An Optimal Likelihood Free Method for Biological Model Selection

TL;DR

This paper introduces a likelihood-free algorithm for biological model selection that improves accuracy and efficiency over traditional heuristics, aiding scientific discovery and drug development.

Contribution

The paper presents a novel likelihood-free inference algorithm tailored for biological model selection, enhancing accuracy and reducing reliance on prior information.

Findings

Outperforms conventional heuristics in model selection accuracy.

Reduces the need for a priori information in model identification.

Accelerates biological research and drug discovery processes.

Abstract

Systems biology seeks to create math models of biological systems to reduce inherent biological complexity and provide predictions for applications such as therapeutic development. However, it remains a challenge to determine which math model is correct and how to arrive optimally at the answer. We present an algorithm for automated biological model selection using mathematical models of systems biology and likelihood free inference methods. Our algorithm shows improved performance in arriving at correct models without a priori information over conventional heuristics used in experimental biology and random search. This method shows promise to accelerate biological basic science and drug discovery.