Automatic mechanistic inference from large families of Boolean models generated by Monte Carlo tree search
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 h...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-08-01
|
| Series: | Frontiers in Cell and Developmental Biology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcell.2023.1198359/full |
| _version_ | 1797737050623967232 |
|---|---|
| author | Bryan J. Glazer Jonathan T. Lifferth Carlos F. Lopez Carlos F. Lopez |
| author_facet | Bryan J. Glazer Jonathan T. Lifferth Carlos F. Lopez Carlos F. Lopez |
| author_sort | Bryan J. Glazer |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T13:22:47Z |
| format | Article |
| id | doaj.art-db377f16c90b42819b56de66913f27a5 |
| institution | Directory Open Access Journal |
| issn | 2296-634X |
| language | English |
| last_indexed | 2024-03-12T13:22:47Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cell and Developmental Biology |
| spelling | doaj.art-db377f16c90b42819b56de66913f27a52023-08-25T16:54:51ZengFrontiers Media S.A.Frontiers in Cell and Developmental Biology2296-634X2023-08-011110.3389/fcell.2023.11983591198359Automatic mechanistic inference from large families of Boolean models generated by Monte Carlo tree searchBryan J. Glazer0Jonathan T. Lifferth1Carlos F. Lopez2Carlos F. Lopez3Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, United StatesDepartment of Human Genetics, Vanderbilt University, Nashville, TN, United StatesDepartment of Biochemistry, Vanderbilt University, Nashville, TN, United StatesAltos Labs, Redwood City, CA, United StatesMany 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.https://www.frontiersin.org/articles/10.3389/fcell.2023.1198359/fullMCTS algorithmBoolean modelmodel inferenceDrosophila developmentsegment polarity networkmulti-model inference |
| spellingShingle | Bryan J. Glazer Jonathan T. Lifferth Carlos F. Lopez Carlos F. Lopez Automatic mechanistic inference from large families of Boolean models generated by Monte Carlo tree search Frontiers in Cell and Developmental Biology MCTS algorithm Boolean model model inference Drosophila development segment polarity network multi-model inference |
| title | Automatic mechanistic inference from large families of Boolean models generated by Monte Carlo tree search |
| title_full | Automatic mechanistic inference from large families of Boolean models generated by Monte Carlo tree search |
| title_fullStr | Automatic mechanistic inference from large families of Boolean models generated by Monte Carlo tree search |
| title_full_unstemmed | Automatic mechanistic inference from large families of Boolean models generated by Monte Carlo tree search |
| title_short | Automatic mechanistic inference from large families of Boolean models generated by Monte Carlo tree search |
| title_sort | automatic mechanistic inference from large families of boolean models generated by monte carlo tree search |
| topic | MCTS algorithm Boolean model model inference Drosophila development segment polarity network multi-model inference |
| url | https://www.frontiersin.org/articles/10.3389/fcell.2023.1198359/full |
| work_keys_str_mv | AT bryanjglazer automaticmechanisticinferencefromlargefamiliesofbooleanmodelsgeneratedbymontecarlotreesearch AT jonathantlifferth automaticmechanisticinferencefromlargefamiliesofbooleanmodelsgeneratedbymontecarlotreesearch AT carlosflopez automaticmechanisticinferencefromlargefamiliesofbooleanmodelsgeneratedbymontecarlotreesearch AT carlosflopez automaticmechanisticinferencefromlargefamiliesofbooleanmodelsgeneratedbymontecarlotreesearch |