Calibrating spatiotemporal models of microbial communities to microscopy data: A review

Area of Science:

• Microbial Ecology
• Computational Biology
• Systems Biology

Background:

• Spatiotemporal models of microbial communities require single-cell data for calibration and validation.
• Advances in microfluidics and data processing have increased accessibility of single-cell data.
• Current validation practices for these models are inconsistent and lack rigor.

Purpose of the Study:

• To review single-cell observation techniques used for microbial community studies.
• To examine calibration strategies for spatiotemporal models of microbial communities.
• To provide resources and highlight new methods for improving model calibration.

Main Methods:

• Literature review of single-cell observation techniques (microscopy, flow cytometry).
• Review of calibration strategies for spatiotemporal ecological models.
• Compilation of summary statistics for microbial community spatiotemporal patterns.

Main Results:

• Identified a wide range of single-cell observation techniques applicable to microbial communities.
• Documented diverse and often unsystematic calibration approaches in modeling studies.
• Compiled a list of relevant summary statistics for quantifying microbial community patterns.

Conclusions:

• There is a need for standardized and rigorous validation methods in microbial spatiotemporal modeling.
• Newly developed techniques, including machine learning, show promise for enhancing model calibration.
• Facilitating better calibration will improve the accuracy and predictive power of microbial community models.