Deriving a genetic regulatory network from an optimization principle
Many biological systems operate near the physical limits to their performance, suggesting that aspects of their behavior and underlying mechanisms could be derived from optimization principles. However, such principles have often been applied only in simplified models. Here, we explore a detailed me...
Saved in:
| Published in | Proceedings of the National Academy of Sciences - PNAS Vol. 122; no. 1; p. e2402925121 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
National Academy of Sciences
07.01.2025
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 0027-8424 1091-6490 1091-6490 |
| DOI | 10.1073/pnas.2402925121 |
Cover
Loading…
| Abstract | Many biological systems operate near the physical limits to their performance, suggesting that aspects of their behavior and underlying mechanisms could be derived from optimization principles. However, such principles have often been applied only in simplified models. Here, we explore a detailed mechanistic model of the gap gene network in the
Drosophila
embryo, optimizing its 50+ parameters to maximize the information that gene expression levels provide about nuclear positions. This optimization is conducted under realistic constraints, such as limits on the number of available molecules. Remarkably, the optimal networks we derive closely match the architecture and spatial gene expression profiles observed in the real organism. Our framework quantifies the tradeoffs involved in maximizing functional performance and allows for the exploration of alternative network configurations, addressing the question of which features are necessary and which are contingent. Our results suggest that multiple solutions to the optimization problem might exist across closely related organisms, offering insights into the evolution of gene regulatory networks. |
|---|---|
| AbstractList | Many biological systems operate near the physical limits to their performance, suggesting that aspects of their behavior and underlying mechanisms could be derived from optimization principles. However, such principles have often been applied only in simplified models. Here, we explore a detailed mechanistic model of the gap gene network in the Drosophila embryo, optimizing its 50+ parameters to maximize the information that gene expression levels provide about nuclear positions. This optimization is conducted under realistic constraints, such as limits on the number of available molecules. Remarkably, the optimal networks we derive closely match the architecture and spatial gene expression profiles observed in the real organism. Our framework quantifies the tradeoffs involved in maximizing functional performance and allows for the exploration of alternative network configurations, addressing the question of which features are necessary and which are contingent. Our results suggest that multiple solutions to the optimization problem might exist across closely related organisms, offering insights into the evolution of gene regulatory networks. Many biological systems operate near the physical limits to their performance, suggesting that aspects of their behavior and underlying mechanisms could be derived from optimization principles. However, such principles have often been applied only in simplified models. Here, we explore a detailed mechanistic model of the gap gene network in the embryo, optimizing its 50+ parameters to maximize the information that gene