Biology Knowledge Discovery for Bacteria Growth

Problem

The Bacteria Growth problem, introduced in LLM-SR: Scientific Equation Discovery via Programming with Large Language Models, is a biology-focused task aiming to discover growth patterns by minimizing mean square error based on environmental parameters.

• Given: Bacteria environment parameters, a set of constant parameters.

• Objective: Minimize the mean square error.

• Constraints:

  • None

Algorithm Design Task

• The task is to design the function to fit the dataset.

  • Inputs: Population density of the bacterial species, substrate concentration, temperature, PH level, numeric constants or parameters to be optimized.

  • Outputs: Predicted value.

Evaluation

• Dataset: Dataset from LLM-SR: Scientific Equation Discovery via Programming with Large Language Models.

• Fitness: Mean Square Error

Template:

template_program = '''
import numpy as np
def equation(b: np.ndarray, s: np.ndarray, temp: np.ndarray, pH: np.ndarray, params: np.ndarray) -> np.ndarray:
    """ Mathematical function for bacterial growth rate
    Args:
        b: A numpy array representing observations of population density of the bacterial species.
        s: A numpy array representing observations of substrate concentration.
        temp: A numpy array representing observations of temperature.
        pH: A numpy array representing observations of pH level.
        params: Array of numeric constants or parameters to be optimized

    Return:
        A numpy array representing bacterial growth rate as the result of applying the mathematical function to the inputs.
    """
    return params[0] * b + params[1] * s + params[2] * temp + params[3] * pH + params[4]

'''

task_description = "Find the mathematical function skeleton that represents E. Coli bacterial growth rate, given data on population density, substrate concentration, temperature, and pH level."