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.Comment: 2022 International Conference on Machine Learning Workshop on
Computational Biolog