02
...
1.31e+02  1.57e+01 1.38e+02 2.27e+05 1.00e+02
...
1.47e+02  1.58e+01 1.38e+02 2.27e+05 1.00e+02
...
1.63e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
1.80e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
1.96e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
2.12e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
2.28e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
2.45e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
2.61e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
2.77e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
2.94e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
3.10e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
3.26e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
3.43e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
3.59e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
3.75e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
3.92e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
4.08e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
4.24e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
4.41e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
4.57e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
4.73e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
4.90e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
5.06e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
5.22e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
5.39e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
5.55e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
5.71e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
5.88e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
6.04e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
6.20e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
6.36e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
6.53e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
6.69e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
6.85e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
7.02e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
7.18e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
7.34e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
7.51e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
7.67e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
7.83e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02
...
8.00e+02  1.59e+01 1.38e+02 2.27e+05 1.00e+02

## 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_2, 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
The perturbation of increasing id_wgvc initial concentration to 10 shows that id_wgvc concentration increases rapidly over time, similar to previous observations. The other species (id_dlm5 and id_acno) also increase, and id_v9bo remains constant.

This suggests that id_wgvc is likely a terminal product or a species that accumulates and is not consumed significantly in the system. The increase in id_dlm5 and id_acno despite the initial id_wgvc perturbation suggests that id_wgvc does not feed back into the production of these species.

Based on all observations, a plausible reaction network is:
- id_v9bo → id_acno
- id_v9bo → id_dlm5
- id_acno → id_wgvc
- id_dlm5 → id_wgvc

I will now try to build this reaction network into the SBML model and simulate it to compare with the observed data.

## Action

### Code
```python
import libsbml

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

# Clear any existing reactions (there are none, but just in case)
model.removeReactions()

# Add reaction: id_v9bo -> id_acno
reaction1 = model.createReaction()
reaction1.setId("r1")
reaction1.setReversible(False)
reaction1.setFast(False)
reactant1 = reaction1.createReactant()
reactant1.setSpecies("id_v9bo")
reactant1.setStoichiometry(1)
reactant1.setConstant(False)
product1 = reaction1.createProduct()
product1.setSpecies("id_acno")
product1.setStoichiometry(1)
product1.setConstant(False)

# Add reaction: id_v9bo -> id_dlm5
reaction2 = model.createReaction()
reaction2.setId("r2")
reaction2.setReversible(False)
reaction2.setFast(False)
reactant2 = reaction2.createReactant()
reactant2.setSpecies("id_v9bo")
reactant2.setStoichiometry(1)
reactant2.setConstant(False)
product2 = reaction2.createProduct()
product2.setSpecies("id_dlm5")
product2.setStoichiometry(1)
product2.setConstant(False)

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

# Add reaction: id_dlm5 -> id_wgvc
reaction4 = model.createReaction()
reaction4.setId("r4")
reaction4.setReversible(False)
reaction4.setFast(False)
reactant4 = reaction4.createReactant()
reactant4.setSpecies("id_dlm5")
reactant4.setStoichiometry(1)
reactant4.setConstant(False)
product4 = reaction4.createProduct()
product4.setSpecies("id_wgvc")
product4.setStoichiometry(1)
product4.setConstant(False)

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

# Save updated model for future reference
shared_variables.add("updated_sbml", updated_sbml)

print("Reactions added to model. Ready for simulation.")
```
# Observation

## Code Stderror
 'Model' object has no attribute 'removeReactions'

## 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_2, 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 6
## Thoughts
The error indicates that the Model object in libsbml does not have a method called removeReactions. Since the initial model has no reactions, I can skip trying to remove reactions and directly add the new reactions.

I will add the four reactions as planned:
- id_v9bo → id_acno
- id_v9bo → id_dlm5
- id_acno → id_wgvc
- id_dlm5 → id_wgvc

After adding the reactions, I will simulate the updated model and compare the simulation results with the observed data to check if the model explains the system behavior.

## Action

### Code
```python
import libsbml

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

# Add reaction: id_v9bo -> id_acno
reaction1 = model.createReaction()
reaction1.setId("r1")
reaction1.setReversible(False)
reaction1.setFast(False)
reactant1 = reaction1.createReactant()
reactant1.setSpecies("id_v9bo")
reactant1.setStoichiometry(1)
reactant1.setConstant(False)
product1 = reaction1.createProduct()
product1.setSpecies("id_acno")
product1.setStoichiometry(1)
product1.setConstant(False)

# Add reaction: id_v9bo -> id_dlm5
reaction2 = model.createReaction()
reaction2.setId("r2")
reaction2.setReversible(False)
reaction2.setFast(False)
reactant2 = reaction2.createReactant()
reactant2.setSpecies("id_v9bo")
reactant2.setStoichiometry(1)
reactant2.setConstant(False)
product2 = reaction2.createProduct()
product2.setSpecies("id_dlm5")
product2.setStoichiometry(1)
product2.setConstant(False)

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

# Add reaction: id_dlm5 -> id_wgvc
reaction4 = model.createReaction()
reaction4.setId("r4")
reaction4.setReversible(False)
reaction4.setFast(False)
reactant4 = reaction4.createReactant()
reactant4.setSpecies("id_dlm5")
reactant4.setStoichiometry(1)
reactant4.setConstant(False)
product4 = reaction4.createProduct()
product4.setSpecies("id_wgvc")
product4.setStoichiometry(1)
product4.setConstant(False)

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

# Save updated model for future reference
shared_variables.add("updated_sbml", updated_sbml)

print("Reactions added to model. Ready for simulation.")
```
# Observation

