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Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism
SIMPLE SUMMARY: The influence of data incompleteness on the correctness of conclusions about the structure and functions of the objects under study is widely discussed in the literature. It was noted that even a small percentage of missing data can lead to incorrect conclusions and imperfect knowled...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9404739/ https://www.ncbi.nlm.nih.gov/pubmed/36009835 http://dx.doi.org/10.3390/biology11081208 |
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author | Kondratyeva, Liya Alekseenko, Irina Chernov, Igor Sverdlov, Eugene |
author_facet | Kondratyeva, Liya Alekseenko, Irina Chernov, Igor Sverdlov, Eugene |
author_sort | Kondratyeva, Liya |
collection | PubMed |
description | SIMPLE SUMMARY: The influence of data incompleteness on the correctness of conclusions about the structure and functions of the objects under study is widely discussed in the literature. It was noted that even a small percentage of missing data can lead to incorrect conclusions and imperfect knowledge. In particular, incompleteness can lead to critical errors in the qualitative and quantitative assessments of interactions in biological systems and a distorted understanding of the functioning mechanisms of living systems. In this brief review, we attempt to demonstrate the extent of this incompleteness in functional information about living systems using the best-studied examples. We suggest that this incompleteness may form seemingly insurmountable barriers in deciphering the mechanisms of the functioning of complex systems with unpredictable properties arising from the interaction of the system components. ABSTRACT: In this brief review, we attempt to demonstrate that the incompleteness of data, as well as the intrinsic heterogeneity of biological systems, may form very strong and possibly insurmountable barriers for researchers trying to decipher the mechanisms of the functioning of live systems. We illustrate this challenge using the two most studied organisms: E. coli, with 34.6% genes lacking experimental evidence of function, and C. elegans, with identified proteins for approximately 50% of its genes. Another striking example is an artificial unicellular entity named JCVI-syn3.0, with a minimal set of genes. A total of 31.5% of the genes of JCVI-syn3.0 cannot be ascribed a specific biological function. The human interactome mapping project identified only 5–10% of all protein interactions in humans. In addition, most of the available data are static snapshots, and it is barely possible to generate realistic models of the dynamic processes within cells. Moreover, the existing interactomes reflect the de facto interaction but not its functional result, which is an unpredictable emerging property. Perhaps the completeness of molecular data on any living organism is beyond our reach and represents an unsolvable problem in biology. |
format | Online Article Text |
id | pubmed-9404739 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94047392022-08-26 Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism Kondratyeva, Liya Alekseenko, Irina Chernov, Igor Sverdlov, Eugene Biology (Basel) Opinion SIMPLE SUMMARY: The influence of data incompleteness on the correctness of conclusions about the structure and functions of the objects under study is widely discussed in the literature. It was noted that even a small percentage of missing data can lead to incorrect conclusions and imperfect knowledge. In particular, incompleteness can lead to critical errors in the qualitative and quantitative assessments of interactions in biological systems and a distorted understanding of the functioning mechanisms of living systems. In this brief review, we attempt to demonstrate the extent of this incompleteness in functional information about living systems using the best-studied examples. We suggest that this incompleteness may form seemingly insurmountable barriers in deciphering the mechanisms of the functioning of complex systems with unpredictable properties arising from the interaction of the system components. ABSTRACT: In this brief review, we attempt to demonstrate that the incompleteness of data, as well as the intrinsic heterogeneity of biological systems, may form very strong and possibly insurmountable barriers for researchers trying to decipher the mechanisms of the functioning of live systems. We illustrate this challenge using the two most studied organisms: E. coli, with 34.6% genes lacking experimental evidence of function, and C. elegans, with identified proteins for approximately 50% of its genes. Another striking example is an artificial unicellular entity named JCVI-syn3.0, with a minimal set of genes. A total of 31.5% of the genes of JCVI-syn3.0 cannot be ascribed a specific biological function. The human interactome mapping project identified only 5–10% of all protein interactions in humans. In addition, most of the available data are static snapshots, and it is barely possible to generate realistic models of the dynamic processes within cells. Moreover, the existing interactomes reflect the de facto interaction but not its functional result, which is an unpredictable emerging property. Perhaps the completeness of molecular data on any living organism is beyond our reach and represents an unsolvable problem in biology. MDPI 2022-08-12 /pmc/articles/PMC9404739/ /pubmed/36009835 http://dx.doi.org/10.3390/biology11081208 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Opinion Kondratyeva, Liya Alekseenko, Irina Chernov, Igor Sverdlov, Eugene Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism |
title | Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism |
title_full | Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism |
title_fullStr | Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism |
title_full_unstemmed | Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism |
title_short | Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism |
title_sort | data incompleteness may form a hard-to-overcome barrier to decoding life’s mechanism |
topic | Opinion |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9404739/ https://www.ncbi.nlm.nih.gov/pubmed/36009835 http://dx.doi.org/10.3390/biology11081208 |
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