Towards heterotic computing with droplets in a fully automated droplet-maker platform

The control and prediction of complex chemical systems is a difficult problem due to the nature of the interactions, transformations and processes occurring. From self-assembly to catalysis and self-organization, complex chemical systems are often heterogeneous mixtures that at the most extreme exhibit system-level functions, such as those that could be observed in a living cell. In this paper, we outline an approach to understand and explore complex chemical systems using an automated droplet maker to control the composition, size and position of the droplets in a predefined chemical environment. By investigating the spatio-temporal dynamics of the droplets, the aim is to understand how to control system-level emergence of complex chemical behaviour and even view the system-level behaviour as a programmable entity capable of information processing. Herein, we explore how our automated droplet-maker platform could be viewed as a prototype chemical heterotic computer with some initial data and example problems that may be viewed as potential chemically embodied computations

Evolution of oil droplets in a chemorobotic platform

Evolution, once the preserve of biology, has been widely emulated in software, while physically embodied systems that can evolve have been limited to electronic and robotic devices and have never been artificially implemented in populations of physically interacting chemical entities. Herein we present a liquid-handling robot built with the aim of investigating the properties of oil droplets as a function of composition via an automated evolutionary process. The robot makes the droplets by mixing four different compounds in different ratios and placing them in a Petri dish after which they are recorded using a camera and the behaviour of the droplets analysed using image recognition software to give a fitness value. In separate experiments, the fitness function discriminates based on movement, division and vibration over 21 cycles, giving successive fitness increases. Analysis and theoretical modelling of the data yields fitness landscapes analogous to the genotype–phenotype correlations found in biological evolution. , Trevor Hinkley, James Ward Taylor Kliment Yane

Development of a 3D printer using scanning projection stereolithography

We have developed a system for the rapid fabrication of low cost 3D devices and systems in the laboratory with micro-scale features yet cm-scale objects. Our system is inspired by maskless lithography, where a digital micromirror device (DMD) is used to project patterns with resolution up to 10 µm onto a layer of photoresist. Large area objects can be fabricated by stitching projected images over a 5cm2 area. The addition of a z-stage allows multiple layers to be stacked to create 3D objects, removing the need for any developing or etching steps but at the same time leading to true 3D devices which are robust, configurable and scalable. We demonstrate the applications of the system by printing a range of micro-scale objects as well as a fully functioning microfluidic droplet device and test its integrity by pumping dye through the channels

The κ Andromedae System: New Constraints on the Companion Mass, System Age, and Further Multiplicity

κ Andromedae is a B9IVn star at 52 pc for which a faint substellar companion separated by 55 ± 2 AU was recently announced. In this work, we present the first spectrum of the companion, "κ And B," using the Project 1640 high-contrast imaging platform. Comparison of our low-resolution YJH-band spectra to empirical brown dwarf spectra suggests an early-L spectral type. Fitting synthetic spectra from PHOENIX model atmospheres to our observed spectrum allows us to constrain the effective temperature to ~2000 K as well as place constraints on the companion surface gravity. Further, we use previously reported log(g) and T_eff measurements of the host star to argue that the κ And system has an isochronal age of 220 ± 100 Myr, older than the 30 Myr age reported previously. This interpretation of an older age is corroborated by the photometric properties of κ And B, which appear to be marginally inconsistent with other 10–100 Myr low-gravity L-dwarfs for the spectral type range we derive. In addition, we use Keck aperture masking interferometry combined with published radial velocity measurements to rule out the existence of any tight stellar companions to κ And A that might be responsible for the system's overluminosity. Further, we show that luminosity enhancements due to a nearly "pole-on" viewing angle coupled with extremely rapid rotation is unlikely. κ And A is thus consistent with its slightly evolved luminosity class (IV), and we propose here that κ And, with a revised age of 220 ± 100 Myr, is an interloper to the 30 Myr Columba association with which it was previously associated. The photometric and spectroscopic evidence for κ And B combined with our reassessment of the system age implies a substellar companion mass of 50^(+16)_(-13) M_Jup, consistent with a brown dwarf rather than a planetary-mass companion

Using Likelihood-Free Inference to Compare Evolutionary Dynamics of the Protein Networks of H. pylori and P. falciparum

