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

Abstract

Spatiotemporal models that account for heterogeneity within microbial communities rely on single-cell data for calibration and validation. Such data, commonly collected via microscopy and flow cytometry, have been made more accessible by recent advances in microfluidics platforms and data processing pipelines. However, validating models against such data poses significant challenges. Validation practices vary widely between modelling studies; systematic and rigorous methods have not been widely adopted. Similar challenges are faced by the (macrobial) ecology community, in which systematic calibration approaches are often employed to improve quantitative predictions from computational models. Here, we review single-cell observation techniques that are being applied to study microbial communities and the calibration strategies that are being employed for accompanying spatiotemporal models. To facilitate future calibration efforts, we have compiled a list of summary statistics relevant for quantifying spatiotemporal patterns in microbial communities. Finally, we highlight some recently developed techniques that hold promise for improved model calibration, including algorithmic guidance of summary statistic selection and machine learning approaches for efficient model simulation.

Calibrating agent-based models to tumor images using representation learning

“…While ABMs are powerful tools that can simulate many different tumor properties and predict emergent behaviors that occur on the spatial level, they are limited in the realm of parameterization. In general, fitting spatial models of cell populations to spatial data is a difficult task, where specific features of the spatial state have to be extracted in order to perform comparisons [ 6 ]. For ABMs, key parameters, such as proliferation rates, cell lifespans, and migration rates, can often be experimentally measured outside of the scope of the modeled system and are commonly found in the literature.…”