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Biomolecular Implementation of a Quasi Sliding Mode Controller using an Ultrasensitive Cell Signalling Pathway
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Biomolecular implementation of a quasi sliding mode feedback controller based on DNA strand displacement reactions
No full textBiomolecular Implementation of a Quasi Sliding Mode Controller using an Ultrasensitive Cell Signalling Pathway
No full textImplementing nonlinear feedback controllers using DNA strand displacement reactions
Get PDFWe 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
Zero-Retroactivity Subtraction Module for Embedded Feedback Control of Chemical Reaction Networks
No full textThe control of biochemical processes is a major goal in systems and synthetic
biology. Current approaches are based on ad-hoc designs, whereas a general and modular
framework would be highly desirable, in order to exploit the well-assessed methods of control
theory. A well-known problem when dealing with complex biosystems is represented by the
retroactivity effect, which can significantly modify the dynamics of interconnected subsystem,
with respect to the behavior they exhibit when disconnected from each other. In the present
work an implementation of a zero-retroactivity Chemical Reaction Network Subtractor (CRNS)
is proposed and its effectiveness is investigated through singular perturbation analysis. The
proposed CRNS represents a first step towards the development of a modular framework for the
design of CRN-based embedded feedback control systems
Ultrasensitive Negative Feedback Control: A Natural Approach for the Design of Synthetic Controllers
No full textBiomolecular implementation of a quasi sliding mode feedback controller based on DNA strand displacement reactions
No full textA 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
Linear time-varying models can reveal non-linear interactions of biomolecular regulatory networks using multiple time-series data
No full text<b>Motivation:</b> Inherent non-linearities in biomolecular interactions make the identification of network interactions difficult. One of the principal problems is that all methods based on the use of linear time-invariant models will have fundamental limitations in their capability to infer certain non-linear network interactions. Another difficulty is the multiplicity of possible solutions, since, for a given dataset, there may be many different possible networks which generate the same time-series expression profiles.
<b>Results:</b> A novel algorithm for the inference of biomolecular interaction networks from temporal expression data is presented. Linear time-varying models, which can represent a much wider class of time-series data than linear time-invariant models, are employed in the algorithm. From time-series expression profiles, the model parameters are identified by solving a non-linear optimization problem. In order to systematically reduce the set of possible solutions for the optimization problem, a filtering process is performed using a phase-portrait analysis with random numerical perturbations. The proposed approach has the advantages of not requiring the system to be in a stable steady state, of using time-series profiles which have been generated by a single experiment, and of allowing non-linear network interactions to be identified. The ability of the proposed algorithm to correctly infer network interactions is illustrated by its application to three examples: a non-linear model for cAMP oscillations in <i>Dictyostelium discoideum</i>, the cell-cycle data for Saccharomyces cerevisiae and a large-scale non-linear model of a group of synchronized <i>Dictyostelium</i> cells
Reverse engineering partially-known interaction networks from noisy data
No full textOne of the most difficult challenges associated with the problem of inferring
functional interaction networks from experimental data is that of dealing with the effects of
measurement noise in the data used for reverse engineering. A second important challenge is
that of taking full advantage of prior knowledge about some elements of the network to improve
the results of the reconstruction process. This paper introduces a new inference algorithm,
PACTLS, which addresses both of the above issues. The algorithm combines methods to exploit
mechanisms underpinning scale–free networks generation, i.e. network growth and preferential
attachment (PA), with a technique to optimally reduce the effects of measurement noise in the
data on the reliability of the inference results, i.e. the Constrained Total Least Squares (CTLS)
algorithm. The technique is assessed through numerical tests on in silico random networks and
is shown to consistently outperform approaches based on Bayesian networks
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