Automatic mechanistic inference from large families of Boolean models generated by Monte Carlo tree search.

ABSTRACT: Many important processes in biology, such as signaling and gene regulation, can be described using logic models. These logic models are typically built to behaviorally emulate experimentally observed phenotypes, which are assumed to be steady states of a biological system. Most models are built by hand and therefore researchers are only able to consider one or perhaps a few potential mechanisms. We present a method to automatically synthesize Boolean logic models with a specified set of steady states. Our method, called MC-Boomer, is based on Monte Carlo Tree Search an efficient, parallel search method using reinforcement learning. Our approach enables users to constrain the model search space using prior knowledge or biochemical interaction databases, thus leading to generation of biologi

SUBMITTER: Glazer BJ 

PROVIDER: S-EPMC10485623 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature