423 research outputs found

    Implementing nonlinear feedback controllers using DNA strand displacement reactions

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    We show how an important class of nonlinear feedback controllers can be designed using idealized abstract chemical reactions and implemented via DNA strand displacement (DSD) reactions. Exploiting chemical reaction networks (CRNs) as a programming language for the design of complex circuits and networks, we show how a set of unimolecular and bimolecular reactions can be used to realize input-output dynamics that produce a nonlinear quasi sliding mode (QSM) feedback controller. The kinetics of the required chemical reactions can then be implemented as enzyme-free, enthalpy/entropy driven DNA reactions using a toehold mediated strand displacement mechanism via Watson-Crick base pairing and branch migration. We demonstrate that the closed loop response of the nonlinear QSM controller outperforms a traditional linear controller by facilitating much faster tracking response dynamics without introducing overshoots in the transient response. The resulting controller is highly modular and is less affected by retroactivity effects than standard linear designs

    Biomolecular implementation of a quasi sliding mode feedback controller based on DNA strand displacement reactions

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    A fundamental aim of synthetic biology is to achieve the capability to design and implement robust embedded biomolecular feedback control circuits. An approach to realize this objective is to use abstract chemical reaction networks (CRNs) as a programming language for the design of complex circuits and networks. Here, we employ this approach to facilitate the implementation of a class of nonlinear feedback controllers based on sliding mode control theory. We show how a set of two-step irreversible reactions with ultrasensitive response dynamics can provide a biomolecular implementation of a nonlinear quasi sliding mode (QSM) controller. We implement our controller in closed-loop with a prototype of a biological pathway and demonstrate that the nonlinear QSM controller outperforms a traditional linear controller by facilitating faster tracking response dynamics without introducing overshoots in the transient response

    “I Will Rise Again”: The Life and Legacy of the U.S.S. Monitor

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    About the author: Declan Riley Kunkel is an award winning writer, author, and consultant. Originally from Fort Worth, Texas, Declan writes about history, politics, and philosophy. He is pursing a degree in history at Yale

    Reverse-Engineering Biological Interaction Networks from Noisy Data using Regularized Least Squares and Instrumental Variables

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    The problem of reverse engineering the topology of a biological network from noisy time–series measurements is one of the most important challenges in the field of Systems Biology. In this work, we develop a new inference approach which combines the Regularized Least Squares (RLS) technique with a technique to avoid the introduction of bias and nonconsistency due to measurement noise in the estimation of the parameters in the standard Least Squares (LS) formulation, the Instrumental Variables (IV) method. We test our approach on a set of nonlinear in silico networks and show that the combined exploitation of RLS and IV methods improves the predictions with respect to other standard approaches
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