expression levels provide about nuclear positions. This optimization is conducted under realistic constraints, such as limits on the number of available molecules. Remarkably, the optimal networks we derive closely match the architecture and spatial gene expression profiles observed in the real organism. Our framework quantifies the tradeoffs involved in maximizing functional performance and allows for the exploration of alternative network configurations, addressing the question of which features are necessary and which are contingent. Our results suggest that multiple solutions to the optimization problem might exist across closely related organisms, offering insights into the evolution of gene regulatory networks. Many biological systems operate near the physical limits to their performance, suggesting that aspects of their behavior and underlying mechanisms could be derived from optimization principles. However, such principles have often been applied only in simplified models. Here, we explore a detailed mechanistic model of the gap gene network in the Drosophila embryo, optimizing its 50+ parameters to maximize the information that gene expression levels provide about nuclear positions. This optimization is conducted under realistic constraints, such as limits on the number of available molecules. Remarkably, the optimal networks we derive closely match the architecture and spatial gene expression profiles observed in the real organism. Our framework quantifies the tradeoffs involved in maximizing functional performance and allows for the exploration of alternative network configurations, addressing the question of which features are necessary and which are contingent. Our results suggest that multiple solutions to the optimization problem might exist across closely related organisms, offering insights into the evolution of gene regulatory networks.Many biological systems operate near the physical limits to their performance, suggesting that aspects of their behavior and underlying mechanisms could be derived from optimization principles. However, such principles have often been applied only in simplified models. Here, we explore a detailed mechanistic model of the gap gene network in the Drosophila embryo, optimizing its 50+ parameters to maximize the information that gene expression levels provide about nuclear positions. This optimization is conducted under realistic constraints, such as limits on the number of available molecules. Remarkably, the optimal networks we derive closely match the architecture and spatial gene expression profiles observed in the real organism. Our framework quantifies the tradeoffs involved in maximizing functional performance and allows for the exploration of alternative network configurations, addressing the question of which features are necessary and which are contingent. Our results suggest that multiple solutions to the optimization problem might exist across closely related organisms, offering insights into the evolution of gene regulatory networks. Many biological systems approach physical limits to their performance, motivating the idea that their behavior and underlying mechanisms could be determined by such optimality. Nevertheless, optimization as a predictive principle has only been applied in very simplified setups. Here, in contrast, we explore a mechanisticallydetailed class of models for the gap gene network of the Drosophila embryo, and determine its 50+ parameters by optimizing the