| Chapter title |
Gene Regulatory Networks
|
|---|---|
| Chapter number | 6 |
| Book title |
Gene Regulatory Networks
|
| Published in |
Methods in molecular biology, January 2012
|
| DOI | 10.1007/978-1-61779-292-2_6 |
| Pubmed ID | |
| Book ISBNs |
978-1-61779-291-5, 978-1-61779-292-2
|
| Authors |
Sylvie Rockel, Marcel Geertz, Sebastian J. Maerkl, Rockel, Sylvie, Geertz, Marcel, Maerkl, Sebastian J. |
| Abstract |
Gene regulatory networks (GRNs) consist of transcription factors (TFs) that determine the level of gene expression by binding to specific DNA sequences. Mapping all TF-DNA interactions and elucidating their dynamics is a major goal to generate comprehensive models of GRNs. Measuring quantitative binding affinities of large sets of TF-DNA interactions requires the application of novel tools and methods. These tools need to cope with the difficulties related to the facts that TFs tend to be expressed at low levels in vivo, and often form only transient interactions with both DNA and their protein partners. Our approach describes a high-throughput microfluidic platform with a novel detection principle based on the mechanically induced trapping of molecular interactions (MITOMI). MITOMI allows the detection of transient and low-affinity TF-DNA interactions in high-throughput. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 2% |
| Switzerland | 1 | 2% |
| Unknown | 41 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 12 | 28% |
| Student > Ph. D. Student | 9 | 21% |
| Researcher | 7 | 16% |
| Student > Bachelor | 5 | 12% |
| Student > Doctoral Student | 3 | 7% |
| Other | 3 | 7% |
| Unknown | 4 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 16 | 37% |
| Biochemistry, Genetics and Molecular Biology | 12 | 28% |
| Engineering | 6 | 14% |
| Physics and Astronomy | 2 | 5% |
| Computer Science | 1 | 2% |
| Other | 2 | 5% |
| Unknown | 4 | 9% |