Gene duplication with subsequent interaction divergence is one of the primary driving forces in the evolution of genetic systems. Yet little is known about the precise mechanisms and the role of duplication divergence in the evolution of protein networks from the prokaryote and eukaryote domains. We developed a novel, model-based approach for Bayesian inference on biological network data that centres on approximate Bayesian computation, or likelihood-free inference. Instead of computing the intractable likelihood of the protein network topology, our method summarizes key features of the network and, based on these, uses a MCMC algorithm to approximate the posterior distribution of the model parameters. This allowed us to reliably fit a flexible mixture model that captures hallmarks of evolution by gene duplication and subfunctionalization to protein interaction network data of Helicobacter pylori and Plasmodium falciparum. The 80% credible intervals for the duplication–divergence component are [0.64, 0.98] for H. pylori and [0.87, 0.99] for P. falciparum. The remaining parameter estimates are not inconsistent with sequence data. An extensive sensitivity analysis showed that incompleteness of PIN data does not largely affect the analysis of models of protein network evolution, and that the degree sequence alone barely captures the evolutionary footprints of protein networks relative to other statistics. Our likelihood-free inference approach enables a fully Bayesian analysis of a complex and highly stochastic system that is otherwise intractable at present. Modelling the evolutionary history of PIN data, it transpires that only the simultaneous analysis of several global aspects of protein networks enables credible and consistent inference to be made from available datasets. Our results indicate that gene duplication has played a larger part in the network evolution of the eukaryote than in the prokaryote, and suggests that single gene duplications with immediate divergence alone may explain more than 60% of biological network data in both domains

Constraining the presence of giant planets in two-belt debris disk systems with VLT/SPHERE direct imaging and dynamical arguments

Giant, wide-separation planets often lie in the gap between multiple, distinct rings of circumstellar debris: this is the case for the HR 8799 and HD 95086 systems, and even the solar system where the Asteroid and Kuiper belts enclose the four gas and ice giants. In the case that a debris disk, inferred from an infrared excess in the SED, is best modelled as two distinct temperatures, we infer the presence of two spatially separated rings of debris. Giant planets may well exist between these two belts of debris, and indeed could be responsible for the formation of the gap between these belts. We observe 24 such two-belt systems using the VLT/SPHERE high contrast imager, and interpret our results under the assumption that the gap is indeed formed by one or more giant planets. A theoretical minimum mass for each planet can then be calculated, based on the predicted dynamical timescales to clear debris. The typical dynamical lower limit is ˜0.2MJ in this work, and in some cases exceeds 1MJ. Direct imaging data, meanwhile, is typically sensitive to planets down to ˜3.6MJ at 1", and 1.7MJ in the best case. Together, these two limits tightly constrain the possible planetary systems present around each target, many of which will be detectable with the next generation of high-contrast imagers

Organic synthesis in a modular robotic system driven by a chemical programming language

The synthesis of complex organic compounds is largely a manual process that is often incompletely documented. To address these shortcomings, we developed an abstraction that maps commonly reported methodological instructions into discrete steps amenable to automation. These unit operations were implemented in a modular robotic platform using a chemical programming language which formalizes and controls the assembly of the molecules. We validated the concept by directing the automated system to synthesize three pharmaceutical compounds, Nytol, rufinamide, and sildenafil, without any human intervention. Yields and purities of products and intermediates were comparable to or better than those achieved manually. The syntheses are captured as digital code that can be published, versioned, and transferred flexibly between platforms with no modification, thereby greatly enhancing reproducibility and reliable access to complex molecules

Using evolutionary algorithms and machine learning to explore sequence space for the discovery of antimicrobial peptides

We present a proof-of-concept methodology for efficiently optimizing a chemical trait by using an artificial evolutionary workflow. We demonstrate this by optimizing the efficacy of antimicrobial peptides (AMPs). In particular, we used a closed-loop approach that combines a genetic algorithm, machine learning, and in vitro evaluation to improve the antimicrobial activity of peptides against Escherichia coli. Starting with a 13-mer natural AMP, we identified 44 highly potent peptides, achieving up to a ca. 160-fold increase in antimicrobial activity within just three rounds of experiments. During these experiments, the conformation of the peptides selected was changed from a random coil to an α-helical form. This strategy not only establishes the potential of in vitro molecule evolution using an algorithmic genetic system but also accelerates the discovery of antimicrobial peptides and other functional molecules within a relatively small number of experiments, allowing the exploration of broad sequence and structural space

Assessing Predicted HIV-1 Replicative Capacity in a Clinical Setting

HIV-1 replicative capacity (RC) provides a measure of within-host fitness and is determined in the context of phenotypic drug resistance testing. However it is unclear how these in-vitro measurements relate to in-vivo processes. Here we assess RCs in a clinical setting by combining a previously published machine-learning tool, which predicts RC values from partial pol sequences with genotypic and clinical data from the Swiss HIV Cohort Study. The machine-learning tool is based on a training set consisting of 65000 RC measurements paired with their corresponding partial pol sequences. We find that predicted RC values (pRCs) correlate significantly with the virus load measured in 2073 infected but drug naïve individuals. Furthermore, we find that, for 53 pairs of sequences, each pair sampled in the same infected individual, the pRC was significantly higher for the sequence sampled later in the infection and that the increase in pRC was also significantly correlated with the increase in plasma viral load and with the length of the time-interval between the sampling points. These findings indicate that selection within a patient favors the evolution of higher replicative capacities and that these in-vitro fitness measures are indicative of in-vivo HIV virus load