Biological Networks
Networks of coordinated interactions among biological entities govern a myriad of biological functions that span a wide range of both length and time scales—from ecosystems to individual cells and from years to milliseconds. For these networks, the concept “the whole is greater than the sum of its p...
Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| Format: | Online |
| Langue: | anglais |
| Publié: |
MDPI - Multidisciplinary Digital Publishing Institute
2021
|
| Sujets: | |
| Accès en ligne: | 29842 |
| Tags: |
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1869514198305734656 |
|---|---|
| author | Neda Bagheri (Ed.) Rudiyanto Gunawan (Ed.) |
| author_browse | Neda Bagheri (Ed.) Rudiyanto Gunawan (Ed.) |
| author_facet | Neda Bagheri (Ed.) Rudiyanto Gunawan (Ed.) |
| author_sort | Neda Bagheri (Ed.) |
| collection | Directory of Open Access Books |
| description | Networks of coordinated interactions among biological entities govern a myriad of biological functions that span a wide range of both length and time scales—from ecosystems to individual cells and from years to milliseconds. For these networks, the concept “the whole is greater than the sum of its parts” applies as a norm rather than an exception. Meanwhile, continued advances in molecular biology and high-throughput technology have enabled a broad and systematic interrogation of whole-cell networks, allowing the investigation of biological processes and functions at unprecedented breadth and resolution—even down to the single-cell level. The explosion of biological data, especially molecular-level intracellular data, necessitates new paradigms for unraveling the complexity of biological networks and for understanding how biological functions emerge from such networks. These paradigms introduce new challenges related to the analysis of networks in which quantitative approaches such as machine learning and mathematical modeling play an indispensable role. The Special Issue on “Biological Networks” showcases advances in the development and application of in silico network modeling and analysis of biological systems. |
| format | Online |
| id | doab-20.500.12854ir-42229 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-422292024-04-05T12:31:44Z Biological Networks Neda Bagheri (Ed.) Rudiyanto Gunawan (Ed.) QH301-705.5 TA1-2040 RC109-216 Pathway crosstalk Alzheimer’s disease Bioenergy crops Model identification Metabolic networks Host–pathogen interactions Single cell Parameter sensitivity Tuberculosis Multivariate statistical analysis Systems biology Biological networks Mathematical modeling Lignin biosynthesis Design of experiments thema EDItEUR::P Mathematics and Science::PS Biology, life sciences Networks of coordinated interactions among biological entities govern a myriad of biological functions that span a wide range of both length and time scales—from ecosystems to individual cells and from years to milliseconds. For these networks, the concept “the whole is greater than the sum of its parts” applies as a norm rather than an exception. Meanwhile, continued advances in molecular biology and high-throughput technology have enabled a broad and systematic interrogation of whole-cell networks, allowing the investigation of biological processes and functions at unprecedented breadth and resolution—even down to the single-cell level. The explosion of biological data, especially molecular-level intracellular data, necessitates new paradigms for unraveling the complexity of biological networks and for understanding how biological functions emerge from such networks. These paradigms introduce new challenges related to the analysis of networks in which quantitative approaches such as machine learning and mathematical modeling play an indispensable role. The Special Issue on “Biological Networks” showcases advances in the development and application of in silico network modeling and analysis of biological systems. 2021-02-11T09:09:09Z 2021-02-11T09:09:09Z 2019-01-10 11:14:23 2019 book 29842 9783038974345 9783038974338 https://directory.doabooks.org/handle/20.500.12854/42229 eng image/jpeg Attribution-NonCommercial-NoDerivatives 4.0 International https://play.google.com/books/publish/a/14935057684283403269#details/ISBN:9783038974338 https://www.mdpi.com/books/pdfview/book/1057 https://www.mdpi.com/books/pdfview/book/1057 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03897-434-5 10.3390/books978-3-03897-434-5 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783038974345 9783038974338 174 open access |
| spellingShingle | QH301-705.5 TA1-2040 RC109-216 Pathway crosstalk Alzheimer’s disease Bioenergy crops Model identification Metabolic networks Host–pathogen interactions Single cell Parameter sensitivity Tuberculosis Multivariate statistical analysis Systems biology Biological networks Mathematical modeling Lignin biosynthesis Design of experiments thema EDItEUR::P Mathematics and Science::PS Biology, life sciences Neda Bagheri (Ed.) Rudiyanto Gunawan (Ed.) Biological Networks |
| title | Biological Networks |
| title_full | Biological Networks |
| title_fullStr | Biological Networks |
| title_full_unstemmed | Biological Networks |
| title_short | Biological Networks |
| title_sort | biological networks |
| topic | QH301-705.5 TA1-2040 RC109-216 Pathway crosstalk Alzheimer’s disease Bioenergy crops Model identification Metabolic networks Host–pathogen interactions Single cell Parameter sensitivity Tuberculosis Multivariate statistical analysis Systems biology Biological networks Mathematical modeling Lignin biosynthesis Design of experiments thema EDItEUR::P Mathematics and Science::PS Biology, life sciences |
| topic_facet | QH301-705.5 TA1-2040 RC109-216 Pathway crosstalk Alzheimer’s disease Bioenergy crops Model identification Metabolic networks Host–pathogen interactions Single cell Parameter sensitivity Tuberculosis Multivariate statistical analysis Systems biology Biological networks Mathematical modeling Lignin biosynthesis Design of experiments thema EDItEUR::P Mathematics and Science::PS Biology, life sciences |
| url | 29842 |
| work_keys_str_mv | AT nedabagheried biologicalnetworks AT rudiyantogunawaned biologicalnetworks |