## Code Stdout
Reactions added to model. Ready for simulation.

## 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_2, iteration_3, iteration_4)
- `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 7
## Thoughts
The reactions have been successfully added to the model. The next step is to simulate this updated model and compare the simulation results with the experimental data from the initial observation (iteration_0). This comparison will help assess if the added reactions explain the system dynamics well.

I will simulate the updated model and plot or print the time courses of all species to compare with the experimental data.

## Action

### Code
```python
import matplotlib.pyplot as plt

# Access the updated SBML model
updated_sbml = shared_variables.access("updated_sbml")

# Simulate the updated model
sim_data = simulate(updated_sbml)

# Access the original observation data for comparison
obs_data = experiment_history["iteration_0"]

# Print the first few rows of simulation and observation data for comparison
print("Simulation data (first 5 rows):")
print(sim_data.head())

print("\nObservation data (first 5 rows):")
print(obs_data.head())

# Plot simulation vs observation for each species
import matplotlib.pyplot as plt

species_ids = ["id_dlm5", "id_acno", "id_wgvc", "id_v9bo"]

plt.figure(figsize=(12, 8))
for i, species in enumerate(species_ids, 1):
    plt.subplot(2, 2, i)
    plt.plot(obs_data["Time"], obs_data[species], label="Observed")
    plt.plot(sim_data["Time"], sim_data[species], label="Simulated", linestyle='dashed')
    plt.title(species)
    plt.xlabel("Time")
    plt.ylabel("Concentration")
    plt.legend()

plt.tight_layout()
plt.show()
```
# 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: 13/20



### Available Global Variables
- `input_sbml_string`: Original incomplete model
- `experiment_history`: Results from all previous experiments (iteration_0, iteration_2, iteration_3, iteration_4)
- `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 8
## Thoughts
Plotting is not allowed due to module restrictions. Instead, I will print summary statistics and the first few rows of the simulation and observation data to compare them numerically. This will help assess if the model captures the system dynamics well.

I will calculate the mean and final concentrations of each species in both datasets and print them for comparison.

## Action

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

# Simulate the updated model
sim_data = simulate(updated_sbml)

# Access the original observation data for comparison
obs_data = experiment_history["iteration_0"]

species_ids = ["id_dlm5", "id_acno", "id_wgvc", "id_v9bo"]

print("First 5 rows of simulation data:")
print(sim_data.head())

print("\nFirst 5 rows of observation data:")
print(obs_data.head())

print("\nSummary statistics comparison (mean and final concentrations):")
for species in species_ids:
    sim_mean = sim_data[species].mean()
    sim_final = sim_data[species].iloc[-1]
    obs_mean = obs_data[species].mean()
    obs_final = obs_data[species].iloc[-1]
    print(f"{species}: Sim mean={sim_mean:.2e}, Sim final={sim_final:.2e} | Obs mean={obs_mean:.2e}, Obs final={obs_final:.2e}")
```
# Observation

## Code Stdout
First 5 rows of simulation data:
   id_dlm5  id_acno  id_wgvc  id_v9bo  Time
0      0.0      0.0      0.0    100.0  0.00
1      0.0      0.0      0.0    100.0  0.08
2      0.0      0.0      0.0    100.0  0.16
3      0.0      0.0      0.0    100.0  0.24
4      0.0      0.0      0.0    100.0  0.32

First 5 rows of observation data:
    id_dlm5    id_acno      id_wgvc  id_v9bo  Time
0  0.000000   0.000000     0.000000    100.0  0.00
1  0.003458   4.896884   793.701528    100.0  0.08
2  0.008172   8.862260  1585.543516    100.0  0.16
3  0.013668  11.867208  2375.560921    100.0  0.24
4  0.019877  14.316097  3163.356640    100.0  0.32

Summary statistics comparison (mean and final concentrations):
id_dlm5: Sim mean=0.00e+00, Sim final=0.00e+00 | Obs mean=1.52e+01, Obs final=1.59e+01
id_acno: Sim mean=0.00e+00, Sim final=0.00e+00 | Obs mean=1.36e+02, Obs final=1.38e+02
id_wgvc: Sim mean=0.00e+00, Sim final=0.00e+00 | Obs mean=2.21e+05, Obs final=2.27e+05
id_v9bo: Sim mean=1.00e+02, Sim final=1.00e+02 | Obs mean=1.00e+02, Obs final=1.00e+02

## 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_2, iteration_3, iteration_4)
- `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 9
## Thoughts
The simulation results show no change in concentrations of id_dlm5, id_acno, and id_wgvc over time, while the observed data shows significant increases. This indicates that the reactions added are not active, likely because no kinetic laws or rate parameters were defined for the reactions.

To fix this, I need to add kinetic laws with rate constants to each reaction to allow the conversion of species. I will add simple mass-action kinetics with arbitrary positive rate constants to each reaction and then simulate again.

## Action

### Code
```python
import libsbml

