{"doi":"10.7554/elife.88742.2","title":"A Logic-incorporated Gene Regulatory Network Deciphers Principles in Cell Fate Decisions","abstract":"<jats:p>Abstract</jats:p>\n                <jats:p>Organisms utilize gene regulatory networks (GRNs) to make fate decisions, but the regulatory mechanisms of transcription factors (TFs) in GRNs are exceedingly intricate. A longstanding question in this field is how these tangled interactions synergistically contribute to decision- making procedures. To comprehensively understand the role of regulatory logic in cell fate decisions, we constructed a logic-incorporated GRN model and examined its behavior under two distinct driving forces (noise-driven and signal-driven). Under the noise-driven mode, we distilled the relationship among fate bias, regulatory logic, and noise profile. Under the signal-driven mode, we bridged regulatory logic and progression-accuracy trade-off, and uncovered distinctive trajectories of reprogramming influenced by logic motifs. In differentiation, we characterized a special logic-dependent priming stage by the solution landscape. Finally, we applied our findings to decipher three biological instances: hematopoiesis, embryogenesis, and trans-differentiation. Orthogonal to the classical analysis of expression profile, we harnessed noise patterns to construct the GRN corresponding to fate transition. Our work presents a generalizable framework for top- down fate-decision studies and a practical approach to the taxonomy of cell fate decisions.</jats:p>","journal":null,"year":null,"id":644636,"datarank":0.17706632462263217,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.012274481322415697,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.012274481322415697,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":2,"citers_with_citation_signal":1,"citers_with_endowment":1,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":978842,"name":"Xiaoyi Zhang","orcid":"0000-0002-4988-819X","position":1,"is_corresponding":false},{"id":1529349,"name":"Wanqi Li","orcid":null,"position":2,"is_corresponding":false},{"id":1128466,"name":"Lu Zhang","orcid":"0000-0001-8358-6112","position":3,"is_corresponding":false},{"id":1529351,"name":"Zongxu Zhang","orcid":null,"position":4,"is_corresponding":false},{"id":1407096,"name":"Xiaolin Zhou","orcid":"0000-0001-7363-4360","position":5,"is_corresponding":false},{"id":987672,"name":"Di Zhang","orcid":"0000-0001-8760-1412","position":6,"is_corresponding":false},{"id":1427431,"name":"Lei Zhang","orcid":"0000-0001-9972-2051","position":7,"is_corresponding":false},{"id":329314,"name":"Zhiyuan Li","orcid":"0000-0001-6662-2636","position":8,"is_corresponding":false},{"id":978261,"name":"Gang Xue","orcid":"0000-0002-4116-5819","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"A Logic-incorporated Gene Regulatory Network Deciphers Principles in Cell Fate Decisions","abstract":"<jats:p>Abstract</jats:p>\n                <jats:p>Organisms utilize gene regulatory networks (GRNs) to make fate decisions, but the regulatory mechanisms of transcription factors (TFs) in GRNs are exceedingly intricate. A longstanding question in this field is how these tangled interactions synergistically contribute to decision- making procedures. To comprehensively understand the role of regulatory logic in cell fate decisions, we constructed a logic-incorporated GRN model and examined its behavior under two distinct driving forces (noise-driven and signal-driven). Under the noise-driven mode, we distilled the relationship among fate bias, regulatory logic, and noise profile. Under the signal-driven mode, we bridged regulatory logic and progression-accuracy trade-off, and uncovered distinctive trajectories of reprogramming influenced by logic motifs. In differentiation, we characterized a special logic-dependent priming stage by the solution landscape. Finally, we applied our findings to decipher three biological instances: hematopoiesis, embryogenesis, and trans-differentiation. Orthogonal to the classical analysis of expression profile, we harnessed noise patterns to construct the GRN corresponding to fate transition. Our work presents a generalizable framework for top- down fate-decision studies and a practical approach to the taxonomy of cell fate decisions.</jats:p>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":null,"authors":[],"funders":[{"funder_name":"National Key Research and Development Program of China","grant_id":"2021YFF1200500","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"12225102","title":null},{"funder_name":"National Key Research and Development Program of China","grant_id":"2021YFA0910700","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"12050002","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"12226316","title":null}],"total_grants":5,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://doi.org/10.7554/elife.88742.2","host_type":"publisher"},{"url":"https://cdn.elifesciences.org/preprints/88742/elife-preprint-88742-v2.pdf","host_type":"publisher"},{"url":"https://cdn.elifesciences.org/preprints/88742/elife-preprint-88742-v2.xml","host_type":"publisher"},{"url":"https://elifesciences.org/reviewed-preprints/88742v2/pdf","host_type":"publisher"}],"fields_of_study":[],"mesh_terms":[],"keywords":[],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-09T01:43:44.932211Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}