Dooders / Experiments

Repository for all specific experiments and tests
0 stars 0 forks source link

Feature: Design and Implement Neural Network-Based Cell Division Simulation #33

Open csmangum opened 4 hours ago

csmangum commented 4 hours ago

We need to design and implement a simulation framework for cell division, where each cell is represented as a neural network. The "genetic material" of the cell will be encapsulated in the parameters (weights and biases) of the neural network, which can replicate, mutate, and evolve over time.


Tasks:

  1. Design Phase:

    • Define the structure of the neural network representing the cell.
    • Specify parameters for the cell lifecycle:
      • Growth phase.
      • Division threshold.
      • Mutation rate and scale.
    • Outline environmental interactions:
      • Resource allocation.
      • Interaction between cells.
  2. Implementation Phase:

    • Create a Cell class:
      • Encapsulate the neural network as the cell's genetic material.
      • Implement lifecycle methods:
      • interphase(): Growth and function execution.
      • check_division(): Determine when the cell divides.
      • divide(): Replicate neural network parameters with mutation.
    • Implement the simulation environment:
      • Track cell interactions and environmental effects.
      • Manage resources and placement of daughter cells.
    • Add mutation mechanics for parameter evolution:
      • Random perturbations controlled by mutation rate and scale.
  3. Optimization and Visualization:

    • Optimize the simulation loop for scalability.
    • Create visualizations for cell states, divisions, and parameter evolution.
    • Explore batch processing to simulate many cells efficiently.
  4. Testing and Debugging:

    • Write unit tests for cell lifecycle methods.
    • Validate the accuracy of parameter replication and mutation.
    • Test edge cases (e.g., low resources, high mutation rates).
  5. Documentation:

    • Provide clear documentation for the Cell class and simulation framework.
    • Include examples of how to initialize and run the simulation.

Acceptance Criteria:

csmangum commented 4 hours ago

Modeling Cell Division with Neural Networks Representing Cells

To model cell division in a simulation where each cell is represented as a neural network and its genetic material is encapsulated in its parameters (weights and biases), you can simulate biological processes computationally. Here's how you can approach this:


1. Representing Cells as Neural Networks


2. Simulating the Cell Cycle

The cell cycle comprises phases that prepare the cell for division:


3. Replicating Genetic Material (Parameters)


4. Simulating Mitosis (Cell Division)


5. Post-Division Cell Functioning


6. Incorporating Evolutionary Dynamics


7. Advanced Modeling Concepts

a. Meiosis and Sexual Reproduction (Optional)

b. Epigenetic Factors


8. Practical Implementation Tips


9. Example Workflow

  1. Initialization:

    • Start with a single cell (neural network) with initial parameters.
  2. Simulation Loop:

    • For each time step:
      • Interphase:
      • Cells process inputs, perform functions, and possibly adapt parameters through learning algorithms.
      • Check for Division Conditions:
      • Cells decide to divide based on criteria (e.g., reaching a certain 'age' or resource threshold).
      • Division:
      • Cells replicate parameters (with mutations) and create daughter cells.
      • Environmental Interaction:
      • Cells interact with the environment and other cells.
      • Update Environment:
      • Adjust environmental factors based on cell actions.
  3. Termination:

    • The simulation runs for a set number of time steps or until certain conditions are met.

10. Example Code Snippet

class Cell:
    def __init__(self, parameters):
        self.network = NeuralNetwork(parameters)
        self.age = 0
        self.resources = initial_resources

    def interphase(self):
        # Cell performs functions and may update parameters
        inputs = self.sense_environment()
        outputs = self.network.forward(inputs)
        self.act_on_environment(outputs)
        self.age += 1

    def check_division(self):
        return self.resources > division_threshold

    def divide(self):
        # Replicate parameters with possible mutations
        new_params = replicate_parameters(self.network.parameters())
        daughter_cell = Cell(new_params)
        # Split resources
        self.resources /= 2
        daughter_cell.resources = self.resources
        return daughter_cell

# Simulation loop
cells = [Cell(initial_parameters)]
for timestep in range(max_timesteps):
    new_cells = []
    for cell in cells:
        cell.interphase()
        if cell.check_division():
            new_cell = cell.divide()
            new_cells.append(new_cell)
    cells.extend(new_cells)