# Load the input 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)

# Helper function to add reaction with mass-action kinetics
def add_reaction(model, rid, reactant_id, product_id, k_id, k_value):
    reaction = model.createReaction()
    reaction.setId(rid)
    reaction.setReversible(False)
    reaction.setFast(False)
    reactant = reaction.createReactant()
    reactant.setSpecies(reactant_id)
    reactant.setStoichiometry(1)
    reactant.setConstant(False)
    product = reaction.createProduct()
    product.setSpecies(product_id)
    product.setStoichiometry(1)
    product.setConstant(False)
    # Create kinetic law
    kl = reaction.createKineticLaw()
    # Create parameter for rate constant
    param = kl.createParameter()
    param.setId(k_id)
    param.setValue(k_value)
    param.setConstant(True)
    # Set kinetic law formula (mass action)
    kl.setFormula(f"{k_id} * {reactant_id}")

# Add reactions with rate constants
add_reaction(model, "r1", "id_v9bo", "id_acno", "k1", 0.1)
add_reaction(model, "r2", "id_v9bo", "id_dlm5", "k2", 0.05)
add_reaction(model, "r3", "id_acno", "id_wgvc", "k3", 0.01)
add_reaction(model, "r4", "id_dlm5", "id_wgvc", "k4", 0.02)

# Write the updated SBML model to a string
writer = libsbml.SBMLWriter()
updated_sbml_with_kinetics = writer.writeSBMLToString(sbml_doc)

# Save updated model for future reference
shared_variables.add("updated_sbml_with_kinetics", updated_sbml_with_kinetics)

print("Reactions with kinetics added to model. Ready for simulation.")
```
# Observation

## Code Stdout
Reactions with kinetics added to model. Ready for simulation.

## 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_2, iteration_3, iteration_4)
- `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 10
## Thoughts
The reactions with kinetic laws have been added successfully. The next step is to simulate this updated model with kinetics and compare the simulation results with the observed data from iteration_0. This will help verify if the model with kinetics captures the system dynamics.

I will simulate the updated model with kinetics and print summary statistics and first few rows of the simulation data alongside the observed data for comparison.

## Action

### Code
```python
# Access 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 original observation data for comparison
obs_data = experiment_history["iteration_0"]

species_ids = ["id_dlm5", "id_acno", "id_wgvc", "id_v9bo"]

print("First 5 rows of simulation data with kinetics:")
print(sim_data_kinetics.head())

print("\nFirst 5 rows of observation data:")
print(obs_data.head())

print("\nSummary statistics comparison (mean and final concentrations):")
for species in species_ids:
    sim_mean = sim_data_kinetics[species].mean()
    sim_final = sim_data_kinetics[species].iloc[-1]
    obs_mean = obs_data[species].mean()
    obs_final = obs_data[species].iloc[-1]
    print(f"{species}: Sim mean={sim_mean:.2e}, Sim final={sim_final:.2e} | Obs mean={obs_mean:.2e}, Obs final={obs_final:.2e}")
```
# Observation

## Code Stdout
First 5 rows of simulation data with kinetics:
    id_dlm5   id_acno   id_wgvc  id_v9bo  Time
0  0.000000  0.000000  0.000000    100.0  0.00
1  0.399680  0.799680  0.000640    100.0  0.08
2  0.798721  1.598721  0.002558    100.0  0.16
3  1.197125  2.397122  0.005753    100.0  0.24
4  1.594891  3.194885  0.010224    100.0  0.32

First 5 rows of observation data:
    id_dlm5    id_acno      id_wgvc  id_v9bo  Time
0  0.000000   0.000000     0.000000    100.0  0.00
1  0.003458   4.896884   793.701528    100.0  0.08
2  0.008172   8.862260  1585.543516    100.0  0.16
3  0.013668  11.867208  2375.560921    100.0  0.24
4  0.019877  14.316097  3163.356640    100.0  0.32

Summary statistics comparison (mean and final concentrations):
id_dlm5: Sim mean=2.34e+02, Sim final=2.50e+02 | Obs mean=1.52e+01, Obs final=1.59e+01
id_acno: Sim mean=8.75e+02, Sim final=1.00e+03 | Obs mean=1.36e+02, Obs final=1.38e+02
id_wgvc: Sim mean=4.89e+03, Sim final=1.08e+04 | Obs mean=2.21e+05, Obs final=2.27e+05
id_v9bo: Sim mean=1.00e+02, Sim final=1.00e+02 | Obs mean=1.00e+02, Obs final=1.00e+02

## 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_2, iteration_3, iteration_4)
- `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 11
## Thoughts
The simulation with kinetics shows the expected increase in species concentrations over time, unlike the previous simulation without kinetics. However, the simulated concentrations for id_dlm5, id_acno, and id_wgvc are much higher than the observed data, indicating that the rate constants might be too high or the model is missing some degradation or consumption reactions.

The observed data shows a much larger increase in id_wgvc compared to id_acno and id_dlm5, suggesting that the conversion rates to id_wgvc might be higher or there might be additional amplification steps.

To improve the model, I will try to adjust the rate constants to better match the observed data trends. I will reduce the rate constants for the first two reactions (id_v9bo to id_acno and id_dlm5) and increase the rate constants for the conversion to id_wgvc.

## Action

### Code
```python
import libsbml

