James Taylor

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.

Semi-automated image analysis for the assessment of megafaunal densities at the Arctic deep-sea observatory HAUSGARTEN

Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS.

The HCV Non-Nucleoside Inhibitor Tegobuvir Utilizes a Novel Mechanism of Action to Inhibit NS5B Polymerase Function

Tegobuvir (TGV) is a novel non-nucleoside inhibitor (NNI) of HCV RNA replication with demonstrated antiviral activity in patients with genotype 1 chronic HCV infection. The mechanism of action of TGV has not been clearly defined despite the identification of resistance mutations mapping to the NS5B polymerase region. TGV does not inhibit NS5B enzymatic activity in biochemical assays in vitro, suggesting a more complex antiviral mechanism with cellular components. Here, we demonstrate that TGV exerts anti-HCV activity utilizing a unique chemical activation and subsequent direct interaction with the NS5B protein. Treatment of HCV subgenomic replicon cells with TGV results in a modified form of NS5B with a distinctly altered mobility on a SDS-PAGE gel. Further analysis reveals that the aberrantly migrating NS5B species contains the inhibitor molecule. Formation of this complex does not require the presence of any other HCV proteins. The intensity of the aberrantly migrating NS5B species is strongly dependent on cellular glutathione levels as well as CYP 1A activity. Furthermore analysis of NS5B protein purified from a heterologous expression system treated with TGV by mass spectrometry suggests that TGV undergoes a CYP- mediated intracellular activation step and the resulting metabolite, after forming a glutathione conjugate, directly and specifically interacts with NS5B. Taken together, these data demonstrate that upon metabolic activation TGV is a specific, covalent inhibitor of the HCV NS5B polymerase and is mechanistically distinct from other classes of the non-nucleoside inhibitors (NNI) of the viral polymerase.

Autonomous model protocell division driven by molecular replication

The coupling of compartmentalisation with molecular replication is thought to be crucial for the emergence of the first evolvable chemical systems. Minimal artificial replicators have been designed based on molecular recognition, inspired by the template copying of DNA, but none yet have been coupled to compartmentalisation. Here, we present an oil-in-water droplet system comprising an amphiphilic imine dissolved in chloroform that catalyses its own formation by bringing together a hydrophilic and a hydrophobic precursor, which leads to repeated droplet division. We demonstrate that the presence of the amphiphilic replicator, by lowering the interfacial tension between droplets of the reaction mixture and the aqueous phase, causes them to divide. Periodic sampling by a droplet-robot demonstrates that the extent of fission is increased as the reaction progresses, producing more compartments with increased self-replication. This bridges a divide, showing how replication at the molecular level can be used to drive macroscale droplet fission.Coupling compartmentalisation and molecular replication is essential for the development of evolving chemical systems. Here the authors show an oil-in-water droplet containing a self-replicating amphiphilic imine that can undergo repeated droplet division.

Adaptive artificial evolution of droplet protocells in a 3D-printed fluidic chemorobotic platform with configurable environments

Evolution via natural selection is governed by the persistence and propagation of living things in an environment. The environment is important since it enabled life to emerge, and shapes evolution today. Although evolution has been widely studied in a variety of fields from biology to computer science, still little is known about the impact of environmental changes on an artificial chemical evolving system outside of computer simulations. Here we develop a fully automated 3D-printed chemorobotic fluidic system that is able to generate and select droplet protocells in real time while changing the surroundings where they undergo artificial evolution. The system is produced using rapid prototyping and explicitly introduces programmable environments as an experimental variable. Our results show that the environment not only acts as an active selector over the genotypes, but also enhances the capacity for individual genotypes to undergo adaptation in response to environmental pressures.Few studies have explored the effect of a changing environment on artificial chemical evolution. Here, the authors develop an evolutionary platform that alters the physical environment of droplet protocells, showing that a population of simple chemical species can adapt to its surroundings, in analogy to natural evolution.

Artificial intelligence exploration of unstable protocells leads to predictable properties and discovery of collective behavior

Significance Exploring and understanding the emergence of complex behaviors is difficult even in “simple” chemical systems since the dynamics can rest on a knife edge between stability and instability. Herein, we study the complex dynamics of a simple protocell system, comprising four-component oil droplets in an aqueous environment using an automated platform equipped with artificial intelligence. The system autonomously selects and performs oil-in-water droplet experiments, and then records and classifies the behavior of the droplets using image recognition. The data acquired are then used to build predictive models of the system. Physical properties such as viscosity, surface tension, and density are related to behaviors, as well as to droplet behavioral niches, such as collective swarming. Protocell models are used to investigate how cells might have first assembled on Earth. Some, like oil-in-water droplets, can be seemingly simple models, while able to exhibit complex and unpredictable behaviors. How such simple oil-in-water systems can come together to yield complex and life-like behaviors remains a key question. Herein, we illustrate how the combination of automated experimentation and image processing, physicochemical analysis, and machine learning allows significant advances to be made in understanding the driving forces behind oil-in-water droplet behaviors. Utilizing >7,000 experiments collected using an autonomous robotic platform, we illustrate how smart automation cannot only help with exploration, optimization, and discovery of new behaviors, but can also be core to developing fundamental understanding of such systems. Using this process, we were able to relate droplet formulation to behavior via predicted physical properties, and to identify and predict more occurrences of a rare collective droplet behavior, droplet swarming. Proton NMR spectroscopic and qualitative pH methods enabled us to better understand oil dissolution, chemical change, phase transitions, and droplet and aqueous phase flows, illustrating the utility of the combination of smart-automation and traditional analytical chemistry techniques. We further extended our study for the simultaneous exploration of both the oil and aqueous phases using a robotic platform. Overall, this work shows that the combination of chemistry, robotics, and artificial intelligence enables discovery, prediction, and mechanistic understanding in ways that no one approach could achieve alone.

The evolution of active droplets in chemorobotic platforms.

There is great interest in oil-in-water droplets as simple systems that display astonishingly complex behaviours. Recently, we reported a chemorobotic platform capable of autonomously exploring and evolving the behaviours these droplets can exhibit. The platform enabled us to undertake a large number of reproducible experiments, allowing us to probe the non-linear relationship between droplet composition and behaviour. Herein we introduce this work, and also report on the recent developments we have made to this system. These include new platforms to simultaneously evolve the droplets’ physical and chemical environments and the inclusion of selfreplicating molecules in the droplets.