## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'grams of fiber' and 'milligrams of vitamin B5'. Let's denote 'grams of fiber' as $x_1$ and 'milligrams of vitamin B5' as $x_2$. The objective function to minimize is $1 \cdot x_1 + 2 \cdot x_2$. The constraints are:
- $4x_1 + 4x_2 \geq 1$ (cognitive performance index)
- $3x_1 + 4x_2 \geq 6$ (kidney support index)
- $1x_1 + 3x_2 \geq 6$ (digestive support index)
- $-2x_1 + 4x_2 \geq 0$
- $4x_1 + 4x_2 \leq 11$ (cognitive performance index upper bound)
- $3x_1 + 4x_2 \leq 26$ (kidney support index upper bound)
- $1x_1 + 3x_2 \leq 22$ (digestive support index upper bound)

## Step 2: Convert the problem into a Gurobi-compatible format
We need to express the problem in a way that Gurobi can understand. This involves defining the variables, the objective function, and the constraints.

## 3: Write down the symbolic representation of the problem
The symbolic representation is as follows:
```json
{
'sym_variables': [('x1', 'grams of fiber'), ('x2', 'milligrams of vitamin B5')],
'objective_function': '1*x1 + 2*x2',
'constraints': [
    '4*x1 + 4*x2 >= 1',
    '3*x1 + 4*x2 >= 6',
    '1*x1 + 3*x2 >= 6',
    '-2*x1 + 4*x2 >= 0',
    '4*x1 + 4*x2 <= 11',
    '3*x1 + 4*x2 <= 26',
    '1*x1 + 3*x2 <= 22'
]
}
```

## 4: Implement the problem in Gurobi
Now, let's implement this problem using Gurobi in Python.

```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="grams_of_fiber", lb=0)  # Assuming non-negative
    x2 = model.addVar(name="milligrams_of_vitamin_B5", lb=0)  # Assuming non-negative

    # Define the objective function
    model.setObjective(1 * x1 + 2 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(4 * x1 + 4 * x2 >= 1, name="cognitive_performance_index_min")
    model.addConstr(3 * x1 + 4 * x2 >= 6, name="kidney_support_index_min")
    model.addConstr(1 * x1 + 3 * x2 >= 6, name="digestive_support_index_min")
    model.addConstr(-2 * x1 + 4 * x2 >= 0, name="vitamin_B5_support")
    model.addConstr(4 * x1 + 4 * x2 <= 11, name="cognitive_performance_index_max")
    model.addConstr(3 * x1 + 4 * x2 <= 26, name="kidney_support_index_max")
    model.addConstr(1 * x1 + 3 * x2 <= 22, name="digestive_support_index_max")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Grams of fiber: {x1.varValue}")
        print(f"Milligrams of vitamin B5: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```