A Comprehensive Guide to Simulation-based Inference in Computational Biology

TL;DR

This paper provides guidelines for choosing between neural and statistical Simulation-Based Inference methods in biological models, highlighting their trade-offs in simulation efficiency and estimation bias with real-world data.

Contribution

It offers comprehensive decision guidelines for SBI in biological modeling and compares neural and statistical SBI methods using real-world cellular dynamics data.

Findings

Neural SBI requires fewer simulations but produces biased estimates.

Statistical SBI improves accuracy with more simulations.

Given sufficient resources, statistical SBI can outperform neural SBI.

Abstract

Computational models are invaluable in capturing the complexities of real-world biological processes. Yet, the selection of appropriate algorithms for inference tasks, especially when dealing with real-world observational data, remains a challenging and underexplored area. This gap has spurred the development of various parameter estimation algorithms, particularly within the realm of Simulation-Based Inference (SBI), such as neural and statistical SBI methods. Limited research exists on how to make informed choices on SBI methods when faced with real-world data, which often results in some form of model misspecification. In this paper, we provide comprehensive guidelines for deciding between SBI approaches for complex biological models. We apply the guidelines to two agent-based models that describe cellular dynamics using real-world data. Our study unveils a critical insight: while neural SBI methods demand significantly fewer simulations for inference results, they tend to yield biased estimations, a trend persistent even with robust variants of these algorithms. On the other hand, the accuracy of statistical SBI methods enhances substantially as the number of simulations increases. This finding suggests that, given a sufficient computational budget, statistical SBI can surpass neural SBI in performance. Our results not only shed light on the efficacy of different SBI methodologies in real-world scenarios but also suggest potential avenues for enhancing neural SBI approaches. This study is poised to be a useful resource for computational biologists navigating the intricate landscape of SBI in biological modeling.