information that gene expression levels convey about nuclear positions, subject to physical constraints on the number of available molecules. Optimal networks recapitulate the architecture and spatial gene expression profiles of the real organism. Our framework makes precise the many tradeoffs involved in maximizing functional performance, and allows us to explore alternative networks to address the questions of necessity vs contingency. Multiple solutions to the optimization problem may be realized in closely related organisms. Many biological systems operate near the physical limits to their performance, suggesting that aspects of their behavior and underlying mechanisms could be derived from optimization principles. However, such principles have often been applied only in simplified models. Here, we explore a detailed mechanistic model of the gap gene network in the Drosophila embryo, optimizing its 50+ parameters to maximize the information that gene expression levels provide about nuclear positions. This optimization is conducted under realistic constraints, such as limits on the number of available molecules. Remarkably, the optimal networks we derive closely match the architecture and spatial gene expression profiles observed in the real organism. Our framework quantifies the tradeoffs involved in maximizing functional performance and allows for the exploration of alternative network configurations, addressing the question of which features are necessary and which are contingent. Our results suggest that multiple solutions to the optimization problem might exist across closely related organisms, offering insights into the evolution of gene regulatory networks. Information crucial for life is represented by surprisingly low concentrations of key molecules, yet cells use these small signals to make reliable decisions. Could the mechanisms of life have been tuned to extract as much information as possible from a limited number of molecules? We apply this physical principle to a genetic network that controls pattern formation in early development of the fruit fly embryo, searching for the settings of 50+ internal parameters that give each cell the maximum possible information about its position in the embryo. The resulting optimal networks recapitulate many features of the real network, quantitatively. This approach makes tradeoffs explicit, rationalizes the network architecture, and provides perspectives on the interplay of chance and necessity. Many biological systems operate near the physical limits to their performance, suggesting that aspects of their behavior and underlying mechanisms could be derived from optimization principles. However, such principles have often been applied only in simplified models. Here, we explore a detailed mechanistic model of the gap gene network in the Drosophila embryo, optimizing its 50+ parameters to maximize the information that gene expression levels provide about nuclear positions. This optimization is conducted under realistic constraints, such as limits on the number of available molecules. Remarkably, the optimal networks we derive closely match the architecture and spatial gene expression profiles observed in the real organism. Our framework quantifies the tradeoffs involved in maximizing functional performance and allows for the exploration of alternative network configurations, addressing the question of which features are necessary and which are contingent. Our results suggest that multiple solutions to the optimization problem might exist across closely related organisms, offering insights into the evolution of gene regulatory networks. |
