Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties.
Understanding complexity in physical, biological, social and information systems is predicated on describing interactions amongst different components. Advances in genomics are facilitating the high-throughput identification of molecular interactions, and graphs are emerging as indispensable tools i...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2018-04-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC5945062?pdf=render |
| _version_ | 1811197591459725312 |
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| author | Wilberforce Zachary Ouma Katja Pogacar Erich Grotewold |
| author_facet | Wilberforce Zachary Ouma Katja Pogacar Erich Grotewold |
| author_sort | Wilberforce Zachary Ouma |
| collection | DOAJ |
| description | Understanding complexity in physical, biological, social and information systems is predicated on describing interactions amongst different components. Advances in genomics are facilitating the high-throughput identification of molecular interactions, and graphs are emerging as indispensable tools in explaining how the connections in the network drive organismal phenotypic plasticity. Here, we describe the architectural organization and associated emergent topological properties of gene regulatory networks (GRNs) that describe protein-DNA interactions (PDIs) in several model eukaryotes. By analyzing GRN connectivity, our results show that the anticipated scale-free network architectures are characterized by organism-specific power law scaling exponents. These exponents are independent of the fraction of the GRN experimentally sampled, enabling prediction of properties of the complete GRN for an organism. We further demonstrate that the exponents describe inequalities in transcription factor (TF)-target gene recognition across GRNs. These observations have the important biological implication that they predict the existence of an intrinsic organism-specific trans and/or cis regulatory landscape that constrains GRN topologies. Consequently, architectural GRN organization drives not only phenotypic plasticity within a species, but is also likely implicated in species-specific phenotype. |
| first_indexed | 2024-04-12T01:17:31Z |
| format | Article |
| id | doaj.art-508e58b7c34c4bada41323f3f31ef70e |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-12T01:17:31Z |
| publishDate | 2018-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-508e58b7c34c4bada41323f3f31ef70e2022-12-22T03:53:55ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582018-04-01144e100609810.1371/journal.pcbi.1006098Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties.Wilberforce Zachary OumaKatja PogacarErich GrotewoldUnderstanding complexity in physical, biological, social and information systems is predicated on describing interactions amongst different components. Advances in genomics are facilitating the high-throughput identification of molecular interactions, and graphs are emerging as indispensable tools in explaining how the connections in the network drive organismal phenotypic plasticity. Here, we describe the architectural organization and associated emergent topological properties of gene regulatory networks (GRNs) that describe protein-DNA interactions (PDIs) in several model eukaryotes. By analyzing GRN connectivity, our results show that the anticipated scale-free network architectures are characterized by organism-specific power law scaling exponents. These exponents are independent of the fraction of the GRN experimentally sampled, enabling prediction of properties of the complete GRN for an organism. We further demonstrate that the exponents describe inequalities in transcription factor (TF)-target gene recognition across GRNs. These observations have the important biological implication that they predict the existence of an intrinsic organism-specific trans and/or cis regulatory landscape that constrains GRN topologies. Consequently, architectural GRN organization drives not only phenotypic plasticity within a species, but is also likely implicated in species-specific phenotype.http://europepmc.org/articles/PMC5945062?pdf=render |
| spellingShingle | Wilberforce Zachary Ouma Katja Pogacar Erich Grotewold Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties. PLoS Computational Biology |
| title | Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties. |
| title_full | Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties. |
| title_fullStr | Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties. |
| title_full_unstemmed | Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties. |
| title_short | Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties. |
| title_sort | topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties |
| url | http://europepmc.org/articles/PMC5945062?pdf=render |
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