Agent-based modeling (ABM) is a powerful simulation technique which describes a complex dynamic system based on its interacting constituent entities. While the flexibility of ABM enables broad application, the complexity of real-world models demands intensive computing resources and computational time; however, a metamodel may be constructed to gain insight at less computational expense. Here, we developed a model in NetLogo to describe the growth of a microbial population consisting of Pantoea. We applied 13 parameters that defined the model and actively changed seven of the parameters to modulate the evolution of the population curve in response to these changes. We efficiently performed more than 3,000 simulations using a Python wrapper, NL4Py. Upon evaluation of the correlation between the active parameters and outputs by random forest regression, we found that the parameters which define the depth of medium and glucose concentration affect the population curves significantly. Subsequently, we constructed a metamodel, a dense neural network, to predict the simulation outputs from the active parameters and found that it achieves high prediction accuracy, reaching an R² coefficient of determination value up to 0.92. Our approach of using a combination of ABM with random forest regression and neural network reduces the number of required ABM simulations. The simplified and refined metamodels may provide insights into the complex dynamic system before their transition to more sophisticated models that run on high-performance computing systems. The ultimate goal is to build a bridge between simulation and experiment, allowing model validation by comparing the simulated data to experimental data in microbiology.

基于代理的建模 (ABM) 是一种强大的模拟技术，它描述了基于其相互作用的组成实体的复杂动态系统。虽然 ABM 的灵活性可以实现广泛的应用，但现实世界模型的复杂性需要大量的计算资源和计算时间；然而，可以构建元模型以较少的计算开销获得洞察力。在这里，我们开发了一个模型网络标志 描述微生物种群的增长，包括 泛菌属. 我们应用了定义模型的 13 个参数，并主动更改了其中的 7 个参数，以调节人口曲线的演变以响应这些变化。我们使用 Python 包装器有效地执行了 3,000 多次模拟，NL4Py. 通过随机森林回归评估活动参数和输出之间的相关性，我们发现定义培养基深度和葡萄糖浓度的参数显着影响种群曲线。随后，我们构建了一个元模型，一个密集的神经网络，从活动参数预测模拟输出，发现它实现了很高的预测精度，达到了电阻²决定系数值高达0.92。我们将 ABM 与随机森林回归和神经网络结合使用的方法减少了所需的 ABM 模拟次数。简化和精炼的元模型可以在过渡到在高性能计算系统上运行的更复杂模型之前提供对复杂动态系统的洞察。最终目标是在模拟和实验之间架起一座桥梁，通过将模拟数据与微生物学中的实验数据进行比较来验证模型。