Open csmangum opened 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:
The cell cycle comprises phases that prepare the cell for division:
Parameter Duplication:
Introducing Mutations:
import copy
import torch
def replicate_parameters(parameters, mutation_rate=0.01, mutation_scale=0.1): new_parameters = copy.deepcopy(parameters) for param in new_parameters: if torch.rand(1).item() < mutation_rate: param += torch.randn_like(param) * mutation_scale return new_parameters
Division Process:
Cytokinesis:
Independent Operation:
Environmental Interaction:
Selection Pressures:
Genetic Diversity:
Recombination of Parameters:
Reducing Parameters:
Data Management:
Performance Optimization:
Visualization:
Initialization:
Simulation Loop:
Termination:
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)
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:
Design Phase:
Implementation Phase:
Cell
class:interphase()
: Growth and function execution.check_division()
: Determine when the cell divides.divide()
: Replicate neural network parameters with mutation.Optimization and Visualization:
Testing and Debugging:
Documentation:
Cell
class and simulation framework.Acceptance Criteria:
Cell
class capable of lifecycle management and division.