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

Frontiers in Cell and Developmental Biology

Cell and Developmental Biology

Carlos F. Lopez¹  Bryan J. Glazer²  Jonathan T. Lifferth³ 

[1] Department of Biochemistry, Vanderbilt University, Nashville, TN, United States;Altos Labs, Redwood City, CA, United States;Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, United States;Department of Human Genetics, Vanderbilt University, Nashville, TN, United States;

关键词: MCTS algorithm;    Boolean model;    model inference;    Drosophila;    segment polarity network;    multi-model inference;   

DOI  :  10.3389/fcell.2023.1198359

 received in 2023-04-01, accepted in 2023-08-07,  发布年份 2023

来源: Frontiers

【 摘 要 】

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 biologically plausible mechanistic hypotheses. Our approach can generate very large numbers of data-consistent models. To help develop mechanistic insight from these models, we developed analytical tools for multi-model inference and model selection. These tools reveal the key sets of interactions that govern the behavior of the models. We demonstrate that MC-Boomer works well at reconstructing randomly generated models. Then, using single time point measurements and reasonable biological constraints, our method generates hundreds of thousands of candidate models that match experimentally validated in-vivo behaviors of the Drosophila segment polarity network. Finally we outline how our multi-model analysis procedures elucidate potentially novel biological mechanisms and provide opportunities for model-driven experimental validation.

【 授权许可】

Unknown   
Copyright © 2023 Glazer, Lifferth and Lopez.