## Step 1: Define the symbolic representation of the variables
The variables are defined as follows:
- $x_1$ represents the milligrams of calcium
- $x_2$ represents the grams of protein
- $x_3$ represents the milligrams of vitamin B4
- $x_4$ represents the milligrams of vitamin B5

## Step 2: Translate the objective function into symbolic notation
The objective function to minimize is $2.64x_1 + 7.45x_2 + 4.13x_3 + 4.48x_4$.

## 3: Translate the constraints into symbolic notation
The constraints are:
- $16x_1 \leq 195$ (Implicitly, $x_1$ has an upper bound, but it's not directly given. However, we have specific indices: $x_1 = 16$, $x_2 = 23$, $x_3 = 8$, $x_4 = 22$)
- $23x_2$ is not directly constrained but $x_2$ has an index
- $8x_3$ is not directly constrained but $x_3$ has an index
- $22x_4$ is not directly constrained but $x_4$ has an index
- $23x_2 + 22x_4 \geq 34$
- $16x_1 + 8x_3 \geq 19$
- $8x_3 + 22x_4 \geq 17$
- $23x_2 + 8x_3 + 22x_4 \geq 41$
- $16x_1 + 8x_3 + 22x_4 \geq 41$
- $23x_2 + 8x_3 + 22x_4 \geq 41$
- $16x_1 + 23x_2 + 8x_3 + 22x_4 \geq 41$
- $6x_1 - x_3 \geq 0$
- $-8x_3 + 4x_4 \geq 0$
- $23x_2 + 22x_4 \leq 91$
- $16x_1 + 22x_4 \leq 114$
- $8x_3 + 22x_4 \leq 163$
- $16x_1 + 23x_2 \leq 161$
- $23x_2 + 8x_3 + 22x_4 \leq 86$
- $16x_1 + 23x_2 + 22x_4 \leq 89$

## 4: Define the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'milligrams of calcium'), 
        ('x2', 'grams of protein'), 
        ('x3', 'milligrams of vitamin B4'), 
        ('x4', 'milligrams of vitamin B5')
    ], 
    'objective_function': '2.64*x1 + 7.45*x2 + 4.13*x3 + 4.48*x4', 
    'constraints': [
        'x1 = 16', 
        'x2 = 23', 
        'x3 = 8', 
        'x4 = 22', 
        '23*x2 + 22*x4 >= 34', 
        '16*x1 + 8*x3 >= 19', 
        '8*x3 + 22*x4 >= 17', 
        '23*x2 + 8*x3 + 22*x4 >= 41', 
        '16*x1 + 8*x3 + 22*x4 >= 41', 
        '23*x2 + 8*x3 + 22*x4 >= 41', 
        '16*x1 + 23*x2 + 8*x3 + 22*x4 >= 41', 
        '6*x1 - x3 >= 0', 
        '-8*x3 + 4*x4 >= 0', 
        '23*x2 + 22*x4 <= 91', 
        '16*x1 + 22*x4 <= 114', 
        '8*x3 + 22*x4 <= 163', 
        '16*x1 + 23*x2 <= 161', 
        '23*x2 + 8*x3 + 22*x4 <= 86', 
        '16*x1 + 23*x2 + 22*x4 <= 89'
    ]
}
```

## 5: Implement the optimization problem using Gurobi
```python
import gurobi

def solve_optimization_problem():
    model = gurobi.Model()

    # Define variables
    x1 = model.addVar(name="milligrams_of_calcium", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name="grams_of_protein", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
    x3 = model.addVar(name="milligrams_of_vitamin_B4", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
    x4 = model.addVar(name="milligrams_of_vitamin_B5", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)

    # Objective function
    model.setObjective(2.64*x1 + 7.45*x2 + 4.13*x3 + 4.48*x4, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(x1 == 16)
    model.addConstr(x2 == 23)
    model.addConstr(x3 == 8)
    model.addConstr(x4 == 22)
    model.addConstr(23*x2 + 22*x4 >= 34)
    model.addConstr(16*x1 + 8*x3 >= 19)
    model.addConstr(8*x3 + 22*x4 >= 17)
    model.addConstr(23*x2 + 8*x3 + 22*x4 >= 41)
    model.addConstr(16*x1 + 8*x3 + 22*x4 >= 41)
    model.addConstr(23*x2 + 8*x3 + 22*x4 >= 41)
    model.addConstr(16*x1 + 23*x2 + 8*x3 + 22*x4 >= 41)
    model.addConstr(6*x1 - x3 >= 0)
    model.addConstr(-8*x3 + 4*x4 >= 0)
    model.addConstr(23*x2 + 22*x4 <= 91)
    model.addConstr(16*x1 + 22*x4 <= 114)
    model.addConstr(8*x3 + 22*x4 <= 163)
    model.addConstr(16*x1 + 23*x2 <= 161)
    model.addConstr(23*x2 + 8*x3 + 22*x4 <= 86)
    model.addConstr(16*x1 + 23*x2 + 22*x4 <= 89)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of calcium: {x1.varValue}")
        print(f"Grams of protein: {x2.varValue}")
        print(f"Milligrams of vitamin B4: {x3.varValue}")
        print(f"Milligrams of vitamin B5: {x4.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```