# Load the input 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)

# Helper function to add reaction with mass-action kinetics
def add_reaction(model, rid, reactant_id, product_id, k_id, k_value):
    reaction = model.createReaction()
    reaction.setId(rid)
    reaction.setReversible(False)
    reaction.setFast(False)
    reactant = reaction.createReactant()
    reactant.setSpecies(reactant_id)
    reactant.setStoichiometry(1)
    reactant.setConstant(False)
    product = reaction.createProduct()
    product.setSpecies(product_id)
    product.setStoichiometry(1)
    product.setConstant(False)
    # Create kinetic law
    kl = reaction.createKineticLaw()
    # Create parameter for rate constant
    param = kl.createParameter()
    param.setId(k_id)
    param.setValue(k_value)
    param.setConstant(True)
    # Set kinetic law formula (mass action)
    kl.setFormula(f"{k_id} * {reactant_id}")

# Adjusted rate constants based on previous simulation
add_reaction(model, "r1", "id_v9bo", "id_acno", "k1", 0.01)  # reduced
add_reaction(model, "r2", "id_v9bo", "id_dlm5", "k2", 0.005)  # reduced
add_reaction(model, "r3", "id_acno", "id_wgvc", "k3", 0.05)  # increased
add_reaction(model, "r4", "id_dlm5", "id_wgvc", "k4", 0.1)  # increased

# Write the updated SBML model to a string
writer = libsbml.SBMLWriter()
updated_sbml_adjusted = writer.writeSBMLToString(sbml_doc)

# Save updated model for future reference
shared_variables.add("updated_sbml_adjusted", updated_sbml_adjusted)

print("Adjusted reactions with new kinetics added to model. Ready for simulation.")
```
# Observation

## Code Stdout
Adjusted reactions with new kinetics added to model. Ready for simulation.

## 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_2, iteration_3, iteration_4)
- `shared_variables`: all variables you have saved from the previous iterations. (updated_sbml, updated_sbml_with_kinetics, updated_sbml_adjusted)

### 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 adjusted model with new rate constants has been created. The next step is to simulate this adjusted model and compare the simulation results with the observed data from iteration_0. This will help verify if the adjusted rate constants improve the model's fit to the data.

I will simulate the adjusted model and print summary statistics and first few rows of the simulation data alongside the observed data for comparison.

## Action

### Code
```python
# Access the updated SBML model with adjusted kinetics
updated_sbml_adjusted = shared_variables.access("updated_sbml_adjusted")

# Simulate the updated model with adjusted kinetics
sim_data_adjusted = simulate(updated_sbml_adjusted)

# Access the original observation data for comparison
obs_data = experiment_history["iteration_0"]

species_ids = ["id_dlm5", "id_acno", "id_wgvc", "id_v9bo"]

print("First 5 rows of simulation data with adjusted kinetics:")
print(sim_data_adjusted.head())

print("\nFirst 5 rows of observation data:")
print(obs_data.head())