| Author | Tkačik, Gašper Sokolowski, Thomas R. Bialek, William Gregor, Thomas |
| Author_xml | – sequence: 1 givenname: Thomas R. surname: Sokolowski fullname: Sokolowski, Thomas R. – sequence: 2 givenname: Thomas orcidid: 0000-0001-9460-139X surname: Gregor fullname: Gregor, Thomas – sequence: 3 givenname: William orcidid: 0000-0002-7823-3862 surname: Bialek fullname: Bialek, William – sequence: 4 givenname: Gašper surname: Tkačik fullname: Tkačik, Gašper |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39752518$$D View this record in MEDLINE/PubMed https://pasteur.hal.science/pasteur-03988951$$DView record in HAL |
| BookMark | eNp1kc9PFDEcxRuDkQU9ezOTePEy8O2vaXsyBFRINuGC56bT6S7FmXZsZ9bAX0_XBcVNPDVpP-_1-77vCB2EGBxC7zGcYBD0dAwmnxAGRBGOCX6FFhgUrhum4AAtAIioJSPsEB3lfAcAikt4gw6pErwI5AJdXLjkNz6sK1OtXXCTt1Vy67k3U0z3Vbn4FdOPapXiUJlQxXHyg38wk4-hGpMP1o-9e4ter0yf3bun8xh9__rl5vyyXl5_uzo_W9aWNTDVeGWsbTumFLNOgFBOcGeoMJSAIh0VreVStpK0qm0s66wlnALvuGiASgP0GH3e-Y5zO7jOujAl0-syx2DSvY7G639fgr_V67jRGAvChaTFod453O7pLs-WejR5cnPSQJWUiuMNKfynpx9T_Dm7POnBZ-v63gQX56wp5phxIuUW_biH3sU5hbKPQpUApClcoT68jPBnhudGCsB3gE0x5-RW2vrp975LIt9rDHrbvN42r_82X3Sne7pn6_8pHgEgabCh |
| CitedBy_id | crossref_primary_10_1103_PhysRevE_111_034406 |
| Cites_doi | 10.1371/journal.pone.0002774 10.1126/science.aam5979 10.1038/s41576-018-0069-z 10.1038/msb.2012.72 10.1073/pnas.1324186111 10.1038/nature04485 10.1242/dev.176065 10.1016/j.bpj.2011.05.060 10.1146/annurev-conmatphys-031214-014803 10.1371/journal.pcbi.0020051 10.1103/PhysRevLett.103.258101 10.1371/journal.pcbi.1000303 10.1103/PhysRevE.81.041905 10.7554/eLife.04785 10.1371/journal.pgen.1006052 10.1103/PhysRevE.85.041903 10.1242/dev.129452 10.1016/j.biosystems.2014.06.003 10.1371/journal.pbio.0020271 10.1016/j.cell.2019.01.007 10.1371/journal.pbio.2003174 10.1103/PhysRevE.91.062710 10.1371/journal.pcbi.1000548 10.1073/pnas.1903887116 10.1038/287795a0 10.1073/pnas.2006731117 10.1534/genetics.114.171850 10.1371/journal.pcbi.1005285 10.1103/PhysRevE.93.022404 10.1007/s00239-002-2335-z 10.1016/j.neuron.2021.01.020 10.1242/dev.048033 10.1093/molbev/mst170 10.1038/348027a0 10.1007/978-3-319-32979-6 10.1242/jeb.29.4.667 10.1093/genetics/iyae191 10.1371/journal.pbio.1000049 10.1016/j.cell.2013.07.025 10.1073/pnas.0501865102 10.1073/pnas.1315642110 10.1016/j.cell.2007.05.025 10.1073/pnas.0911663107 10.1016/j.ydbio.2004.08.021 10.1073/pnas.0509483102 10.1016/j.cell.2016.06.012 10.1073/pnas.2113651119 10.1016/S0022-5193(69)80016-0 10.1038/nature02678 10.1016/j.cell.2018.09.056 10.1103/RevModPhys.70.1027 10.1038/nature06496 10.1086/414425 10.1016/j.coisb.2022.100435 10.1038/s41587-019-0315-8 10.1007/s00018-010-0536-y 10.1103/PhysRevE.80.031920 10.1371/journal.pcbi.1002654 10.7554/eLife.64543 10.1038/nature01149 10.1073/pnas.0806077105 10.1016/j.tig.2014.06.002 10.1016/S0006-3495(77)85544-6 10.1093/oso/9780198830870.001.0001 10.1006/jtbi.2001.2335 10.1073/pnas.2123152119 10.1038/381607a0 10.1103/PhysRevLett.104.048704 10.1371/journal.pone.0163628 10.1038/msb4100192 10.1242/dev.170480 10.1126/science.1177894 10.1073/pnas.162041399 10.1534/genetics.104.027334 10.7554/eLife.42832 |
| ContentType | Journal Article |
| Copyright | Copyright National Academy of Sciences Jan 7, 2024 Attribution Copyright © 2025 the Author(s). Published by PNAS. 2025 |
