A computational method for the investigation of multistable systems and its application to genetic switches

BACKGROUND: Genetic switches exhibit multistability, form the basis of epigenetic memory, and are found in natural decision making systems, such as cell fate determination in developmental pathways. Synthetic genetic switches can be used for recording the presence of different environmental signals, for changing phenotype using synthetic inputs and as building blocks for higher-level sequential logic circuits. Understanding how multistable switches can be constructed and how they function within larger biological systems is therefore key to synthetic biology. RESULTS: Here we present a new computational tool, called StabilityFinder, that takes advantage of sequential Monte Carlo methods to identify regions of parameter space capable of producing multistable behaviour, while handling uncertainty in biochemical rate constants and initial conditions. The algorithm works by clustering trajectories in phase space, and iteratively minimizing a distance metric. Here we examine a collection of models of genetic switches, ranging from the deterministic Gardner toggle switch to stochastic models containing different positive feedback connections. We uncover the design principles behind making bistable, tristable and quadristable switches, and find that rate of gene expression is a key parameter. We demonstrate the ability of the framework to examine more complex systems and examine the design principles of a three gene switch. Our framework allows us to relax the assumptions that are often used in genetic switch models and we show that more complex abstractions are still capable of multistable behaviour. CONCLUSIONS: Our results suggest many ways in which genetic switches can be enhanced and offer designs for the construction of novel switches. Our analysis also highlights subtle changes in correlation of experimentally tunable parameters that can lead to bifurcations in deterministic and stochastic systems. Overall we demonstrate that StabilityFinder will be a valuable tool in the future design and construction of novel gene networks

A Statistical Approach Reveals Designs for the Most Robust Stochastic Gene Oscillators

The engineering of transcriptional networks presents many challenges due to the inherent uncertainty in the system structure, changing cellular context, and stochasticity in the governing dynamics. One approach to address these problems is to design and build systems that can function across a range of conditions; that is they are robust to uncertainty in their constituent components. Here we examine the parametric robustness landscape of transcriptional oscillators, which underlie many important processes such as circadian rhythms and the cell cycle, plus also serve as a model for the engineering of complex and emergent phenomena. The central questions that we address are: Can we build genetic oscillators that are more robust than those already constructed? Can we make genetic oscillators arbitrarily robust? These questions are technically challenging due to the large model and parameter spaces that must be efficiently explored. Here we use a measure of robustness that coincides with the Bayesian model evidence, combined with an efficient Monte Carlo method to traverse model space and concentrate on regions of high robustness, which enables the accurate evaluation of the relative robustness of gene network models governed by stochastic dynamics. We report the most robust two and three gene oscillator systems, plus examine how the number of interactions, the presence of autoregulation, and degradation of mRNA and protein affects the frequency, amplitude, and robustness of transcriptional oscillators. We also find that there is a limit to parametric robustness, beyond which there is nothing to be gained by adding additional feedback. Importantly, we provide predictions on new oscillator systems that can be constructed to verify the theory and advance design and modeling approaches to systems and synthetic biology

The HI Content of Compact Groups of Galaxies

The HI content of Hickson Compact Groups in the southern hemisphere is measured using data from the HI Parkes All Sky Survey (HIPASS), and dedicated observations using the narrowband filter on the Multibeam instrument on the Parkes telescope. The expected HI mass of these groups was estimated using the luminosity, diameter and morphological types of the member galaxies, calibrated from published data. Taking careful account of non-detection limits, the results show that the compact group population that has been detected by these observations has an HI content similar to that of galaxies in the reference field sample. The upper limits for the undetected groups lie within the normal range; improvement of these limits will require a large increase in sensitivity.Comment: 27 pages, 5 figures. Accepted for publication in PAS

Verrucous carcinoma (carcinoma cuniculatum) of the head and neck: what do we know now that we did not know a decade ago?

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Towards an Aspect-Oriented Design and Modelling Framework for Synthetic Biology

Work on synthetic biology has largely used a component-based metaphor for system construction. While this paradigm has been successful for the construction of numerous systems, the incorporation of contextual design issues—either compositional, host or environmental—will be key to realising more complex applications. Here, we present a design framework that radically steps away from a purely parts-based paradigm by using aspect-oriented software engineering concepts. We believe that the notion of concerns is a powerful and biologically credible way of thinking about system synthesis. By adopting this approach, we can separate core concerns, which represent modular aims of the design, from cross-cutting concerns, which represent system-wide attributes. The explicit handling of cross-cutting concerns allows for contextual information to enter the design process in a modular way. As a proof-of-principle, we implemented the aspect-oriented approach in the Python tool, SynBioWeaver, which enables the combination, or weaving, of core and cross-cutting concerns. The power and flexibility of this framework is demonstrated through a number of examples covering the inclusion of part context, combining circuit designs in a context dependent manner, and the generation of rule, logic and reaction models from synthetic circuit designs

A comparative study of semiconductor-based plasmonic metamaterials

Recent metamaterial (MM) research faces several problems when using metal-based plasmonic components as building blocks for MMs. The use of conventional metals for MMs is limited by several factors: metals such as gold and silver have high losses in the visible and near-infrared (NIR) ranges and very large negative real permittivity values, and in addition, their optical properties cannot be tuned. These issues that put severe constraints on the device applications of MMs could be overcome if semiconductors are used as plasmonic materials instead of metals. Heavily doped, wide bandgap oxide semiconductors could exhibit both a small negative real permittivity and relatively small losses in the NIR. Heavily doped oxides of zinc and indium were already reported to be good, low loss alternatives to metals in the NIR range. Here, we consider these transparent conducting oxides (TCOs) as alternative plasmonic materials for many specific applications ranging from surface-plasmon-polariton waveguides to MMs with hyperbolic dispersion and epsilon-near-zero (ENZ) materials. We show that TCOs outperform conventional metals for ENZ and other MM-applications in the NIR.Comment: 16 pages, 7 figure