print("\nSummary statistics comparison (mean and final concentrations):")
for species in species_ids:
    sim_mean = sim_data_adjusted[species].mean()
    sim_final = sim_data_adjusted[species].iloc[-1]
    obs_mean = obs_data[species].mean()
    obs_final = obs_data[species].iloc[-1]
    print(f"{species}: Sim mean={sim_mean:.2e}, Sim final={sim_final:.2e} | Obs mean={obs_mean:.2e}, Obs final={obs_final:.2e}")
```
# Observation

## Code Stdout
First 5 rows of simulation data with adjusted kinetics:
    id_dlm5   id_acno   id_wgvc  id_v9bo  Time
0  0.000000  0.000000  0.000000    100.0  0.00
1  0.039840  0.079840  0.000319    100.0  0.08
2  0.079363  0.159362  0.001275    100.0  0.16
3  0.118571  0.238566  0.002863    100.0  0.24
4  0.157467  0.317454  0.005079    100.0  0.32

First 5 rows of observation data:
    id_dlm5    id_acno      id_wgvc  id_v9bo  Time
0  0.000000   0.000000     0.000000    100.0  0.00
1  0.003458   4.896884   793.701528    100.0  0.08
2  0.008172   8.862260  1585.543516    100.0  0.16
3  0.013668  11.867208  2375.560921    100.0  0.24
4  0.019877  14.316097  3163.356640    100.0  0.32

Summary statistics comparison (mean and final concentrations):
id_dlm5: Sim mean=4.94e+00, Sim final=5.00e+00 | Obs mean=1.52e+01, Obs final=1.59e+01
id_acno: Sim mean=1.95e+01, Sim final=2.00e+01 | Obs mean=1.36e+02, Obs final=1.38e+02
id_wgvc: Sim mean=5.76e+02, Sim final=1.17e+03 | Obs mean=2.21e+05, Obs final=2.27e+05
id_v9bo: Sim mean=1.00e+02, Sim final=1.00e+02 | Obs mean=1.00e+02, Obs final=1.00e+02

## 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_2, iteration_3, iteration_4)
- `shared_variables`: all variables you have saved from the previous iterations. (updated_sbml, updated_sbml_with_kinetics, updated_sbml_adjusted)

### 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 adjusted model with new rate constants shows improved agreement with the observed data for id_dlm5 and id_acno, but the simulated id_wgvc concentration is still much lower than observed. This suggests that the production of id_wgvc might be underestimated or there might be an additional amplification or autocatalytic step missing.

Given the large increase in id_wgvc in the observed data, I hypothesize that id_wgvc might catalyze its own production or there is a positive feedback loop.

I will test this hypothesis by adding an autocatalytic reaction where id_wgvc catalyzes the conversion of id_acno to id_wgvc, i.e., id_acno + id_wgvc → 2 id_wgvc.

## Action

### Code
```python
import libsbml

# Load the input SBML model
sbml_doc = libsbml.readSBMLFromString(shared_variables.access("updated_sbml_adjusted"))
model = sbml_doc.getModel()

# Add autocatalytic reaction: id_acno + id_wgvc -> 2 id_wgvc
reaction = model.createReaction()
reaction.setId("r5")
reaction.setReversible(False)
reaction.setFast(False)

# Reactants
reactant1 = reaction.createReactant()
reactant1.setSpecies("id_acno")
reactant1.setStoichiometry(1)
reactant1.setConstant(False)

reactant2 = reaction.createReactant()
reactant2.setSpecies("id_wgvc")
reactant2.setStoichiometry(1)
reactant2.setConstant(False)

# Products
product = reaction.createProduct()
product.setSpecies("id_wgvc")
product.setStoichiometry(2)
product.setConstant(False)

# Kinetic law with rate constant k5
kl = reaction.createKineticLaw()
param = kl.createParameter()
param.setId("k5")
param.setValue(0.001)  # small rate constant to start
param.setConstant(True)
kl.setFormula("k5 * id_acno * id_wgvc")

# Write the updated SBML model to a string
writer = libsbml.SBMLWriter()
updated_sbml_autocat = writer.writeSBMLToString(sbml_doc)

# Save updated model for future reference
shared_variables.add("updated_sbml_autocat", updated_sbml_autocat)

print("Added autocatalytic reaction to model. Ready for simulation.")
```
# Observation

## Code Stdout
Added autocatalytic reaction to model. Ready for simulation.

## 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_2, iteration_3, iteration_4)
- `shared_variables`: all variables you have saved from the previous iterations. (updated_sbml, updated_sbml_with_kinetics, updated_sbml_adjusted, updated_sbml_autocat)

### 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 autocatalytic reaction has been added to the model. The next step is to simulate this updated model and compare the simulation results with the observed data from iteration_0. This will help verify if the autocatalytic reaction improves the model's fit to the data, especially for id_wgvc.

I will simulate the updated model with the autocatalytic reaction and print summary statistics and first few rows of the simulation data alongside the observed data for comparison.

## Action

### Code
```python
# Access the updated SBML model with autocatalytic reaction
updated_sbml_autocat = shared_variables.access("updated_sbml_autocat")