| Copyright_xml | – notice: Copyright National Academy of Sciences Jan 7, 2024 – notice: Attribution – notice: Copyright © 2025 the Author(s). Published by PNAS. 2025 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QG 7QL 7QP 7QR 7SN 7SS 7T5 7TK 7TM 7TO 7U9 8FD C1K FR3 H94 M7N P64 RC3 7X8 1XC VOOES 5PM |
| DOI | 10.1073/pnas.2402925121 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Animal Behavior Abstracts Bacteriology Abstracts (Microbiology B) Calcium & Calcified Tissue Abstracts Chemoreception Abstracts Ecology Abstracts Entomology Abstracts (Full archive) Immunology Abstracts Neurosciences Abstracts Nucleic Acids Abstracts Oncogenes and Growth Factors Abstracts Virology and AIDS Abstracts Technology Research Database Environmental Sciences and Pollution Management Engineering Research Database AIDS and Cancer Research Abstracts Algology Mycology and Protozoology Abstracts (Microbiology C) Biotechnology and BioEngineering Abstracts Genetics Abstracts MEDLINE - Academic Hyper Article en Ligne (HAL) Hyper Article en Ligne (HAL) (Open Access) PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Virology and AIDS Abstracts Oncogenes and Growth Factors Abstracts Technology Research Database Nucleic Acids Abstracts Ecology Abstracts Neurosciences Abstracts Biotechnology and BioEngineering Abstracts Environmental Sciences and Pollution Management Entomology Abstracts Genetics Abstracts Animal Behavior Abstracts Bacteriology Abstracts (Microbiology B) Algology Mycology and Protozoology Abstracts (Microbiology C) AIDS and Cancer Research Abstracts Chemoreception Abstracts Immunology Abstracts Engineering Research Database Calcium & Calcified Tissue Abstracts MEDLINE - Academic |
| DatabaseTitleList | Virology and AIDS Abstracts MEDLINE MEDLINE - Academic CrossRef |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Sciences (General) Physics |
| EISSN | 1091-6490 |
| ExternalDocumentID | PMC11725783 oai_HAL_pasteur_03988951v2 39752518 10_1073_pnas_2402925121 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: Simons Foundation (SF) grantid: 821703 – fundername: NIDDK NIH HHS grantid: U01 DK127429 – fundername: John Simon Guggenheim Memorial Foundation (JSGMF) grantid: (none) – fundername: Austrian Science Fund (FWF) grantid: FWF P28844 – fundername: HHS | National Institutes of Health (NIH) grantid: U01DA047730 – fundername: HHS | National Institutes of Health (NIH) grantid: U01DK127429 – fundername: Human Frontier Science Program (HFSP) grantid: (none_) – fundername: NIGMS NIH HHS grantid: R01 GM097275 – fundername: NIDA NIH HHS grantid: U01 DA047730 – fundername: HHS | National Institutes of Health (NIH) grantid: R01GM097275 – fundername: ; grantid: PHY-1734030 – fundername: ; grantid: 821703 – fundername: ; grantid: U01DK127429 – fundername: ; grantid: (none) – fundername: ; grantid: FWF P28844 – fundername: ; grantid: R01GM097275 – fundername: ; grantid: U01DA047730 – fundername: ; grantid: (none_) |
| GroupedDBID | --- -DZ -~X .55 0R~ 123 2FS 2WC 4.4 5RE 5VS 85S AACGO AAFWJ AANCE AAYXX ABOCM ABPLY ABPPZ ABTLG ABZEH ACGOD ACIWK ACNCT ACPRK AENEX AFFNX AFHIN AFOSN AFRAH ALMA_UNASSIGNED_HOLDINGS BKOMP CITATION CS3 D0L DIK DU5 E3Z EBS F5P FRP GX1 H13 HH5 JLS JSG KQ8 L7B LU7 N9A N~3 O9- OK1 PNE PQQKQ R.V RHI RNA RNS RPM RXW SJN TAE TN5 UKR WH7 WOQ X7M XSW Y6R YBH YKV YSK ZCA ~02 ~KM 29P CGR CUY CVF ECM EIF HYE NPM W8F WOW 53G 7QG 7QL 7QP 7QR 7SN 7SS 7T5 7TK 7TM 7TO 7U9 8FD C1K FR3 H94 M7N P64 RC3 7X8 1XC UMC VOOES 5PM |
| ID | FETCH-LOGICAL-c460t-1faccbd4994ce7079e75ea37a32092d37bc588b82b9b6c4dcc25305d576038a03 |
