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pro vyhledávání: '"Noah Olsman"'
Architectural Principles for Characterizing the Performance of Antithetic Integral Feedback Networks
Publikováno v:
iScience, Vol 14, Iss , Pp 277-291 (2019)
Summary: As we begin to design increasingly complex synthetic biomolecular systems, it is essential to develop rational design methodologies that yield predictable circuit performance. Here we apply mathematical tools from the theory of control and d
Externí odkaz:
https://doaj.org/article/f764f0f14a584c268232e98c0ece11fc
Publikováno v:
PLoS Computational Biology, Vol 11, Iss 5, p e1004264 (2015)
An approach combining genetic, proteomic, computational, and physiological analysis was used to define a protein network that regulates fat storage in budding yeast (Saccharomyces cerevisiae). A computational analysis of this network shows that it is
Externí odkaz:
https://doaj.org/article/e09c0872ccae4c93b438b6fc701a90e4
Architectural Principles for Characterizing the Performance of Antithetic Integral Feedback Networks
Publikováno v:
iScience, Vol 14, Iss, Pp 277-291 (2019)
iScience
iScience
Summary As we begin to design increasingly complex synthetic biomolecular systems, it is essential to develop rational design methodologies that yield predictable circuit performance. Here we apply mathematical tools from the theory of control and dy
Autor:
Lea Goentoro, Noah Olsman
Publikováno v:
Current Opinion in Biotechnology. 54:72-79
Motifs, circuits, and networks are core conceptual elements in modern systems and synthetic biology. While there are still undoubtedly more fascinating computations to discover at network level, there are also rich computations that we are only begin
Publikováno v:
Cell Systems. 7:352-355
Transcription is an episodic process characterized by probabilistic bursts; but how the transcriptional noise from these bursts is modulated by cellular physiology remains unclear. Using simulations and single-molecule RNA counting, we examined how c
Autor:
Richard M. Murray, Fangzhou Xiao, Yoke Peng Leong, Noah Olsman, John Doyle, Ania-Ariadna Baetica
Feedback regulation is pervasive in biology at both the organismal and cellular level. In this article, we explore the properties of a particular biomolecular feedback mechanism called antithetic integral feedback, which can be implemented using the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::841e4502876ad216390dad27ad69ac76
https://resolver.caltech.edu/CaltechAUTHORS:20190708-153642582
https://resolver.caltech.edu/CaltechAUTHORS:20190708-153642582
Publikováno v:
CDC
Biological control systems often contain a wide variety of feedforward and feedback mechanisms that regulate a given process. While it is generally assumed that this apparent redundancy has evolved for a reason, it is often unclear how exactly the ce
SummaryAs we begin to design increasingly complex synthetic biomolecular systems, it is essential to develop rational design methodologies that yield predictable circuit performance. Here we apply theoretical tools from the theory of control and dyna
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e8fdb93b9eda83fd27538a66a2e967a3
Autor:
Noah Olsman, Johan Paulsson
Publikováno v:
Nature. 570:452-453
A module for implementing robust feedback control in synthetic cellular networks has been reported. Its design is first proved mathematically to be universal for all networks, and then implemented in living cells. A module for robust perfect adaptati
Autor:
Georgios Piliouras, John T. Ormerod, Bader Al-Anzi, Noah Olsman, Sherif Gerges, Christopher M. Ormerod, Kai Zinn
Biological networks, like most engineered networks, are not the product of a singular design but rather are the result of a long process of refinement and optimization. Many large real-world networks are comprised of well-defined and meaningful small
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eeacef9d639347c1f2f85757c5048683
https://resolver.caltech.edu/CaltechAUTHORS:20170417-102739427
https://resolver.caltech.edu/CaltechAUTHORS:20170417-102739427