# Simulate the updated model with autocatalytic reaction
sim_data_autocat = simulate(updated_sbml_autocat)

# Access the original observation data for comparison
obs_data = experiment_history["iteration_0"]

species_ids = ["id_dlm5", "id_acno", "id_wgvc", "id_v9bo"]

print("First 5 rows of simulation data with autocatalytic reaction:")
print(sim_data_autocat.head())

print("\nFirst 5 rows of observation data:")
print(obs_data.head())

print("\nSummary statistics comparison (mean and final concentrations):")
for species in species_ids:
    sim_mean = sim_data_autocat[species].mean()
    sim_final = sim_data_autocat[species].iloc[-1]
    obs_mean = obs_data[species].mean()
    obs_final = obs_data[species].iloc[-1]
    print(f"{species}: Sim mean={sim_mean:.2e}, Sim final={sim_final:.2e} | Obs mean={obs_mean:.2e}, Obs final={obs_final:.2e}")
```
# Observation

## Code Stdout
First 5 rows of simulation data with autocatalytic reaction:
    id_dlm5   id_acno   id_wgvc  id_v9bo  Time
0  0.000000  0.000000  0.000000    100.0  0.00
1  0.039840  0.079840  0.000319    100.0  0.08
2  0.079363  0.159362  0.001275    100.0  0.16
3  0.118571  0.238566  0.002863    100.0  0.24
4  0.157467  0.317453  0.005079    100.0  0.32

First 5 rows of observation data:
    id_dlm5    id_acno      id_wgvc  id_v9bo  Time
0  0.000000   0.000000     0.000000    100.0  0.00
1  0.003458   4.896884   793.701528    100.0  0.08
2  0.008172   8.862260  1585.543516    100.0  0.16
3  0.013668  11.867208  2375.560921    100.0  0.24
4  0.019877  14.316097  3163.356640    100.0  0.32

Summary statistics comparison (mean and final concentrations):
id_dlm5: Sim mean=4.94e+00, Sim final=5.00e+00 | Obs mean=1.52e+01, Obs final=1.59e+01
id_acno: Sim mean=2.69e+00, Sim final=8.05e-01 | Obs mean=1.36e+02, Obs final=1.38e+02
id_wgvc: Sim mean=5.92e+02, Sim final=1.19e+03 | Obs mean=2.21e+05, Obs final=2.27e+05
id_v9bo: Sim mean=1.00e+02, Sim final=1.00e+02 | Obs mean=1.00e+02, Obs final=1.00e+02

## 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_2, iteration_3, iteration_4)
- `shared_variables`: all variables you have saved from the previous iterations. (updated_sbml, updated_sbml_with_kinetics, updated_sbml_adjusted, updated_sbml_autocat)

### 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
The autocatalytic reaction did not significantly improve the fit for id_acno and id_wgvc concentrations. The id_acno concentration is much lower than observed, and id_wgvc is still far below observed values.

This suggests that the model might be missing additional reactions or mechanisms. One possibility is that id_acno is produced from id_dlm5 or vice versa, or there is a reversible reaction between them.

I will test adding a reversible reaction between id_dlm5 and id_acno to see if this improves the model fit.

## Action

### Code
```python
import libsbml

# Load the input SBML model
sbml_doc = libsbml.readSBMLFromString(shared_variables.access("updated_sbml_autocat"))
model = sbml_doc.getModel()

# Add reversible reaction: id_dlm5 <-> id_acno
reaction = model.createReaction()
reaction.setId("r6")
reaction.setReversible(True)
reaction.setFast(False)

# Reactants and products for forward reaction
reactant = reaction.createReactant()
reactant.setSpecies("id_dlm5")
reactant.setStoichiometry(1)
reactant.setConstant(False)

product = reaction.createProduct()
product.setSpecies("id_acno")
product.setStoichiometry(1)
product.setConstant(False)

# Kinetic law with forward and reverse rate constants k6f and k6r
kl = reaction.createKineticLaw()
param_f = kl.createParameter()
param_f.setId("k6f")
param_f.setValue(0.01)
param_f.setConstant(True)

param_r = kl.createParameter()
param_r.setId("k6r")
param_r.setValue(0.005)
param_r.setConstant(True)

kl.setFormula("k6f * id_dlm5 - k6r * id_acno")

# Write the updated SBML model to a string
writer = libsbml.SBMLWriter()
updated_sbml_reversible = writer.writeSBMLToString(sbml_doc)

# Save updated model for future reference
shared_variables.add("updated_sbml_reversible", updated_sbml_reversible)

print("Added reversible reaction between id_dlm5 and id_acno. Ready for simulation.")
```
# Observation

## Code Stdout
Added reversible reaction between id_dlm5 and id_acno. Ready for simulation.

## 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_2, iteration_3, iteration_4)
- `shared_variables`: all variables you have saved from the previous iterations. (updated_sbml, updated_sbml_with_kinetics, updated_sbml_adjusted, updated_sbml_autocat, updated_sbml_reversible)

### 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 reversible reaction between id_dlm5 and id_acno has been added. The next step is to simulate this updated model and compare the simulation results with the observed data from iteration_0. This will help verify if the reversible reaction improves the model's fit to the data.

I will simulate the updated model with the reversible reaction and print summary statistics and first few rows of the simulation data alongside the observed data for comparison.

## Action

### Code
```python
# Access the updated SBML model with reversible reaction
updated_sbml_reversible = shared_variables.access("updated_sbml_reversible")

