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Validation of an agent-based model for cell interactions in a microfluidic chip.

Simona Panunzi1, Marcello Pompa1, Pietro Marco D'Angelo2

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This study combines microfluidic cell co-culture with computational modeling and Approximate Bayesian Computation (ABC) to accurately predict cellular behavior in vitro. This approach enhances early therapeutic evaluation and understanding of cell-environment interactions.

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Area of Science:

  • In vitro modeling
  • Computational biology
  • Bioengineering

Background:

  • Microfluidic cell co-culture, tissue co-culture, and Organ-on-Chip (OoC) technologies facilitate in vitro modeling of tissues and organs.
  • These technologies enable studies of cell-environment interactions and early therapeutic evaluations.
  • Hybrid computational models (physiology-based, agent-based models (ABMs), cellular automata) combined with in vitro models offer tools for quantifying and predicting biological phenomena.

Purpose of the Study:

  • To extend a hybrid agent-based model (ABM) for cell co-culture experiments on a chip.
  • To address the challenge of parameter estimation for hybrid computational models using observational data.
  • To utilize Sequential Monte Carlo Approximate Bayesian Computation (ABC-SMC) for parameter estimation.

Main Methods:

  • Development and extension of a hybrid agent-based model (ABM) for microfluidic cell co-culture.
  • Application of Sequential Monte Carlo Approximate Bayesian Computation (ABC-SMC) for parameter estimation.
  • Utilizing cell tracking data as observational input for model parameterization.

Main Results:

  • The developed hybrid computational model accurately reproduces observed cellular behavior in vitro.
  • The model successfully distinguishes between different experimental conditions.
  • Parameter estimation using ABC-SMC proved effective for the hybrid ABM.

Conclusions:

  • The integration of microfluidic co-culture technology with hybrid computational models and ABC-SMC offers a robust framework.
  • This framework enhances the prediction of in vitro cellular behavior.
  • It improves the potential for early therapeutic evaluation and understanding of cell-environment interactions.