期刊
METHODS
卷 62, 期 1, 页码 91-98出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ymeth.2013.05.014
关键词
Modeling; Transcriptional regulation; Synthetic biology
资金
- NIH [RO1 RR07801, P50 GM081892, R01 GM70444]
- P50 group at the Chicago Center for Systems Biology
- University of Chicago
- NATIONAL CENTER FOR RESEARCH RESOURCES [R01RR007801] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [P50GM081892, R01GM070444] Funding Source: NIH RePORTER
Synthetic biology offers novel opportunities for elucidating transcriptional regulatory mechanisms and enhancer logic. Complex cis-regulatory sequences-like the ones driving expression of the Drosophila even-skipped gene have proven difficult to design from existing knowledge, presumably due to the large number of protein-protein interactions needed to drive the correct expression patterns of genes in multicellular organisms. This work discusses two novel computational methods for the custom design of enhancers that employ a sophisticated, empirically validated transcriptional model, optimization algorithms, and synthetic biology. These synthetic elements have both utilitarian and academic value, including improving existing regulatory models as well as evolutionary questions. The first method involves the use of simulated annealing to explore the sequence space for synthetic enhancers whose expression output fit a given search criterion. The second method uses a novel optimization algorithm to find functionally accessible pathways between two enhancer sequences. These paths describe a set of mutations wherein the predicted expression pattern does not significantly vary at any point along the path. Both methods rely on a predictive mathematical framework that maps the enhancer sequence space to functional output. (C) 2013 Elsevier Inc. All rights reserved.
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