# Simulate the updated model with reversible reaction
sim_data_reversible = simulate(updated_sbml_reversible)

# Access the original observation data for comparison
obs_data = experiment_history["iteration_0"]

species_ids = ["id_dlm5", "id_acno", "id_wgvc", "id_v9bo"]

print("First 5 rows of simulation data with reversible reaction:")
print(sim_data_reversible.head())

print("\nFirst 5 rows of observation data:")
print(obs_data.head())

print("\nSummary statistics comparison (mean and final concentrations):")
for species in species_ids:
    sim_mean = sim_data_reversible[species].mean()
    sim_final = sim_data_reversible[species].iloc[-1]
    obs_mean = obs_data[species].mean()
    obs_final = obs_data[species].iloc[-1]
    print(f"{species}: Sim mean={sim_mean:.2e}, Sim final={sim_final:.2e} | Obs mean={obs_mean:.2e}, Obs final={obs_final:.2e}")
```
# Observation

## Code Stdout
First 5 rows of simulation data with reversible reaction:
    id_dlm5   id_acno   id_wgvc  id_v9bo  Time
0  0.000000  0.000000  0.000000    100.0  0.00
1  0.039840  0.079840  0.000319    100.0  0.08
2  0.079364  0.159362  0.001275    100.0  0.16
3  0.118572  0.238565  0.002863    100.0  0.24
4  0.157468  0.317452  0.005079    100.0  0.32

First 5 rows of observation data:
    id_dlm5    id_acno      id_wgvc  id_v9bo  Time
0  0.000000   0.000000     0.000000    100.0  0.00
1  0.003458   4.896884   793.701528    100.0  0.08
2  0.008172   8.862260  1585.543516    100.0  0.16
3  0.013668  11.867208  2375.560921    100.0  0.24
4  0.019877  14.316097  3163.356640    100.0  0.32

Summary statistics comparison (mean and final concentrations):
id_dlm5: Sim mean=4.62e+00, Sim final=4.58e+00 | Obs mean=1.52e+01, Obs final=1.59e+01
id_acno: Sim mean=2.74e+00, Sim final=8.38e-01 | Obs mean=1.36e+02, Obs final=1.38e+02
id_wgvc: Sim mean=5.93e+02, Sim final=1.19e+03 | Obs mean=2.21e+05, Obs final=2.27e+05
id_v9bo: Sim mean=1.00e+02, Sim final=1.00e+02 | Obs mean=1.00e+02, Obs final=1.00e+02

## 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_2, iteration_3, iteration_4)
- `shared_variables`: all variables you have saved from the previous iterations. (updated_sbml, updated_sbml_with_kinetics, updated_sbml_adjusted, updated_sbml_autocat, updated_sbml_reversible)

### 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 reversible reaction between id_dlm5 and id_acno did not significantly improve the fit for id_acno or id_wgvc concentrations. The simulated id_acno remains much lower than observed, and id_wgvc is still far below observed values.

This suggests that the model might be missing additional reactions or mechanisms. The large increase in id_wgvc in the observed data suggests a strong amplification or production mechanism.

Given the data, I hypothesize that id_v9bo might directly produce id_wgvc in addition to producing id_acno and id_dlm5. I will add a direct reaction from id_v9bo to id_wgvc to test this.

## Action

### Code
```python
import libsbml

# Load the input SBML model
sbml_doc = libsbml.readSBMLFromString(shared_variables.access("updated_sbml_reversible"))
model = sbml_doc.getModel()

# Add reaction: id_v9bo -> id_wgvc
reaction = model.createReaction()
reaction.setId("r7")
reaction.setReversible(False)
reaction.setFast(False)

reactant = reaction.createReactant()
reactant.setSpecies("id_v9bo")
reactant.setStoichiometry(1)
reactant.setConstant(False)

product = reaction.createProduct()
product.setSpecies("id_wgvc")
product.setStoichiometry(1)
product.setConstant(False)

# Kinetic law with rate constant k7
kl = reaction.createKineticLaw()
param = kl.createParameter()
param.setId("k7")
param.setValue(0.1)  # moderate rate constant
param.setConstant(True)
kl.setFormula("k7 * id_v9bo")

# Write the updated SBML model to a string
writer = libsbml.SBMLWriter()
updated_sbml_direct_wgvc = writer.writeSBMLToString(sbml_doc)

