Coordinated Ionospheric Reconstruction CubeSat Experiment (CIRCE) mission overview

The Coordinated Ionospheric Reconstruction Cubesat Experiment (CIRCE) is a joint US/UK mission consisting of two 6U CubeSats actively maintaining a lead-follow configuration in the same low Earth orbit with a launch planned for the 2020 timeframe. These nanosatellites will each feature multiple space weather payloads. From the US, the Naval Research Laboratory will provide two 1U Triple Tiny Ionospheric Photometers (Tri-TIPs) on each satellite, observing the ultraviolet 135.6 nm emission of atomic oxygen at nighttime. The primary objective is to characterize the twodimensional distribution of electrons in the Equatorial Ionization Anomaly (EIA). The methodology used to reconstruct the nighttime ionosphere employs continuous UV photometry from four distinct viewing angles in combination with an additional data source, such as in situ plasma density measurements, with advanced image space reconstruction algorithm tomography techniques. From the UK, the Defence Science and Technology Laboratory (Dstl) is providing the In-situ and Remote Ionospheric Sensing suite consisting of an Ion/Neutral Mass Spectrometer, a triple-frequency GPS receiver for ionospheric sensing, and a radiation environment monitor. We present our mission concept, simulations illustrating the imaging capability of the Tri-TIP sensor suite, and a range of science questions addressable via these measurements

Coordinated Ionospheric Reconstruction CubeSat Experiment (CIRCE), In situ and Remote Ionospheric Sensing (IRIS) suite

The UK’s Defence Science and Technology Laboratory (Dstl) is partnering with the US Naval Research Laboratory (NRL) on a joint mission to launch miniature sensors that will advance space weather measurement and modelling capabilities. The Coordinated Ionospheric Reconstruction Cubesat Experiment (CIRCE) comprises two 6U cube-satellites that will be launched into a near-polar low earth orbit (LEO), targeting 500 km altitude, in 2021. The UK contribution to CIRCE is the In situ and Remote Ionospheric Sensing (IRIS) suite, complementary to NRL sensors, and comprising three highly miniaturised payloads provided to Dstl by University College London (UCL), University of Bath, and University of Surrey/Surrey Satellite Technology Ltd (SSTL). One IRIS suite will be flown on each satellite, and incorporates an ion/neutral mass spectrometer, a tri-band global positioning system (GPS) receiver for ionospheric remote sensing, and a radiation environment monitor. From the US, NRL have provided two 1U Triple Tiny Ionospheric Photometers (Tri-TIPs) on each satellite (Nicholas et al., 2019), observing the ultraviolet 135.6 nm emission of atomic oxygen at night-time to characterize the two-dimensional distribution of electrons

Modeling Structure-Function Relationships in Synthetic DNA Sequences using Attribute Grammars

Recognizing that certain biological functions can be associated with specific DNA sequences has led various fields of biology to adopt the notion of the genetic part. This concept provides a finer level of granularity than the traditional notion of the gene. However, a method of formally relating how a set of parts relates to a function has not yet emerged. Synthetic biology both demands such a formalism and provides an ideal setting for testing hypotheses about relationships between DNA sequences and phenotypes beyond the gene-centric methods used in genetics. Attribute grammars are used in computer science to translate the text of a program source code into the computational operations it represents. By associating attributes with parts, modifying the value of these attributes using rules that describe the structure of DNA sequences, and using a multi-pass compilation process, it is possible to translate DNA sequences into molecular interaction network models. These capabilities are illustrated by simple example grammars expressing how gene expression rates are dependent upon single or multiple parts. The translation process is validated by systematically generating, translating, and simulating the phenotype of all the sequences in the design space generated by a small library of genetic parts. Attribute grammars represent a flexible framework connecting parts with models of biological function. They will be instrumental for building mathematical models of libraries of genetic constructs synthesized to characterize the function of genetic parts. This formalism is also expected to provide a solid foundation for the development of computer assisted design applications for synthetic biology

Rational Diversification of a Promoter Providing Fine-Tuned Expression and Orthogonal Regulation for Synthetic Biology

Yeast is an ideal organism for the development and application of synthetic biology, yet there remain relatively few well-characterised biological parts suitable for precise engineering of this chassis. In order to address this current need, we present here a strategy that takes a single biological part, a promoter, and re-engineers it to produce a fine-graded output range promoter library and new regulated promoters desirable for orthogonal synthetic biology applications. A highly constitutive Saccharomyces cerevisiae promoter, PFY1p, was identified by bioinformatic approaches, characterised in vivo and diversified at its core sequence to create a 36-member promoter library. TetR regulation was introduced into PFY1p to create a synthetic inducible promoter (iPFY1p) that functions in an inverter device. Orthogonal and scalable regulation of synthetic promoters was then demonstrated for the first time using customisable Transcription Activator-Like Effectors (TALEs) modified and designed to act as orthogonal repressors for specific PFY1-based promoters. The ability to diversify a promoter at its core sequences and then independently target Transcription Activator-Like Orthogonal Repressors (TALORs) to virtually any of these sequences shows great promise toward the design and construction of future synthetic gene networks that encode complex “multi-wire” logic functions

Mapping the Environmental Fitness Landscape of a Synthetic Gene Circuit

Gene expression actualizes the organismal phenotypes encoded within the genome in an environment-dependent manner. Among all encoded phenotypes, cell population growth rate (fitness) is perhaps the most important, since it determines how well-adapted a genotype is in various environments. Traditional biological measurement techniques have revealed the connection between the environment and fitness based on the gene expression mean. Yet, recently it became clear that cells with identical genomes exposed to the same environment can differ dramatically from the population average in their gene expression and division rate (individual fitness). For cell populations with bimodal gene expression, this difference is particularly pronounced, and may involve stochastic transitions between two cellular states that form distinct sub-populations. Currently it remains unclear how a cell population's growth rate and its subpopulation fractions emerge from the molecular-level kinetics of gene networks and the division rates of single cells. To address this question we developed and quantitatively characterized an inducible, bistable synthetic gene circuit controlling the expression of a bifunctional antibiotic resistance gene in Saccharomyces cerevisiae. Following fitness and fluorescence measurements in two distinct environments (inducer alone and antibiotic alone), we applied a computational approach to predict cell population fitness and subpopulation fractions in the combination of these environments based on stochastic cellular movement in gene expression space and fitness space. We found that knowing the fitness and nongenetic (cellular) memory associated with specific gene expression states were necessary for predicting the overall fitness of cell populations in combined environments. We validated these predictions experimentally and identified environmental conditions that defined a “sweet spot” of drug resistance. These findings may provide a roadmap for connecting the molecular-level kinetics of gene networks to cell population fitness in well-defined environments, and may have important implications for phenotypic variability of drug resistance in natural settings