1,721,000 research outputs found
Reverse Engineering Biological Interaction Networks by Exploiting Prior Knowledge and Topological Features
No full textMathematical modelling of local calcium and regulated exocytosis during inhibition and stimulation of glucagon secretion from pancreatic alpha-cells
No full textReverse Engineering Partially-Known Interaction Networks from Noisy DataProceedings of the 18th IFAC World Congress
No full textExploiting prior knowledge and preferential attachment to infer biological interaction networks2009 17th Mediterranean Conference on Control and Automation
No full textGeometric slow-fast analysis of a hybrid pituitary cell model with stochastic ion channel dynamics
Get PDFTo obtain explicit understanding of the behavior of dynamical systems, geometrical methods and slow-fast analysis have proved to be highly useful. Such methods are standard for smooth dynamical systems and increasingly used for continuous, non-smooth dynamical systems. However, they are much less used for random dynamical systems, in particular for hybrid models with discrete, random dynamics. Here we propose a geometrical method that works directly with the hybrid system. We illustrate our approach through an application to a hybrid pituitary cell model in which the stochastic dynamics of very few active large-conductance potassium (BK) channels is coupled to a deterministic model of the other ion channels and calcium dynamics. To employ our geometric approach, we exploit the slow-fast structure of the model. The random fast subsystem is analyzed by considering discrete phase planes, corresponding to the discrete number of open BK channels, and stochastic events correspond to jumps between these planes. The evolution within each plane can be understood from nullclines and limit cycles, and the overall dynamics, e.g., whether the model produces a spike or a burst, is determined by the location at which the system jumps from one plane to another. Our approach is generally applicable to other scenarios to study discrete random dynamical systems defined by hybrid stochastic-deterministic models
Biomolecular Implementation of a Quasi Sliding Mode Controller using an Ultrasensitive Cell Signalling Pathway
No full textBiomolecular Implementation of a Quasi Sliding Mode Controller using an Ultrasensitive Cell Signalling Pathway
No full textBiomolecular implementation of a quasi sliding mode feedback controller based on DNA strand displacement reactions
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
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