| Chapter title |
Systems Biology to Support Nanomaterial Grouping.
|
|---|---|
| Chapter number | 6 |
| Book title |
Modelling the Toxicity of Nanoparticles
|
| Published in |
Advances in experimental medicine and biology, February 2017
|
| DOI | 10.1007/978-3-319-47754-1_6 |
| Pubmed ID | |
| Book ISBNs |
978-3-31-947752-7, 978-3-31-947754-1
|
| Authors |
Christian Riebeling, Harald Jungnickel, Andreas Luch, Andrea Haase, Riebeling, Christian, Jungnickel, Harald, Luch, Andreas, Haase, Andrea |
| Editors |
Lang Tran, Miguel A. Bañares, Robert Rallo |
| Abstract |
The assessment of potential health risks of engineered nanomaterials (ENMs) is a challenging task due to the high number and great variety of already existing and newly emerging ENMs. Reliable grouping or categorization of ENMs with respect to hazards could help to facilitate prioritization and decision making for regulatory purposes. The development of grouping criteria, however, requires a broad and comprehensive data basis. A promising platform addressing this challenge is the systems biology approach. The different areas of systems biology, most prominently transcriptomics, proteomics and metabolomics, each of which provide a wealth of data that can be used to reveal novel biomarkers and biological pathways involved in the mode-of-action of ENMs. Combining such data with classical toxicological data would enable a more comprehensive understanding and hence might lead to more powerful and reliable prediction models. Physico-chemical data provide crucial information on the ENMs and need to be integrated, too. Overall statistical analysis should reveal robust grouping and categorization criteria and may ultimately help to identify meaningful biomarkers and biological pathways that sufficiently characterize the corresponding ENM subgroups. This chapter aims to give an overview on the different systems biology technologies and their current applications in the field of nanotoxicology, as well as to identify the existing challenges. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 7% |
| Unknown | 14 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 33% |
| Student > Doctoral Student | 3 | 20% |
| Other | 2 | 13% |
| Student > Ph. D. Student | 2 | 13% |
| Student > Master | 1 | 7% |
| Other | 1 | 7% |
| Unknown | 1 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 3 | 20% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 13% |
| Medicine and Dentistry | 2 | 13% |
| Nursing and Health Professions | 1 | 7% |
| Business, Management and Accounting | 1 | 7% |
| Other | 2 | 13% |
| Unknown | 4 | 27% |