Alamoudi, E. | bioRxiv Science

bioRxiv2023

FitMultiCell: Simulating and parameterizing computational models of multi-scale and multi-cellular processes

MotivationBiological tissues are dynamic and highly organized. Multi-scale models are helpful tools to analyze and understand the processes determining tissue dynamics. These models usually depend on parameters that need to be inferred from experimental data to achieve a quantitative understanding, to predict the response to perturbations, and to evaluate competing hypotheses. However, even advanced inference approaches such as Approximate Bayesian Computation (ABC) are difficult to apply due to the computational complexity of the simulation of multi-scale models. Thus, there is a need for a scalable pipeline for modeling, simulating, and parameterizing multi-scale models of multi-cellular processes. ResultsHere, we present FitMultiCell, a computationally efficient and user-friendly open-source pipeline that can handle the full workflow of modeling, simulating, and parameterizing for multi-scale models of multi-cellular processes. The pipeline is modular and integrates the modeling and simulation tool Morpheus and the statistical inference tool pyABC. The easy integration of high-performance infrastructure allows to scale to computationally expensive problems. The introduction of a novel standard for the formulation of parameter inference problems for multi-scale models additionally ensures reproducibility and reusability. By applying the pipeline to multiple biological problems, we demonstrate its broad applicability, which will benefit in particular image-based systems biology. AvailabilityFitMultiCell is available open-source at https://gitlab.com/fitmulticell/fit. Contactjan.hasenauer@uni-bonn.de Supplementary informationSupplementary data are available at https://doi.org/10.5281/zenodo.7646287 online.

systems biology↗

bioRxiv2021

Robust adaptive distance functions for approximate Bayesian inference on outlier-corrupted data

Approximate Bayesian Computation (ABC) is a likelihood-free parameter inference method for complex stochastic models in systems biology and other research areas. While conceptually simple, its practical performance relies on the ability to efficiently compare relevant features in simulated and observed data via distance functions. Complications can arise particularly from the presence of outliers in the data, which can severely impair the inference. Thus, robust methods are required that provide reliable estimates also from outlier-corrupted data. We illustrate how established ABC distance functions are highly sensitive to outliers, and can in practice yield erroneous or highly uncertain parameter estimates and model predictions. We introduce self-tuned outlier-insensitive distance functions, based on a popular adaptive distance weighting concept, complemented by a simulation-based online outlier detection and downweighting routine. We evaluate and compare the presented methods on six test models covering different model types, problem features, and outlier scenarios. Our evaluation demonstrates substantial improvements on outlier-corrupted data, while giving at least comparable performance on outlier-free data. The developed methods have been made available as part of the open-source Python package pyABC (https://github.com/icb-dcm/pyabc).

systems biology↗