Linked Life Data

21601673

Source:http://linkedlifedata.com/resource/pubmed/id/21601673

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2011-5-23
pubmed:abstractText
DNA sequences are now far more readily available in silico than as physical DNA. De novo gene synthesis is an increasingly cost-effective method for building genetic constructs, and effectively removes the constraint of basing constructs on extant sequences. This allows scientists and engineers to experimentally test their hypotheses relating sequence to function. Molecular biologists, and now synthetic biologists, are characterizing and cataloging genetic elements with specific functions, aiming to combine them to perform complex functions. However, the most common purpose of synthetic genes is for the expression of an encoded protein. The huge number of different proteins makes it impossible to characterize and catalog each functional gene. Instead, it is necessary to abstract design principles from experimental data: data that can be generated by making predictions followed by synthesizing sequences to test those predictions. Because of the degeneracy of the genetic code, design of gene sequences to encode proteins is a high-dimensional problem, so there is no single simple formula to guarantee success. Nevertheless, there are several straightforward steps that can be taken to greatly increase the probability that a designed sequence will result in expression of the encoded protein. In this chapter, we discuss gene sequence parameters that are important for protein expression. We also describe algorithms for optimizing these parameters, and troubleshooting procedures that can be helpful when initial attempts fail. Finally, we show how many of these methods can be accomplished using the synthetic biology software tool Gene Designer.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1557-7988
pubmed:author
pubmed:copyrightInfo
Copyright © 2011 Elsevier Inc. All rights reserved.
pubmed:issnType
Electronic
pubmed:volume
498
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
43-66
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Designing genes for successful protein expression.
pubmed:affiliation
DNA2.0, Inc., Suite A, Menlo Park, California, USA.
pubmed:publicationType
Journal Article