| ISSN | 0027-8424 1091-6490 |
| IngestDate | Thu Aug 21 18:32:56 EDT 2025 Fri May 09 12:06:51 EDT 2025 Thu Jul 10 17:01:50 EDT 2025 Mon Jun 30 09:42:38 EDT 2025 Mon Jul 21 06:03:32 EDT 2025 Tue Jul 01 02:37:24 EDT 2025 Thu Apr 24 22:54:44 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | gene regulatory networks evolution optimization Drosophila |
| Language | English |
| License | Attribution: http://creativecommons.org/licenses/by This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND). |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c460t-1faccbd4994ce7079e75ea37a32092d37bc588b82b9b6c4dcc25305d576038a03 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Contributed by William Bialek; received February 13, 2024; accepted November 13, 2024; reviewed by Dmitri B. Chklovskii, Angela H. DePace, Michael M. Desai, and Jane Kondev |
| ORCID | 0000-0002-7823-3862 0000-0001-9460-139X |
| OpenAccessLink | https://pasteur.hal.science/pasteur-03988951 |
| PMID | 39752518 |
| PQID | 3160326528 |
| PQPubID | 42026 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_11725783 hal_primary_oai_HAL_pasteur_03988951v2 proquest_miscellaneous_3151452882 proquest_journals_3160326528 pubmed_primary_39752518 crossref_citationtrail_10_1073_pnas_2402925121 crossref_primary_10_1073_pnas_2402925121 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2025-01-07 |
| PublicationDateYYYYMMDD | 2025-01-07 |
| PublicationDate_xml | – month: 01 year: 2025 text: 2025-01-07 day: 07 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: Washington |
| PublicationTitle | Proceedings of the National Academy of Sciences - PNAS |
| PublicationTitleAlternate | Proc Natl Acad Sci U S A |
| PublicationYear | 2025 |
| Publisher | National Academy of Sciences |
| Publisher_xml | – name: National Academy of Sciences |
| References | e_1_3_4_3_2 e_1_3_4_1_2 Lawrence P. A. (e_1_3_4_23_2) 1992 e_1_3_4_61_2 e_1_3_4_9_2 e_1_3_4_63_2 e_1_3_4_7_2 e_1_3_4_40_2 e_1_3_4_5_2 e_1_3_4_80_2 e_1_3_4_44_2 e_1_3_4_69_2 Bialek W. (e_1_3_4_6_2) 2012 e_1_3_4_21_2 e_1_3_4_42_2 e_1_3_4_27_2 e_1_3_4_48_2 e_1_3_4_65_2 e_1_3_4_25_2 e_1_3_4_46_2 e_1_3_4_67_2 e_1_3_4_29_2 Wilkins A. S. (e_1_3_4_81_2) 2002 e_1_3_4_72_2 e_1_3_4_74_2 e_1_3_4_30_2 e_1_3_4_51_2 e_1_3_4_70_2 e_1_3_4_11_2 e_1_3_4_34_2 e_1_3_4_57_2 e_1_3_4_55_2 e_1_3_4_32_2 e_1_3_4_59_2 e_1_3_4_53_2 e_1_3_4_15_2 e_1_3_4_38_2 e_1_3_4_76_2 e_1_3_4_13_2 e_1_3_4_36_2 e_1_3_4_78_2 e_1_3_4_19_2 e_1_3_4_17_2 e_1_3_4_2_2 e_1_3_4_62_2 e_1_3_4_41_2 e_1_3_4_4_2 e_1_3_4_22_2 e_1_3_4_45_2 e_1_3_4_68_2 e_1_3_4_20_2 e_1_3_4_43_2 e_1_3_4_26_2 e_1_3_4_49_2 e_1_3_4_64_2 e_1_3_4_24_2 e_1_3_4_47_2 e_1_3_4_66_2 Jacob F. (e_1_3_4_60_2) 1982 e_1_3_4_28_2 Karklin Y. (e_1_3_4_8_2) 2011; 24 e_1_3_4_71_2 e_1_3_4_73_2 e_1_3_4_52_2 e_1_3_4_50_2 e_1_3_4_79_2 e_1_3_4_12_2 e_1_3_4_33_2 e_1_3_4_58_2 e_1_3_4_54_2 e_1_3_4_10_2 e_1_3_4_31_2 e_1_3_4_75_2 e_1_3_4_16_2 e_1_3_4_37_2 e_1_3_4_77_2 e_1_3_4_14_2 e_1_3_4_35_2 e_1_3_4_56_2 e_1_3_4_18_2 e_1_3_4_39_2 |
