Inspired by natural time-keeping devices controlling the circadian clock, managing information processing in the brain and coordinating physiological activities on a daily (feeding and sleeping) or seasonal timescale (reproductive activity or hibernation), synthetic biologists have successfully assembled functional synthetic clocks from cataloged genetic components with standardized activities and arranging them in transcription circuits containing positive and negative feedback loops with integrated time-delay dynamics. While the positive feedback loop drives the clock like the (balance) spring in a mechanical watch the negative time-delay circuit represents the pulse generator defining a minimal time unit and precision of the clock like the pendulum fallback or the movement of the balance wheel in a classical mechanic watch. This basic design principle enabled the construction of a variety of synthetic oscillators whose design details are concisely covered in this review.
"Minimal designs have been identified that yield robust oscillations with either tunable amplitude or frequency, and these match architectures observed in natural oscillator systems. Thus iterative synthetic cycles have been useful in defining the space of oscillatory networks, and in distinguishing bare bones oscillator designs from slightly more complex designs that show far more robust behaviors or more specialized classes of behaviors (Atkinson et al., 2003; Fung et al., 2005; Stricker et al., 2008; Tsai et al., 2008; Tigges et al., 2009, 2010; Aubel and Fussenegger, 2010). "
[Show abstract][Hide abstract] ABSTRACT: A challenge in biology is to understand how complex molecular networks in the cell execute sophisticated regulatory functions. Here we explore the idea that there are common and general principles that link network structures to biological functions, principles that constrain the design solutions that evolution can converge upon for accomplishing a given cellular task. We describe approaches for classifying networks based on abstract architectures and functions, rather than on the specific molecular components of the networks. For any common regulatory task, can we define the space of all possible molecular solutions? Such inverse approaches might ultimately allow the assembly of a design table of core molecular algorithms that could serve as a guide for building synthetic networks and modulating disease networks.
[Show abstract][Hide abstract] ABSTRACT: This paper reviews the FEOL technologies for fabricating the transistor for high performance logic and system LSI devices of 100 nm node, especially, doping process, gate dielectric formation and shallow junction formation using spike annealing. The optimization of ion implantation for gate doping contact source drain, and gate oxide, spike annealing are described
Ion Implantation Technology, 2000. Conference on; 02/2000
[Show abstract][Hide abstract] ABSTRACT: The rapid development of synthetic biology is a paradigm of how the molecular diversity of naturally occurring gene control components can be used to design synthetic control devices and gene networks that provide precisely programmed transgene expression dynamics in space and time. Here we offer an overview on recent advances in the modular design of trigger-inducible mammalian expression devices that are either responsive by exogenous stimuli such as chemicals and physical cues or controlled by endogenous metabolites driving prosthetic circuits to treat metabolic disorders in a self-sufficient manner. Compatible genetic switches can also be assembled to synthetic gene networks that show highly complex expression dynamics such as temporally resolved band-detect functions or oscillating transgene expression profiles. The ongoing metagenomic discovery and characterization of the unexplored sequence space is constantly increasing the molecular diversity in fundamental control components that fuels the further development of synthetic biology.
Current opinion in chemical biology 04/2011; 15(3):414-20. DOI:10.1016/j.cbpa.2011.03.003 · 6.81 Impact Factor
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