Forward individualized medicine from personal genomes to interactomes
When considering the variation in the genome, transcriptome, proteome and metabolome, and their interaction with the environment, every individual can be rightfully considered as a unique biological entity. Individualized medicine promises to take this uniqueness into account to optimize disease tre...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2015-12-01
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| Series: | Frontiers in Physiology |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fphys.2015.00364/full |
| _version_ | 1811269049863110656 |
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| author | Xiang eZhang Jan Albert Kuivenhoven Albert K. Groen Albert K. Groen |
| author_facet | Xiang eZhang Jan Albert Kuivenhoven Albert K. Groen Albert K. Groen |
| author_sort | Xiang eZhang |
| collection | DOAJ |
| description | When considering the variation in the genome, transcriptome, proteome and metabolome, and their interaction with the environment, every individual can be rightfully considered as a unique biological entity. Individualized medicine promises to take this uniqueness into account to optimize disease treatment and thereby improve health benefits for every patient. The success of individualized medicine relies on a precise understanding of the genotype-phenotype relationship. Although omics technologies advance rapidly, there are several challenges that need to be overcome: Next generation sequencing can efficiently decipher genomic sequences, epigenetic changes, and transcriptomic variation in patients, but it does not automatically indicate how or whether the identified variation will cause pathological changes. This is likely due to the inability to account for 1) the consequences of gene-gene and gene-environment interactions, and 2) (post)transcriptional as well as (post)translational processes that eventually determine the concentration of key metabolites. The technologies to accurately measure changes in these latter layers are still under development, and such measurements in humans are also mainly restricted to blood and circulating cells. Despite these challenges, it is already possible to track dynamic changes in the human interactome in healthy and diseased states by using the integration of multi-omics data. In this review, we evaluate the potential value of current major bioinformatics and systems biology-based approaches, including genome wide association studies, epigenetics, gene regulatory and protein-protein interaction networks, and genome-scale metabolic modeling. Moreover, we address the question whether integrative analysis of personal multi-omics data will help understanding of personal genotype-phenotype relationships. |
| first_indexed | 2024-04-12T21:35:01Z |
| format | Article |
| id | doaj.art-d39c411097a54e02bf9f91309c7374d5 |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-04-12T21:35:01Z |
| publishDate | 2015-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-d39c411097a54e02bf9f91309c7374d52022-12-22T03:15:56ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2015-12-01610.3389/fphys.2015.00364168659Forward individualized medicine from personal genomes to interactomesXiang eZhang0Jan Albert Kuivenhoven1Albert K. Groen2Albert K. Groen3University of Groningen, University Medical Center GroningenUniversity of Groningen, University Medical Center GroningenUniversity of Groningen, University Medical Center GroningenUniversity of Groningen, University Medical Center GroningenWhen considering the variation in the genome, transcriptome, proteome and metabolome, and their interaction with the environment, every individual can be rightfully considered as a unique biological entity. Individualized medicine promises to take this uniqueness into account to optimize disease treatment and thereby improve health benefits for every patient. The success of individualized medicine relies on a precise understanding of the genotype-phenotype relationship. Although omics technologies advance rapidly, there are several challenges that need to be overcome: Next generation sequencing can efficiently decipher genomic sequences, epigenetic changes, and transcriptomic variation in patients, but it does not automatically indicate how or whether the identified variation will cause pathological changes. This is likely due to the inability to account for 1) the consequences of gene-gene and gene-environment interactions, and 2) (post)transcriptional as well as (post)translational processes that eventually determine the concentration of key metabolites. The technologies to accurately measure changes in these latter layers are still under development, and such measurements in humans are also mainly restricted to blood and circulating cells. Despite these challenges, it is already possible to track dynamic changes in the human interactome in healthy and diseased states by using the integration of multi-omics data. In this review, we evaluate the potential value of current major bioinformatics and systems biology-based approaches, including genome wide association studies, epigenetics, gene regulatory and protein-protein interaction networks, and genome-scale metabolic modeling. Moreover, we address the question whether integrative analysis of personal multi-omics data will help understanding of personal genotype-phenotype relationships.http://journal.frontiersin.org/Journal/10.3389/fphys.2015.00364/fullpersonalized medicineInteractomeIntegrative Genomicsgenome-scale metabolic modelsgene regulatory networks (GRN)Network medicine |
| spellingShingle | Xiang eZhang Jan Albert Kuivenhoven Albert K. Groen Albert K. Groen Forward individualized medicine from personal genomes to interactomes Frontiers in Physiology personalized medicine Interactome Integrative Genomics genome-scale metabolic models gene regulatory networks (GRN) Network medicine |
| title | Forward individualized medicine from personal genomes to interactomes |
| title_full | Forward individualized medicine from personal genomes to interactomes |
| title_fullStr | Forward individualized medicine from personal genomes to interactomes |
| title_full_unstemmed | Forward individualized medicine from personal genomes to interactomes |
| title_short | Forward individualized medicine from personal genomes to interactomes |
| title_sort | forward individualized medicine from personal genomes to interactomes |
| topic | personalized medicine Interactome Integrative Genomics genome-scale metabolic models gene regulatory networks (GRN) Network medicine |
| url | http://journal.frontiersin.org/Journal/10.3389/fphys.2015.00364/full |
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