| References_xml | – ident: e_1_3_4_45_2 doi: 10.1371/journal.pone.0002774 – ident: e_1_3_4_19_2 doi: 10.1126/science.aam5979 – ident: e_1_3_4_65_2 doi: 10.1038/s41576-018-0069-z – ident: e_1_3_4_47_2 doi: 10.1038/msb.2012.72 – ident: e_1_3_4_55_2 doi: 10.1073/pnas.1324186111 – ident: e_1_3_4_10_2 doi: 10.1038/nature04485 – ident: e_1_3_4_27_2 doi: 10.1242/dev.176065 – ident: e_1_3_4_74_2 doi: 10.1016/j.bpj.2011.05.060 – ident: e_1_3_4_7_2 doi: 10.1146/annurev-conmatphys-031214-014803 – ident: e_1_3_4_31_2 doi: 10.1371/journal.pcbi.0020051 – ident: e_1_3_4_53_2 doi: 10.1103/PhysRevLett.103.258101 – volume-title: The Evolution of Developmental Pathways year: 2002 ident: e_1_3_4_81_2 – ident: e_1_3_4_32_2 doi: 10.1371/journal.pcbi.1000303 – ident: e_1_3_4_43_2 doi: 10.1103/PhysRevE.81.041905 – ident: e_1_3_4_63_2 doi: 10.7554/eLife.04785 – ident: e_1_3_4_78_2 doi: 10.1371/journal.pgen.1006052 – volume: 24 start-page: 999 year: 2011 ident: e_1_3_4_8_2 article-title: Efficient coding of natural images with a population of noisy linear-nonlinear neurons publication-title: Adv. Neural. Inf. Process. Syst. – ident: e_1_3_4_49_2 doi: 10.1103/PhysRevE.85.041903 – ident: e_1_3_4_22_2 doi: 10.1242/dev.129452 – ident: e_1_3_4_62_2 doi: 10.1016/j.biosystems.2014.06.003 – ident: e_1_3_4_72_2 doi: 10.1371/journal.pbio.0020271 – ident: e_1_3_4_26_2 doi: 10.1016/j.cell.2019.01.007 – ident: e_1_3_4_36_2 doi: 10.1371/journal.pbio.2003174 – ident: e_1_3_4_42_2 doi: 10.1103/PhysRevE.91.062710 – ident: e_1_3_4_34_2 doi: 10.1371/journal.pcbi.1000548 – ident: e_1_3_4_11_2 doi: 10.1073/pnas.1903887116 – ident: e_1_3_4_20_2 doi: 10.1038/287795a0 – ident: e_1_3_4_44_2 doi: 10.1073/pnas.2006731117 – ident: e_1_3_4_25_2 doi: 10.1534/genetics.114.171850 – ident: e_1_3_4_35_2 doi: 10.1371/journal.pcbi.1005285 – ident: e_1_3_4_51_2 doi: 10.1103/PhysRevE.93.022404 – ident: e_1_3_4_67_2 doi: 10.1007/s00239-002-2335-z – ident: e_1_3_4_52_2 doi: 10.1016/j.neuron.2021.01.020 – ident: e_1_3_4_77_2 doi: 10.1242/dev.048033 – ident: e_1_3_4_75_2 doi: 10.1093/molbev/mst170 – ident: e_1_3_4_1_2 doi: 10.1038/348027a0 – ident: e_1_3_4_18_2 doi: 10.1007/978-3-319-32979-6 – ident: e_1_3_4_4_2 doi: 10.1242/jeb.29.4.667 – ident: e_1_3_4_57_2 doi: 10.1093/genetics/iyae191 – ident: e_1_3_4_33_2 doi: 10.1371/journal.pbio.1000049 – ident: e_1_3_4_39_2 doi: 10.1016/j.cell.2013.07.025 – ident: e_1_3_4_61_2 doi: 10.1073/pnas.0501865102 – ident: e_1_3_4_24_2 doi: 10.1073/pnas.1315642110 – ident: e_1_3_4_41_2 doi: 10.1016/j.cell.2007.05.025 – ident: e_1_3_4_16_2 doi: 10.1073/pnas.0911663107 – ident: e_1_3_4_64_2 doi: 10.1016/j.ydbio.2004.08.021 – ident: e_1_3_4_66_2 doi: 10.1073/pnas.0509483102 – ident: e_1_3_4_68_2 doi: 10.1016/j.cell.2016.06.012 – ident: e_1_3_4_37_2 doi: 10.1073/pnas.2113651119 – ident: e_1_3_4_17_2 doi: 10.1016/S0022-5193(69)80016-0 – ident: e_1_3_4_29_2 doi: 10.1038/nature02678 – ident: e_1_3_4_40_2 doi: 10.1016/j.cell.2018.09.056 – ident: e_1_3_4_3_2 doi: 10.1103/RevModPhys.70.1027 – ident: e_1_3_4_73_2 doi: 10.1038/nature06496 – ident: e_1_3_4_38_2 doi: 10.1086/414425 – ident: e_1_3_4_69_2 doi: 10.1016/j.coisb.2022.100435 – ident: e_1_3_4_58_2 doi: 10.1038/s41587-019-0315-8 – ident: e_1_3_4_21_2 doi: 10.1007/s00018-010-0536-y – ident: e_1_3_4_48_2 doi: 10.1103/PhysRevE.80.031920 – volume-title: The Possible and the Actual year: 1982 ident: e_1_3_4_60_2 – ident: e_1_3_4_54_2 doi: 10.1371/journal.pcbi.1002654 – ident: e_1_3_4_59_2 doi: 10.7554/eLife.64543 – volume-title: Biophysics: Searching for Principles year: 2012 ident: e_1_3_4_6_2 – ident: e_1_3_4_12_2 doi: 10.1038/nature01149 – ident: e_1_3_4_15_2 doi: 10.1073/pnas.0806077105 – ident: e_1_3_4_71_2 doi: 10.1016/j.tig.2014.06.002 – ident: e_1_3_4_5_2 doi: 10.1016/S0006-3495(77)85544-6 – ident: e_1_3_4_2_2 doi: 10.1093/oso/9780198830870.001.0001 – volume-title: The Making of a Fly: The Genetics of Animal Design year: 1992 ident: e_1_3_4_23_2 – ident: e_1_3_4_28_2 doi: 10.1006/jtbi.2001.2335 – ident: e_1_3_4_56_2 doi: 10.1073/pnas.2123152119 – ident: e_1_3_4_9_2 doi: 10.1038/381607a0 – ident: e_1_3_4_14_2 doi: 10.1103/PhysRevLett.104.048704 – ident: e_1_3_4_50_2 doi: 10.1371/journal.pone.0163628 – ident: e_1_3_4_76_2 doi: 10.1038/msb4100192 – ident: e_1_3_4_80_2 doi: 10.1242/dev.170480 – ident: e_1_3_4_70_2 – ident: e_1_3_4_13_2 doi: 10.1126/science.1177894 – ident: e_1_3_4_46_2 doi: 10.1073/pnas.162041399 – ident: e_1_3_4_30_2 doi: 10.1534/genetics.104.027334 – ident: e_1_3_4_79_2 doi: 10.7554/eLife.42832 |
| SSID | ssj0009580 |
| Score | 2.4992785 |
| Snippet | Many biological systems operate near the physical limits to their performance, suggesting that aspects of their behavior and underlying mechanisms could be... Many biological systems approach physical limits to their performance, motivating the idea that their behavior and underlying mechanisms could be determined by... Information crucial for life is represented by surprisingly low concentrations of key molecules, yet cells use these small signals to make reliable decisions.... |
| SourceID | pubmedcentral hal proquest pubmed crossref |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | e2402925121 |
| SubjectTerms | Animals Biological Physics Configuration management Drosophila - genetics Drosophila melanogaster - genetics Embryo, Nonmammalian - metabolism Gene expression Gene Expression Regulation, Developmental Gene Regulatory Networks Models, Genetic Optimization Physical Sciences Physics |
| Title | Deriving a genetic regulatory network from an optimization principle |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/39752518 https://www.proquest.com/docview/3160326528 https://www.proquest.com/docview/3151452882 https://pasteur.hal.science/pasteur-03988951 https://pubmed.ncbi.nlm.nih.gov/PMC11725783 |
| Volume | 122 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3db9MwELe68cILYnwWBjISoKEqJXFsx3ms6LYKlTKJVupb5DgJmwZptbYg7a_n_JE02Yo0eIla27Et3-Xnu_P5DqG3XCgiAiY9VVBQUEJfeNJnhZfFrABULApuovN_mfDRjH6es3mnc928XbJO--p6572S_6EqlAFd9S3Zf6Bs3SkUwG-gLzyBwvC8E42HMIgxCEidCTm34ZhNbnl9cl5aD297g0RnoAB0-OmuXfaWlZG9KZ2e1bvZqvIdmFTGwsH26onDg1XP651NtomMvy0uAUl_u0TY1vOo13Tw-W5NAw2fJGOdhy3KQLIz_dS2hEtpJN3hhak9hX_s3SBYOndiZ6kgzFgqoga4gmzicWrTg_bzHWUVIhNyi_Usvub6MCjWMlmwE_8BsHTS4lKu-u2W7Ujbk6_JyWw8TqbH8-keukdAxdDZL07nQSNgs7CRLNz8qrBQUfjxRvctiWbvXPvT3lZWbvrcNoSY6UP0wGkfeGBZ6QB18vIROqjoiY9cEPIPj9Gw4i0sseMtvOUt7HgLa97CssRN3sI1bz1Bs5Pj6aeR5zJueIpyf-0FhVQqzUALpirXsRPziOUyjGRI_JhkYZQqJkQqSBqnXNFMKcJgw8hAafVDIf3wKdovF2X-HGHOC0ppHBGeKdgXijQVWUThfU5TEmdxF_WrVUuUC0evs6L8SIxbRBQmepmT7TJ30VH9wtJGYvl70_dAhrqVjqA-GoyTpQTg3FwlfhgLAZrFL9JFhxWlEvddr5JQZ14nnBHRRW_qakBdfZQmy3yx0W1A0YAWArp4ZglbDwcSPoNpwNuiRfLWfNo15cW5iewegDoBW2j44g4Dv0T3tx_ZIdpfX23yVyAgr9PXhpP_AODruww |
| linkProvider | Geneva Foundation for Medical Education and Research |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Deriving+a+genetic+regulatory+network+from+an+optimization+principle&rft.jtitle=Proceedings+of+the+National+Academy+of+Sciences+-+PNAS&rft.au=Sokolowski%2C+Thomas+R&rft.au=Gregor%2C+Thomas&rft.au=Bialek%2C+William&rft.au=Tka%C4%8Dik%2C+Ga%C5%A1per&rft.date=2025-01-07&rft.issn=1091-6490&rft.eissn=1091-6490&rft.volume=122&rft.issue=1&rft.spage=e2402925121&rft_id=info:doi/10.1073%2Fpnas.2402925121&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0027-8424&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0027-8424&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0027-8424&client=summon |