Information Thermodynamics and Reducibility of Large Gene Networks
Gene regulatory networks (GRNs) control biological processes like pluripotency, differentiation, and apoptosis. Omics methods can identify a large number of putative network components (on the order of hundreds or thousands) but it is possible that in many cases a small subset of genes control the s...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/1/63 |
| _version_ | 1797542673538613248 |
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| author | Swarnavo Sarkar Joseph B. Hubbard Michael Halter Anne L. Plant |
| author_facet | Swarnavo Sarkar Joseph B. Hubbard Michael Halter Anne L. Plant |
| author_sort | Swarnavo Sarkar |
| collection | DOAJ |
| description | Gene regulatory networks (GRNs) control biological processes like pluripotency, differentiation, and apoptosis. Omics methods can identify a large number of putative network components (on the order of hundreds or thousands) but it is possible that in many cases a small subset of genes control the state of GRNs. Here, we explore how the topology of the interactions between network components may indicate whether the effective state of a GRN can be represented by a small subset of genes. We use methods from information theory to model the regulatory interactions in GRNs as cascading and superposing information channels. We propose an information loss function that enables identification of the conditions by which a small set of genes can represent the state of all the other genes in the network. This information-theoretic analysis extends to a measure of free energy change due to communication within the network, which provides a new perspective on the reducibility of GRNs. Both the information loss and relative free energy depend on the density of interactions and edge communication error in a network. Therefore, this work indicates that a loss in mutual information between genes in a GRN is directly coupled to a thermodynamic cost, i.e., a reduction of relative free energy, of the system. |
| first_indexed | 2024-03-10T13:33:53Z |
| format | Article |
| id | doaj.art-6986c5859fa64dd5a3ba72aa09b4ac97 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T13:33:53Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-6986c5859fa64dd5a3ba72aa09b4ac972023-11-21T07:40:42ZengMDPI AGEntropy1099-43002021-01-012316310.3390/e23010063Information Thermodynamics and Reducibility of Large Gene NetworksSwarnavo Sarkar0Joseph B. Hubbard1Michael Halter2Anne L. Plant3National Institute of Standards and Technology, Gaithersburg, MD 20899, USANational Institute of Standards and Technology, Gaithersburg, MD 20899, USANational Institute of Standards and Technology, Gaithersburg, MD 20899, USANational Institute of Standards and Technology, Gaithersburg, MD 20899, USAGene regulatory networks (GRNs) control biological processes like pluripotency, differentiation, and apoptosis. Omics methods can identify a large number of putative network components (on the order of hundreds or thousands) but it is possible that in many cases a small subset of genes control the state of GRNs. Here, we explore how the topology of the interactions between network components may indicate whether the effective state of a GRN can be represented by a small subset of genes. We use methods from information theory to model the regulatory interactions in GRNs as cascading and superposing information channels. We propose an information loss function that enables identification of the conditions by which a small set of genes can represent the state of all the other genes in the network. This information-theoretic analysis extends to a measure of free energy change due to communication within the network, which provides a new perspective on the reducibility of GRNs. Both the information loss and relative free energy depend on the density of interactions and edge communication error in a network. Therefore, this work indicates that a loss in mutual information between genes in a GRN is directly coupled to a thermodynamic cost, i.e., a reduction of relative free energy, of the system.https://www.mdpi.com/1099-4300/23/1/63gene regulatory networksmutual informationchannel cascadesfree energynetwork reducibility |
| spellingShingle | Swarnavo Sarkar Joseph B. Hubbard Michael Halter Anne L. Plant Information Thermodynamics and Reducibility of Large Gene Networks Entropy gene regulatory networks mutual information channel cascades free energy network reducibility |
| title | Information Thermodynamics and Reducibility of Large Gene Networks |
| title_full | Information Thermodynamics and Reducibility of Large Gene Networks |
| title_fullStr | Information Thermodynamics and Reducibility of Large Gene Networks |
| title_full_unstemmed | Information Thermodynamics and Reducibility of Large Gene Networks |
| title_short | Information Thermodynamics and Reducibility of Large Gene Networks |
| title_sort | information thermodynamics and reducibility of large gene networks |
| topic | gene regulatory networks mutual information channel cascades free energy network reducibility |
| url | https://www.mdpi.com/1099-4300/23/1/63 |
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