# Save updated model for future reference
shared_variables.add("updated_sbml_direct_wgvc", updated_sbml_direct_wgvc)

print("Added direct reaction from id_v9bo to id_wgvc. Ready for simulation.")
```
# Observation

## Code Stdout
Added direct reaction from id_v9bo to id_wgvc. Ready for simulation.

## 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_2, iteration_3, iteration_4)
- `shared_variables`: all variables you have saved from the previous iterations. (updated_sbml, updated_sbml_with_kinetics, updated_sbml_adjusted, updated_sbml_autocat, updated_sbml_reversible, updated_sbml_direct_wgvc)

### 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 direct reaction from id_v9bo to id_wgvc has been added. The next step is to simulate this updated model and compare the simulation results with the observed data from iteration_0. This will help verify if the direct production of id_wgvc from id_v9bo improves the model's fit to the data.

I will simulate the updated model with the direct id_v9bo to id_wgvc reaction and print summary statistics and first few rows of the simulation data alongside the observed data for comparison.

## Action

### Code
```python
# Access the updated SBML model with direct id_v9bo to id_wgvc reaction
updated_sbml_direct_wgvc = shared_variables.access("updated_sbml_direct_wgvc")

# Simulate the updated model with direct id_v9bo to id_wgvc reaction
sim_data_direct_wgvc = simulate(updated_sbml_direct_wgvc)

# Access the original observation data for comparison
obs_data = experiment_history["iteration_0"]

species_ids = ["id_dlm5", "id_acno", "id_wgvc", "id_v9bo"]

print("First 5 rows of simulation data with direct id_v9bo to id_wgvc reaction:")
print(sim_data_direct_wgvc.head())

print("\nFirst 5 rows of observation data:")
print(obs_data.head())

print("\nSummary statistics comparison (mean and final concentrations):")
for species in species_ids:
    sim_mean = sim_data_direct_wgvc[species].mean()
    sim_final = sim_data_direct_wgvc[species].iloc[-1]
    obs_mean = obs_data[species].mean()
    obs_final = obs_data[species].iloc[-1]
    print(f"{species}: Sim mean={sim_mean:.2e}, Sim final={sim_final:.2e} | Obs mean={obs_mean:.2e}, Obs final={obs_final:.2e}")
```
# Observation

## Code Stdout
First 5 rows of simulation data with direct id_v9bo to id_wgvc reaction:
    id_dlm5   id_acno   id_wgvc  id_v9bo  Time
0  0.000000  0.000000  0.000000    100.0  0.00
1  0.039840  0.079838  0.800321    100.0  0.08
2  0.079364  0.159348  1.601288    100.0  0.16
3  0.118572  0.238519  2.402909    100.0  0.24
4  0.157468  0.317344  3.205188    100.0  0.32

First 5 rows of observation data:
    id_dlm5    id_acno      id_wgvc  id_v9bo  Time
0  0.000000   0.000000     0.000000    100.0  0.00
1  0.003458   4.896884   793.701528    100.0  0.08
2  0.008172   8.862260  1585.543516    100.0  0.16
3  0.013668  11.867208  2375.560921    100.0  0.24
4  0.019877  14.316097  3163.356640    100.0  0.32

Summary statistics comparison (mean and final concentrations):
id_dlm5: Sim mean=4.52e+00, Sim final=4.55e+00 | Obs mean=1.52e+01, Obs final=1.59e+01
id_acno: Sim mean=5.22e-01, Sim final=1.13e-01 | Obs mean=1.36e+02, Obs final=1.38e+02
id_wgvc: Sim mean=4.59e+03, Sim final=9.20e+03 | Obs mean=2.21e+05, Obs final=2.27e+05
id_v9bo: Sim mean=1.00e+02, Sim final=1.00e+02 | Obs mean=1.00e+02, Obs final=1.00e+02

## 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_2, iteration_3, iteration_4)
- `shared_variables`: all variables you have saved from the previous iterations. (updated_sbml, updated_sbml_with_kinetics, updated_sbml_adjusted, updated_sbml_autocat, updated_sbml_reversible, updated_sbml_direct_wgvc)

### 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 addition of the direct reaction from id_v9bo to id_wgvc improved the id_wgvc concentration significantly, but it is still much lower than observed. The id_acno concentration is very low compared to observed, and id_dlm5 is also underestimated.

The model captures the qualitative behavior but not the quantitative scale. Given the limited iterations left, I will submit the current model as the best hypothesis. It includes:
- id_v9bo → id_acno
- id_v9bo → id_dlm5
- id_acno → id_wgvc
- id_dlm5 → id_wgvc
- autocatalytic id_acno + id_wgvc → 2 id_wgvc
- reversible id_dlm5 ↔ id_acno
- direct id_v9bo → id_wgvc

This model captures the main pathways and feedbacks observed.

## Action

### Submit
```python
final_sbml = shared_variables.access("updated_sbml_direct_wgvc")
```
