(Gene Tuning)
(Gene Tuning)
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[[File:wiki_GeneTuning.png|thumb|right|alt=gfp under control of placI.|GFP under control of pLacI.]]
 
[[File:wiki_GeneTuning.png|thumb|right|alt=gfp under control of placI.|GFP under control of pLacI.]]
 
Transcription networks are commonly used in synthetic biology to implement a wide variety of regulatory, logic, and temporal functions.  Tuning of transcription networks is commonly achieved by design of the gene promoter regions.  Characterized promoter libraries serve well in this design as an initial starting point (Hammer 2006).  As with any engineered system, however, precise behavior is attained by in vivo fine tuning.  Model based tuning is made difficult by inherent biological model overparametrization (Gutenkunst 2008).  Tuning using high throughput assays is also prohibitive both in terms of experimental workload and precision. Georgiev Lab is working on developing precise model-free tuning protocols.  These are protocols that are able to identify small differences between competing promoter designs and isolate the ones that yield desirable behaviors, e.g., robustness to common perturbations, fast activation times, sufficient temporal spacings, and correct equilibrium concentrations.
 
Transcription networks are commonly used in synthetic biology to implement a wide variety of regulatory, logic, and temporal functions.  Tuning of transcription networks is commonly achieved by design of the gene promoter regions.  Characterized promoter libraries serve well in this design as an initial starting point (Hammer 2006).  As with any engineered system, however, precise behavior is attained by in vivo fine tuning.  Model based tuning is made difficult by inherent biological model overparametrization (Gutenkunst 2008).  Tuning using high throughput assays is also prohibitive both in terms of experimental workload and precision. Georgiev Lab is working on developing precise model-free tuning protocols.  These are protocols that are able to identify small differences between competing promoter designs and isolate the ones that yield desirable behaviors, e.g., robustness to common perturbations, fast activation times, sufficient temporal spacings, and correct equilibrium concentrations.
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==Modular Transcriptional Riboregulation==
 
==Modular Transcriptional Riboregulation==

Revision as of 15:28, 5 March 2015

Gene Tuning

gfp under control of placI.
GFP under control of pLacI.

Transcription networks are commonly used in synthetic biology to implement a wide variety of regulatory, logic, and temporal functions. Tuning of transcription networks is commonly achieved by design of the gene promoter regions. Characterized promoter libraries serve well in this design as an initial starting point (Hammer 2006). As with any engineered system, however, precise behavior is attained by in vivo fine tuning. Model based tuning is made difficult by inherent biological model overparametrization (Gutenkunst 2008). Tuning using high throughput assays is also prohibitive both in terms of experimental workload and precision. Georgiev Lab is working on developing precise model-free tuning protocols. These are protocols that are able to identify small differences between competing promoter designs and isolate the ones that yield desirable behaviors, e.g., robustness to common perturbations, fast activation times, sufficient temporal spacings, and correct equilibrium concentrations.

Modular Transcriptional Riboregulation

Programmable Dynamic Riboregulation

Division Control

gfp under control of placI.
Accurate protein partitioning through spatial-temporal mechanisms.

Individual cells are constantly subject to perturbations: exogenous perturbations such as temperature fluctuations, brownian motion related perturbations, and perturbations caused by cell division. Cell division related perturbations are primarily caused by random partitioning of molecules between daughter cells and can be difficult to attenuate. Important molecules, e.g., chromosomes, implement complex mechanisms to ensure equal partitioning. Other molecules, e.g., the majority of proteins, are simply partitioned at random. Georgiev Lab is interested in developing simple mechanisms to regulate general protein partitioning.

Time lapse of E. coli with an integrated Min D::GFP fusion protein. Observed spatial-temporal oscillations are critical for correct cell division.

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