Abstract
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.
Highlights
The Earth may be described as a microbial planet with microbes found in complex communities that perform a wide range of important functions, from microbial communities in the human gut, to communities in soil that affect the growth of plants (Leveau et al, 2018)
This is because agent-based modeling (ABM) of microbiology produce local and global information of microbial growth
Global information can inform on how a particular microbial population grows as a whole
Summary
The Earth may be described as a microbial planet with microbes found in complex communities that perform a wide range of important functions, from microbial communities in the human gut, to communities in soil that affect the growth of plants (Leveau et al, 2018). Agent-Based Model of Pantoea and composition of microbial communities, our ability to predict and manipulate the functions of microbial communities for desired outcomes is limited (Kreft et al, 2017). Complementing these experimental approaches, simulation techniques, such as agent-based modeling (ABM), are moving to the forefront as a powerful tool to unravel how interactions between microbes lead to emergent traits at the community level (Kreft et al, 1998, 2017; Hellweger et al, 2008, 2016; Leveau et al, 2018). During the validation process, one can focus on varying the independent parameters only, thereby reducing the computational burden
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