AI-driven automated discovery tools reveal diverse behavioral competencies of biological networks
Many applications in biomedicine and synthetic bioengineering rely on understanding, mapping, predicting, and controlling the complex behavior of chemical and genetic networks. The emerging field of diverse intelligence investigates the problem-solving capacities of unconventional agents. However, f...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
eLife Sciences Publications Ltd
2025-01-01
|
| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/92683 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841543290552320000 |
|---|---|
| author | Mayalen Etcheverry Clément Moulin-Frier Pierre-Yves Oudeyer Michael Levin |
| author_facet | Mayalen Etcheverry Clément Moulin-Frier Pierre-Yves Oudeyer Michael Levin |
| author_sort | Mayalen Etcheverry |
| collection | DOAJ |
| description | Many applications in biomedicine and synthetic bioengineering rely on understanding, mapping, predicting, and controlling the complex behavior of chemical and genetic networks. The emerging field of diverse intelligence investigates the problem-solving capacities of unconventional agents. However, few quantitative tools exist for exploring the competencies of non-conventional systems. Here, we view gene regulatory networks (GRNs) as agents navigating a problem space and develop automated tools to map the robust goal states GRNs can reach despite perturbations. Our contributions include: (1) Adapting curiosity-driven exploration algorithms from AI to discover the range of reachable goal states of GRNs, and (2) Proposing empirical tests inspired by behaviorist approaches to assess their navigation competencies. Our data shows that models inferred from biological data can reach a wide spectrum of steady states, exhibiting various competencies in physiological network dynamics without requiring structural changes in network properties or connectivity. We also explore the applicability of these ‘behavioral catalogs’ for comparing evolved competencies across biological networks, for designing drug interventions in biomedical contexts and synthetic gene networks for bioengineering. These tools and the emphasis on behavior-shaping open new paths for efficiently exploring the complex behavior of biological networks. For the interactive version of this paper, please visit https://developmentalsystems.org/curious-exploration-of-grn-competencies. |
| format | Article |
| id | doaj-art-0cf9cb80644a4f0387eccda562877d88 |
| institution | Kabale University |
| issn | 2050-084X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj-art-0cf9cb80644a4f0387eccda562877d882025-01-13T14:41:27ZengeLife Sciences Publications LtdeLife2050-084X2025-01-011310.7554/eLife.92683AI-driven automated discovery tools reveal diverse behavioral competencies of biological networksMayalen Etcheverry0https://orcid.org/0000-0001-9568-6081Clément Moulin-Frier1Pierre-Yves Oudeyer2Michael Levin3https://orcid.org/0000-0001-7292-8084INRIA, University of Bordeaux, Bordeaux, France; Poietis, Pessac, FranceINRIA, University of Bordeaux, Bordeaux, FranceINRIA, University of Bordeaux, Bordeaux, FranceAllen Discovery Center, Tufts University, Medford, United StatesMany applications in biomedicine and synthetic bioengineering rely on understanding, mapping, predicting, and controlling the complex behavior of chemical and genetic networks. The emerging field of diverse intelligence investigates the problem-solving capacities of unconventional agents. However, few quantitative tools exist for exploring the competencies of non-conventional systems. Here, we view gene regulatory networks (GRNs) as agents navigating a problem space and develop automated tools to map the robust goal states GRNs can reach despite perturbations. Our contributions include: (1) Adapting curiosity-driven exploration algorithms from AI to discover the range of reachable goal states of GRNs, and (2) Proposing empirical tests inspired by behaviorist approaches to assess their navigation competencies. Our data shows that models inferred from biological data can reach a wide spectrum of steady states, exhibiting various competencies in physiological network dynamics without requiring structural changes in network properties or connectivity. We also explore the applicability of these ‘behavioral catalogs’ for comparing evolved competencies across biological networks, for designing drug interventions in biomedical contexts and synthetic gene networks for bioengineering. These tools and the emphasis on behavior-shaping open new paths for efficiently exploring the complex behavior of biological networks. For the interactive version of this paper, please visit https://developmentalsystems.org/curious-exploration-of-grn-competencies.https://elifesciences.org/articles/92683basal cognitiondiverse intelligencegene regulatory networkcuriosity-driven explorationmachine learningAI for science |
| spellingShingle | Mayalen Etcheverry Clément Moulin-Frier Pierre-Yves Oudeyer Michael Levin AI-driven automated discovery tools reveal diverse behavioral competencies of biological networks eLife basal cognition diverse intelligence gene regulatory network curiosity-driven exploration machine learning AI for science |
| title | AI-driven automated discovery tools reveal diverse behavioral competencies of biological networks |
| title_full | AI-driven automated discovery tools reveal diverse behavioral competencies of biological networks |
| title_fullStr | AI-driven automated discovery tools reveal diverse behavioral competencies of biological networks |
| title_full_unstemmed | AI-driven automated discovery tools reveal diverse behavioral competencies of biological networks |
| title_short | AI-driven automated discovery tools reveal diverse behavioral competencies of biological networks |
| title_sort | ai driven automated discovery tools reveal diverse behavioral competencies of biological networks |
| topic | basal cognition diverse intelligence gene regulatory network curiosity-driven exploration machine learning AI for science |
| url | https://elifesciences.org/articles/92683 |
| work_keys_str_mv | AT mayalenetcheverry aidrivenautomateddiscoverytoolsrevealdiversebehavioralcompetenciesofbiologicalnetworks AT clementmoulinfrier aidrivenautomateddiscoverytoolsrevealdiversebehavioralcompetenciesofbiologicalnetworks AT pierreyvesoudeyer aidrivenautomateddiscoverytoolsrevealdiversebehavioralcompetenciesofbiologicalnetworks AT michaellevin aidrivenautomateddiscoverytoolsrevealdiversebehavioralcompetenciesofbiologicalnetworks |