1.

Record Nr.

UNINA9910298412703321

Autore

Kadkhodaei

Titolo

Cis/Transgene Optimization : Systematic Discovery of Novel Gene Expression Elements Using Bioinformatics and Computational Biology Approaches / / by Saeid Kadkhodaei, Farahnaz Sadat Golestan Hashemi, Morvarid Akhavan Rezaei, Sahar Abbasiliasi, Joo Shun Tan, Hamid Rajabi Memari, Faruku Bande, Ali Baradaran, Mahdi Moradpour, Arbakariya B, Ariff

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018

ISBN

3-319-90391-8

Edizione

[1版. 2018.]

Descrizione fisica

1 online resource (XVII, 120 p. 24 illus.)

Collana

SpringerBriefs in Systems Biology, , 2193-4746

Altri autori (Persone)

Saeid

Disciplina

615.32

Soggetti

Bioinformatics

Computational biology

Systems biology

Proteomics

Computer Appl. in Life Sciences

Systems Biology

Computational Biology/Bioinformatics

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di contenuto

1. Introduction -- 2. Systematic Strategies -- 3. Outcomes Assessment -- 4. Conceptual Models.

Sommario/riassunto

This book is a practical review which focuses on computational analysis and on in silicoapproaches towards the systematic discovery of various key functional gene expression elements in microalgae as a model. So far, in this regard very little information is available. Efficient stepwise procedures for analysing the matrix attachment regions (MARs) are outlined, as well as for translation initiation sites (TIS), signal peptide (SP) sequences, gene optimization and transformation systems. These outlines can be efficiently deployed as practical models for the systematic discovery of key expression elements and for the optimization of cis/transgenes in other micro/organisms. The first



chapter is an introduction on the key gene expression elements analysed in this book, including scaffold/matrix attachment regions, translation initiation sites, signal peptides as well as gene optimization. Chapter 2 focuses on systematic strategies and computational approaches toward in silico analysis of each factor. The analyses outcomes is assessed individually in chapter 3 followed by developing the specific conceptual models for each element in Chapter 4. The concluding remarks are discussed in Chapter 5. This work is of interest to computational and experimental biologists interested in transcriptional regulation analysis as well as to researchers and scientists who wish to consider the use of bioinformatics and computational biology in design, analysis, or regulatory reviews of key gene expression elements for the production of recombinant proteins experiments.